From e874eb78ddd4453b1b84fa3f795819fd78864e07 Mon Sep 17 00:00:00 2001 From: Tommy in Tongji <36354458+TommyZihao@users.noreply.github.com> Date: Tue, 4 Apr 2023 18:44:41 +0800 Subject: [PATCH] Add files via upload --- ...3\205\351\205\215\347\275\256MMPose.ipynb" | 1792 ++++++++++++ ...\351\242\204\346\265\213-Python API.ipynb" | 1012 +++++++ ...345\221\275\344\273\244\350\241\214.ipynb" | 388 +++ ...345\236\213\351\242\204\346\265\213.ipynb" | 231 ++ ...346\265\213\346\250\241\345\236\213.ipynb" | 2259 ++++++++++++++ ...345\236\213\351\242\204\346\265\213.ipynb" | 226 ++ ...345\236\213\351\242\204\346\265\213.ipynb" | 92 + ...345\236\213\351\242\204\346\265\213.ipynb" | 229 ++ ...345\236\213\351\242\204\346\265\213.ipynb" | 217 ++ ...345\236\213\351\242\204\346\265\213.ipynb" | 315 ++ ...345\236\213\351\242\204\346\265\213.ipynb" | 352 +++ .../img/nvgesture.png" | Bin 0 -> 342687 bytes ...4\260\345\256\211\350\243\205MMPose.ipynb" | 459 +++ 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"2023/0404/\343\200\220C\343\200\221\344\270\213\350\275\275\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\225\260\346\215\256\351\233\206.ipynb" create mode 100644 "2023/0404/\343\200\220D1\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\350\256\255\347\273\203.ipynb" create mode 100644 "2023/0404/\343\200\220D2\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\351\242\204\346\265\213.ipynb" create mode 100644 "2023/0404/\343\200\220E1\343\200\221RTMPose\350\256\255\347\273\203\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" create mode 100644 "2023/0404/\343\200\220E2\343\200\221RTMPose\346\235\203\351\207\215\346\226\207\344\273\266\350\275\273\351\207\217\345\214\226\345\244\204\347\220\206.ipynb" create mode 100644 "2023/0404/\343\200\220F1\343\200\221RTMPose\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" diff --git "a/2022/\343\200\220A\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" "b/2022/\343\200\220A\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" new file mode 100644 index 0000000..501e2fb --- /dev/null +++ "b/2022/\343\200\220A\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" @@ -0,0 +1,1792 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bc3d1961-9081-49c9-be56-ad0c748decf1", + "metadata": {}, + "source": [ + "# 安装配置MMPose" + ] + }, + { + "cell_type": "markdown", + "id": "0fc8ebd8-db6e-4a17-895f-2f87fc98ca79", + "metadata": {}, + "source": [ + "按照顺序逐行运行本代码，即可安装配置 MMCV、MMPose环境\n", + "\n", + "代码测试[云GPU环境](https://featurize.cn?s=d7ce99f842414bfcaea5662a97581bd1)：GPU RTX 3060、CUDA v11.2\n", + "\n", + "OpenMMLab主页：https://openmmlab.com/\n", + "\n", + "MMPose主页：https://github.com/open-mmlab/mmpose\n", + "\n", + "作者：同济子豪兄 2022-6-6" + ] + }, + { + "cell_type": "markdown", + "id": "63d0dc47-4601-49a3-8d25-12b4d24fa6f0", + "metadata": {}, + "source": [ + "> 提示：以下代码运行时，若长时间运行卡着不动，可重启 kernel 后重新运行一遍" + ] + }, + { + "cell_type": "markdown", + "id": "6fc1c686-1267-4503-b9e4-bcb188a7f974", + "metadata": { + "tags": [] + }, + "source": [ + "## 下载安装Pytorch（大约需要两分钟）" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ebac87ae-6b96-417f-88f2-5016e8860bb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple, https://download.pytorch.org/whl/cu113\n", + "Requirement already satisfied: torch in /environment/miniconda3/lib/python3.7/site-packages (1.10.0+cu113)\n", + "Requirement already satisfied: torchvision in /environment/miniconda3/lib/python3.7/site-packages (0.11.1+cu113)\n", + "Requirement already satisfied: torchaudio in /environment/miniconda3/lib/python3.7/site-packages (0.10.0+cu113)\n", + "Requirement already satisfied: typing-extensions in /environment/miniconda3/lib/python3.7/site-packages (from torch) (4.0.1)\n", + "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /environment/miniconda3/lib/python3.7/site-packages (from torchvision) (8.4.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from torchvision) (1.21.4)\n" + ] + } + ], + "source": [ + "!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113" + ] + }, + { + "cell_type": "markdown", + "id": "30ca00a2-4f39-44f3-9379-8889e3a24b39", + "metadata": {}, + "source": [ + "## 下载安装 mmcv-full（大约需要两分钟）" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "69d80570-7eb5-4bf4-a84d-39106d23f73b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Collecting mmcv-full\n", + " Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/mmcv_full-1.5.3-cp37-cp37m-manylinux1_x86_64.whl (48.5 MB)\n", + "\u001b[K |████████████████████████████████| 48.5 MB 76.5 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: Pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (8.4.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (0.31.0)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (6.0)\n", + "Collecting addict\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (21.3)\n", + "Requirement already satisfied: opencv-python>=3 in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (4.5.4.60)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (1.21.4)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /environment/miniconda3/lib/python3.7/site-packages (from packaging->mmcv-full) (3.0.6)\n", + "Installing collected packages: addict, mmcv-full\n", + "Successfully installed addict-2.4.0 mmcv-full-1.5.3\n" + ] + } + ], + "source": [ + "# 安装mmcv -full\n", + "!pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html" + ] + }, + { + "cell_type": "markdown", + "id": "38c55520-071b-41de-91f4-3ec78e16bf27", + "metadata": {}, + "source": [ + "## 安装其它工具包（大约需要一分钟）" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb4b3373-f117-46a9-ab43-e67753eb7c61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting mmdet\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b0/11/52ad3563ccb589269a559b8b52b4ce0c6ee5e701a0483baa267f25d88416/mmdet-2.25.0-py3-none-any.whl (1.4 MB)\n", + "\u001b[K |████████████████████████████████| 1.4 MB 41.7 MB/s eta 0:00:01\n", + "\u001b[?25hCollecting seaborn\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/10/5b/0479d7d845b5ba410ca702ffcd7f2cd95a14a4dfff1fde2637802b258b9b/seaborn-0.11.2-py3-none-any.whl (292 kB)\n", + "\u001b[K |████████████████████████████████| 292 kB 63.4 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: tqdm in /environment/miniconda3/lib/python3.7/site-packages (4.61.2)\n", + "Collecting imageio-ffmpeg\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e5/3b/fdf3e75462e93b7806ffecad6c5aa35f2cc76b9f2faaedf5e43194ceff09/imageio_ffmpeg-0.4.7-py3-none-manylinux2010_x86_64.whl (26.9 MB)\n", + "\u001b[K |████████████████████████████████| 26.9 MB 49.0 MB/s eta 0:00:01\n", + "\u001b[?25hCollecting terminaltables\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c4/fb/ea621e0a19733e01fe4005d46087d383693c0f4a8f824b47d8d4122c87e0/terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmdet) (3.5.0)\n", + "Requirement already satisfied: six in /environment/miniconda3/lib/python3.7/site-packages (from mmdet) (1.16.0)\n", + "Collecting pycocotools\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/75/5c/ac61ea715d7a89ecc31c090753bde28810238225ca8b71778dfe3e6a68bc/pycocotools-2.0.4.tar.gz (106 kB)\n", + "\u001b[K |████████████████████████████████| 106 kB 92.7 MB/s eta 0:00:01\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet) (1.21.4)\n", + "Requirement already satisfied: scipy>=1.0 in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (1.7.3)\n", + "Requirement already satisfied: pandas>=0.23 in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (1.3.4)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (4.28.3)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (6.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (2.8.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (8.4.0)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (0.11.0)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (21.3)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (3.0.6)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet) (1.3.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2021.3)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet) (1.2.2)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet) (52.0.0.post20210125)\n", + "Building wheels for collected packages: pycocotools\n", + " Building wheel for pycocotools (PEP 517) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for pycocotools: filename=pycocotools-2.0.4-cp37-cp37m-linux_x86_64.whl size=370198 sha256=420e14e6e52dea4fe30db65df0cba16c853561ed5e2cb48cc5a98900665092bb\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/c0/01/5f/670dfd20204fc9cc6bf843db4e014acb998f411922e3abc49f\n", + "Successfully built pycocotools\n", + "Installing collected packages: terminaltables, pycocotools, seaborn, mmdet, imageio-ffmpeg\n", + "Successfully installed imageio-ffmpeg-0.4.7 mmdet-2.25.0 pycocotools-2.0.4 seaborn-0.11.2 terminaltables-3.1.10\n" + ] + } + ], + "source": [ + "!pip install mmdet seaborn tqdm imageio-ffmpeg -i https://pypi.tuna.tsinghua.edu.cn/simple" + ] + }, + { + "cell_type": "markdown", + "id": "0defb2f8-d5ab-4ee4-a66f-85cc17c221de", + "metadata": { + "tags": [] + }, + "source": [ + "## 下载安装 MMPose" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "89026868-58b1-4755-aca3-ded98451e906", + "metadata": {}, + "outputs": [], + "source": [ + "# 删掉原有的 mmpose 文件夹（如有）\n", + "!rm -rf mmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "09c51fee-d040-46cf-8d86-c0248ab7fe84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmpose'...\n", + "remote: Enumerating objects: 19313, done.\u001b[K\n", + "remote: Counting objects: 100% (389/389), done.\u001b[K\n", + "remote: Compressing objects: 100% (253/253), done.\u001b[K\n", + "remote: Total 19313 (delta 186), reused 279 (delta 135), pack-reused 18924\u001b[K\n", + "Receiving objects: 100% (19313/19313), 24.87 MiB | 17.20 MiB/s, done.\n", + "Resolving deltas: 100% (13670/13670), done.\n", + "Updating files: 100% (1611/1611), done.\n" + ] + } + ], + "source": [ + "# 从 github 上下载最新的 mmpose 源代码\n", + "!git clone https://github.com/open-mmlab/mmpose.git" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21f59cb9-bcd6-4d00-93f2-e533c406d484", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入 mmpose 主目录\n", + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1703a0a7-453e-4414-a14e-c452a8c64fba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Ignoring dataclasses: markers 'python_version == \"3.6\"' don't 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"Requirement already satisfied: certifi>=2017.4.17 in /environment/miniconda3/lib/python3.7/site-packages (from requests->-r requirements/optional.txt (line 6)) (2021.5.30)\n", + "Building wheels for collected packages: poseval, chumpy, PyOpenGL\n", + " Building wheel for poseval (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for poseval: filename=poseval-0.1.0-py3-none-any.whl size=25993 sha256=4487d904302697e0b80678f154d44e3ee5d1f392047c011497ebc9233a0e0d3b\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-oe4m5xkw/wheels/45/df/45/59dc4b86f2c20fd298f88676bc7351833e058b33b024cb1c98\n", + " Building wheel for chumpy (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for chumpy: filename=chumpy-0.70-py3-none-any.whl size=58285 sha256=9b8474aaeac5bb7c0a52ac4cf0344931b2db7e55e30d1d8f51ab98a46e85cbba\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/2f/ff/a6/ddb75bc3aa8fa8ba036634f428f414ed87f966d7e1ad33c514\n", + " Building wheel for PyOpenGL (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for PyOpenGL: filename=PyOpenGL-3.1.0-py3-none-any.whl size=1745211 sha256=3101811b7a6898d6b9c2766d8c40216e4e599980391dcae1e0eaaf148f14c196\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/db/12/8c/13833f5306d89d3b121123d10da1b2fa9f1ad84ab6273a8299\n", + "Successfully built poseval chumpy PyOpenGL\n", + "Installing collected packages: xmltodict, trimesh, shapely, PyOpenGL, pyglet, py, motmetrics, iniconfig, freetype-py, cython, xtcocotools, xdoctest, smplx, pytest-runner, pytest, pyrender, poseval, onnxruntime, onnx, munkres, json-tricks, isort, interrogate, coverage, chumpy\n", + " Attempting uninstall: isort\n", + " Found existing installation: isort 5.10.1\n", + " Uninstalling isort-5.10.1:\n", + " Successfully uninstalled isort-5.10.1\n", + "Successfully installed PyOpenGL-3.1.0 chumpy-0.70 coverage-6.4.1 cython-0.29.30 freetype-py-2.3.0 iniconfig-1.1.1 interrogate-1.5.0 isort-4.3.21 json-tricks-3.15.5 motmetrics-1.2.5 munkres-1.1.4 onnx-1.12.0 onnxruntime-1.11.1 poseval-0.1.0 py-1.11.0 pyglet-1.5.26 pyrender-0.1.45 pytest-7.1.2 pytest-runner-6.0.0 shapely-1.8.2 smplx-0.1.28 trimesh-3.12.7 xdoctest-1.0.0 xmltodict-0.13.0 xtcocotools-1.12\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Obtaining file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmpose\n", + "Requirement already satisfied: chumpy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (0.70)\n", + "Requirement already satisfied: json_tricks in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (3.15.5)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (3.5.0)\n", + "Requirement already satisfied: munkres in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (1.1.4)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (1.21.4)\n", + "Requirement already satisfied: opencv-python in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (4.5.4.60)\n", + "Requirement already satisfied: pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (8.4.0)\n", + "Requirement already satisfied: scipy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (1.7.3)\n", + "Requirement already satisfied: torchvision in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (0.11.1+cu113)\n", + "Requirement already satisfied: xtcocotools>=1.12 in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==0.28.0) (1.12)\n", + "Requirement already satisfied: setuptools>=18.0 in /environment/miniconda3/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==0.28.0) (52.0.0.post20210125)\n", + "Requirement already satisfied: cython>=0.27.3 in /environment/miniconda3/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==0.28.0) (0.29.30)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (3.0.6)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (2.8.2)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (6.3.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (1.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (4.28.3)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (21.3)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib->mmpose==0.28.0) (1.16.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmpose==0.28.0) (1.2.2)\n", + "Requirement already satisfied: torch==1.10.0 in /environment/miniconda3/lib/python3.7/site-packages (from torchvision->mmpose==0.28.0) (1.10.0+cu113)\n", + "Requirement already satisfied: typing-extensions in /environment/miniconda3/lib/python3.7/site-packages (from torch==1.10.0->torchvision->mmpose==0.28.0) (4.0.1)\n", + "Installing collected packages: mmpose\n", + " Running setup.py develop for mmpose\n", + "Successfully installed mmpose-0.28.0\n" + ] + } + ], + "source": [ + "!pip install -r requirements.txt\n", + "\n", + "!pip3 install -e ." + ] + }, + { + "cell_type": "markdown", + "id": "57588840-d990-44c6-a1e2-151d869dab8d", + "metadata": {}, + "source": [ + "## 安装MMTracking" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a448d99e-068b-4e68-a66c-21423daa087e", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('../')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78babbeb-f833-48e5-a038-e8bdfa143a00", + "metadata": {}, + "outputs": [], + "source": [ + "# 删掉原有的 mmtracking 文件夹（如有）\n", + "!rm -rf mmtracking" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cf386ad1-86c4-4ecb-bcaa-bd808742145f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmtracking'...\n", + "remote: Enumerating objects: 4504, done.\u001b[K\n", + "remote: Counting objects: 100% (230/230), done.\u001b[K\n", + "remote: Compressing objects: 100% (186/186), done.\u001b[K\n", + "remote: Total 4504 (delta 74), reused 147 (delta 41), pack-reused 4274\u001b[K\n", + "Receiving objects: 100% (4504/4504), 1.92 MiB | 5.32 MiB/s, done.\n", + "Resolving deltas: 100% (2633/2633), done.\n" + ] + } + ], + "source": [ + "# 从 github 上下载最新的 mmtracking 源代码\n", + "!git clone https://github.com/open-mmlab/mmtracking.git" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "855da0dc-006a-4c58-a68b-7431e6dc0d8f", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入 mmtracking 主目录\n", + "os.chdir('mmtracking')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "09d4f839-01a5-4a92-b813-c751e42b9ab9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: cython in /environment/miniconda3/lib/python3.7/site-packages (from -r requirements/build.txt (line 1)) (0.29.30)\n", + "Collecting numba==0.53.0\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/92/e8/4f67aa44b86333528e04dc8820f7e8058753582d8a0867324a2b818ef516/numba-0.53.0-cp37-cp37m-manylinux2014_x86_64.whl (3.4 MB)\n", + "\u001b[K |████████████████████████████████| 3.4 MB 100.1 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from -r requirements/build.txt (line 3)) (1.21.4)\n", + "Collecting llvmlite<0.37,>=0.36.0rc1\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/54/25/2b4015e2b0c3be2efa6870cf2cf2bd969dd0e5f937476fc13c102209df32/llvmlite-0.36.0-cp37-cp37m-manylinux2010_x86_64.whl (25.3 MB)\n", + "\u001b[K |████████████████████████████████| 25.3 MB 98.6 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from numba==0.53.0->-r requirements/build.txt (line 2)) (52.0.0.post20210125)\n", + "Installing collected packages: llvmlite, numba\n", + "Successfully installed llvmlite-0.36.0 numba-0.53.0\n", + "Using pip 21.1.3 from /environment/miniconda3/lib/python3.7/site-packages/pip (python 3.7)\n", + "Non-user install because site-packages writeable\n", + "Created temporary directory: /tmp/pip-ephem-wheel-cache-u57vwwsm\n", + "Created temporary directory: /tmp/pip-req-tracker-gnol5xvg\n", + "Initialized build tracking at /tmp/pip-req-tracker-gnol5xvg\n", + "Created build tracker: /tmp/pip-req-tracker-gnol5xvg\n", + "Entered build tracker: /tmp/pip-req-tracker-gnol5xvg\n", + "Created temporary directory: /tmp/pip-install-gyultzk6\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Obtaining file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmtracking\n", + " Added file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmtracking to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Running setup.py (path:/home/featurize/work/MMPose教程/mmtracking/setup.py) egg_info for package from file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmtracking\n", + " Created temporary directory: /tmp/pip-pip-egg-info-7hr3chgu\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info\n", + " writing /tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/dependency_links.txt\n", + " writing requirements to /tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/requires.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no files found matching 'mmtrack/.mim/model-index.yml'\n", + " warning: no files found matching '*.py' under directory 'mmtrack/.mim/configs'\n", + " warning: no files found matching '*.yml' under directory 'mmtrack/.mim/configs'\n", + " warning: no files found matching '*.sh' under directory 'mmtrack/.mim/tools'\n", + " warning: no files found matching '*.py' under directory 'mmtrack/.mim/tools'\n", + " writing manifest file '/tmp/pip-pip-egg-info-7hr3chgu/mmtrack.egg-info/SOURCES.txt'\n", + " Source in /home/featurize/work/MMPose教程/mmtracking has version 0.13.0, which satisfies requirement mmtrack==0.13.0 from file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmtracking\n", + " Removed mmtrack==0.13.0 from file:///home/featurize/work/MMPose%E6%95%99%E7%A8%8B/mmtracking from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "1 location(s) to search for versions of attributee:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "Starting new HTTPS connection (1): pypi.tuna.tsinghua.edu.cn:443\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/attributee/ HTTP/1.1\" 200 952\n", + "Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\"\n", + "Caching due to etag\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e1/41/027dab4f27f972d903c41587016b57c55ac018224e152012ac37e964a044/attributee-0.1.0.tar.gz#sha256=c339ff232b47b2128bb27435e0f33bc74affc4398fbd2a1e7ed72b3e54df4a31 (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.0\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/7c/c9/52ba4a4f9f433146c2a569cf7e8090fa7d096e06c5c69d8bec084b8774f5/attributee-0.1.1-py3.8.egg#sha256=464a253066bce57241b418bd816867d5768550a2d35cf04c124b6e592abeb16c (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/dc/31/1387d9f506e59f60defdbe23d85812e396711a59f956c33b927f71a58b8f/attributee-0.1.1.tar.gz#sha256=3a6e2a10c69e4db4e9058b74e7bbb50480e1fce936fac3cfb929bcde16d65b77 (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b9/e9/57079e099d50e74310801152a49648ca188f97b6e8460087c3d9cc8d40eb/attributee-0.1.3.tar.gz#sha256=067b706c1ff3c449eeedebee5b960cd2895e8aa2684a03b5427419aa859bba71 (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.3\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/98/8e/4dae44b19e87268af37301e865ad8d39887a0e176d908697e8d01019b5eb/attributee-0.1.4.tar.gz#sha256=7848fc213446177b3587bc1b48d29a5bc824a64011a61071ef7e3b323bf8809d (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.4\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz#sha256=a9c14efe6f540e70d226a44ecea10bc606521c4f81d4e8142170ed5c2b81feb9 (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.5\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/58/72/fb13fce1c0603a4ae64f063203a4eaafc05c7ff29f675c5813f7486e6f68/attributee-0.1.6.tar.gz#sha256=462a8d677d8baddea5c03bbc92e88b4318e04d05b10fc2e254f62ccbc4bd4faa (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.6\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/15/40/13b697d3b7048d36ed8dabcf6c2719ef77d7df3709a8baab736f9d009e27/attributee-0.1.7.tar.gz#sha256=a853a3a6fdeb19d7927201d60e0e9c80d67b1ccb4087a4eabec1bb476cec8666 (from https://pypi.tuna.tsinghua.edu.cn/simple/attributee/) (requires-python:>=3.5), version: 0.1.7\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/attributee/\n", + "Given no hashes to check 1 links for project 'attributee': discarding no candidates\n", + "Collecting attributee==0.1.5\n", + " Created temporary directory: /tmp/pip-unpack-g5z7rqt8\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz HTTP/1.1\" 200 11033\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz (11 kB)\n", + " Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz\"\n", + " Added attributee==0.1.5 from https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz#sha256=a9c14efe6f540e70d226a44ecea10bc606521c4f81d4e8142170ed5c2b81feb9 (from mmtrack==0.13.0) to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Running setup.py (path:/tmp/pip-install-gyultzk6/attributee_1f4ddd965cd24399b6b52ec45967913b/setup.py) egg_info for package attributee\n", + " Created temporary directory: /tmp/pip-pip-egg-info-k0a86tp3\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info\n", + " writing /tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/dependency_links.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/SOURCES.txt'\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file '/tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/SOURCES.txt'\n", + " writing manifest file '/tmp/pip-pip-egg-info-k0a86tp3/attributee.egg-info/SOURCES.txt'\n", + " Source in /tmp/pip-install-gyultzk6/attributee_1f4ddd965cd24399b6b52ec45967913b has version 0.1.5, which satisfies requirement attributee==0.1.5 from https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz#sha256=a9c14efe6f540e70d226a44ecea10bc606521c4f81d4e8142170ed5c2b81feb9 (from mmtrack==0.13.0)\n", + " Removed attributee==0.1.5 from https://pypi.tuna.tsinghua.edu.cn/packages/b6/c2/50123286524e8dfee4b5502e349bae96c42185592479c9dc31c40f9f8259/attributee-0.1.5.tar.gz#sha256=a9c14efe6f540e70d226a44ecea10bc606521c4f81d4e8142170ed5c2b81feb9 (from mmtrack==0.13.0) from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "1 location(s) to search for versions of dotty-dict:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/dotty-dict/ HTTP/1.1\" 200 1956\n", + "Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\"\n", + "Caching due to etag\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b9/ec/49cedba03c1fd9ae5e3c8bdfac73d98e0208df29cd466fc7b0738f003ae9/dotty_dict-0.1.1-py2.py3-none-any.whl#sha256=ad0f60072417e32a45736083e098d1162ebeea762c6adc5f0963640a87e85130 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a5/ae/6603f1185b2e51ce1a24963e20d03a592abbcd27150eba57fafe5adf2093/dotty_dict-0.1.1.tar.gz#sha256=291417d1966467582f2d398c0bd592f33e14180dd084f58dbafb70bbf0803d72 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d3/d3/4828feea639306024e33f7c0f338eef5d51a451a66705f5b3cb27308dcd9/dotty_dict-0.1.2-py2.py3-none-any.whl#sha256=66b06a94d01ad080f4698eb49e4ab2f26f33d025d9dfc3f8e3f58746b2acfa71 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1a/45/ba519a550ca0b367b892bbf86d0f6eb3295ad21e5cdb21e2ad61a979b46d/dotty_dict-0.1.2.tar.gz#sha256=378b152a45f2f16787181023670286b7e0a30cef9f94d12db90c0b032b78f78f (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/2d/14/dd6f753f53fba9da89fc2512e521972036361c411a69b3a10fb9ac347d02/dotty_dict-0.1.7-py2.py3-none-any.whl#sha256=77520ddce5eeb2c0d9c41cde6f9fda690f3372d784e152ce49ec4c27a4cdcc2a (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.7\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/98/79/692b7a10aa7891483ccba4c915a178e50bd3cf38ad9962c83e463f513335/dotty_dict-0.1.7.tar.gz#sha256=063828bc6d7e354a93b88be90c9b23fa55a8a70083941cf59ae714704d17715a (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.7\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/23/3a/161acefb1650fb386ed06fb9328779b2adf7f1f774306c3a911c03d881d7/dotty_dict-0.1.8-py2.py3-none-any.whl#sha256=d8b78c06def1e4f810b40a5f789e48ca16f4eac7666e6d7596dcfe3f82e7576c (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.8\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/60/8b/ac1b3eaecd707ce44d1fa510e20e8c2fa74bbbb3b0043294ec9b6ea36e09/dotty_dict-0.1.8.tar.gz#sha256=ad9afc6dc7be91919e2fddb042ccf46d6c59bef752f7398d5cdc784afea649f4 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.8\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/27/01/c5e14a775ca0c9a6f7818c313aa02b4c5a146e6c6f85842ce627dc920f7a/dotty_dict-0.1.9-py2.py3-none-any.whl#sha256=877bd27728761d8913dddb6cba20122b9025f284bc0d7b45070827ae85924adb (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.9\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/db/0f/d5b10ae67ca08dc02bc7b764dbff7caa16579abc31c6a7cd1af5fb210f91/dotty_dict-0.1.9.tar.gz#sha256=35045e484fc9bb66fc2eb18c2db6f0a58ff9627f36c14361bfc0607f7d3af12f (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 0.1.9\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/cf/91/2e85f8db91e363e5b05fbfc3a2caa064ec096f5cce50f4498c98b117e516/dotty_dict-1.0.0.tar.gz#sha256=1d3f33fb718d453ec51c59154b46ad294393a000b221e6b16282ec9c7cd44de7 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.0.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e1/c7/541104682d6ddbe09fe3c5458142f88a41362de66649fc5e94361e88a3d4/dotty_dict-1.0.1.tar.gz#sha256=aad735bc1c52c733b008357d01bd87ca0d1eead93c5fbd6c4e2a8018076c56f0 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.0.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1d/ce/2006037bd6ec7dcd8d180e39996600319a5c322882472e7bf1a792c2a5f9/dotty_dict-1.0.2.tar.gz#sha256=eeff2b42e9af79144e302b9c068f2700099162f4e3a65671be5ea828ae449869 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.0.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8c/33/896f79def5c10a6a8c6a1228d8571d24e4e6eeeaad576a8952322d87f43b/dotty_dict-1.1.0.tar.gz#sha256=bfe2ca59e8d1831b35cb4b591aaae9d4679b347f281676187cf6b31cf4523fc4 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.1.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/15/5e/364a4eebef491936d048e96deda26800a7cd9848e658db956a9c10bbd2c3/dotty_dict-1.1.1.tar.gz#sha256=2d290a86bad0e7eb1cc5e748bcbb6ddf30597e2fb64613c5eca745a9b263ab91 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.1.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d0/08/43acdabe71b0c47e2cb3886336667869987a10d3a2f0facee7c7811cadda/dotty_dict-1.1.2.tar.gz#sha256=2e11232771d41e37896119c4f4b2c24b177117d53ac25f65e6019038177c415b (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.1.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7f/cc/ccbadadbdbe62d66ac9cdd9dc0dae88e3e495245e16cf3edab2b43e8cb62/dotty_dict-1.2.0.tar.gz#sha256=92d561e7cb27d8abcfa3fa252a5ef954e562146edb5e1caba9beafe8b97c25c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.2.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3a/47/84d990d040ddefb9ad905dd3dc1608762f7d57220c65f0c1c1f4b1140e31/dotty_dict-1.2.1.tar.gz#sha256=0b6f1ebc26a442f4cb1f963dbda7f5a56a5993a7c1311b1feca77e0bfcc1a13b (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.2.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz#sha256=eb0035a3629ecd84397a68f1f42f1e94abd1c34577a19cd3eacad331ee7cbaf0 (from https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/), version: 1.3.0\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/dotty-dict/\n", + "Given no hashes to check 19 links for project 'dotty-dict': discarding no candidates\n", + "Collecting dotty_dict\n", + " Created temporary directory: /tmp/pip-unpack-3m5rb93z\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz HTTP/1.1\" 200 32267\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz (32 kB)\n", + " Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz\"\n", + " Added dotty_dict from https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz#sha256=eb0035a3629ecd84397a68f1f42f1e94abd1c34577a19cd3eacad331ee7cbaf0 (from mmtrack==0.13.0) to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Running setup.py (path:/tmp/pip-install-gyultzk6/dotty-dict_eaf4f138a1f647fe8cb84557c3fd7647/setup.py) egg_info for package dotty-dict\n", + " Created temporary directory: /tmp/pip-pip-egg-info-7k6m8_30\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info\n", + " writing /tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/dependency_links.txt\n", + " writing requirements to /tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/requires.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/SOURCES.txt'\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file '/tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no previously-included files matching '__pycache__' found under directory '*'\n", + " warning: no previously-included files matching '*.py[co]' found under directory '*'\n", + " warning: no files found matching '*' under directory 'vo'\n", + " warning: no files found matching '*.jpg' under directory 'docs'\n", + " warning: no files found matching '*.png' under directory 'docs'\n", + " warning: no files found matching '*.gif' under directory 'docs'\n", + " writing manifest file '/tmp/pip-pip-egg-info-7k6m8_30/dotty_dict.egg-info/SOURCES.txt'\n", + " Source in /tmp/pip-install-gyultzk6/dotty-dict_eaf4f138a1f647fe8cb84557c3fd7647 has version 1.3.0, which satisfies requirement dotty_dict from https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz#sha256=eb0035a3629ecd84397a68f1f42f1e94abd1c34577a19cd3eacad331ee7cbaf0 (from mmtrack==0.13.0)\n", + " Removed dotty_dict from https://pypi.tuna.tsinghua.edu.cn/packages/a7/da/fc25898c4edb9549b2aac0f7329fec027d654e94d4c4b89849d4c5fff0a4/dotty_dict-1.3.0.tar.gz#sha256=eb0035a3629ecd84397a68f1f42f1e94abd1c34577a19cd3eacad331ee7cbaf0 (from mmtrack==0.13.0) from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "1 location(s) to search for versions of lap:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/lap/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/lap/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/lap/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/lap/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/lap/ HTTP/1.1\" 200 364\n", + "Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/simple/lap/\"\n", + "Caching due to etag\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/0b/17/d179f806407e1ccbce2c4ad5a265851c1c8d78f5b6ec5e1d1d6101135086/lap-0.3.0.tar.gz#sha256=f0464186e7f4a29073e3d960e0fe9d0eaed4515ebf936e6d48662c5e76906799 (from https://pypi.tuna.tsinghua.edu.cn/simple/lap/), version: 0.3.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz#sha256=c4dad9976f0e9f276d8a676a6d03632c3cb7ab7c80142e3b27303d49f0ed0e3b (from https://pypi.tuna.tsinghua.edu.cn/simple/lap/), version: 0.4.0\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/lap/\n", + "Given no hashes to check 2 links for project 'lap': discarding no candidates\n", + "Collecting lap\n", + " Created temporary directory: /tmp/pip-unpack-c5tar9nb\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz HTTP/1.1\" 200 1501497\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz (1.5 MB)\n", + "\u001b[K |███████████████████████████████▉| 1.5 MB 73.1 MB/s eta 0:00:01 Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz\"\n", + "\u001b[K |████████████████████████████████| 1.5 MB 73.1 MB/s \n", + "\u001b[?25h Added lap from https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz#sha256=c4dad9976f0e9f276d8a676a6d03632c3cb7ab7c80142e3b27303d49f0ed0e3b (from mmtrack==0.13.0) to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Running setup.py (path:/tmp/pip-install-gyultzk6/lap_5e1a2e2bc34541c28164661ddc5444b6/setup.py) egg_info for package lap\n", + " Created temporary directory: /tmp/pip-pip-egg-info-arge1zc3\n", + " Running command python setup.py egg_info\n", + " Partial import of lap during the build process.\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-arge1zc3/lap.egg-info\n", + " writing /tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/dependency_links.txt\n", + " writing requirements to /tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/requires.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/SOURCES.txt'\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file '/tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no files found matching 'setup.cfg'\n", + " warning: no files found matching '*.pxd' under directory 'lap'\n", + " warning: no files found matching '*.pxi' under directory 'lap'\n", + " warning: no files found matching '*.c' under directory 'lap'\n", + " warning: no files found matching '*.md' under directory 'lap'\n", + " warning: no previously-included files matching '*~' found anywhere in distribution\n", + " warning: no previously-included files matching '*.pyc' found anywhere in distribution\n", + " writing manifest file '/tmp/pip-pip-egg-info-arge1zc3/lap.egg-info/SOURCES.txt'\n", + " Source in /tmp/pip-install-gyultzk6/lap_5e1a2e2bc34541c28164661ddc5444b6 has version 0.4.0, which satisfies requirement lap from https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz#sha256=c4dad9976f0e9f276d8a676a6d03632c3cb7ab7c80142e3b27303d49f0ed0e3b (from mmtrack==0.13.0)\n", + " Removed lap from https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz#sha256=c4dad9976f0e9f276d8a676a6d03632c3cb7ab7c80142e3b27303d49f0ed0e3b (from mmtrack==0.13.0) from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (3.5.0)\n", + "1 location(s) to search for versions of mmcls:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/mmcls/ HTTP/1.1\" 200 None\n", + "Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\"\n", + "Caching due to etag\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/85/9b/6ef2d4ffb70f560a7e6376916bd582af2500ab843e0412764a29a990f867/mmcls-0.10.0-py2.py3-none-any.whl#sha256=4e7715f30d0da65d29c275d1abf915db5946b6514f36f389e2a676d28d4a3502 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.10.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/f6/d0/c4202ab4a4b1e367e01dd43a2d44bb39503bdea6bb15a37f5e41db855228/mmcls-0.10.0.tar.gz#sha256=84a2af00233e034452cebf50c7582ff357a26a618c648611b8d4cc8511ce463f (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.10.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/5e/f7/a650cd9c2506bd723aa7fafb12b40e0d53410a84e5b1cd2e974971b606af/mmcls-0.11.0-py2.py3-none-any.whl#sha256=c914e5cfdc340ca630a178d3f5684100201124d9a0476575bcb91f9c28234760 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.11.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/08/d0/f5386b66f92c7fa871377cf88f097adc02d67f066a518359b0b9555c0fa7/mmcls-0.11.0.tar.gz#sha256=8d5f527a8f32b4c07b0e13295e6578e210e114759a52d3ae41a87faafa8e34bf (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.11.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3a/2f/81cf1b258ba43ce741487e6c3d66dbd65e24e26540d36246d0abac6d3d9f/mmcls-0.11.1-py2.py3-none-any.whl#sha256=45b6926b57c76a492f29a943e7bc4fdd67d09cf33c69cf8e5edad57e8ed197ff (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.11.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/40/41/80ff30a794b5b8725592aed3f5a5cbc564b216e6c5144df86dca27fbd7b0/mmcls-0.11.1.tar.gz#sha256=f8d2a2f011f6230637c7662192854602007c5bcd7e3c5c30666069868a9dedb6 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.11.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3a/ad/1ccf8f11d22fffedad33a7078c905daa4ccb96e39954ea72aa90d62b7930/mmcls-0.12.0-py2.py3-none-any.whl#sha256=92fbe371f604cb93e7d7c1bc2ded486f08ba14611d6ad978446c7191834cac4d (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.12.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b6/0a/7dd6c6fa4c27d569df3edaf89be42a869c8af2a849f98c83027145345f41/mmcls-0.12.0.tar.gz#sha256=688a9493bacaf9e5e7e26faf5161e72b70047fe74d3caaa1659b1debfbde1145 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.12.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/f6/33/7ba1b7a234d3c60b4282ea85adcc438c43eb3a2bf92ee9bdaf69a176d723/mmcls-0.13.0-py2.py3-none-any.whl#sha256=fef1d14f2d05abd9f360ed8855a5bbe87e0b71e3b46030ec6d289f17f9dcc8fe (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.13.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/24/12/e6da3e76009c3e34d674e2a640d220f3d371810b03e107c0bb6e2d0d4c4f/mmcls-0.13.0.tar.gz#sha256=cf7eb142f6889f6c7075d5823fc60cceabc5c876e82466b85d3d4eed8d3ee1f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.13.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/fa/dd/2dfe7501ee91d7bae96bd88b38860fe7b4cb5811ecd19325bbdc07c0094a/mmcls-0.14.0-py2.py3-none-any.whl#sha256=328975a828aa49e33995b98eb59b47ddd74dd83ea5c097a2899098c84a66b5c8 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.14.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1e/13/dc005f6c08c753041a4ce12e08e8bac191dc3edd64da7e95c3c45c18ab77/mmcls-0.14.0.tar.gz#sha256=30986dd543c87a56791580ef000b1bfc2fbba1ae916cf454d403ddddf0785d55 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.14.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/6f/c6/2d4d8677435bc3b4fc843df859d28d9460c9f5bf281eab390076bbc41a60/mmcls-0.15.0-py2.py3-none-any.whl#sha256=32a3923c3d312a5c5c17a1bc25dae0b1a2cf9c2c6642d47161e4e0a2eddeff44 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.15.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/2e/82/d7d543c2572750b8783492e99fd4e464a5cbbf0e8e1b739d0b4e7ff7a86c/mmcls-0.15.0.tar.gz#sha256=17f4210ae4c45deb614146385c0ed34678e121aa8ad59f287c2fb73a7886c8f8 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.15.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d6/92/a639941d9a2d9d45912cfa7aae3c6277050a4f4fb8231f2ae27cd6812855/mmcls-0.16.0-py2.py3-none-any.whl#sha256=8d29ec9eab98c2dcc23cae00607357b67993e600c3402223c18b7aa55e9f8011 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.16.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/63/cc/2bdfb7c10c8ae1d5f0dd6f350b163cd8181481ff1662b2bbf5eb08d0da83/mmcls-0.16.0.tar.gz#sha256=6dcead18234aa2ad8b8c38842f85fb41f1604a8df7150cca913fa4a36b9ec378 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.16.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/11/ff/4c55b0e7257d4bdc249cdb24e2da0651d59d6be687d719913eeaaea429d5/mmcls-0.17.0-py2.py3-none-any.whl#sha256=7982c36a032fdaa1c1fe0bcd555a6106d4ccde6a721800447ab560d15e326468 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.17.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/be/f5/1cc2fb292fdd853360f017837688f4b1c3b656a5616db3b97efd63f148bd/mmcls-0.17.0.tar.gz#sha256=50b532feb3547bd2bebb4d3742860ce645072b3945071de3f634cad1f75c2993 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.17.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d4/81/7a5712a5b393a02a49148a184ab7dc6c192f6adeee4ce17ff1958e06b230/mmcls-0.18.0-py2.py3-none-any.whl#sha256=267f028de581b60cad6a1fd29b221eaa62c64720516239be1e6de78111a86003 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.18.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/30/65/9e90ced0c0a7bef6947819838bf3e8d787be63435831d979744e0ba102ec/mmcls-0.18.0.tar.gz#sha256=5b0584ae0682b110a3ce154d3249fabc7b4f428633472122325165d2641adf5b (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.18.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/21/43/c43508f8932ae074809a6a2b76bdf12841309697a851f4e68087e3618d19/mmcls-0.19.0-py2.py3-none-any.whl#sha256=9d3f7b9cb44c3a04af78f16cdd450b5a5b39b8a1f943553bce94c7b2d9553992 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.19.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/77/3a/6c7bd85c96b8a18f68d0177972882d3cb6e55c143997abc55f3c29ff7a34/mmcls-0.19.0.tar.gz#sha256=a93f3191b33ab587fb1d31148c76341832f70ce1a6395201cee71682bf853d40 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.19.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/19/31/f6f553ce0967104308adb2d5d6fe8ea1d000b9deca698791586ac3a13672/mmcls-0.20.0-py2.py3-none-any.whl#sha256=bce0499fd5efc5b7f0bb91097a58280480b49a908be231dfe7bbf3dd742237eb (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.20.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/79/d8/bd6cc771cc3bfedc665008110752ad49c5344fee9b73ed8e42bfa806cf89/mmcls-0.20.0.tar.gz#sha256=6c57107ec4eccba8304ca561aea88f8350629ebb692d9d1321267ee0883b30cd (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.20.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/f6/f6/9464bc39ea63e33b64d4ba1d33df2d83e35f674640ba71b74e2c910ec5e0/mmcls-0.20.1-py2.py3-none-any.whl#sha256=7cc4bc91f87af87114fcc8e71c984fe53b5318fd8aea438bb5557dd27ca192cb (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.20.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/17/0b/aeb8a0e039e588bfaa212025ea38a37b6b44caaa44459c1ad10ec6108d80/mmcls-0.20.1.tar.gz#sha256=28460b4ae32efd6baaf532ead8d9de9426486511815a8a6cc13b6f05a410e987 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.20.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/ef/c8/b319cb91649b0c2649a3c7446630ddba002c5754d156a5468e323c227da6/mmcls-0.21.0-py2.py3-none-any.whl#sha256=02a3c1186d100f4dbb5c5f99138499ebfcc2110cca447ae0938ed046c76a6e98 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.21.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d6/1c/918b7f8d566c0bf9240699b083f44c6d2e6f6d411b9bd57f25a7843e3a7e/mmcls-0.21.0.tar.gz#sha256=29b3e63cad030fba008325d05868ec28a20b9f1904fcbca09ff2a579285ca6c4 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.21.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/99/32/233363eaa9a0a71f6b3d9689dd4c06bd96bccae80cd008419a60435c296f/mmcls-0.22.0-py2.py3-none-any.whl#sha256=d5a60bb4dab36f2a31248049dc57d933c147c556d1d2e4c7e172650ab1547bb2 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.22.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/0d/7f/e94ed849e560174f9ff880e916c65a2496b552b3fc1a944cc0a2ae26c08d/mmcls-0.22.0.tar.gz#sha256=62bb8b2c8431271d1d026522d2c2aaf658bb4ebb28d4ae224b4f8aa57eabd3da (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.22.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/c8/69/6b614906fd2a4fba4164f80b4490fb0ab1c9a8338b170ab877aaab791bc6/mmcls-0.22.1-py2.py3-none-any.whl#sha256=6c41f1e11d125d593e7f7d680489984e14d02694d3ecb52fede8fa65b5398b90 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.22.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e3/f3/b37706eb449b1e529e1deea952f68e29ec394995d07100467b261a16c047/mmcls-0.22.1.tar.gz#sha256=de6cf99c92b11f01e966e0691827fa21392d33c41106253bd148fec23d75a696 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.22.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/82/df/49b63e1e43b483697005c48752a8609fabb5303225db02781bd9e4a04111/mmcls-0.23.0-py2.py3-none-any.whl#sha256=bd0c31f2540f3a76353fb0154281df5ac4cc6e2661e5d32789ba010a8db16dc3 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.23.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/46/e5/a409566de449f288cc947d8ea1eb2fd0b98492bd975110d95b290158a452/mmcls-0.23.0.tar.gz#sha256=b09ffa12678dff3fee91d52318874e77394165a6ab9fbbf27a494114da262ef8 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.23.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl#sha256=dc2e72572859a9f137251648c8a6f5e38f13101d1dba72eb2eb6ed9add5c8e84 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.23.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/ac/0f/5bdf838c82c1f7165aadb4a7518f081263c20e4f76efd4e3eb58e1523ce4/mmcls-0.23.1.tar.gz#sha256=195345f6301f8fb095598dc51f35ff2f75feeded2aded90c09f311582b3cbba3 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.23.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8e/9a/1a1aab4f1bc2ad38f2c1dd632bddd4c9e48bba62cbde6d9129329c556726/mmcls-0.6.0-py2.py3-none-any.whl#sha256=edab9cf3d5ff6ab488eaabf0260e37abadaf0178973017bf1adfce8d7ec28000 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.6.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7b/7e/2e82d753ae85f95cbf702913b516c6738a90bfbf349a86df65b207b10446/mmcls-0.6.0.tar.gz#sha256=fe720a38e7f33c7f0a009a554798ba0082754d32b668f96de8b8ec2e9d579f7e (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.6.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/89/da/6e629aaf9c14fecef6c369409edc2c4d4d9402793d9552efd0eedd30cb0b/mmcls-0.7.0-py2.py3-none-any.whl#sha256=04e0b454e95a01f8ced043492394e023ea9d856e099f79b4fa79a29a1090d4ba (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.7.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/fe/65/5c4f601c85067788cdad1541f2af11fad1d0608fc30cac70bdbc015b9b1a/mmcls-0.7.0.tar.gz#sha256=7aad71c77ed631499c3c32b630cd6b93050df7cacef1465d6ea80cde0ab679d9 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.7.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/82/f9/98c2a039c9a93a8dd92965f1805d1c378e5dd1e5f411c5b254253a3c49b6/mmcls-0.8.0-py2.py3-none-any.whl#sha256=90edf229fafcb463bfb6682d20680b1ad494c33f3c2f8fcf55333059784fb9cd (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.8.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8b/fe/c42e1eafa4cf3ce8f83278b5c5e4bf52106c8a4745e5b46cd720d77e40a5/mmcls-0.8.0.tar.gz#sha256=56df1cda187e981e687cac50743a66e7de8af53b3ee1c94007fb5118374bebd6 (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.8.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1b/06/6327a2a89f7ec1e4e89d2bb361f73e8ef2ef01c46fe7971711f0d8e89344/mmcls-0.9.0-py2.py3-none-any.whl#sha256=c3e74a4a5f54bc1d1ce98524bbd41378de551f3f37b6ffceebb008e1e8adb6cc (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.9.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a8/50/9f4928b12eb757f270711718227e898a8f36f80bb93a657d37fd25191044/mmcls-0.9.0.tar.gz#sha256=cab5d0edea2020937b348898cd9bf39d2706412bb190b20023276da7863d88bb (from https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/), version: 0.9.0\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/mmcls/\n", + "Given no hashes to check 22 links for project 'mmcls': discarding no candidates\n", + "Collecting mmcls>=0.16.0\n", + " Created temporary directory: /tmp/pip-unpack-0b49c4k_\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl HTTP/1.1\" 200 577265\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl (577 kB)\n", + "\u001b[K |███████████████████████████████▉| 573 kB 72.1 MB/s eta 0:00:01 Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl\"\n", + "\u001b[K |████████████████████████████████| 577 kB 72.1 MB/s \n", + "\u001b[?25h Added mmcls>=0.16.0 from https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl#sha256=dc2e72572859a9f137251648c8a6f5e38f13101d1dba72eb2eb6ed9add5c8e84 (from mmtrack==0.13.0) to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Removed mmcls>=0.16.0 from https://pypi.tuna.tsinghua.edu.cn/packages/16/2d/c50efb993f6f14f13631a10a72f72135fd47ae4173c60a8c120095d1bc7e/mmcls-0.23.1-py2.py3-none-any.whl#sha256=dc2e72572859a9f137251648c8a6f5e38f13101d1dba72eb2eb6ed9add5c8e84 (from mmtrack==0.13.0) from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "Requirement already satisfied: motmetrics in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (1.2.5)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (21.3)\n", + "Requirement already satisfied: pandas<=1.3.5 in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (1.3.4)\n", + "1 location(s) to search for versions of pycocotools:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/pycocotools/ HTTP/1.1\" 304 0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/96/84/9a07b1095fd8555ba3f3d519517c8743c2554a245f9476e5e39869f948d2/pycocotools-2.0.0.tar.gz#sha256=cbb8c2fbab80450a67ee9879c63b0bc8a69e58dd9a0153d55de404c0d383a94b (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/), version: 2.0.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/5c/82/bcaf4d21d7027fe5165b88e3aef1910a36ed02c3e99d3385d1322ea0ba29/pycocotools-2.0.1.tar.gz#sha256=1c06e73a85ed9874c1174d47064524b9fb2759b95a6997437775652f20c1711f (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/), version: 2.0.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/), version: 2.0.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/66/45/36556573d509349a4a1a49bc96fdc3dd6046d691c6660fc0416d93fb1547/pycocotools-2.0.2a1.tar.gz#sha256=5138269c32d42772a7d1bd76815203680b1ca9f051929f6c0affa08e594c76fc (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/), version: 2.0.2a1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/2e/1c/4fd663fc57be418cecf6f89d0d141ffa815d0fd6538ccddeccf767e8aace/pycocotools-2.0.3.tar.gz#sha256=3829024930013771156521a4b8db4b3aef590556cfa3a8dd3fab027d39b215e1 (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/), version: 2.0.3\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/75/5c/ac61ea715d7a89ecc31c090753bde28810238225ca8b71778dfe3e6a68bc/pycocotools-2.0.4.tar.gz#sha256=2ab586aa389b9657b6d73c2b9a827a3681f8d00f36490c2e8ab05902e3fd9e93 (from https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/) (requires-python:>=3.5), version: 2.0.4\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/pycocotools/\n", + "Given no hashes to check 3 links for project 'pycocotools': discarding no candidates\n", + "Collecting pycocotools<=2.0.2\n", + " Created temporary directory: /tmp/pip-unpack-big215pj\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz HTTP/1.1\" 200 23527\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz (23 kB)\n", + " Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz\"\n", + " Added pycocotools<=2.0.2 from https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a (from mmtrack==0.13.0) to build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + " Running setup.py (path:/tmp/pip-install-gyultzk6/pycocotools_0cf896d5ca664f87837892912d63c17d/setup.py) egg_info for package pycocotools\n", + " Created temporary directory: /tmp/pip-pip-egg-info-7ftcew5j\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info\n", + " writing /tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/dependency_links.txt\n", + " writing requirements to /tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/requires.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/SOURCES.txt'\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file '/tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " writing manifest file '/tmp/pip-pip-egg-info-7ftcew5j/pycocotools.egg-info/SOURCES.txt'\n", + " Source in /tmp/pip-install-gyultzk6/pycocotools_0cf896d5ca664f87837892912d63c17d has version 2.0.2, which satisfies requirement pycocotools<=2.0.2 from https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a (from mmtrack==0.13.0)\n", + " Removed pycocotools<=2.0.2 from https://pypi.tuna.tsinghua.edu.cn/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a (from mmtrack==0.13.0) from build tracker '/tmp/pip-req-tracker-gnol5xvg'\n", + "Requirement already satisfied: scipy<=1.7.3 in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (1.7.3)\n", + "Requirement already satisfied: seaborn in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (0.11.2)\n", + "Requirement already satisfied: terminaltables in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (3.1.10)\n", + "Requirement already satisfied: tqdm in /environment/miniconda3/lib/python3.7/site-packages (from mmtrack==0.13.0) (4.61.2)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmcls>=0.16.0->mmtrack==0.13.0) (1.21.4)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas<=1.3.5->mmtrack==0.13.0) (2.8.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas<=1.3.5->mmtrack==0.13.0) (2021.3)\n", + "Requirement already satisfied: setuptools>=18.0 in /environment/miniconda3/lib/python3.7/site-packages (from pycocotools<=2.0.2->mmtrack==0.13.0) (52.0.0.post20210125)\n", + "Requirement already satisfied: cython>=0.27.3 in /environment/miniconda3/lib/python3.7/site-packages (from pycocotools<=2.0.2->mmtrack==0.13.0) (0.29.30)\n", + "Requirement already satisfied: pillow>=6.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (8.4.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (3.0.6)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (4.28.3)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (0.11.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (1.3.2)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmtrack==0.13.0) (6.3.2)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas<=1.3.5->mmtrack==0.13.0) (1.16.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmtrack==0.13.0) (1.2.2)\n", + "Requirement already satisfied: xmltodict>=0.12.0 in /environment/miniconda3/lib/python3.7/site-packages (from motmetrics->mmtrack==0.13.0) (0.13.0)\n", + "Created temporary directory: /tmp/pip-unpack-58flwnk7\n", + "Building wheels for collected packages: attributee, pycocotools, dotty-dict, lap\n", + " Created temporary directory: /tmp/pip-wheel-p26k0ilq\n", + " Building wheel for attributee (setup.py) ... \u001b[?25l Destination directory: /tmp/pip-wheel-p26k0ilq\n", + " Running command /environment/miniconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-gyultzk6/attributee_1f4ddd965cd24399b6b52ec45967913b/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-gyultzk6/attributee_1f4ddd965cd24399b6b52ec45967913b/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-p26k0ilq\n", + " running bdist_wheel\n", + " running build\n", + " running build_py\n", + " creating build\n", + " creating build/lib\n", + " creating build/lib/attributee\n", + " copying attributee/io.py -> build/lib/attributee\n", + " copying attributee/__init__.py -> build/lib/attributee\n", + " copying attributee/containers.py -> build/lib/attributee\n", + " copying attributee/object.py -> build/lib/attributee\n", + " copying attributee/tests.py -> build/lib/attributee\n", + " copying attributee/primitives.py -> build/lib/attributee\n", + " running egg_info\n", + " writing attributee.egg-info/PKG-INFO\n", + " writing dependency_links to attributee.egg-info/dependency_links.txt\n", + " writing top-level names to attributee.egg-info/top_level.txt\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file 'attributee.egg-info/SOURCES.txt'\n", + " writing manifest file 'attributee.egg-info/SOURCES.txt'\n", + " installing to build/bdist.linux-x86_64/wheel\n", + " running install\n", + " running install_lib\n", + " creating build/bdist.linux-x86_64\n", + " creating build/bdist.linux-x86_64/wheel\n", + " creating build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/io.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/__init__.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/containers.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/object.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/tests.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " copying build/lib/attributee/primitives.py -> build/bdist.linux-x86_64/wheel/attributee\n", + " running install_egg_info\n", + " Copying attributee.egg-info to build/bdist.linux-x86_64/wheel/attributee-0.1.5-py3.7.egg-info\n", + " running install_scripts\n", + " adding license file \"LICENSE.md\" (matched pattern \"LICEN[CS]E*\")\n", + " creating build/bdist.linux-x86_64/wheel/attributee-0.1.5.dist-info/WHEEL\n", + " creating '/tmp/pip-wheel-p26k0ilq/attributee-0.1.5-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n", + " adding 'attributee/__init__.py'\n", + " adding 'attributee/containers.py'\n", + " adding 'attributee/io.py'\n", + " adding 'attributee/object.py'\n", + " adding 'attributee/primitives.py'\n", + " adding 'attributee/tests.py'\n", + " adding 'attributee-0.1.5.dist-info/LICENSE.md'\n", + " adding 'attributee-0.1.5.dist-info/METADATA'\n", + " adding 'attributee-0.1.5.dist-info/WHEEL'\n", + " adding 'attributee-0.1.5.dist-info/top_level.txt'\n", + " adding 'attributee-0.1.5.dist-info/RECORD'\n", + " removing build/bdist.linux-x86_64/wheel\n", + "\u001b[?25hdone\n", + " Created wheel for attributee: filename=attributee-0.1.5-py3-none-any.whl size=12067 sha256=794fe1274bd5935aa602d1f81157145a58786f354f75a6872864079bb05345af\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/f2/fb/c3/8cde21929efecfc7bea5ee163a51f20ff008b13332392011b3\n", + " Created temporary directory: /tmp/pip-wheel-qwxniqou\n", + " Building wheel for pycocotools (setup.py) ... \u001b[?25l Destination directory: /tmp/pip-wheel-qwxniqou\n", + " Running command /environment/miniconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-gyultzk6/pycocotools_0cf896d5ca664f87837892912d63c17d/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-gyultzk6/pycocotools_0cf896d5ca664f87837892912d63c17d/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-qwxniqou\n", + " running bdist_wheel\n", + " running build\n", + " running build_py\n", + " creating build\n", + " creating build/lib.linux-x86_64-3.7\n", + " creating build/lib.linux-x86_64-3.7/pycocotools\n", + " copying pycocotools/cocoeval.py -> build/lib.linux-x86_64-3.7/pycocotools\n", + " copying pycocotools/__init__.py -> build/lib.linux-x86_64-3.7/pycocotools\n", + " copying pycocotools/coco.py -> build/lib.linux-x86_64-3.7/pycocotools\n", + " copying pycocotools/mask.py -> build/lib.linux-x86_64-3.7/pycocotools\n", + " running build_ext\n", + " cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n", + " /environment/miniconda3/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /tmp/pip-install-gyultzk6/pycocotools_0cf896d5ca664f87837892912d63c17d/pycocotools/_mask.pyx\n", + " tree = Parsing.p_module(s, pxd, full_module_name)\n", + " building 'pycocotools._mask' extension\n", + " creating build/temp.linux-x86_64-3.7\n", + " creating build/temp.linux-x86_64-3.7/common\n", + " creating build/temp.linux-x86_64-3.7/pycocotools\n", + " gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/environment/miniconda3/lib/python3.7/site-packages/numpy/core/include -I./common -I/environment/miniconda3/include/python3.7m -c ./common/maskApi.c -o build/temp.linux-x86_64-3.7/./common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n", + " ./common/maskApi.c: In function ‘rleDecode’:\n", + " ./common/maskApi.c:46:7: warning: this ‘for’ clause does not guard... [-Wmisleading-indentation]\n", + " 46 | for( k=0; k2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n", + " | ^~\n", + " ./common/maskApi.c:228:34: note: ...this statement, but the latter is misleadingly indented as if it were guarded by the ‘if’\n", + " 228 | if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n", + " | ^~~~\n", + " gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/environment/miniconda3/lib/python3.7/site-packages/numpy/core/include -I./common -I/environment/miniconda3/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n", + " gcc -pthread -shared -B /environment/miniconda3/compiler_compat -L/environment/miniconda3/lib -Wl,-rpath=/environment/miniconda3/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/./common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\n", + " installing to build/bdist.linux-x86_64/wheel\n", + " running install\n", + " running install_lib\n", + " creating build/bdist.linux-x86_64\n", + " creating build/bdist.linux-x86_64/wheel\n", + " creating build/bdist.linux-x86_64/wheel/pycocotools\n", + " copying build/lib.linux-x86_64-3.7/pycocotools/cocoeval.py -> build/bdist.linux-x86_64/wheel/pycocotools\n", + " copying build/lib.linux-x86_64-3.7/pycocotools/__init__.py -> build/bdist.linux-x86_64/wheel/pycocotools\n", + " copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/pycocotools\n", + " copying build/lib.linux-x86_64-3.7/pycocotools/coco.py -> build/bdist.linux-x86_64/wheel/pycocotools\n", + " copying build/lib.linux-x86_64-3.7/pycocotools/mask.py -> build/bdist.linux-x86_64/wheel/pycocotools\n", + " running install_egg_info\n", + " running egg_info\n", + " writing pycocotools.egg-info/PKG-INFO\n", + " writing dependency_links to pycocotools.egg-info/dependency_links.txt\n", + " writing requirements to pycocotools.egg-info/requires.txt\n", + " writing top-level names to pycocotools.egg-info/top_level.txt\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file 'pycocotools.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " writing manifest file 'pycocotools.egg-info/SOURCES.txt'\n", + " Copying pycocotools.egg-info to build/bdist.linux-x86_64/wheel/pycocotools-2.0.2-py3.7.egg-info\n", + " running install_scripts\n", + " creating build/bdist.linux-x86_64/wheel/pycocotools-2.0.2.dist-info/WHEEL\n", + " creating '/tmp/pip-wheel-qwxniqou/pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n", + " adding 'pycocotools/__init__.py'\n", + " adding 'pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so'\n", + " adding 'pycocotools/coco.py'\n", + " adding 'pycocotools/cocoeval.py'\n", + " adding 'pycocotools/mask.py'\n", + " adding 'pycocotools-2.0.2.dist-info/METADATA'\n", + " adding 'pycocotools-2.0.2.dist-info/WHEEL'\n", + " adding 'pycocotools-2.0.2.dist-info/top_level.txt'\n", + " adding 'pycocotools-2.0.2.dist-info/RECORD'\n", + " removing build/bdist.linux-x86_64/wheel\n", + "\u001b[?25hdone\n", + " Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=371400 sha256=d7bd93f2b480b920b11c5a3366f16a53254be5b5498f20f86c0dd651d2d23997\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/f0/4e/80/5c67caf3bcf78ccd860093035add754e4d05e2a54b06dd2cb9\n", + " Created temporary directory: /tmp/pip-wheel-q7lfb3x1\n", + " Building wheel for dotty-dict (setup.py) ... \u001b[?25l Destination directory: /tmp/pip-wheel-q7lfb3x1\n", + " Running command /environment/miniconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-gyultzk6/dotty-dict_eaf4f138a1f647fe8cb84557c3fd7647/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-gyultzk6/dotty-dict_eaf4f138a1f647fe8cb84557c3fd7647/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-q7lfb3x1\n", + " running bdist_wheel\n", + " running build\n", + " running build_py\n", + " creating build\n", + " creating build/lib\n", + " creating build/lib/dotty_dict\n", + " copying dotty_dict/__init__.py -> build/lib/dotty_dict\n", + " copying dotty_dict/dotty_dict.py -> build/lib/dotty_dict\n", + " running egg_info\n", + " writing dotty_dict.egg-info/PKG-INFO\n", + " writing dependency_links to dotty_dict.egg-info/dependency_links.txt\n", + " writing requirements to dotty_dict.egg-info/requires.txt\n", + " writing top-level names to dotty_dict.egg-info/top_level.txt\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file 'dotty_dict.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no previously-included files matching '__pycache__' found under directory '*'\n", + " warning: no previously-included files matching '*.py[co]' found under directory '*'\n", + " warning: no files found matching '*' under directory 'vo'\n", + " warning: no files found matching '*.jpg' under directory 'docs'\n", + " warning: no files found matching '*.png' under directory 'docs'\n", + " warning: no files found matching '*.gif' under directory 'docs'\n", + " writing manifest file 'dotty_dict.egg-info/SOURCES.txt'\n", + " installing to build/bdist.linux-x86_64/wheel\n", + " running install\n", + " running install_lib\n", + " creating build/bdist.linux-x86_64\n", + " creating build/bdist.linux-x86_64/wheel\n", + " creating build/bdist.linux-x86_64/wheel/dotty_dict\n", + " copying build/lib/dotty_dict/__init__.py -> build/bdist.linux-x86_64/wheel/dotty_dict\n", + " copying build/lib/dotty_dict/dotty_dict.py -> build/bdist.linux-x86_64/wheel/dotty_dict\n", + " running install_egg_info\n", + " Copying dotty_dict.egg-info to build/bdist.linux-x86_64/wheel/dotty_dict-1.3.0-py3.7.egg-info\n", + " running install_scripts\n", + " adding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\")\n", + " adding license file \"AUTHORS.rst\" (matched pattern \"AUTHORS*\")\n", + " creating build/bdist.linux-x86_64/wheel/dotty_dict-1.3.0.dist-info/WHEEL\n", + " creating '/tmp/pip-wheel-q7lfb3x1/dotty_dict-1.3.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n", + " adding 'dotty_dict/__init__.py'\n", + " adding 'dotty_dict/dotty_dict.py'\n", + " adding 'dotty_dict-1.3.0.dist-info/AUTHORS.rst'\n", + " adding 'dotty_dict-1.3.0.dist-info/LICENSE'\n", + " adding 'dotty_dict-1.3.0.dist-info/METADATA'\n", + " adding 'dotty_dict-1.3.0.dist-info/WHEEL'\n", + " adding 'dotty_dict-1.3.0.dist-info/top_level.txt'\n", + " adding 'dotty_dict-1.3.0.dist-info/RECORD'\n", + " removing build/bdist.linux-x86_64/wheel\n", + "\u001b[?25hdone\n", + " Created wheel for dotty-dict: filename=dotty_dict-1.3.0-py3-none-any.whl size=7658 sha256=0c2a0d3e2b9e5ad68a4d84bebbd8fccb47387ba42a7f132a86f20c5b434e9120\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/32/2c/ea/e609d3faf1d9017eaa685b124274022e6e5cb5bda2ed2d5a7a\n", + " Created temporary directory: /tmp/pip-wheel-_s5l0yat\n", + " Building wheel for lap (setup.py) ... \u001b[?25l Destination directory: /tmp/pip-wheel-_s5l0yat\n", + " Running command /environment/miniconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-gyultzk6/lap_5e1a2e2bc34541c28164661ddc5444b6/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-gyultzk6/lap_5e1a2e2bc34541c28164661ddc5444b6/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-_s5l0yat\n", + " Partial import of lap during the build process.\n", + " Generating cython files\n", + " running bdist_wheel\n", + " running build\n", + " running config_cc\n", + " unifing config_cc, config, build_clib, build_ext, build commands --compiler options\n", + " running config_fc\n", + " unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\n", + " running build_src\n", + " build_src\n", + " building extension \"lap._lapjv\" sources\n", + " building data_files sources\n", + " build_src: building npy-pkg config files\n", + " running build_py\n", + " creating build\n", + " creating build/lib.linux-x86_64-3.7\n", + " creating build/lib.linux-x86_64-3.7/lap\n", + " copying lap/lapmod.py -> build/lib.linux-x86_64-3.7/lap\n", + " copying lap/__init__.py -> build/lib.linux-x86_64-3.7/lap\n", + " running build_ext\n", + " customize UnixCCompiler\n", + " customize UnixCCompiler using build_ext\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-march=native)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " creating /tmp/tmp9_uxv5s2/environment\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib/python3.7\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib/python3.7/site-packages\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib/python3.7/site-packages/numpy\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib/python3.7/site-packages/numpy/distutils\n", + " creating /tmp/tmp9_uxv5s2/environment/miniconda3/lib/python3.7/site-packages/numpy/distutils/checks\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-march=native'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-O3)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-O3'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-Werror)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-Werror'\n", + " CCompilerOpt.__init__[1701] : check requested baseline\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-msse)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-msse2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse2'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSE' with flags (-msse -msse2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSE2' with flags (-msse -msse2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-msse3)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse3'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSE3' with flags (-msse -msse2 -msse3)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -Werror'\n", + " CCompilerOpt.__init__[1710] : check requested dispatch-able features\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mssse3)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mssse3'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-msse4.1)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse4.1'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mpopcnt)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mpopcnt'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-msse4.2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse4.2'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSE42' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSSE3' with flags (-msse -msse2 -msse3 -mssse3)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'SSE41' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mf16c)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mf16c'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx2'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX2' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mavx2)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mavx2 -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mfma)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mfma'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512f)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512f'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512F' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'POPCNT' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'FMA3' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'F16C' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512cd)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512cd'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512CD' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512vl -mavx512bw -mavx512dq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512vl -mavx512bw -mavx512dq'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512ifma -mavx512vbmi)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512ifma -mavx512vbmi'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_CNL' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512ifma -mavx512vbmi)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512ifma -mavx512vbmi -Werror'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_SKX' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512vnni)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512vnni'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_CLX' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512vnni)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512vnni -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_ICL' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512vnni -mavx512ifma -mavx512vbmi -mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512vnni -mavx512ifma -mavx512vbmi -mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx512er -mavx512pf)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx512er -mavx512pf'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_KNL' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512er -mavx512pf)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512er -mavx512pf -Werror'\n", + " CCompilerOpt.cc_test_flags[1013] : testing flags (-mavx5124fmaps -mavx5124vnniw -mavx512vpopcntdq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-mavx5124fmaps -mavx5124vnniw -mavx512vpopcntdq'\n", + " CCompilerOpt.feature_test[1468] : testing feature 'AVX512_KNM' with flags (-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512er -mavx512pf -mavx5124fmaps -mavx5124vnniw -mavx512vpopcntdq)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512er -mavx512pf -mavx5124fmaps -mavx5124vnniw -mavx512vpopcntdq -Werror'\n", + " CCompilerOpt.__init__[1723] : skip features (SSE2 SSE3 SSE) since its part of baseline\n", + " CCompilerOpt.__init__[1726] : initialize targets groups\n", + " CCompilerOpt.__init__[1728] : parse target group simd_test\n", + " CCompilerOpt._parse_target_tokens[1941] : skip targets (VSX ASIMD XOP FMA4 NEON VSX2 VSX3) not part of baseline or dispatch-able features\n", + " CCompilerOpt._parse_policy_not_keepbase[2051] : skip baseline features (SSE2)\n", + " CCompilerOpt.generate_dispatch_header[2272] : generate CPU dispatch header: (build/src.linux-x86_64-3.7/numpy/distutils/include/npy_cpu_dispatch_config.h)\n", + " CCompilerOpt.generate_dispatch_header[2283] : dispatch header dir build/src.linux-x86_64-3.7/numpy/distutils/include does not exist, creating it\n", + " CCompilerOpt.feature_extra_checks[1546] : Testing extra checks for feature 'AVX512F' (AVX512F_REDUCE)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -Werror'\n", + " CCompilerOpt.feature_extra_checks[1546] : Testing extra checks for feature 'AVX512_SKX' (AVX512BW_MASK AVX512DQ_MASK)\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -Werror'\n", + " C compiler: gcc -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC\n", + "\n", + " compile options: '-I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3 -mssse3 -msse4.1 -mpopcnt -msse4.2 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -Werror'\n", + " customize UnixCCompiler\n", + " customize UnixCCompiler using build_ext\n", + " building 'lap._lapjv' extension\n", + " compiling C++ sources\n", + " C compiler: g++ -pthread -B /environment/miniconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC\n", + "\n", + " creating build/temp.linux-x86_64-3.7/lap\n", + " compile options: '-I/environment/miniconda3/lib/python3.7/site-packages/numpy/core/include -Ilap -I/environment/miniconda3/lib/python3.7/site-packages/numpy/core/include -Ibuild/src.linux-x86_64-3.7/numpy/distutils/include -I/environment/miniconda3/include/python3.7m -c'\n", + " extra options: '-msse -msse2 -msse3'\n", + " g++: lap/_lapjv.cppg++: lap/lapjv.cpp\n", + "\n", + " g++: lap/lapmod.cpp\n", + " In file included from /environment/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1969,\n", + " from /environment/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n", + " from /environment/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n", + " from lap/_lapjv.cpp:581:\n", + " /environment/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n", + " 17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n", + " | ^~~~~~~\n", + " g++ -pthread -shared -B /environment/miniconda3/compiler_compat -L/environment/miniconda3/lib -Wl,-rpath=/environment/miniconda3/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/lap/_lapjv.o build/temp.linux-x86_64-3.7/lap/lapjv.o build/temp.linux-x86_64-3.7/lap/lapmod.o -o build/lib.linux-x86_64-3.7/lap/_lapjv.cpython-37m-x86_64-linux-gnu.so\n", + " installing to build/bdist.linux-x86_64/wheel\n", + " running install\n", + " running install_lib\n", + " creating build/bdist.linux-x86_64\n", + " creating build/bdist.linux-x86_64/wheel\n", + " creating build/bdist.linux-x86_64/wheel/lap\n", + " copying build/lib.linux-x86_64-3.7/lap/_lapjv.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/lap\n", + " copying build/lib.linux-x86_64-3.7/lap/lapmod.py -> build/bdist.linux-x86_64/wheel/lap\n", + " copying build/lib.linux-x86_64-3.7/lap/__init__.py -> build/bdist.linux-x86_64/wheel/lap\n", + " running install_data\n", + " creating build/bdist.linux-x86_64/wheel/lap/tests\n", + " copying lap/tests/test_arr_loop.py -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " copying lap/tests/cost_eps.csv.gz -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " copying lap/tests/__init__.py -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " copying lap/tests/test_lapjv.py -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " copying lap/tests/test_utils.py -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " copying lap/tests/test_lapmod.py -> build/bdist.linux-x86_64/wheel/lap/tests/\n", + " running install_clib\n", + " customize UnixCCompiler\n", + " running install_egg_info\n", + " running egg_info\n", + " creating lap.egg-info\n", + " writing lap.egg-info/PKG-INFO\n", + " writing dependency_links to lap.egg-info/dependency_links.txt\n", + " writing requirements to lap.egg-info/requires.txt\n", + " writing top-level names to lap.egg-info/top_level.txt\n", + " writing manifest file 'lap.egg-info/SOURCES.txt'\n", + " listing git files failed - pretending there aren't any\n", + " reading manifest file 'lap.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no files found matching 'setup.cfg'\n", + " warning: no files found matching '*.pxd' under directory 'lap'\n", + " warning: no files found matching '*.pxi' under directory 'lap'\n", + " warning: no files found matching '*.c' under directory 'lap'\n", + " warning: no files found matching '*.md' under directory 'lap'\n", + " warning: no previously-included files matching '*~' found anywhere in distribution\n", + " warning: no previously-included files matching '*.pyc' found anywhere in distribution\n", + " writing manifest file 'lap.egg-info/SOURCES.txt'\n", + " Copying lap.egg-info to build/bdist.linux-x86_64/wheel/lap-0.4.0-py3.7.egg-info\n", + " running install_scripts\n", + " adding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\")\n", + " creating build/bdist.linux-x86_64/wheel/lap-0.4.0.dist-info/WHEEL\n", + " creating '/tmp/pip-wheel-_s5l0yat/lap-0.4.0-cp37-cp37m-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n", + " adding 'lap/__init__.py'\n", + " adding 'lap/_lapjv.cpython-37m-x86_64-linux-gnu.so'\n", + " adding 'lap/lapmod.py'\n", + " adding 'lap/tests/__init__.py'\n", + " adding 'lap/tests/cost_eps.csv.gz'\n", + " adding 'lap/tests/test_arr_loop.py'\n", + " adding 'lap/tests/test_lapjv.py'\n", + " adding 'lap/tests/test_lapmod.py'\n", + " adding 'lap/tests/test_utils.py'\n", + " adding 'lap-0.4.0.dist-info/LICENSE'\n", + " adding 'lap-0.4.0.dist-info/METADATA'\n", + " adding 'lap-0.4.0.dist-info/WHEEL'\n", + " adding 'lap-0.4.0.dist-info/top_level.txt'\n", + " adding 'lap-0.4.0.dist-info/RECORD'\n", + " removing build/bdist.linux-x86_64/wheel\n", + "\n", + " ########### EXT COMPILER OPTIMIZATION ###########\n", + " Platform :\n", + " Architecture: x64\n", + " Compiler : gcc\n", + "\n", + " CPU baseline :\n", + " Requested : 'min'\n", + " Enabled : SSE SSE2 SSE3\n", + " Flags : -msse -msse2 -msse3\n", + " Extra checks: none\n", + "\n", + " CPU dispatch :\n", + " Requested : 'max -xop -fma4'\n", + " Enabled : SSSE3 SSE41 POPCNT SSE42 AVX F16C FMA3 AVX2 AVX512F AVX512CD AVX512_KNL AVX512_KNM AVX512_SKX AVX512_CLX AVX512_CNL AVX512_ICL\n", + " Generated : none\n", + " CCompilerOpt.cache_flush[809] : write cache to path -> /tmp/pip-install-gyultzk6/lap_5e1a2e2bc34541c28164661ddc5444b6/build/temp.linux-x86_64-3.7/ccompiler_opt_cache_ext.py\n", + "\u001b[?25hdone\n", + " Created wheel for lap: filename=lap-0.4.0-cp37-cp37m-linux_x86_64.whl size=1655045 sha256=be715151b34f32b18a038566de86741ed3d5be5e43970e9627d87d3f9d0cb575\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/5c/d0/d2/e331d17a999666b1e2eb99743cfa1742629f9d26c55c657001\n", + "Successfully built attributee pycocotools dotty-dict lap\n", + "Installing collected packages: pycocotools, mmcls, lap, dotty-dict, attributee, mmtrack\n", + " Attempting uninstall: pycocotools\n", + " Found existing installation: pycocotools 2.0.4\n", + " Uninstalling pycocotools-2.0.4:\n", + " Created temporary directory: /environment/miniconda3/lib/python3.7/site-packages/~ycocotools-2.0.4.dist-info\n", + " Removing file or directory /environment/miniconda3/lib/python3.7/site-packages/pycocotools-2.0.4.dist-info/\n", + " Created temporary directory: /environment/miniconda3/lib/python3.7/site-packages/~ycocotools\n", + " Removing file or directory /environment/miniconda3/lib/python3.7/site-packages/pycocotools/\n", + " Successfully uninstalled pycocotools-2.0.4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " Running setup.py develop for mmtrack\n", + " Running command /environment/miniconda3/bin/python -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/home/featurize/work/MMPose教程/mmtracking/setup.py'\"'\"'; __file__='\"'\"'/home/featurize/work/MMPose教程/mmtracking/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n", + " running develop\n", + " running egg_info\n", + " creating mmtrack.egg-info\n", + " writing mmtrack.egg-info/PKG-INFO\n", + " writing dependency_links to mmtrack.egg-info/dependency_links.txt\n", + " writing requirements to mmtrack.egg-info/requires.txt\n", + " writing top-level names to mmtrack.egg-info/top_level.txt\n", + " writing manifest file 'mmtrack.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " writing manifest file 'mmtrack.egg-info/SOURCES.txt'\n", + " /environment/miniconda3/lib/python3.7/site-packages/torch/utils/cpp_extension.py:381: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n", + " warnings.warn(msg.format('we could not find ninja.'))\n", + " running build_ext\n", + " Creating /environment/miniconda3/lib/python3.7/site-packages/mmtrack.egg-link (link to .)\n", + " Adding mmtrack 0.13.0 to easy-install.pth file\n", + "\n", + " Installed /home/featurize/work/MMPose教程/mmtracking\n", + "Successfully installed attributee-0.1.5 dotty-dict-1.3.0 lap-0.4.0 mmcls-0.23.1 mmtrack-0.13.0 pycocotools-2.0.2\n", + "Removed build tracker: '/tmp/pip-req-tracker-gnol5xvg'\n" + ] + } + ], + "source": [ + "# 安装 mmtracking\n", + "!pip install -r requirements/build.txt\n", + "!pip install -v -e ." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "eac85003-df21-4178-b7b0-bab7a927cf9d", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('../')" + ] + }, + { + "cell_type": "markdown", + "id": "a828bfe4-82e5-497a-9bda-cb9837df2cb3", + "metadata": {}, + "source": [ + "## 下载预训练模型权重文件和视频素材" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bd9f5185-e124-4436-8d38-a83456760147", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5aac1d0c-dbf5-478c-ab80-7e2aa0ab0f01", + "metadata": {}, + "outputs": [], + "source": [ + "# 创建 checkpoints 文件夹，用于存放预训练模型权重文件\n", + "os.mkdir('checkpoints')\n", + "\n", + "# 创建 outputs 文件夹，用于存放预测结果\n", + "os.mkdir('outputs')\n", + "\n", + "# 创建 data 文件夹，用于存放视频\n", + "os.mkdir('data')" + ] + }, + { + "cell_type": "markdown", + "id": "e4fb1cb6-2b9b-418c-9230-6b413c748095", + "metadata": {}, + "source": [ + "## 下载素材\n", + "\n", + "如果报错`Unable to establish SSL connection.`，重新运行代码块即可。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6b68526d-a28b-487b-9b31-312c2315f254", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-07-06 15:23:18-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/fly.mp4\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 167194 (163K) [video/mp4]\n", + "Saving to: ‘data/fly.mp4’\n", + "\n", + "data/fly.mp4 100%[===================>] 163.28K 743KB/s in 0.2s \n", + "\n", + "2022-07-06 15:23:18 (743 KB/s) - ‘data/fly.mp4’ saved [167194/167194]\n", + "\n" + ] + } + ], + "source": [ + "# 小虫子视频，来源：https://user-images.githubusercontent.com/87690686/165095600-f68e0d42-830d-4c22-8940-c90c9f3bb817.mp4\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/fly.mp4 -O data/fly.mp4\n", + "\n", + "# 单人跳舞视频\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/solo_dance.mp4 -O data/solo_dance.mp4\n", + "# 两个跳舞的人，视频来源：https://www.youtube.com/watch?v=fP_IZKfc4vo\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/two_dancers.mp4 -O data/two_dancers.mp4\n", + "\n", + "# 弹钢琴视频，来源：https://mixkit.co/free-stock-video/hands-of-a-pianist-performing-a-song-on-a-piano-41667/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/play_piano.mp4 -O data/play_piano.mp4\n", + "\n", + "# 弹钢琴图片，来源：https://www.pexels.com/zh-cn/photo/6671953/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/piano.jpeg -O data/piano.jpeg\n", + "\n", + "# 孩子的笑脸视频，来源：https://mixkit.co/free-stock-video/teacher-and-students-waving-with-painted-hands-36029/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/face_child.mp4 -O data/face_child.mp4\n", + "\n", + "# 妈妈和女儿跳舞，视频来源：https://mixkit.co/free-stock-video/mother-and-daughters-in-a-kitchen-dancing-4565/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/mother.mp4 -O data/mother.mp4\n", + "\n", + "# 多人，图片来源：https://www.pexels.com/zh-cn/photo/2168292/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/multi-person.jpeg -O data/multi-person.jpeg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "53116869-a25e-4219-ae38-00ae0b54f7f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-07-06 15:23:25-- https://zihao-download.obs.cn-east-3.myhuaweicloud.com/detectron2/TongjiDancerClub.MOV\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 5052215 (4.8M) [binary/octet-stream]\n", + "Saving to: ‘data/TongjiDancerClub.MOV’\n", + "\n", + "data/TongjiDancerCl 100%[===================>] 4.82M 10.1MB/s in 0.5s \n", + "\n", + "2022-07-06 15:23:26 (10.1 MB/s) - ‘data/TongjiDancerClub.MOV’ saved [5052215/5052215]\n", + "\n", + "--2022-07-06 15:23:26-- https://zihao-download.obs.cn-east-3.myhuaweicloud.com/detectron2/TongjiDancer.png\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 10777141 (10M) [image/png]\n", + "Saving to: ‘data/TongjiDancer.png’\n", + "\n", + "data/TongjiDancer.p 100%[===================>] 10.28M 14.9MB/s in 0.7s \n", + "\n", + "2022-07-06 15:23:27 (14.9 MB/s) - ‘data/TongjiDancer.png’ saved [10777141/10777141]\n", + "\n", + "--2022-07-06 15:23:27-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/horse1.mp4\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 16879394 (16M) [video/mp4]\n", + "Saving to: ‘data/horse1.mp4’\n", + "\n", + "data/horse1.mp4 100%[===================>] 16.10M 15.6MB/s in 1.0s \n", + "\n", + "2022-07-06 15:23:29 (15.6 MB/s) - ‘data/horse1.mp4’ saved [16879394/16879394]\n", + "\n", + "--2022-07-06 15:23:29-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/macaque1.mp4\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 38198269 (36M) [video/mp4]\n", + "Saving to: ‘data/macaque1.mp4’\n", + "\n", + "data/macaque1.mp4 100%[===================>] 36.43M 19.1MB/s in 1.9s \n", + "\n", + "2022-07-06 15:23:31 (19.1 MB/s) - ‘data/macaque1.mp4’ saved [38198269/38198269]\n", + "\n", + "--2022-07-06 15:23:31-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/zihao_hand.mp4\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 44345575 (42M) [video/mp4]\n", + "Saving to: ‘data/zihao_hand.mp4’\n", + "\n", + "data/zihao_hand.mp4 100%[===================>] 42.29M 16.5MB/s in 2.6s \n", + "\n", + "2022-07-06 15:23:34 (16.5 MB/s) - ‘data/zihao_hand.mp4’ saved [44345575/44345575]\n", + "\n", + "--2022-07-06 15:23:34-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220418-mmtracking/data/mot_people_short.mp4\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 1392164 (1.3M) [video/mp4]\n", + "Saving to: ‘data/mot_people_short.mp4’\n", + "\n", + "data/mot_people_sho 100%[===================>] 1.33M 4.01MB/s in 0.3s \n", + "\n", + "2022-07-06 15:23:35 (4.01 MB/s) - ‘data/mot_people_short.mp4’ saved [1392164/1392164]\n", + "\n" + ] + } + ], + "source": [ + "# 同济大学多人舞蹈视频，视频来源：同济大学 C4Family 舞蹈社\n", + "!wget https://zihao-download.obs.cn-east-3.myhuaweicloud.com/detectron2/TongjiDancerClub.MOV -O data/TongjiDancerClub.MOV\n", + "!wget https://zihao-download.obs.cn-east-3.myhuaweicloud.com/detectron2/TongjiDancer.png -O data/TongjiDancer.png\n", + "\n", + "# 马\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/horse1.mp4 -O data/horse1.mp4\n", + "\n", + "# 猕猴\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/macaque1.mp4 -O data/macaque1.mp4\n", + "\n", + "# 手势\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/zihao_hand.mp4 -O data/zihao_hand.mp4\n", + "\n", + "# 行人\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220418-mmtracking/data/mot_people_short.mp4 -O data/mot_people_short.mp4" + ] + }, + { + "cell_type": "markdown", + "id": "fa97e1da-f245-4d32-892d-dec07a6b2c16", + "metadata": {}, + "source": [ + "## 检查是否安装成功" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6be5098e-e5e7-402b-841a-4006e9e7cd25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pytorch 版本 1.10.0+cu113\n", + "torchvision版本 0.11.1+cu113\n", + "CUDA 是否可用 True\n" + ] + } + ], + "source": [ + "# 检查 Pytorch\n", + "import torch, torchvision\n", + "print('Pytorch 版本', torch.__version__)\n", + "print('torchvision版本', torchvision.__version__)\n", + "print('CUDA 是否可用',torch.cuda.is_available())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e4ffef5d-eb49-4785-8d06-2d8f88298014", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDA版本 11.3\n", + "编译器版本 GCC 7.3\n" + ] + } + ], + "source": [ + "# 检查 mmcv\n", + "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n", + "print('CUDA版本', get_compiling_cuda_version())\n", + "print('编译器版本', get_compiler_version())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "851aaea2-6bf8-484c-be38-b42feaa3b53c", + "metadata": {}, + "outputs": [], + "source": [ + "# 检查 mmtracking\n", + "import mmtracking" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "fbd8c8dd-07d2-4c32-9a22-2cdb59e79b26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mmpose版本 0.28.0\n" + ] + } + ], + "source": [ + "# 检查 mmpose\n", + "import mmpose\n", + "print('mmpose版本', mmpose.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "89716d32-b021-40f7-bb8b-72f6b9df6157", + "metadata": {}, + "source": [ + "## 小试牛刀" + ] + }, + { + "cell_type": "markdown", + "id": "6ea9b94f-0222-4986-96aa-d0bf3bdfcdd4", + "metadata": {}, + "source": [ + "使用预训练的“自顶向下“（top down）对`data` 目录下的图像进行预测。" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b68f4286-542e-4457-aaf9-1d1f9b7cdb11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "Downloading: \"https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\" to /home/featurize/.cache/torch/hub/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "100%|█████████████████████████████████████████| 160M/160M [00:01<00:00, 105MB/s]\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n", + "100%|█████████████████████████████████████████| 243M/243M [00:02<00:00, 107MB/s]\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \\\n", + " --img data/TongjiDancer.png \\\n", + " --out-img-root outputs/A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bce7e97f-ac23-4878-83d4-38689804090c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220B1\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" "b/2022/\343\200\220B1\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" new file mode 100644 index 0000000..9db12ae --- /dev/null +++ "b/2022/\343\200\220B1\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" @@ -0,0 +1,1012 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D人体关键点 预训练模型预测-Python API\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_human_pose_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.git',\n", + " '.circleci',\n", + " '.dev_scripts',\n", + " '.github',\n", + " '.gitignore',\n", + " '.pre-commit-config.yaml',\n", + " '.pylintrc',\n", + " '.readthedocs.yml',\n", + " 'CITATION.cff',\n", + " 'LICENSE',\n", + " 'MANIFEST.in',\n", + " 'README.md',\n", + " 'README_CN.md',\n", + " 'configs',\n", + " 'demo',\n", + " 'docker',\n", + " 'docs',\n", + " 'mmpose',\n", + " 'model-index.yml',\n", + " 'pytest.ini',\n", + " 'requirements.txt',\n", + " 'requirements',\n", + " 'resources',\n", + " 'setup.cfg',\n", + " 'setup.py',\n", + " 'tests',\n", + " 'tools',\n", + " 'mmpose.egg-info',\n", + " 'checkpoints',\n", + " 'outputs',\n", + " 'data']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "id": "5cda8aef-0233-4912-b5bb-ad63d1586fe5", + "metadata": {}, + "source": [ + "## 导入工具包" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "393aff46-5634-47b1-836b-86af01dbd8ae", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from mmpose.apis import inference_top_down_pose_model, init_pose_model, vis_pose_result, process_mmdet_results\n", + "from mmdet.apis import inference_detector, init_detector\n", + "\n", + "# 导入可视化工具包 matplotlib，并让绘制的图像嵌入在 notebook 中\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# 定义可视化图像函数，输入图像路径，可视化图像\n", + "def show_img_from_path(img_path):\n", + " '''opencv 读入图像，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n", + " img = cv2.imread(img_path)\n", + " img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + " plt.imshow(img_RGB)\n", + " plt.show()\n", + " \n", + "# 定义可视化图像函数，输入图像 array，可视化图像\n", + "def show_img_from_array(img):\n", + " '''输入 array，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n", + " img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + " plt.imshow(img_RGB)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9dacb5cd-9d4f-4fe3-87eb-1093a74528e6", + "metadata": {}, + "source": [ + "## 指定模型`config`配置文件和`checkpoint`权重文件" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f74b7774-d51e-4d2c-93b3-fd41dcd85964", + "metadata": {}, + "outputs": [], + "source": [ + "# 目标检测模型\n", + "det_config = 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py'\n", + "det_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'\n", + "\n", + "# 人体姿态估计模型\n", + "pose_config = 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py'\n", + "pose_checkpoint = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'\n" + ] + }, + { + "cell_type": "markdown", + "id": "84b45139-e4c0-47c4-8113-5ca953b589fb", + "metadata": {}, + "source": [ + "## 初始化模型" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "58817b3f-d800-48a3-b2d7-9512b5b9c517", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n" + ] + } + ], + "source": [ + "# 目标检测模型\n", + "det_model = init_detector(det_config, det_checkpoint)\n", + "\n", + "# 人体姿态估计模型\n", + "pose_model = init_pose_model(pose_config, pose_checkpoint)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b35d72a4-7792-45aa-aa62-46882daeb42b", + "metadata": {}, + "source": [ + "## 载入待预测图像" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "418e43e1-1f67-4bc0-886f-8c96061987be", + "metadata": {}, + "outputs": [], + "source": [ + "# img_path = 'data/TongjiDancer.png'\n", + "img_path = 'data/multi-person.jpeg'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6ad27a67-9235-4e27-b8fe-0fb07b285192", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "show_img_from_path(img_path)" + ] + }, + { + "cell_type": "markdown", + "id": "00349ab4-1127-432a-8fbb-c327a6f6f9f2", + "metadata": {}, + "source": [ + "## 运行目标检测预测" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b46d2c33-2914-4d29-ad9e-2cbd69030d37", + "metadata": {}, + "outputs": [], + "source": [ + "mmdet_results = inference_detector(det_model, img_path)" + ] + }, + { + "cell_type": "markdown", + "id": "2116a22d-a3ce-47ad-8377-4f1067a0a3e1", + "metadata": {}, + "source": [ + "包含了MS COCO目标检测数据集 80 个类别每个预测框的以下信息：\n", + "\n", + "左上角X坐标、左上角Y坐标、右下角X坐标、右下角Y坐标、置信度" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "39de9e7f-f31e-4b6f-93ae-b822494b07c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(mmdet_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f8a689fa-d2fc-424d-9724-b1268f000615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(33, 5)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 行人\n", + "mmdet_results[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "314583a4-5336-4151-b39d-6e6712abdecc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 5)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 自行车\n", + "mmdet_results[1].shape" + ] + }, + { + "cell_type": "markdown", + "id": "c8b7cc1b-a1f7-4e1f-adac-cc6511e01d50", + "metadata": {}, + "source": [ + "## 提取人体检测框" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a11f53a7-5344-4509-bd00-5e2c210783c0", + "metadata": {}, + "outputs": [], + "source": [ + "# 提取类别 ID 为 1 的 行人 目标检测框\n", + "person_results = process_mmdet_results(mmdet_results, cat_id=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a9dd8d2-1320-450c-b4e7-06f73c39b18b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(person_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fb624717-c321-46d4-b1f2-aebb2c32f045", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'bbox': array([1.7056345e+03, 9.6277733e+01, 2.1145239e+03, 1.3011721e+03,\n", + " 9.9941599e-01], dtype=float32)}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "person_results[0]" + ] + }, + { + "cell_type": "markdown", + "id": "c73e4587-dd00-4dd9-893c-b6b8aed51f2f", + "metadata": {}, + "source": [ + "## 运行`top_down`自顶向下的人体姿态估计算法" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cc2d4ed9-6f01-4a46-913e-407deb7263c7", + "metadata": {}, + "outputs": [], + "source": [ + "pose_results, returned_outputs = inference_top_down_pose_model(pose_model, img_path, person_results, bbox_thr=0.3, format='xyxy', dataset='TopDownCocoDataset')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2d689298-e167-43b4-b0cb-3dd451e429af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'bbox': array([1.7056345e+03, 9.6277733e+01, 2.1145239e+03, 1.3011721e+03,\n", + " 9.9941599e-01], dtype=float32),\n", + " 'keypoints': array([[1.9159626e+03, 1.9864667e+02, 9.5111549e-01],\n", + " [1.9394956e+03, 1.7511359e+02, 9.9092317e-01],\n", + " [1.8924294e+03, 1.7511359e+02, 9.7071761e-01],\n", + " [1.9630288e+03, 1.9864667e+02, 9.5346653e-01],\n", + " [1.8571299e+03, 1.9864667e+02, 9.4796818e-01],\n", + " [2.0100950e+03, 3.1631207e+02, 9.1708946e-01],\n", + " [1.8100637e+03, 3.2807867e+02, 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[2.4522488e+03, 8.0955438e+02, 1.4070162e-01],\n", + " [2.4497603e+03, 8.0955438e+02, 1.5994284e-01]], dtype=float32)}]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pose_results" + ] + }, + { + "cell_type": "markdown", + "id": "2867a455-2181-48d6-8181-669b730b89ba", + "metadata": {}, + "source": [ + "## 可视化人体姿态估计结果" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7f9adcf6-8903-4a98-9119-824bbde0d8f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0mvis_pose_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthickness\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkpt_score_thr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbbox_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TopDownCocoDataset'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mout_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Visualize the detection results on the image.\n", + "\n", + "Args:\n", + " model (nn.Module): The loaded detector.\n", + " img (str | np.ndarray): Image filename or loaded image.\n", + " result (list[dict]): The results to draw over `img`\n", + " (bbox_result, pose_result).\n", + " radius (int): Radius of circles.\n", + " thickness (int): Thickness of lines.\n", + " kpt_score_thr (float): The threshold to visualize the keypoints.\n", + " skeleton (list[tuple()]): Default None.\n", + " show (bool): Whether to show the image. Default True.\n", + " out_file (str|None): The filename of the output visualization image.\n", + "\u001b[0;31mFile:\u001b[0m ~/work/MMPose教程/mmpose/mmpose/apis/inference.py\n", + "\u001b[0;31mType:\u001b[0m function\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vis_pose_result?" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "75830dd9-bfd2-41e0-8a21-21231e0ecf5b", + "metadata": {}, + "outputs": [], + "source": [ + "vis_result = vis_pose_result(pose_model,\n", + " img_path,\n", + " pose_results, \n", + " radius=8,\n", + " thickness=3,\n", + " dataset='TopDownCocoDataset', \n", + " show=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "710c70ca-5a79-44f5-abd9-327eff497636", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1418, 2520, 3)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vis_result.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "efb39d24-be46-4696-8193-c0202ff8d3a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "show_img_from_array(vis_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "267c880a-a043-4cdb-b72c-e4afa5c1a017", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv2.imwrite('outputs/B1_multi_human.jpg', vis_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f22340f-85ca-41ae-ba53-3bf5bd63395f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220B2\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" "b/2022/\343\200\220B2\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" new file mode 100644 index 0000000..ae10963 --- /dev/null +++ "b/2022/\343\200\220B2\343\200\2212D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D人体关键点 预训练模型预测-命令行\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_human_pose_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.git',\n", + " '.circleci',\n", + " '.dev_scripts',\n", + " '.github',\n", + " '.gitignore',\n", + " '.pre-commit-config.yaml',\n", + " '.pylintrc',\n", + " '.readthedocs.yml',\n", + " 'CITATION.cff',\n", + " 'LICENSE',\n", + " 'MANIFEST.in',\n", + " 'README.md',\n", + " 'README_CN.md',\n", + " 'configs',\n", + " 'demo',\n", + " 'docker',\n", + " 'docs',\n", + " 'mmpose',\n", + " 'model-index.yml',\n", + " 'pytest.ini',\n", + " 'requirements.txt',\n", + " 'requirements',\n", + " 'resources',\n", + " 'setup.cfg',\n", + " 'setup.py',\n", + " 'tests',\n", + " 'tools',\n", + " 'mmpose.egg-info',\n", + " 'checkpoints',\n", + " 'outputs',\n", + " 'data']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "id": "c272e015-6112-495d-b407-531fcf46b9bd", + "metadata": { + "tags": [] + }, + "source": [ + "## 自顶向下`top_down`算法" + ] + }, + { + "cell_type": "markdown", + "id": "92d4b2a2-cdf0-40c9-99d6-e5c0bdfae73a", + "metadata": {}, + "source": [ + "### 用目标检测预测框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8cddc453-bb4d-49e8-8b70-b3be1154712c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \\\n", + " --img data/TongjiDancer.png \\\n", + " --out-img-root outputs/B2/B2_1_img" + ] + }, + { + "cell_type": "markdown", + "id": "ee67f933-2a6f-4146-88b5-c53d1399e866", + "metadata": {}, + "source": [ + "### 用标注框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cd1ae34f-1fc2-4374-8ad8-c62169c2a4b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \\\n", + " --img-root tests/data/coco/ \\\n", + " --json-file tests/data/coco/test_coco.json \\\n", + " --out-img-root outputs/B2/B2_2_gt_img" + ] + }, + { + "cell_type": "markdown", + "id": "ea7600f5-c4be-47f5-95b4-147213edabed", + "metadata": {}, + "source": [ + "### 单帧输入模型的视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9928a78-d7f2-42b1-a3fa-e64035e4a4f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 3.9 task/s, elapsed: 25s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --bbox-thr 0.8 \\\n", + " --out-video-root outputs/B2/B2_3_video_single_frame" + ] + }, + { + "cell_type": "markdown", + "id": "6cd383ab-0135-4a44-9c3d-659f32b02954", + "metadata": {}, + "source": [ + "### 多帧输入模型的视频预测" + ] + }, + { + "cell_type": "markdown", + "id": "86c42b28-a6fa-4495-8142-02e0251691e3", + "metadata": {}, + "source": [ + "使用`--use-multi-frames`参数，将视频前后多帧画面输入模型用于姿态预测。\n", + "\n", + "使用`--online`参数，仅输入该帧之前的帧，不输入该帧之后的帧。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "01931340-af91-4d33-922c-1cad7888865c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 0.5 task/s, elapsed: 183s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/B2/B2_4_multi_frames \\\n", + " --use-multi-frames --online" + ] + }, + { + "cell_type": "markdown", + "id": "8e3d85ba-ad01-4c1b-8e15-07642829e028", + "metadata": {}, + "source": [ + "### 全图输入模型的视频预测\n", + "\n", + "不提取人体检测框，直接将全图输入至姿态估计模型中。\n", + "\n", + "仅适用于视频中人体始终在画面中央的场景。\n", + "\n", + "仅适用于单人。\n", + "\n", + "扩展阅读：Mediapipe Blaze Pose单人实时人体姿态估计：https://www.bilibili.com/video/BV1dL4y1h7Q6" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b698955a-ef57-41be-aa6d-3effacb90f3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_coco_256x192-cc43b466_20210624.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_coco_256x192-cc43b466_20210624.pth\" to /home/featurize/.cache/torch/hub/checkpoints/vipnas_res50_coco_256x192-cc43b466_20210624.pth\n", + "100%|██████████████████████████████████████| 28.0M/28.0M [00:00<00:00, 89.6MB/s]\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>> ] 645/729, 17.3 task/s, elapsed: 37s, ETA: 5s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_full_frame_without_det.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vipnas_res50_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_coco_256x192-cc43b466_20210624.pth \\\n", + " --video-path data/solo_dance.mp4 \\\n", + " --out-video-root outputs/B2/B2_5_full_img" + ] + }, + { + "cell_type": "markdown", + "id": "dd36abfc-31f5-4e52-866d-87e9e670e718", + "metadata": {}, + "source": [ + "## 自底向上`Bottom-Up`算法" + ] + }, + { + "cell_type": "markdown", + "id": "5078404b-c30d-49f8-bb97-a32045026523", + "metadata": {}, + "source": [ + "### 单张图像预测" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d434a232-411c-44f3-ae64-f7117b8dcade", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\n", + "100%|█████████████████████████████████████████| 109M/109M [00:01<00:00, 104MB/s]\n", + "[ ] 0/1, elapsed: 0s, ETA:/home/featurize/work/MMPose教程/mmpose/mmpose/core/post_processing/group.py:240: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " y = ind // W\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1/1, 0.1 task/s, elapsed: 13s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/bottom_up_img_demo.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py \\\n", + " https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth \\\n", + " --img data/TongjiDancer.png \\\n", + " --out-img-root outputs/B2/B2_6_bottom_up_img" + ] + }, + { + "cell_type": "markdown", + "id": "8bbca5c2-d7b3-4e93-b1f1-5b3fadcb0c2c", + "metadata": {}, + "source": [ + "### 视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d5eba779-3a45-4c11-baea-ced185ab07ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\n", + "/home/featurize/work/MMPose教程/mmpose/mmpose/core/post_processing/group.py:240: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " y = ind // W\n" + ] + } + ], + "source": [ + "!python demo/bottom_up_video_demo.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py \\\n", + " https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/B2/B2_7_bottom_up_video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4b76e65-1fe0-4bac-b715-063d52c22895", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220B3\343\200\2213D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220B3\343\200\2213D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..1fd6688 --- /dev/null +++ "b/2022/\343\200\220B3\343\200\2213D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 3D人体关键点 预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/3d_human_pose_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "b2aa405a-69f6-4847-8120-2cfb03723809", + "metadata": {}, + "source": [ + "## 3D Human Pose的`two-stage`算法\n", + "\n", + "第一阶段：2D人体关键点检测\n", + "\n", + "第二阶段：2D转3D映射" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "57226a88-b9d1-4442-8f6d-1a4ff1b79ced", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Stage 1: load 2D pose results from Json file.\n", + "Stage 2: 2D-to-3D pose lifting.\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/body3d/simple_baseline/simple3Dbaseline_h36m-f0ad73a4_20210419.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/body3d/simple_baseline/simple3Dbaseline_h36m-f0ad73a4_20210419.pth\" to /home/featurize/.cache/torch/hub/checkpoints/simple3Dbaseline_h36m-f0ad73a4_20210419.pth\n", + "100%|██████████████████████████████████████| 16.4M/16.4M [00:00<00:00, 74.2MB/s]\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 4/4, 5.4 task/s, elapsed: 1s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/body3d_two_stage_img_demo.py \\\n", + " configs/body/3d_kpt_sview_rgb_img/pose_lift/h36m/simplebaseline3d_h36m.py \\\n", + " https://download.openmmlab.com/mmpose/body3d/simple_baseline/simple3Dbaseline_h36m-f0ad73a4_20210419.pth \\\n", + " --json-file tests/data/h36m/h36m_coco.json \\\n", + " --img-root tests/data/h36m \\\n", + " --camera-param-file tests/data/h36m/cameras.pkl \\\n", + " --only-second-stage \\\n", + " --out-img-root outputs/B3/B3_1_3d_img \\\n", + " --rebase-keypoint-height \\\n", + " --show-ground-truth" + ] + }, + { + "cell_type": "markdown", + "id": "ed32af6c-a1c5-4bc9-b067-1439bede7908", + "metadata": {}, + "source": [ + "## 视频预测" + ] + }, + { + "cell_type": "markdown", + "id": "be11ffb8-1a6e-4ad9-bca9-31d42e721951", + "metadata": {}, + "source": [ + "命令行中的三个模型分别是：\n", + "\n", + "2D 人体框检测\n", + "\n", + "2D 人体关键点检测\n", + "\n", + "2d-to-3d pose lifting 直接用2D坐标回归3D坐标，把2D的关键点坐标映射到三维" + ] + }, + { + "cell_type": "markdown", + "id": "b900e7fd-1eb0-41df-81ae-9de80ed2837b", + "metadata": {}, + "source": [ + "### 单帧输入模型的视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d16a2e65-821c-408b-b896-69d7bf0c2b0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stage 1: 2D pose detection.\n", + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth\n", + "Running 2D pose detection inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 554/563, 5.5 task/s, elapsed: 101s, ETA: 2s\n", + "Stage 2: 2D-to-3D pose lifting.\n", + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth\" to /home/featurize/.cache/torch/hub/checkpoints/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth\n", + "100%|███████████████████████████████████████| 64.8M/64.8M [00:00<00:00, 100MB/s]\n", + "Running 2D-to-3D pose lifting inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 554/554, 7.0 task/s, elapsed: 79s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/body3d_two_stage_video_demo.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \\\n", + " configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/videopose3d_h36m_243frames_fullconv_supervised_cpn_ft.py \\\n", + " https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth \\\n", + " --video-path data/two_dancers.mp4 \\\n", + " --out-video-root outputs/B3/B3_2_3d_video \\\n", + " --rebase-keypoint-height" + ] + }, + { + "cell_type": "markdown", + "id": "e4e78715-cc78-4d48-942d-bc71b3e90f42", + "metadata": {}, + "source": [ + "### 多帧输入模型的视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "88957e45-3c02-46d4-b35a-510f0c197835", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stage 1: 2D pose detection.\n", + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "100%|█████████████████████████████████████████| 273M/273M [00:02<00:00, 107MB/s]\n", + "Running 2D pose detection inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 554/563, 2.3 task/s, elapsed: 237s, ETA: 4s\n", + "Stage 2: 2D-to-3D pose lifting.\n", + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth\n", + "Running 2D-to-3D pose lifting inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 554/554, 6.5 task/s, elapsed: 85s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/body3d_two_stage_video_demo.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth \\\n", + " configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/videopose3d_h36m_243frames_fullconv_supervised_cpn_ft.py \\\n", + " https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth \\\n", + " --video-path data/two_dancers.mp4 \\\n", + " --out-video-root outputs/B3/B3_3_3d_video_multi_frames \\\n", + " --rebase-keypoint-height \\\n", + " --use-multi-frames --online" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21ade942-2e4c-4d12-8a70-6eacc13dc578", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220B4\343\200\221\345\234\250\350\207\252\345\267\261\347\232\204\346\225\260\346\215\256\351\233\206\344\270\212\350\256\255\347\273\2032D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" "b/2022/\343\200\220B4\343\200\221\345\234\250\350\207\252\345\267\261\347\232\204\346\225\260\346\215\256\351\233\206\344\270\212\350\256\255\347\273\2032D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" new file mode 100644 index 0000000..033429b --- /dev/null +++ "b/2022/\343\200\220B4\343\200\221\345\234\250\350\207\252\345\267\261\347\232\204\346\225\260\346\215\256\351\233\206\344\270\212\350\256\255\347\273\2032D\344\272\272\344\275\223\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" @@ -0,0 +1,2259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "180b619f-80a0-4131-8982-4095be05e49f", + "metadata": {}, + "source": [ + "# 在自己的数据集上训练2D人体关键点检测模型\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/MMPose_Tutorial.ipynb\n", + "\n", + "作者：同济子豪兄、卢鹏 2022-07-01\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "1a398c89-598e-4f23-9094-bafbd7ad2245", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dbc60778-82fa-4846-854a-ec5eabf949bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.git',\n", + " '.circleci',\n", + " '.dev_scripts',\n", + " '.github',\n", + " '.gitignore',\n", + " '.pre-commit-config.yaml',\n", + " '.pylintrc',\n", + " '.readthedocs.yml',\n", + " 'CITATION.cff',\n", + " 'LICENSE',\n", + " 'MANIFEST.in',\n", + " 'README.md',\n", + " 'README_CN.md',\n", + " 'configs',\n", + " 'demo',\n", + " 'docker',\n", + " 'docs',\n", + " 'mmpose',\n", + " 'model-index.yml',\n", + " 'pytest.ini',\n", + " 'requirements.txt',\n", + " 'requirements',\n", + " 'resources',\n", + " 'setup.cfg',\n", + " 'setup.py',\n", + " 'tests',\n", + " 'tools',\n", + " 'mmpose.egg-info',\n", + " 'checkpoints',\n", + " 'outputs',\n", + " 'data']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "id": "6078a1ce-059e-4baa-9a42-80951984b891", + "metadata": {}, + "source": [ + "## 导入工具包" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "90d80544-4bbb-49a2-9d87-e5b3e314f2c8", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "\n", + "# 导入可视化工具包 matplotlib，并让绘制的图像嵌入在 notebook 中\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# 定义可视化图像函数，输入图像路径，可视化图像\n", + "def show_img_from_path(img_path):\n", + " '''opencv 读入图像，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n", + " img = cv2.imread(img_path)\n", + " img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + " plt.imshow(img_RGB)\n", + " plt.show()\n", + " \n", + "# 定义可视化图像函数，输入图像 array，可视化图像\n", + "def show_img_from_array(img):\n", + " '''输入 array，matplotlib 可视化格式为 RGB，因此需将 BGR 转 RGB，最后可视化出来'''\n", + " img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + " plt.imshow(img_RGB)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "da21ffb0-93f4-4462-8b80-407ea5379e92", + "metadata": {}, + "source": [ + "## 下载数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8a5e6553-acb5-41c9-943c-3a37fd591565", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-07-04 15:15:57-- https://download.openmmlab.com/mmpose/datasets/coco_tiny.tar\n", + "Connecting to 172.16.0.13:5848... connected.\n", + "Proxy request sent, awaiting response... 200 OK\n", + "Length: 16558080 (16M) [application/octet-stream]\n", + "Saving to: ‘data/coco_tiny.tar’\n", + "\n", + "data/coco_tiny.tar 100%[===================>] 15.79M 90.0MB/s in 0.2s \n", + "\n", + "2022-07-04 15:15:57 (90.0 MB/s) - ‘data/coco_tiny.tar’ saved [16558080/16558080]\n", + "\n" + ] + } + ], + "source": [ + "# 下载数据集压缩包至 data 目录\n", + "!wget https://download.openmmlab.com/mmpose/datasets/coco_tiny.tar -O data/coco_tiny.tar" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "49f65072-996e-4dc5-8557-76e9a6963cd8", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入 data 目录\n", + "os.chdir('data')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "56b62fb4-30ca-4257-a1da-9cec9b63fe1a", + "metadata": {}, + "outputs": [], + "source": [ + "# 解压\n", + "!tar -xf coco_tiny.tar" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "30d906d8-754c-4b5c-9216-694528c11489", + "metadata": {}, + "outputs": [], + "source": [ + "# 回到 mmpose 主目录\n", + "os.chdir('../')" + ] + }, + { + "cell_type": "markdown", + "id": "43e286a2-d78c-437b-b72a-c4d98484a6fc", + "metadata": {}, + "source": [ + "## 查看标注文件" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "adaffd85-cbf9-4596-b3a1-9d0ff490293c", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pprint\n", + "\n", + "# 载入训练集标注文件\n", + "anns = json.load(open('data/coco_tiny/train.json'))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a70403d-db24-46d0-8dc4-389a1db05ad9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "75" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(anns)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "457174aa-b05d-43e4-8a70-cedfafae8197", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'bbox': [267.03, 104.32, 229.19, 320],\n", + " 'image_file': '000000537548.jpg',\n", + " 'image_size': [640, 480],\n", + " 'keypoints': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 325, 160, 2, 398,\n", + " 177, 2, 0, 0, 0, 437, 238, 2, 0, 0, 0, 477, 270, 2, 287, 255, 1,\n", + " 339, 267, 2, 0, 0, 0, 423, 314, 2, 0, 0, 0, 355, 367, 2]}\n" + ] + } + ], + "source": [ + "pprint.pprint(anns[0], compact=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6c80a726-d8b9-4b3c-abb5-14467403c0af", + "metadata": {}, + "source": [ + "demo数据集包含100张来自 COCO train2017数据集的图像，训练集75张，测试集25张。" + ] + }, + { + "cell_type": "markdown", + "id": "634a2037-4d24-4870-a331-fe6c3fa433e6", + "metadata": {}, + "source": [ + "## 将数据集转为 mmpose 支持的类" + ] + }, + { + "cell_type": "markdown", + "id": "bd8a3f4d-5c14-4050-b4b6-dac0504ea644", + "metadata": {}, + "source": [ + "把自己的关键点检测数据集，使用 mmpose 训练，有两种方法。\n", + "\n", + "方法一：直接将数据集标注转为 mmpose 支持的数据集格式（例如 MS COCO）。在 mmpose 中使用对应的数据集类（例如 TopdownCOCODataset）训练。文档：https://mmpose.readthedocs.io/en/latest/tutorials/2_new_dataset.html#reorganize-dataset-to-existing-format\n", + "\n", + "方法二：如果你自己的关键点检测数据集的标注格式与标准数据集（例如 COCO）不同。那么，需要手动写一个新的数据集类。在类中，读取标注文件内容并整理为一个 sample list，并实现数据集的 evaluate 接口。\n", + "\n", + "以下展示方法一。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "89539d01-d4aa-4cef-9382-36371c738e36", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_ann_to_coco_style(anns):\n", + " anns_new = {\n", + " 'images': {},\n", + " 'annotations': [],\n", + " 'categories': [{'id': 1, 'name': 'person'}]\n", + " }\n", + "\n", + " for i, item in enumerate(anns):\n", + " if item['image_file'] not in anns_new['images']:\n", + " anns_new['images'][item['image_file']] = {\n", + " 'file_name': item['image_file'],\n", + " 'height': item['image_size'][1],\n", + " 'width': item['image_size'][0],\n", + " 'id': len(anns_new['images']) + 1\n", + " }\n", + " anns_new['annotations'].append({\n", + " 'keypoints': item['keypoints'],\n", + " 'bbox': item['bbox'],\n", + " 'area': item['bbox'][2] * item['bbox'][3],\n", + " 'id': i + 1,\n", + " 'image_id': anns_new['images'][item['image_file']]['id'],\n", + " 'category_id': 1,\n", + " })\n", + "\n", + " anns_new['images'] = list(anns_new['images'].values())\n", + " return anns_new" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5517ea08", + "metadata": {}, + "outputs": [], + "source": [ + "for phase in ('train', 'val'):\n", + " with open(f'data/coco_tiny/{phase}.json', 'r') as f:\n", + " anns = json.load(f)\n", + " with open(f'data/coco_tiny/{phase}.json', 'w') as f:\n", + " json.dump(convert_ann_to_coco_style(anns), f)" + ] + }, + { + "cell_type": "markdown", + "id": "ccb613ee-5004-449c-8215-4c470897e768", + "metadata": {}, + "source": [ + "`data/coco_tiny`目录，新生成了`MS COCO`格式的`train.json`和`val.json`文件" + ] + }, + { + "cell_type": "markdown", + "id": "507d7164-1284-42ba-9a21-58cb8c795509", + "metadata": {}, + "source": [ + "## 创建 config 配置文件" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "efa86b10-6cc5-4ad4-8824-40dc70662ecb", + "metadata": {}, + "outputs": [], + "source": [ + "from mmcv import Config\n", + "\n", + "# 模型 config 配置文件\n", + "cfg = Config.fromfile('./configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_256x192.py')\n", + "\n", + "# 基础配置\n", + "cfg.data_root = 'data/coco_tiny'\n", + "cfg.work_dir = 'work_dirs/hrnet_w32_coco_tiny_256x192'\n", + "cfg.gpu_ids = range(1)\n", + "cfg.seed = 0\n", + "\n", + "# 日志间隔\n", + "cfg.log_config.interval = 1\n", + "\n", + "# 评估指标\n", + "cfg.evaluation.interval = 10\n", + "cfg.evaluation.metric = 'mAP'\n", + "cfg.evaluation.save_best = 'AP'\n", + "\n", + "# 学习率和训练策略\n", + "lr_config = dict(\n", + " policy='step',\n", + " warmup='linear',\n", + " warmup_iters=10,\n", + " warmup_ratio=0.001,\n", + " step=[17, 35])\n", + "cfg.total_epochs = 40\n", + "\n", + "# batch size\n", + "cfg.data.samples_per_gpu = 16\n", + "cfg.data.val_dataloader = dict(samples_per_gpu=16)\n", + "cfg.data.test_dataloader = dict(samples_per_gpu=16)\n", + "\n", + "# 数据集配置\n", + "cfg.data.train.ann_file = f'{cfg.data_root}/train.json'\n", + "cfg.data.train.img_prefix = f'{cfg.data_root}/images/'\n", + "\n", + "cfg.data.val.ann_file = f'{cfg.data_root}/val.json'\n", + "cfg.data.val.img_prefix = f'{cfg.data_root}/images/'\n", + "cfg.data.val.data_cfg.use_gt_bbox = True\n", + "\n", + "cfg.data.test.ann_file = f'{cfg.data_root}/val.json'\n", + "cfg.data.test.img_prefix = f'{cfg.data_root}/images/'\n", + "cfg.data.test.data_cfg.use_gt_bbox = True" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e45a2562-0670-4dd2-922d-046adda784d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint_config = dict(interval=10)\n", + "log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook')])\n", + "log_level = 'INFO'\n", + "load_from = None\n", + "resume_from = None\n", + "dist_params = dict(backend='nccl')\n", + "workflow = [('train', 1)]\n", + "opencv_num_threads = 0\n", + "mp_start_method = 'fork'\n", + "dataset_info = dict(\n", + " dataset_name='coco',\n", + " paper_info=dict(\n", + " author=\n", + " 'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n", + " title='Microsoft coco: Common objects in context',\n", + " container='European conference on computer vision',\n", + " year='2014',\n", + " homepage='http://cocodataset.org/'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),\n", + " 1:\n", + " dict(\n", + " name='left_eye',\n", + " id=1,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_eye'),\n", + " 2:\n", + " dict(\n", + " name='right_eye',\n", + " id=2,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_eye'),\n", + " 3:\n", + " dict(\n", + " name='left_ear',\n", + " id=3,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_ear'),\n", + " 4:\n", + " dict(\n", + " name='right_ear',\n", + " id=4,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_ear'),\n", + " 5:\n", + " dict(\n", + " name='left_shoulder',\n", + " id=5,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_shoulder'),\n", + " 6:\n", + " dict(\n", + " name='right_shoulder',\n", + " id=6,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_shoulder'),\n", + " 7:\n", + " dict(\n", + " name='left_elbow',\n", + " id=7,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_elbow'),\n", + " 8:\n", + " dict(\n", + " name='right_elbow',\n", + " id=8,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_elbow'),\n", + " 9:\n", + " dict(\n", + " name='left_wrist',\n", + " id=9,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_wrist'),\n", + " 10:\n", + " dict(\n", + " name='right_wrist',\n", + " id=10,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_wrist'),\n", + " 11:\n", + " dict(\n", + " name='left_hip',\n", + " id=11,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_hip'),\n", + " 12:\n", + " dict(\n", + " name='right_hip',\n", + " id=12,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_hip'),\n", + " 13:\n", + " dict(\n", + " name='left_knee',\n", + " id=13,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_knee'),\n", + " 14:\n", + " dict(\n", + " name='right_knee',\n", + " id=14,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_knee'),\n", + " 15:\n", + " dict(\n", + " name='left_ankle',\n", + " id=15,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_ankle'),\n", + " 16:\n", + " dict(\n", + " name='right_ankle',\n", + " id=16,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_ankle')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n", + " 1:\n", + " dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n", + " 2:\n", + " dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),\n", + " 3:\n", + " dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),\n", + " 4:\n", + " dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),\n", + " 5:\n", + " dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),\n", + " 6:\n", + " dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),\n", + " 7:\n", + " dict(\n", + " link=('left_shoulder', 'right_shoulder'),\n", + " id=7,\n", + " color=[51, 153, 255]),\n", + " 8:\n", + " dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),\n", + " 9:\n", + " dict(\n", + " link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),\n", + " 10:\n", + " dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),\n", + " 11:\n", + " dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),\n", + " 12:\n", + " dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),\n", + " 13:\n", + " dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n", + " 14:\n", + " dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n", + " 15:\n", + " dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),\n", + " 16:\n", + " dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),\n", + " 17:\n", + " dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),\n", + " 18:\n", + " dict(\n", + " link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])\n", + " }),\n", + " joint_weights=[\n", + " 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0, 1.0, 1.2,\n", + " 1.2, 1.5, 1.5\n", + " ],\n", + " sigmas=[\n", + " 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,\n", + " 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n", + " ])\n", + "evaluation = dict(interval=10, metric='mAP', save_best='AP')\n", + "optimizer = dict(type='Adam', lr=0.0005)\n", + "optimizer_config = dict(grad_clip=None)\n", + "lr_config = dict(\n", + " policy='step',\n", + " warmup='linear',\n", + " warmup_iters=500,\n", + " warmup_ratio=0.001,\n", + " step=[170, 200])\n", + "total_epochs = 40\n", + "channel_cfg = dict(\n", + " num_output_channels=17,\n", + " dataset_joints=17,\n", + " dataset_channel=[[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ]],\n", + " inference_channel=[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ])\n", + "model = dict(\n", + " type='TopDown',\n", + " pretrained=\n", + " 'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth',\n", + " backbone=dict(\n", + " type='HRNet',\n", + " in_channels=3,\n", + " extra=dict(\n", + " stage1=dict(\n", + " num_modules=1,\n", + " num_branches=1,\n", + " block='BOTTLENECK',\n", + " num_blocks=(4, ),\n", + " num_channels=(64, )),\n", + " stage2=dict(\n", + " num_modules=1,\n", + " num_branches=2,\n", + " block='BASIC',\n", + " num_blocks=(4, 4),\n", + " num_channels=(32, 64)),\n", + " stage3=dict(\n", + " num_modules=4,\n", + " num_branches=3,\n", + " block='BASIC',\n", + " num_blocks=(4, 4, 4),\n", + " num_channels=(32, 64, 128)),\n", + " stage4=dict(\n", + " num_modules=3,\n", + " num_branches=4,\n", + " block='BASIC',\n", + " num_blocks=(4, 4, 4, 4),\n", + " num_channels=(32, 64, 128, 256)))),\n", + " keypoint_head=dict(\n", + " type='TopdownHeatmapSimpleHead',\n", + " in_channels=32,\n", + " out_channels=17,\n", + " num_deconv_layers=0,\n", + " extra=dict(final_conv_kernel=1),\n", + " loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),\n", + " train_cfg=dict(),\n", + " test_cfg=dict(\n", + " flip_test=True,\n", + " post_process='default',\n", + " shift_heatmap=True,\n", + " modulate_kernel=11))\n", + "data_cfg = dict(\n", + " image_size=[192, 256],\n", + " heatmap_size=[48, 64],\n", + " num_output_channels=17,\n", + " num_joints=17,\n", + " dataset_channel=[[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ]],\n", + " inference_channel=[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ],\n", + " soft_nms=False,\n", + " nms_thr=1.0,\n", + " oks_thr=0.9,\n", + " vis_thr=0.2,\n", + " use_gt_bbox=False,\n", + " det_bbox_thr=0.0,\n", + " bbox_file=\n", + " 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n", + ")\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),\n", + " dict(type='TopDownRandomFlip', flip_prob=0.5),\n", + " dict(\n", + " type='TopDownHalfBodyTransform',\n", + " num_joints_half_body=8,\n", + " prob_half_body=0.3),\n", + " dict(\n", + " type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(type='TopDownGenerateTarget', sigma=2),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img', 'target', 'target_weight'],\n", + " meta_keys=[\n", + " 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',\n", + " 'rotation', 'bbox_score', 'flip_pairs'\n", + " ])\n", + "]\n", + "val_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img'],\n", + " meta_keys=[\n", + " 'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n", + " 'flip_pairs'\n", + " ])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img'],\n", + " meta_keys=[\n", + " 'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n", + " 'flip_pairs'\n", + " ])\n", + "]\n", + "data_root = 'data/coco_tiny'\n", + "data = dict(\n", + " samples_per_gpu=16,\n", + " workers_per_gpu=2,\n", + " val_dataloader=dict(samples_per_gpu=16),\n", + " test_dataloader=dict(samples_per_gpu=16),\n", + " train=dict(\n", + " type='TopDownCocoDataset',\n", + " ann_file='data/coco_tiny/train.json',\n", + " img_prefix='data/coco_tiny/images/',\n", + " data_cfg=dict(\n", + " image_size=[192, 256],\n", + " heatmap_size=[48, 64],\n", + " num_output_channels=17,\n", + " num_joints=17,\n", + " dataset_channel=[[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ]],\n", + " inference_channel=[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ],\n", + " soft_nms=False,\n", + " nms_thr=1.0,\n", + " oks_thr=0.9,\n", + " vis_thr=0.2,\n", + " use_gt_bbox=False,\n", + " det_bbox_thr=0.0,\n", + " bbox_file=\n", + " 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n", + " ),\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(\n", + " type='TopDownRandomShiftBboxCenter',\n", + " shift_factor=0.16,\n", + " prob=0.3),\n", + " dict(type='TopDownRandomFlip', flip_prob=0.5),\n", + " dict(\n", + " type='TopDownHalfBodyTransform',\n", + " num_joints_half_body=8,\n", + " prob_half_body=0.3),\n", + " dict(\n", + " type='TopDownGetRandomScaleRotation',\n", + " rot_factor=40,\n", + " scale_factor=0.5),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(type='TopDownGenerateTarget', sigma=2),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img', 'target', 'target_weight'],\n", + " meta_keys=[\n", + " 'image_file', 'joints_3d', 'joints_3d_visible', 'center',\n", + " 'scale', 'rotation', 'bbox_score', 'flip_pairs'\n", + " ])\n", + " ],\n", + " dataset_info=dict(\n", + " dataset_name='coco',\n", + " paper_info=dict(\n", + " author=\n", + " 'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n", + " title='Microsoft coco: Common objects in context',\n", + " container='European conference on computer vision',\n", + " year='2014',\n", + " homepage='http://cocodataset.org/'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='nose',\n", + " id=0,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='left_eye',\n", + " id=1,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_eye'),\n", + " 2:\n", + " dict(\n", + " name='right_eye',\n", + " id=2,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_eye'),\n", + " 3:\n", + " dict(\n", + " name='left_ear',\n", + " id=3,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_ear'),\n", + " 4:\n", + " dict(\n", + " name='right_ear',\n", + " id=4,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_ear'),\n", + " 5:\n", + " dict(\n", + " name='left_shoulder',\n", + " id=5,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_shoulder'),\n", + " 6:\n", + " dict(\n", + " name='right_shoulder',\n", + " id=6,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_shoulder'),\n", + " 7:\n", + " dict(\n", + " name='left_elbow',\n", + " id=7,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_elbow'),\n", + " 8:\n", + " dict(\n", + " name='right_elbow',\n", + " id=8,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_elbow'),\n", + " 9:\n", + " dict(\n", + " name='left_wrist',\n", + " id=9,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_wrist'),\n", + " 10:\n", + " dict(\n", + " name='right_wrist',\n", + " id=10,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_wrist'),\n", + " 11:\n", + " dict(\n", + " name='left_hip',\n", + " id=11,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_hip'),\n", + " 12:\n", + " dict(\n", + " name='right_hip',\n", + " id=12,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_hip'),\n", + " 13:\n", + " dict(\n", + " name='left_knee',\n", + " id=13,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_knee'),\n", + " 14:\n", + " dict(\n", + " name='right_knee',\n", + " id=14,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_knee'),\n", + " 15:\n", + " dict(\n", + " name='left_ankle',\n", + " id=15,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_ankle'),\n", + " 16:\n", + " dict(\n", + " name='right_ankle',\n", + " id=16,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_ankle')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n", + " 1:\n", + " dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n", + " 2:\n", + " dict(\n", + " link=('right_ankle', 'right_knee'),\n", + " id=2,\n", + " color=[255, 128, 0]),\n", + " 3:\n", + " dict(\n", + " link=('right_knee', 'right_hip'),\n", + " id=3,\n", + " color=[255, 128, 0]),\n", + " 4:\n", + " dict(\n", + " link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n", + " 255]),\n", + " 5:\n", + " dict(\n", + " link=('left_shoulder', 'left_hip'),\n", + " id=5,\n", + " color=[51, 153, 255]),\n", + " 6:\n", + " dict(\n", + " link=('right_shoulder', 'right_hip'),\n", + " id=6,\n", + " color=[51, 153, 255]),\n", + " 7:\n", + " dict(\n", + " link=('left_shoulder', 'right_shoulder'),\n", + " id=7,\n", + " color=[51, 153, 255]),\n", + " 8:\n", + " dict(\n", + " link=('left_shoulder', 'left_elbow'),\n", + " id=8,\n", + " color=[0, 255, 0]),\n", + " 9:\n", + " dict(\n", + " link=('right_shoulder', 'right_elbow'),\n", + " id=9,\n", + " color=[255, 128, 0]),\n", + " 10:\n", + " dict(\n", + " link=('left_elbow', 'left_wrist'),\n", + " id=10,\n", + " color=[0, 255, 0]),\n", + " 11:\n", + " dict(\n", + " link=('right_elbow', 'right_wrist'),\n", + " id=11,\n", + " color=[255, 128, 0]),\n", + " 12:\n", + " dict(\n", + " link=('left_eye', 'right_eye'),\n", + " id=12,\n", + " color=[51, 153, 255]),\n", + " 13:\n", + " dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n", + " 14:\n", + " dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n", + " 15:\n", + " dict(\n", + " link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n", + " 255]),\n", + " 16:\n", + " dict(\n", + " link=('right_eye', 'right_ear'),\n", + " id=16,\n", + " color=[51, 153, 255]),\n", + " 17:\n", + " dict(\n", + " link=('left_ear', 'left_shoulder'),\n", + " id=17,\n", + " color=[51, 153, 255]),\n", + " 18:\n", + " dict(\n", + " link=('right_ear', 'right_shoulder'),\n", + " id=18,\n", + " color=[51, 153, 255])\n", + " }),\n", + " joint_weights=[\n", + " 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n", + " 1.0, 1.2, 1.2, 1.5, 1.5\n", + " ],\n", + " sigmas=[\n", + " 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n", + " 0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n", + " ])),\n", + " val=dict(\n", + " type='TopDownCocoDataset',\n", + " ann_file='data/coco_tiny/val.json',\n", + " img_prefix='data/coco_tiny/images/',\n", + " data_cfg=dict(\n", + " image_size=[192, 256],\n", + " heatmap_size=[48, 64],\n", + " num_output_channels=17,\n", + " num_joints=17,\n", + " dataset_channel=[[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ]],\n", + " inference_channel=[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ],\n", + " soft_nms=False,\n", + " nms_thr=1.0,\n", + " oks_thr=0.9,\n", + " vis_thr=0.2,\n", + " use_gt_bbox=True,\n", + " det_bbox_thr=0.0,\n", + " bbox_file=\n", + " 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n", + " ),\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img'],\n", + " meta_keys=[\n", + " 'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n", + " 'flip_pairs'\n", + " ])\n", + " ],\n", + " dataset_info=dict(\n", + " dataset_name='coco',\n", + " paper_info=dict(\n", + " author=\n", + " 'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n", + " title='Microsoft coco: Common objects in context',\n", + " container='European conference on computer vision',\n", + " year='2014',\n", + " homepage='http://cocodataset.org/'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='nose',\n", + " id=0,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='left_eye',\n", + " id=1,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_eye'),\n", + " 2:\n", + " dict(\n", + " name='right_eye',\n", + " id=2,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_eye'),\n", + " 3:\n", + " dict(\n", + " name='left_ear',\n", + " id=3,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_ear'),\n", + " 4:\n", + " dict(\n", + " name='right_ear',\n", + " id=4,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_ear'),\n", + " 5:\n", + " dict(\n", + " name='left_shoulder',\n", + " id=5,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_shoulder'),\n", + " 6:\n", + " dict(\n", + " name='right_shoulder',\n", + " id=6,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_shoulder'),\n", + " 7:\n", + " dict(\n", + " name='left_elbow',\n", + " id=7,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_elbow'),\n", + " 8:\n", + " dict(\n", + " name='right_elbow',\n", + " id=8,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_elbow'),\n", + " 9:\n", + " dict(\n", + " name='left_wrist',\n", + " id=9,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_wrist'),\n", + " 10:\n", + " dict(\n", + " name='right_wrist',\n", + " id=10,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_wrist'),\n", + " 11:\n", + " dict(\n", + " name='left_hip',\n", + " id=11,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_hip'),\n", + " 12:\n", + " dict(\n", + " name='right_hip',\n", + " id=12,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_hip'),\n", + " 13:\n", + " dict(\n", + " name='left_knee',\n", + " id=13,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_knee'),\n", + " 14:\n", + " dict(\n", + " name='right_knee',\n", + " id=14,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_knee'),\n", + " 15:\n", + " dict(\n", + " name='left_ankle',\n", + " id=15,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_ankle'),\n", + " 16:\n", + " dict(\n", + " name='right_ankle',\n", + " id=16,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_ankle')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n", + " 1:\n", + " dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n", + " 2:\n", + " dict(\n", + " link=('right_ankle', 'right_knee'),\n", + " id=2,\n", + " color=[255, 128, 0]),\n", + " 3:\n", + " dict(\n", + " link=('right_knee', 'right_hip'),\n", + " id=3,\n", + " color=[255, 128, 0]),\n", + " 4:\n", + " dict(\n", + " link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n", + " 255]),\n", + " 5:\n", + " dict(\n", + " link=('left_shoulder', 'left_hip'),\n", + " id=5,\n", + " color=[51, 153, 255]),\n", + " 6:\n", + " dict(\n", + " link=('right_shoulder', 'right_hip'),\n", + " id=6,\n", + " color=[51, 153, 255]),\n", + " 7:\n", + " dict(\n", + " link=('left_shoulder', 'right_shoulder'),\n", + " id=7,\n", + " color=[51, 153, 255]),\n", + " 8:\n", + " dict(\n", + " link=('left_shoulder', 'left_elbow'),\n", + " id=8,\n", + " color=[0, 255, 0]),\n", + " 9:\n", + " dict(\n", + " link=('right_shoulder', 'right_elbow'),\n", + " id=9,\n", + " color=[255, 128, 0]),\n", + " 10:\n", + " dict(\n", + " link=('left_elbow', 'left_wrist'),\n", + " id=10,\n", + " color=[0, 255, 0]),\n", + " 11:\n", + " dict(\n", + " link=('right_elbow', 'right_wrist'),\n", + " id=11,\n", + " color=[255, 128, 0]),\n", + " 12:\n", + " dict(\n", + " link=('left_eye', 'right_eye'),\n", + " id=12,\n", + " color=[51, 153, 255]),\n", + " 13:\n", + " dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n", + " 14:\n", + " dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n", + " 15:\n", + " dict(\n", + " link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n", + " 255]),\n", + " 16:\n", + " dict(\n", + " link=('right_eye', 'right_ear'),\n", + " id=16,\n", + " color=[51, 153, 255]),\n", + " 17:\n", + " dict(\n", + " link=('left_ear', 'left_shoulder'),\n", + " id=17,\n", + " color=[51, 153, 255]),\n", + " 18:\n", + " dict(\n", + " link=('right_ear', 'right_shoulder'),\n", + " id=18,\n", + " color=[51, 153, 255])\n", + " }),\n", + " joint_weights=[\n", + " 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n", + " 1.0, 1.2, 1.2, 1.5, 1.5\n", + " ],\n", + " sigmas=[\n", + " 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n", + " 0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n", + " ])),\n", + " test=dict(\n", + " type='TopDownCocoDataset',\n", + " ann_file='data/coco_tiny/val.json',\n", + " img_prefix='data/coco_tiny/images/',\n", + " data_cfg=dict(\n", + " image_size=[192, 256],\n", + " heatmap_size=[48, 64],\n", + " num_output_channels=17,\n", + " num_joints=17,\n", + " dataset_channel=[[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ]],\n", + " inference_channel=[\n", + " 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16\n", + " ],\n", + " soft_nms=False,\n", + " nms_thr=1.0,\n", + " oks_thr=0.9,\n", + " vis_thr=0.2,\n", + " use_gt_bbox=True,\n", + " det_bbox_thr=0.0,\n", + " bbox_file=\n", + " 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'\n", + " ),\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='TopDownGetBboxCenterScale', padding=1.25),\n", + " dict(type='TopDownAffine'),\n", + " dict(type='ToTensor'),\n", + " dict(\n", + " type='NormalizeTensor',\n", + " mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]),\n", + " dict(\n", + " type='Collect',\n", + " keys=['img'],\n", + " meta_keys=[\n", + " 'image_file', 'center', 'scale', 'rotation', 'bbox_score',\n", + " 'flip_pairs'\n", + " ])\n", + " ],\n", + " dataset_info=dict(\n", + " dataset_name='coco',\n", + " paper_info=dict(\n", + " author=\n", + " 'Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\\'a}r, Piotr and Zitnick, C Lawrence',\n", + " title='Microsoft coco: Common objects in context',\n", + " container='European conference on computer vision',\n", + " year='2014',\n", + " homepage='http://cocodataset.org/'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='nose',\n", + " id=0,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='left_eye',\n", + " id=1,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_eye'),\n", + " 2:\n", + " dict(\n", + " name='right_eye',\n", + " id=2,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_eye'),\n", + " 3:\n", + " dict(\n", + " name='left_ear',\n", + " id=3,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='right_ear'),\n", + " 4:\n", + " dict(\n", + " name='right_ear',\n", + " id=4,\n", + " color=[51, 153, 255],\n", + " type='upper',\n", + " swap='left_ear'),\n", + " 5:\n", + " dict(\n", + " name='left_shoulder',\n", + " id=5,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_shoulder'),\n", + " 6:\n", + " dict(\n", + " name='right_shoulder',\n", + " id=6,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_shoulder'),\n", + " 7:\n", + " dict(\n", + " name='left_elbow',\n", + " id=7,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_elbow'),\n", + " 8:\n", + " dict(\n", + " name='right_elbow',\n", + " id=8,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_elbow'),\n", + " 9:\n", + " dict(\n", + " name='left_wrist',\n", + " id=9,\n", + " color=[0, 255, 0],\n", + " type='upper',\n", + " swap='right_wrist'),\n", + " 10:\n", + " dict(\n", + " name='right_wrist',\n", + " id=10,\n", + " color=[255, 128, 0],\n", + " type='upper',\n", + " swap='left_wrist'),\n", + " 11:\n", + " dict(\n", + " name='left_hip',\n", + " id=11,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_hip'),\n", + " 12:\n", + " dict(\n", + " name='right_hip',\n", + " id=12,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_hip'),\n", + " 13:\n", + " dict(\n", + " name='left_knee',\n", + " id=13,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_knee'),\n", + " 14:\n", + " dict(\n", + " name='right_knee',\n", + " id=14,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_knee'),\n", + " 15:\n", + " dict(\n", + " name='left_ankle',\n", + " id=15,\n", + " color=[0, 255, 0],\n", + " type='lower',\n", + " swap='right_ankle'),\n", + " 16:\n", + " dict(\n", + " name='right_ankle',\n", + " id=16,\n", + " color=[255, 128, 0],\n", + " type='lower',\n", + " swap='left_ankle')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),\n", + " 1:\n", + " dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),\n", + " 2:\n", + " dict(\n", + " link=('right_ankle', 'right_knee'),\n", + " id=2,\n", + " color=[255, 128, 0]),\n", + " 3:\n", + " dict(\n", + " link=('right_knee', 'right_hip'),\n", + " id=3,\n", + " color=[255, 128, 0]),\n", + " 4:\n", + " dict(\n", + " link=('left_hip', 'right_hip'), id=4, color=[51, 153,\n", + " 255]),\n", + " 5:\n", + " dict(\n", + " link=('left_shoulder', 'left_hip'),\n", + " id=5,\n", + " color=[51, 153, 255]),\n", + " 6:\n", + " dict(\n", + " link=('right_shoulder', 'right_hip'),\n", + " id=6,\n", + " color=[51, 153, 255]),\n", + " 7:\n", + " dict(\n", + " link=('left_shoulder', 'right_shoulder'),\n", + " id=7,\n", + " color=[51, 153, 255]),\n", + " 8:\n", + " dict(\n", + " link=('left_shoulder', 'left_elbow'),\n", + " id=8,\n", + " color=[0, 255, 0]),\n", + " 9:\n", + " dict(\n", + " link=('right_shoulder', 'right_elbow'),\n", + " id=9,\n", + " color=[255, 128, 0]),\n", + " 10:\n", + " dict(\n", + " link=('left_elbow', 'left_wrist'),\n", + " id=10,\n", + " color=[0, 255, 0]),\n", + " 11:\n", + " dict(\n", + " link=('right_elbow', 'right_wrist'),\n", + " id=11,\n", + " color=[255, 128, 0]),\n", + " 12:\n", + " dict(\n", + " link=('left_eye', 'right_eye'),\n", + " id=12,\n", + " color=[51, 153, 255]),\n", + " 13:\n", + " dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),\n", + " 14:\n", + " dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),\n", + " 15:\n", + " dict(\n", + " link=('left_eye', 'left_ear'), id=15, color=[51, 153,\n", + " 255]),\n", + " 16:\n", + " dict(\n", + " link=('right_eye', 'right_ear'),\n", + " id=16,\n", + " color=[51, 153, 255]),\n", + " 17:\n", + " dict(\n", + " link=('left_ear', 'left_shoulder'),\n", + " id=17,\n", + " color=[51, 153, 255]),\n", + " 18:\n", + " dict(\n", + " link=('right_ear', 'right_shoulder'),\n", + " id=18,\n", + " color=[51, 153, 255])\n", + " }),\n", + " joint_weights=[\n", + " 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.5, 1.5, 1.0,\n", + " 1.0, 1.2, 1.2, 1.5, 1.5\n", + " ],\n", + " sigmas=[\n", + " 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,\n", + " 0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089\n", + " ])))\n", + "work_dir = 'work_dirs/hrnet_w32_coco_tiny_256x192'\n", + "gpu_ids = range(0, 1)\n", + "seed = 0\n", + "\n" + ] + } + ], + "source": [ + "print(cfg.pretty_text)" + ] + }, + { + "cell_type": "markdown", + "id": "1cbbd440-2a4f-4683-8ecd-f58db431dbbc", + "metadata": {}, + "source": [ + "## 准备训练" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5b9ed62f-78af-41c0-b28f-f167475e5c45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "=> num_images: 73\n", + "=> load 67 samples\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w32-36af842e.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e0ac83ebee94ae3942727e0e357857c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/126M [00:00 num_images: 24\n", + "=> load 24 samples\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:19:32,014 - mmpose - INFO - Epoch [1][1/5]\tlr: 5.000e-07, eta: 0:09:38, time: 2.909, data_time: 2.346, memory: 2602, heatmap_loss: 0.0021, acc_pose: 0.0295, loss: 0.0021\n", + "2022-07-04 15:19:32,295 - mmpose - INFO - Epoch [1][2/5]\tlr: 1.499e-06, eta: 0:05:15, time: 0.281, data_time: 0.008, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0220, loss: 0.0024\n", + "2022-07-04 15:19:32,562 - mmpose - INFO - Epoch [1][3/5]\tlr: 2.498e-06, eta: 0:03:47, time: 0.267, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.0098, loss: 0.0021\n", + "2022-07-04 15:19:32,819 - mmpose - INFO - Epoch [1][4/5]\tlr: 3.497e-06, eta: 0:03:01, time: 0.257, data_time: 0.004, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0000, loss: 0.0024\n", + "2022-07-04 15:19:33,081 - mmpose - INFO - Epoch [1][5/5]\tlr: 4.496e-06, eta: 0:02:35, time: 0.262, data_time: 0.003, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.0000, loss: 0.0015\n", + "2022-07-04 15:19:35,899 - mmpose - INFO - Epoch [2][1/5]\tlr: 5.495e-06, eta: 0:03:37, time: 2.736, data_time: 2.366, memory: 2919, heatmap_loss: 0.0027, acc_pose: 0.0000, loss: 0.0027\n", + "2022-07-04 15:19:36,180 - mmpose - INFO - Epoch [2][2/5]\tlr: 6.494e-06, eta: 0:03:12, time: 0.282, data_time: 0.007, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0042, loss: 0.0024\n", + "2022-07-04 15:19:36,469 - mmpose - INFO - Epoch [2][3/5]\tlr: 7.493e-06, eta: 0:02:54, time: 0.289, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0074, loss: 0.0022\n", + "2022-07-04 15:19:36,769 - mmpose - INFO - Epoch [2][4/5]\tlr: 8.492e-06, eta: 0:02:40, time: 0.300, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0205, loss: 0.0022\n", + "2022-07-04 15:19:37,037 - mmpose - INFO - Epoch [2][5/5]\tlr: 9.491e-06, eta: 0:02:29, time: 0.267, data_time: 0.004, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0294, loss: 0.0024\n", + "2022-07-04 15:19:39,861 - mmpose - INFO - Epoch [3][1/5]\tlr: 1.049e-05, eta: 0:03:02, time: 2.748, data_time: 2.401, memory: 2919, heatmap_loss: 0.0026, acc_pose: 0.0345, loss: 0.0026\n", + "2022-07-04 15:19:40,174 - mmpose - INFO - Epoch [3][2/5]\tlr: 1.149e-05, eta: 0:02:50, time: 0.313, data_time: 0.006, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.0000, loss: 0.0020\n", + "2022-07-04 15:19:40,482 - mmpose - INFO - Epoch [3][3/5]\tlr: 1.249e-05, eta: 0:02:41, time: 0.308, data_time: 0.003, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.0104, loss: 0.0025\n", + "2022-07-04 15:19:40,805 - mmpose - INFO - Epoch [3][4/5]\tlr: 1.349e-05, eta: 0:02:33, time: 0.323, data_time: 0.003, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.0059, loss: 0.0023\n", + "2022-07-04 15:19:41,073 - mmpose - INFO - Epoch [3][5/5]\tlr: 1.449e-05, eta: 0:02:25, time: 0.268, data_time: 0.004, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.0294, loss: 0.0025\n", + "2022-07-04 15:19:43,863 - mmpose - INFO - Epoch [4][1/5]\tlr: 1.549e-05, eta: 0:02:46, time: 2.703, data_time: 2.373, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.0000, loss: 0.0023\n", + "2022-07-04 15:19:44,133 - mmpose - INFO - Epoch [4][2/5]\tlr: 1.648e-05, eta: 0:02:39, time: 0.270, data_time: 0.007, memory: 2919, heatmap_loss: 0.0027, acc_pose: 0.0098, loss: 0.0027\n", + "2022-07-04 15:19:44,446 - mmpose - INFO - Epoch [4][3/5]\tlr: 1.748e-05, eta: 0:02:32, time: 0.313, data_time: 0.005, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.0164, loss: 0.0021\n", + "2022-07-04 15:19:44,730 - mmpose - INFO - Epoch [4][4/5]\tlr: 1.848e-05, eta: 0:02:26, time: 0.284, data_time: 0.004, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.0065, loss: 0.0019\n", + "2022-07-04 15:19:44,989 - mmpose - INFO - Epoch [4][5/5]\tlr: 1.948e-05, eta: 0:02:20, time: 0.259, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0000, loss: 0.0022\n", + "2022-07-04 15:19:47,816 - mmpose - INFO - Epoch [5][1/5]\tlr: 2.048e-05, eta: 0:02:36, time: 2.737, data_time: 2.390, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.0255, loss: 0.0018\n", + "2022-07-04 15:19:48,102 - mmpose - INFO - Epoch [5][2/5]\tlr: 2.148e-05, eta: 0:02:31, time: 0.287, data_time: 0.007, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0185, loss: 0.0022\n", + "2022-07-04 15:19:48,418 - mmpose - INFO - Epoch [5][3/5]\tlr: 2.248e-05, eta: 0:02:26, time: 0.316, data_time: 0.005, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0163, loss: 0.0024\n", + "2022-07-04 15:19:48,717 - mmpose - INFO - Epoch [5][4/5]\tlr: 2.348e-05, eta: 0:02:21, time: 0.299, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.0045, loss: 0.0021\n", + "2022-07-04 15:19:49,010 - mmpose - INFO - Epoch [5][5/5]\tlr: 2.448e-05, eta: 0:02:17, time: 0.293, data_time: 0.004, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.0000, loss: 0.0013\n", + "2022-07-04 15:19:51,868 - mmpose - INFO - Epoch [6][1/5]\tlr: 2.548e-05, eta: 0:02:29, time: 2.776, data_time: 2.392, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0369, loss: 0.0024\n", + "2022-07-04 15:19:52,194 - mmpose - INFO - Epoch [6][2/5]\tlr: 2.647e-05, eta: 0:02:25, time: 0.326, data_time: 0.010, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.0259, loss: 0.0020\n", + "2022-07-04 15:19:52,508 - mmpose - INFO - Epoch [6][3/5]\tlr: 2.747e-05, eta: 0:02:21, time: 0.314, data_time: 0.004, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.0182, loss: 0.0025\n", + "2022-07-04 15:19:52,800 - mmpose - INFO - Epoch [6][4/5]\tlr: 2.847e-05, eta: 0:02:17, time: 0.292, data_time: 0.004, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.0245, loss: 0.0025\n", + "2022-07-04 15:19:53,047 - mmpose - INFO - Epoch [6][5/5]\tlr: 2.947e-05, eta: 0:02:13, time: 0.247, data_time: 0.004, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.0000, loss: 0.0013\n", + "2022-07-04 15:19:55,826 - mmpose - INFO - Epoch [7][1/5]\tlr: 3.047e-05, eta: 0:02:22, time: 2.704, data_time: 2.358, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.0322, loss: 0.0019\n", + "2022-07-04 15:19:56,118 - mmpose - INFO - Epoch [7][2/5]\tlr: 3.147e-05, eta: 0:02:19, time: 0.293, data_time: 0.007, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.0483, loss: 0.0023\n", + "2022-07-04 15:19:56,392 - mmpose - INFO - Epoch [7][3/5]\tlr: 3.247e-05, eta: 0:02:15, time: 0.274, data_time: 0.003, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.0483, loss: 0.0025\n", + "2022-07-04 15:19:56,675 - mmpose - INFO - Epoch [7][4/5]\tlr: 3.347e-05, eta: 0:02:12, time: 0.284, data_time: 0.004, memory: 2919, heatmap_loss: 0.0027, acc_pose: 0.0964, loss: 0.0027\n", + "2022-07-04 15:19:56,907 - mmpose - INFO - Epoch [7][5/5]\tlr: 3.447e-05, eta: 0:02:08, time: 0.231, data_time: 0.003, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0000, loss: 0.0022\n", + "2022-07-04 15:19:59,688 - mmpose - INFO - Epoch [8][1/5]\tlr: 3.547e-05, eta: 0:02:16, time: 2.706, data_time: 2.345, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.1179, loss: 0.0025\n", + "2022-07-04 15:19:59,989 - mmpose - INFO - Epoch [8][2/5]\tlr: 3.646e-05, eta: 0:02:13, time: 0.301, data_time: 0.006, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.0267, loss: 0.0023\n", + "2022-07-04 15:20:00,285 - mmpose - INFO - Epoch [8][3/5]\tlr: 3.746e-05, eta: 0:02:10, time: 0.296, data_time: 0.004, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0965, loss: 0.0024\n", + "2022-07-04 15:20:00,580 - mmpose - INFO - Epoch [8][4/5]\tlr: 3.846e-05, eta: 0:02:07, time: 0.296, data_time: 0.004, memory: 2919, heatmap_loss: 0.0026, acc_pose: 0.1237, loss: 0.0026\n", + "2022-07-04 15:20:00,845 - mmpose - INFO - Epoch [8][5/5]\tlr: 3.946e-05, eta: 0:02:04, time: 0.265, data_time: 0.006, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.0588, loss: 0.0018\n", + "2022-07-04 15:20:03,668 - mmpose - INFO - Epoch [9][1/5]\tlr: 4.046e-05, eta: 0:02:11, time: 2.742, data_time: 2.353, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.0925, loss: 0.0024\n", + "2022-07-04 15:20:03,970 - mmpose - INFO - Epoch [9][2/5]\tlr: 4.146e-05, eta: 0:02:08, time: 0.302, data_time: 0.008, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.1302, loss: 0.0024\n", + "2022-07-04 15:20:04,254 - mmpose - INFO - Epoch [9][3/5]\tlr: 4.246e-05, eta: 0:02:05, time: 0.284, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.0889, loss: 0.0022\n", + "2022-07-04 15:20:04,521 - mmpose - INFO - Epoch [9][4/5]\tlr: 4.346e-05, eta: 0:02:03, time: 0.267, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.1038, loss: 0.0021\n", + "2022-07-04 15:20:04,748 - mmpose - INFO - Epoch [9][5/5]\tlr: 4.446e-05, eta: 0:02:00, time: 0.226, data_time: 0.003, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.1538, loss: 0.0015\n", + "2022-07-04 15:20:07,582 - mmpose - INFO - Epoch [10][1/5]\tlr: 4.545e-05, eta: 0:02:06, time: 2.746, data_time: 2.388, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.1522, loss: 0.0021\n", + "2022-07-04 15:20:07,870 - mmpose - INFO - Epoch [10][2/5]\tlr: 4.645e-05, eta: 0:02:03, time: 0.288, data_time: 0.006, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.0793, loss: 0.0021\n", + "2022-07-04 15:20:08,164 - mmpose - INFO - Epoch [10][3/5]\tlr: 4.745e-05, eta: 0:02:01, time: 0.294, data_time: 0.006, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.0957, loss: 0.0021\n", + "2022-07-04 15:20:08,456 - mmpose - INFO - Epoch [10][4/5]\tlr: 4.845e-05, eta: 0:01:59, time: 0.292, data_time: 0.004, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.1522, loss: 0.0025\n", + "2022-07-04 15:20:08,731 - mmpose - INFO - Epoch [10][5/5]\tlr: 4.945e-05, eta: 0:01:56, time: 0.275, data_time: 0.004, memory: 2919, heatmap_loss: 0.0024, acc_pose: 0.1778, loss: 0.0024\n", + "2022-07-04 15:20:08,801 - mmpose - INFO - Saving checkpoint at 10 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 24/24, 38.4 task/s, elapsed: 1s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:20:10,728 - mmpose - INFO - Epoch(val) [10][2]\tAP: 0.0000, AP .5: 0.0000, AP .75: 0.0000, AP (M): 0.0000, AP (L): 0.0000, AR: 0.0000, AR .5: 0.0000, AR .75: 0.0000, AR (M): 0.0000, AR (L): 0.0000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.000\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:20:13,386 - mmpose - INFO - Epoch [11][1/5]\tlr: 5.045e-05, eta: 0:02:01, time: 2.650, data_time: 2.323, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1275, loss: 0.0020\n", + "2022-07-04 15:20:13,649 - mmpose - INFO - Epoch [11][2/5]\tlr: 5.145e-05, eta: 0:01:58, time: 0.264, data_time: 0.006, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.1881, loss: 0.0022\n", + "2022-07-04 15:20:13,921 - mmpose - INFO - Epoch [11][3/5]\tlr: 5.245e-05, eta: 0:01:56, time: 0.271, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.1448, loss: 0.0021\n", + "2022-07-04 15:20:14,184 - mmpose - INFO - Epoch [11][4/5]\tlr: 5.345e-05, eta: 0:01:54, time: 0.264, data_time: 0.004, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.1687, loss: 0.0023\n", + "2022-07-04 15:20:14,415 - mmpose - INFO - Epoch [11][5/5]\tlr: 5.445e-05, eta: 0:01:52, time: 0.230, data_time: 0.003, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.0476, loss: 0.0018\n", + "2022-07-04 15:20:17,185 - mmpose - INFO - Epoch [12][1/5]\tlr: 5.544e-05, eta: 0:01:56, time: 2.692, data_time: 2.357, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.2110, loss: 0.0023\n", + "2022-07-04 15:20:17,467 - mmpose - INFO - Epoch [12][2/5]\tlr: 5.644e-05, eta: 0:01:54, time: 0.282, data_time: 0.007, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1384, loss: 0.0020\n", + "2022-07-04 15:20:17,774 - mmpose - INFO - Epoch [12][3/5]\tlr: 5.744e-05, eta: 0:01:52, time: 0.307, data_time: 0.004, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1964, loss: 0.0020\n", + "2022-07-04 15:20:18,030 - mmpose - INFO - Epoch [12][4/5]\tlr: 5.844e-05, eta: 0:01:50, time: 0.256, data_time: 0.004, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1393, loss: 0.0020\n", + "2022-07-04 15:20:18,245 - mmpose - INFO - Epoch [12][5/5]\tlr: 5.944e-05, eta: 0:01:48, time: 0.216, data_time: 0.004, memory: 2919, heatmap_loss: 0.0009, acc_pose: 0.0000, loss: 0.0009\n", + "2022-07-04 15:20:21,038 - mmpose - INFO - Epoch [13][1/5]\tlr: 6.044e-05, eta: 0:01:51, time: 2.687, data_time: 2.360, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.1976, loss: 0.0021\n", + "2022-07-04 15:20:21,358 - mmpose - INFO - Epoch [13][2/5]\tlr: 6.144e-05, eta: 0:01:49, time: 0.321, data_time: 0.010, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1809, loss: 0.0020\n", + "2022-07-04 15:20:21,665 - mmpose - INFO - Epoch [13][3/5]\tlr: 6.244e-05, eta: 0:01:47, time: 0.307, data_time: 0.006, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1568, loss: 0.0020\n", + "2022-07-04 15:20:21,930 - mmpose - INFO - Epoch [13][4/5]\tlr: 6.344e-05, eta: 0:01:46, time: 0.265, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.1393, loss: 0.0022\n", + "2022-07-04 15:20:22,162 - mmpose - INFO - Epoch [13][5/5]\tlr: 6.444e-05, eta: 0:01:44, time: 0.232, data_time: 0.004, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.0000, loss: 0.0013\n", + "2022-07-04 15:20:25,000 - mmpose - INFO - Epoch [14][1/5]\tlr: 6.544e-05, eta: 0:01:47, time: 2.757, data_time: 2.380, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.1932, loss: 0.0018\n", + "2022-07-04 15:20:25,331 - mmpose - INFO - Epoch [14][2/5]\tlr: 6.643e-05, eta: 0:01:45, time: 0.331, data_time: 0.008, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.1970, loss: 0.0022\n", + "2022-07-04 15:20:25,651 - mmpose - INFO - Epoch [14][3/5]\tlr: 6.743e-05, eta: 0:01:43, time: 0.320, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.0711, loss: 0.0017\n", + "2022-07-04 15:20:25,957 - mmpose - INFO - Epoch [14][4/5]\tlr: 6.843e-05, eta: 0:01:42, time: 0.307, data_time: 0.005, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2207, loss: 0.0019\n", + "2022-07-04 15:20:26,200 - mmpose - INFO - Epoch [14][5/5]\tlr: 6.943e-05, eta: 0:01:40, time: 0.243, data_time: 0.004, memory: 2919, heatmap_loss: 0.0025, acc_pose: 0.2353, loss: 0.0025\n", + "2022-07-04 15:20:29,062 - mmpose - INFO - Epoch [15][1/5]\tlr: 7.043e-05, eta: 0:01:43, time: 2.802, data_time: 2.430, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.1946, loss: 0.0022\n", + "2022-07-04 15:20:29,383 - mmpose - INFO - Epoch [15][2/5]\tlr: 7.143e-05, eta: 0:01:41, time: 0.321, data_time: 0.006, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.2169, loss: 0.0021\n", + "2022-07-04 15:20:29,687 - mmpose - INFO - Epoch [15][3/5]\tlr: 7.243e-05, eta: 0:01:40, time: 0.305, data_time: 0.004, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2117, loss: 0.0019\n", + "2022-07-04 15:20:29,977 - mmpose - INFO - Epoch [15][4/5]\tlr: 7.343e-05, eta: 0:01:38, time: 0.290, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.1415, loss: 0.0017\n", + "2022-07-04 15:20:30,235 - mmpose - INFO - Epoch [15][5/5]\tlr: 7.443e-05, eta: 0:01:36, time: 0.258, data_time: 0.004, memory: 2919, heatmap_loss: 0.0011, acc_pose: 0.0000, loss: 0.0011\n", + "2022-07-04 15:20:33,025 - mmpose - INFO - Epoch [16][1/5]\tlr: 7.543e-05, eta: 0:01:39, time: 2.713, data_time: 2.350, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2313, loss: 0.0019\n", + "2022-07-04 15:20:33,322 - mmpose - INFO - Epoch [16][2/5]\tlr: 7.642e-05, eta: 0:01:37, time: 0.297, data_time: 0.006, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.1914, loss: 0.0017\n", + "2022-07-04 15:20:33,613 - mmpose - INFO - Epoch [16][3/5]\tlr: 7.742e-05, eta: 0:01:35, time: 0.291, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.1931, loss: 0.0022\n", + "2022-07-04 15:20:33,906 - mmpose - INFO - Epoch [16][4/5]\tlr: 7.842e-05, eta: 0:01:34, time: 0.292, data_time: 0.006, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2290, loss: 0.0019\n", + "2022-07-04 15:20:34,174 - mmpose - INFO - Epoch [16][5/5]\tlr: 7.942e-05, eta: 0:01:32, time: 0.268, data_time: 0.006, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.2667, loss: 0.0013\n", + "2022-07-04 15:20:36,967 - mmpose - INFO - Epoch [17][1/5]\tlr: 8.042e-05, eta: 0:01:34, time: 2.717, data_time: 2.356, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1762, loss: 0.0020\n", + "2022-07-04 15:20:37,280 - mmpose - INFO - Epoch [17][2/5]\tlr: 8.142e-05, eta: 0:01:33, time: 0.313, data_time: 0.010, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2323, loss: 0.0018\n", + "2022-07-04 15:20:37,550 - mmpose - INFO - Epoch [17][3/5]\tlr: 8.242e-05, eta: 0:01:31, time: 0.270, data_time: 0.003, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2720, loss: 0.0018\n", + "2022-07-04 15:20:37,811 - mmpose - INFO - Epoch [17][4/5]\tlr: 8.342e-05, eta: 0:01:30, time: 0.261, data_time: 0.004, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.2937, loss: 0.0020\n", + "2022-07-04 15:20:38,036 - mmpose - INFO - Epoch [17][5/5]\tlr: 8.442e-05, eta: 0:01:28, time: 0.225, data_time: 0.003, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.3472, loss: 0.0015\n", + "2022-07-04 15:20:40,898 - mmpose - INFO - Epoch [18][1/5]\tlr: 8.542e-05, eta: 0:01:30, time: 2.778, data_time: 2.415, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.2299, loss: 0.0021\n", + "2022-07-04 15:20:41,209 - mmpose - INFO - Epoch [18][2/5]\tlr: 8.641e-05, eta: 0:01:29, time: 0.311, data_time: 0.006, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2758, loss: 0.0018\n", + "2022-07-04 15:20:41,496 - mmpose - INFO - Epoch [18][3/5]\tlr: 8.741e-05, eta: 0:01:27, time: 0.287, data_time: 0.003, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.2024, loss: 0.0016\n", + "2022-07-04 15:20:41,790 - mmpose - INFO - Epoch [18][4/5]\tlr: 8.841e-05, eta: 0:01:26, time: 0.294, data_time: 0.003, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3246, loss: 0.0019\n", + "2022-07-04 15:20:42,038 - mmpose - INFO - Epoch [18][5/5]\tlr: 8.941e-05, eta: 0:01:25, time: 0.247, data_time: 0.003, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.1562, loss: 0.0020\n", + "2022-07-04 15:20:44,818 - mmpose - INFO - Epoch [19][1/5]\tlr: 9.041e-05, eta: 0:01:26, time: 2.684, data_time: 2.353, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.2444, loss: 0.0020\n", + "2022-07-04 15:20:45,104 - mmpose - INFO - Epoch [19][2/5]\tlr: 9.141e-05, eta: 0:01:25, time: 0.287, data_time: 0.006, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.2326, loss: 0.0020\n", + "2022-07-04 15:20:45,374 - mmpose - INFO - Epoch [19][3/5]\tlr: 9.241e-05, eta: 0:01:23, time: 0.269, data_time: 0.003, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3090, loss: 0.0018\n", + "2022-07-04 15:20:45,643 - mmpose - INFO - Epoch [19][4/5]\tlr: 9.341e-05, eta: 0:01:22, time: 0.269, data_time: 0.004, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.1945, loss: 0.0019\n", + "2022-07-04 15:20:45,866 - mmpose - INFO - Epoch [19][5/5]\tlr: 9.441e-05, eta: 0:01:21, time: 0.223, data_time: 0.003, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.2692, loss: 0.0016\n", + "2022-07-04 15:20:48,663 - mmpose - INFO - Epoch [20][1/5]\tlr: 9.540e-05, eta: 0:01:22, time: 2.719, data_time: 2.382, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2861, loss: 0.0018\n", + "2022-07-04 15:20:48,983 - mmpose - INFO - Epoch [20][2/5]\tlr: 9.640e-05, eta: 0:01:21, time: 0.320, data_time: 0.008, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2931, loss: 0.0019\n", + "2022-07-04 15:20:49,286 - mmpose - INFO - Epoch [20][3/5]\tlr: 9.740e-05, eta: 0:01:19, time: 0.303, data_time: 0.005, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.3311, loss: 0.0022\n", + "2022-07-04 15:20:49,558 - mmpose - INFO - Epoch [20][4/5]\tlr: 9.840e-05, eta: 0:01:18, time: 0.272, data_time: 0.003, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2431, loss: 0.0018\n", + "2022-07-04 15:20:49,780 - mmpose - INFO - Epoch [20][5/5]\tlr: 9.940e-05, eta: 0:01:17, time: 0.223, data_time: 0.003, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.5417, loss: 0.0015\n", + "2022-07-04 15:20:49,858 - mmpose - INFO - Saving checkpoint at 20 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 24/24, 41.6 task/s, elapsed: 1s, ETA: 0sLoading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.054\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.233\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.031\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.067\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.100\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.417\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.117\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.094\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:20:52,939 - mmpose - INFO - Now best checkpoint is saved as best_AP_epoch_20.pth.\n", + "2022-07-04 15:20:52,940 - mmpose - INFO - Best AP is 0.0535 at 20 epoch.\n", + "2022-07-04 15:20:52,943 - mmpose - INFO - Epoch(val) [20][2]\tAP: 0.0535, AP .5: 0.2327, AP .75: 0.0000, AP (M): 0.0311, AP (L): 0.0668, AR: 0.1000, AR .5: 0.4167, AR .75: 0.0000, AR (M): 0.1167, AR (L): 0.0944\n", + "2022-07-04 15:20:55,613 - mmpose - INFO - Epoch [21][1/5]\tlr: 1.004e-04, eta: 0:01:18, time: 2.662, data_time: 2.344, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2796, loss: 0.0019\n", + "2022-07-04 15:20:55,926 - mmpose - INFO - Epoch [21][2/5]\tlr: 1.014e-04, eta: 0:01:17, time: 0.313, data_time: 0.008, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.2464, loss: 0.0018\n", + "2022-07-04 15:20:56,191 - mmpose - INFO - Epoch [21][3/5]\tlr: 1.024e-04, eta: 0:01:15, time: 0.265, data_time: 0.005, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3100, loss: 0.0019\n", + "2022-07-04 15:20:56,478 - mmpose - INFO - Epoch [21][4/5]\tlr: 1.034e-04, eta: 0:01:14, time: 0.287, data_time: 0.005, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3305, loss: 0.0019\n", + "2022-07-04 15:20:56,713 - mmpose - INFO - Epoch [21][5/5]\tlr: 1.044e-04, eta: 0:01:13, time: 0.235, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.2000, loss: 0.0017\n", + "2022-07-04 15:20:59,471 - mmpose - INFO - Epoch [22][1/5]\tlr: 1.054e-04, eta: 0:01:14, time: 2.683, data_time: 2.334, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.1958, loss: 0.0017\n", + "2022-07-04 15:20:59,758 - mmpose - INFO - Epoch [22][2/5]\tlr: 1.064e-04, eta: 0:01:12, time: 0.288, data_time: 0.006, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3504, loss: 0.0016\n", + "2022-07-04 15:21:00,047 - mmpose - INFO - Epoch [22][3/5]\tlr: 1.074e-04, eta: 0:01:11, time: 0.289, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3244, loss: 0.0017\n", + "2022-07-04 15:21:00,320 - mmpose - INFO - Epoch [22][4/5]\tlr: 1.084e-04, eta: 0:01:10, time: 0.273, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.2687, loss: 0.0017\n", + "2022-07-04 15:21:00,531 - mmpose - INFO - Epoch [22][5/5]\tlr: 1.094e-04, eta: 0:01:09, time: 0.211, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.0882, loss: 0.0017\n", + "2022-07-04 15:21:03,329 - mmpose - INFO - Epoch [23][1/5]\tlr: 1.104e-04, eta: 0:01:10, time: 2.721, data_time: 2.358, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3405, loss: 0.0019\n", + "2022-07-04 15:21:03,623 - mmpose - INFO - Epoch [23][2/5]\tlr: 1.114e-04, eta: 0:01:08, time: 0.294, data_time: 0.007, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2883, loss: 0.0019\n", + "2022-07-04 15:21:03,912 - mmpose - INFO - Epoch [23][3/5]\tlr: 1.124e-04, eta: 0:01:07, time: 0.290, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.3253, loss: 0.0021\n", + "2022-07-04 15:21:04,171 - mmpose - INFO - Epoch [23][4/5]\tlr: 1.134e-04, eta: 0:01:06, time: 0.258, data_time: 0.004, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3392, loss: 0.0019\n", + "2022-07-04 15:21:04,377 - mmpose - INFO - Epoch [23][5/5]\tlr: 1.144e-04, eta: 0:01:05, time: 0.206, data_time: 0.004, memory: 2919, heatmap_loss: 0.0012, acc_pose: 0.4375, loss: 0.0012\n", + "2022-07-04 15:21:07,161 - mmpose - INFO - Epoch [24][1/5]\tlr: 1.154e-04, eta: 0:01:06, time: 2.704, data_time: 2.340, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3057, loss: 0.0016\n", + "2022-07-04 15:21:07,453 - mmpose - INFO - Epoch [24][2/5]\tlr: 1.164e-04, eta: 0:01:04, time: 0.292, data_time: 0.005, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3236, loss: 0.0017\n", + "2022-07-04 15:21:07,742 - mmpose - INFO - Epoch [24][3/5]\tlr: 1.174e-04, eta: 0:01:03, time: 0.289, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3649, loss: 0.0018\n", + "2022-07-04 15:21:08,026 - mmpose - INFO - Epoch [24][4/5]\tlr: 1.184e-04, eta: 0:01:02, time: 0.284, data_time: 0.004, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.3821, loss: 0.0020\n", + "2022-07-04 15:21:08,255 - mmpose - INFO - Epoch [24][5/5]\tlr: 1.194e-04, eta: 0:01:01, time: 0.229, data_time: 0.003, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.5490, loss: 0.0017\n", + "2022-07-04 15:21:11,110 - mmpose - INFO - Epoch [25][1/5]\tlr: 1.204e-04, eta: 0:01:02, time: 2.776, data_time: 2.411, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3555, loss: 0.0016\n", + "2022-07-04 15:21:11,428 - mmpose - INFO - Epoch [25][2/5]\tlr: 1.214e-04, eta: 0:01:00, time: 0.319, data_time: 0.007, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.3074, loss: 0.0013\n", + "2022-07-04 15:21:11,745 - mmpose - INFO - Epoch [25][3/5]\tlr: 1.224e-04, eta: 0:00:59, time: 0.316, data_time: 0.004, memory: 2919, heatmap_loss: 0.0021, acc_pose: 0.2891, loss: 0.0021\n", + "2022-07-04 15:21:12,039 - mmpose - INFO - Epoch [25][4/5]\tlr: 1.234e-04, eta: 0:00:58, time: 0.294, data_time: 0.005, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.2327, loss: 0.0019\n", + "2022-07-04 15:21:12,292 - mmpose - INFO - Epoch [25][5/5]\tlr: 1.244e-04, eta: 0:00:57, time: 0.253, data_time: 0.004, memory: 2919, heatmap_loss: 0.0022, acc_pose: 0.2941, loss: 0.0022\n", + "2022-07-04 15:21:15,093 - mmpose - INFO - Epoch [26][1/5]\tlr: 1.254e-04, eta: 0:00:58, time: 2.712, data_time: 2.362, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3939, loss: 0.0016\n", + "2022-07-04 15:21:15,410 - mmpose - INFO - Epoch [26][2/5]\tlr: 1.264e-04, eta: 0:00:57, time: 0.317, data_time: 0.009, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.4134, loss: 0.0019\n", + "2022-07-04 15:21:15,705 - mmpose - INFO - Epoch [26][3/5]\tlr: 1.274e-04, eta: 0:00:56, time: 0.296, data_time: 0.006, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3218, loss: 0.0018\n", + "2022-07-04 15:21:15,990 - mmpose - INFO - Epoch [26][4/5]\tlr: 1.284e-04, eta: 0:00:54, time: 0.285, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.4029, loss: 0.0018\n", + "2022-07-04 15:21:16,224 - mmpose - INFO - Epoch [26][5/5]\tlr: 1.294e-04, eta: 0:00:53, time: 0.234, data_time: 0.004, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.2333, loss: 0.0014\n", + "2022-07-04 15:21:19,012 - mmpose - INFO - Epoch [27][1/5]\tlr: 1.304e-04, eta: 0:00:54, time: 2.705, data_time: 2.341, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4350, loss: 0.0017\n", + "2022-07-04 15:21:19,307 - mmpose - INFO - Epoch [27][2/5]\tlr: 1.314e-04, eta: 0:00:53, time: 0.296, data_time: 0.006, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.2851, loss: 0.0015\n", + "2022-07-04 15:21:19,599 - mmpose - INFO - Epoch [27][3/5]\tlr: 1.324e-04, eta: 0:00:52, time: 0.292, data_time: 0.004, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.3918, loss: 0.0020\n", + "2022-07-04 15:21:19,875 - mmpose - INFO - Epoch [27][4/5]\tlr: 1.334e-04, eta: 0:00:51, time: 0.276, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4145, loss: 0.0017\n", + "2022-07-04 15:21:20,086 - mmpose - INFO - Epoch [27][5/5]\tlr: 1.344e-04, eta: 0:00:50, time: 0.211, data_time: 0.003, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3333, loss: 0.0016\n", + "2022-07-04 15:21:22,866 - mmpose - INFO - Epoch [28][1/5]\tlr: 1.354e-04, eta: 0:00:50, time: 2.708, data_time: 2.351, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3398, loss: 0.0017\n", + "2022-07-04 15:21:23,158 - mmpose - INFO - Epoch [28][2/5]\tlr: 1.364e-04, eta: 0:00:49, time: 0.293, data_time: 0.006, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3664, loss: 0.0019\n", + "2022-07-04 15:21:23,439 - mmpose - INFO - Epoch [28][3/5]\tlr: 1.374e-04, eta: 0:00:48, time: 0.281, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3704, loss: 0.0017\n", + "2022-07-04 15:21:23,704 - mmpose - INFO - Epoch [28][4/5]\tlr: 1.384e-04, eta: 0:00:47, time: 0.265, data_time: 0.003, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3751, loss: 0.0017\n", + "2022-07-04 15:21:23,915 - mmpose - INFO - Epoch [28][5/5]\tlr: 1.394e-04, eta: 0:00:46, time: 0.211, data_time: 0.003, memory: 2919, heatmap_loss: 0.0023, acc_pose: 0.3235, loss: 0.0023\n", + "2022-07-04 15:21:26,715 - mmpose - INFO - Epoch [29][1/5]\tlr: 1.404e-04, eta: 0:00:46, time: 2.724, data_time: 2.350, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4467, loss: 0.0017\n", + "2022-07-04 15:21:27,011 - mmpose - INFO - Epoch [29][2/5]\tlr: 1.414e-04, eta: 0:00:45, time: 0.296, data_time: 0.007, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3907, loss: 0.0016\n", + "2022-07-04 15:21:27,294 - mmpose - INFO - Epoch [29][3/5]\tlr: 1.424e-04, eta: 0:00:44, time: 0.283, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3425, loss: 0.0018\n", + "2022-07-04 15:21:27,558 - mmpose - INFO - Epoch [29][4/5]\tlr: 1.434e-04, eta: 0:00:43, time: 0.264, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4081, loss: 0.0016\n", + "2022-07-04 15:21:27,807 - mmpose - INFO - Epoch [29][5/5]\tlr: 1.444e-04, eta: 0:00:42, time: 0.248, data_time: 0.004, memory: 2919, heatmap_loss: 0.0011, acc_pose: 0.3333, loss: 0.0011\n", + "2022-07-04 15:21:30,582 - mmpose - INFO - Epoch [30][1/5]\tlr: 1.454e-04, eta: 0:00:42, time: 2.687, data_time: 2.354, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3392, loss: 0.0016\n", + "2022-07-04 15:21:30,848 - mmpose - INFO - Epoch [30][2/5]\tlr: 1.464e-04, eta: 0:00:41, time: 0.266, data_time: 0.005, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3693, loss: 0.0016\n", + "2022-07-04 15:21:31,115 - mmpose - INFO - Epoch [30][3/5]\tlr: 1.474e-04, eta: 0:00:40, time: 0.267, data_time: 0.003, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.3824, loss: 0.0015\n", + "2022-07-04 15:21:31,374 - mmpose - INFO - Epoch [30][4/5]\tlr: 1.484e-04, eta: 0:00:39, time: 0.259, data_time: 0.003, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.3720, loss: 0.0019\n", + "2022-07-04 15:21:31,597 - mmpose - INFO - Epoch [30][5/5]\tlr: 1.494e-04, eta: 0:00:38, time: 0.223, data_time: 0.003, memory: 2919, heatmap_loss: 0.0020, acc_pose: 0.5098, loss: 0.0020\n", + "2022-07-04 15:21:31,692 - mmpose - INFO - Saving checkpoint at 30 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 24/24, 35.4 task/s, elapsed: 1s, ETA: 0sLoading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.239\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.714\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.129\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.265\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.235\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.296\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.792\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.125\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.300\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.294\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:21:33,961 - mmpose - INFO - The previous best checkpoint /home/featurize/work/MMPose教程/mmpose/work_dirs/hrnet_w32_coco_tiny_256x192/best_AP_epoch_20.pth was removed\n", + "2022-07-04 15:21:35,206 - mmpose - INFO - Now best checkpoint is saved as best_AP_epoch_30.pth.\n", + "2022-07-04 15:21:35,207 - mmpose - INFO - Best AP is 0.2386 at 30 epoch.\n", + "2022-07-04 15:21:35,208 - mmpose - INFO - Epoch(val) [30][2]\tAP: 0.2386, AP .5: 0.7137, AP .75: 0.1287, AP (M): 0.2654, AP (L): 0.2350, AR: 0.2958, AR .5: 0.7917, AR .75: 0.1250, AR (M): 0.3000, AR (L): 0.2944\n", + "2022-07-04 15:21:37,931 - mmpose - INFO - Epoch [31][1/5]\tlr: 1.504e-04, eta: 0:00:38, time: 2.716, data_time: 2.356, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.3365, loss: 0.0015\n", + "2022-07-04 15:21:38,226 - mmpose - INFO - Epoch [31][2/5]\tlr: 1.513e-04, eta: 0:00:37, time: 0.295, data_time: 0.007, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4115, loss: 0.0017\n", + "2022-07-04 15:21:38,516 - mmpose - INFO - Epoch [31][3/5]\tlr: 1.523e-04, eta: 0:00:36, time: 0.291, data_time: 0.007, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4354, loss: 0.0016\n", + "2022-07-04 15:21:38,801 - mmpose - INFO - Epoch [31][4/5]\tlr: 1.533e-04, eta: 0:00:35, time: 0.284, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.4153, loss: 0.0018\n", + "2022-07-04 15:21:39,033 - mmpose - INFO - Epoch [31][5/5]\tlr: 1.543e-04, eta: 0:00:34, time: 0.232, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3824, loss: 0.0018\n", + "2022-07-04 15:21:41,856 - mmpose - INFO - Epoch [32][1/5]\tlr: 1.553e-04, eta: 0:00:34, time: 2.742, data_time: 2.356, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4155, loss: 0.0016\n", + "2022-07-04 15:21:42,151 - mmpose - INFO - Epoch [32][2/5]\tlr: 1.563e-04, eta: 0:00:33, time: 0.295, data_time: 0.005, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.3689, loss: 0.0018\n", + "2022-07-04 15:21:42,440 - mmpose - INFO - Epoch [32][3/5]\tlr: 1.573e-04, eta: 0:00:32, time: 0.289, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3707, loss: 0.0016\n", + "2022-07-04 15:21:42,710 - mmpose - INFO - Epoch [32][4/5]\tlr: 1.583e-04, eta: 0:00:31, time: 0.270, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.5127, loss: 0.0017\n", + "2022-07-04 15:21:42,918 - mmpose - INFO - Epoch [32][5/5]\tlr: 1.593e-04, eta: 0:00:30, time: 0.208, data_time: 0.003, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.2059, loss: 0.0017\n", + "2022-07-04 15:21:45,685 - mmpose - INFO - Epoch [33][1/5]\tlr: 1.603e-04, eta: 0:00:30, time: 2.688, data_time: 2.362, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4753, loss: 0.0017\n", + "2022-07-04 15:21:46,031 - mmpose - INFO - Epoch [33][2/5]\tlr: 1.613e-04, eta: 0:00:29, time: 0.347, data_time: 0.011, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4192, loss: 0.0014\n", + "2022-07-04 15:21:46,322 - mmpose - INFO - Epoch [33][3/5]\tlr: 1.623e-04, eta: 0:00:28, time: 0.291, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4421, loss: 0.0017\n", + "2022-07-04 15:21:46,589 - mmpose - INFO - Epoch [33][4/5]\tlr: 1.633e-04, eta: 0:00:27, time: 0.267, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4325, loss: 0.0016\n", + "2022-07-04 15:21:46,813 - mmpose - INFO - Epoch [33][5/5]\tlr: 1.643e-04, eta: 0:00:26, time: 0.223, data_time: 0.003, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.4118, loss: 0.0013\n", + "2022-07-04 15:21:49,558 - mmpose - INFO - Epoch [34][1/5]\tlr: 1.653e-04, eta: 0:00:26, time: 2.668, data_time: 2.334, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.3580, loss: 0.0016\n", + "2022-07-04 15:21:49,831 - mmpose - INFO - Epoch [34][2/5]\tlr: 1.663e-04, eta: 0:00:25, time: 0.273, data_time: 0.006, memory: 2919, heatmap_loss: 0.0012, acc_pose: 0.4401, loss: 0.0012\n", + "2022-07-04 15:21:50,098 - mmpose - INFO - Epoch [34][3/5]\tlr: 1.673e-04, eta: 0:00:24, time: 0.267, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4374, loss: 0.0016\n", + "2022-07-04 15:21:50,359 - mmpose - INFO - Epoch [34][4/5]\tlr: 1.683e-04, eta: 0:00:23, time: 0.261, data_time: 0.004, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4773, loss: 0.0014\n", + "2022-07-04 15:21:50,578 - mmpose - INFO - Epoch [34][5/5]\tlr: 1.693e-04, eta: 0:00:22, time: 0.218, data_time: 0.003, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.4744, loss: 0.0018\n", + "2022-07-04 15:21:53,407 - mmpose - INFO - Epoch [35][1/5]\tlr: 1.703e-04, eta: 0:00:22, time: 2.752, data_time: 2.348, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.3721, loss: 0.0014\n", + "2022-07-04 15:21:53,706 - mmpose - INFO - Epoch [35][2/5]\tlr: 1.713e-04, eta: 0:00:21, time: 0.299, data_time: 0.008, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.4150, loss: 0.0015\n", + "2022-07-04 15:21:53,997 - mmpose - INFO - Epoch [35][3/5]\tlr: 1.723e-04, eta: 0:00:20, time: 0.291, data_time: 0.004, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.4350, loss: 0.0018\n", + "2022-07-04 15:21:54,261 - mmpose - INFO - Epoch [35][4/5]\tlr: 1.733e-04, eta: 0:00:20, time: 0.264, data_time: 0.004, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.4552, loss: 0.0015\n", + "2022-07-04 15:21:54,492 - mmpose - INFO - Epoch [35][5/5]\tlr: 1.743e-04, eta: 0:00:19, time: 0.231, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.2604, loss: 0.0017\n", + "2022-07-04 15:21:57,312 - mmpose - INFO - Epoch [36][1/5]\tlr: 1.753e-04, eta: 0:00:18, time: 2.740, data_time: 2.346, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4173, loss: 0.0016\n", + "2022-07-04 15:21:57,669 - mmpose - INFO - Epoch [36][2/5]\tlr: 1.763e-04, eta: 0:00:17, time: 0.357, data_time: 0.008, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4557, loss: 0.0014\n", + "2022-07-04 15:21:57,992 - mmpose - INFO - Epoch [36][3/5]\tlr: 1.773e-04, eta: 0:00:16, time: 0.323, data_time: 0.004, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.4488, loss: 0.0017\n", + "2022-07-04 15:21:58,299 - mmpose - INFO - Epoch [36][4/5]\tlr: 1.783e-04, eta: 0:00:16, time: 0.307, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4255, loss: 0.0016\n", + "2022-07-04 15:21:58,566 - mmpose - INFO - Epoch [36][5/5]\tlr: 1.793e-04, eta: 0:00:15, time: 0.267, data_time: 0.003, memory: 2919, heatmap_loss: 0.0019, acc_pose: 0.4020, loss: 0.0019\n", + "2022-07-04 15:22:01,331 - mmpose - INFO - Epoch [37][1/5]\tlr: 1.803e-04, eta: 0:00:14, time: 2.687, data_time: 2.359, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4200, loss: 0.0014\n", + "2022-07-04 15:22:01,621 - mmpose - INFO - Epoch [37][2/5]\tlr: 1.813e-04, eta: 0:00:13, time: 0.290, data_time: 0.007, memory: 2919, heatmap_loss: 0.0017, acc_pose: 0.3720, loss: 0.0017\n", + "2022-07-04 15:22:01,913 - mmpose - INFO - Epoch [37][3/5]\tlr: 1.823e-04, eta: 0:00:13, time: 0.292, data_time: 0.004, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4393, loss: 0.0014\n", + "2022-07-04 15:22:02,203 - mmpose - INFO - Epoch [37][4/5]\tlr: 1.833e-04, eta: 0:00:12, time: 0.290, data_time: 0.004, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.4778, loss: 0.0015\n", + "2022-07-04 15:22:02,456 - mmpose - INFO - Epoch [37][5/5]\tlr: 1.843e-04, eta: 0:00:11, time: 0.253, data_time: 0.004, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4167, loss: 0.0014\n", + "2022-07-04 15:22:05,308 - mmpose - INFO - Epoch [38][1/5]\tlr: 1.853e-04, eta: 0:00:10, time: 2.773, data_time: 2.419, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.4734, loss: 0.0015\n", + "2022-07-04 15:22:05,625 - mmpose - INFO - Epoch [38][2/5]\tlr: 1.863e-04, eta: 0:00:10, time: 0.317, data_time: 0.006, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.4248, loss: 0.0015\n", + "2022-07-04 15:22:05,950 - mmpose - INFO - Epoch [38][3/5]\tlr: 1.873e-04, eta: 0:00:09, time: 0.325, data_time: 0.005, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.5283, loss: 0.0015\n", + "2022-07-04 15:22:06,228 - mmpose - INFO - Epoch [38][4/5]\tlr: 1.883e-04, eta: 0:00:08, time: 0.278, data_time: 0.004, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.5060, loss: 0.0015\n", + "2022-07-04 15:22:06,488 - mmpose - INFO - Epoch [38][5/5]\tlr: 1.893e-04, eta: 0:00:07, time: 0.260, data_time: 0.005, memory: 2919, heatmap_loss: 0.0012, acc_pose: 0.2000, loss: 0.0012\n", + "2022-07-04 15:22:09,249 - mmpose - INFO - Epoch [39][1/5]\tlr: 1.903e-04, eta: 0:00:06, time: 2.682, data_time: 2.333, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4644, loss: 0.0016\n", + "2022-07-04 15:22:09,535 - mmpose - INFO - Epoch [39][2/5]\tlr: 1.913e-04, eta: 0:00:06, time: 0.286, data_time: 0.007, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.4143, loss: 0.0013\n", + "2022-07-04 15:22:09,821 - mmpose - INFO - Epoch [39][3/5]\tlr: 1.923e-04, eta: 0:00:05, time: 0.286, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.4414, loss: 0.0016\n", + "2022-07-04 15:22:10,108 - mmpose - INFO - Epoch [39][4/5]\tlr: 1.933e-04, eta: 0:00:04, time: 0.287, data_time: 0.004, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.5028, loss: 0.0015\n", + "2022-07-04 15:22:10,339 - mmpose - INFO - Epoch [39][5/5]\tlr: 1.943e-04, eta: 0:00:03, time: 0.231, data_time: 0.004, memory: 2919, heatmap_loss: 0.0015, acc_pose: 0.3824, loss: 0.0015\n", + "2022-07-04 15:22:13,114 - mmpose - INFO - Epoch [40][1/5]\tlr: 1.953e-04, eta: 0:00:03, time: 2.698, data_time: 2.351, memory: 2919, heatmap_loss: 0.0013, acc_pose: 0.5609, loss: 0.0013\n", + "2022-07-04 15:22:13,416 - mmpose - INFO - Epoch [40][2/5]\tlr: 1.963e-04, eta: 0:00:02, time: 0.302, data_time: 0.007, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.3719, loss: 0.0014\n", + "2022-07-04 15:22:13,713 - mmpose - INFO - Epoch [40][3/5]\tlr: 1.973e-04, eta: 0:00:01, time: 0.298, data_time: 0.004, memory: 2919, heatmap_loss: 0.0014, acc_pose: 0.4303, loss: 0.0014\n", + "2022-07-04 15:22:14,000 - mmpose - INFO - Epoch [40][4/5]\tlr: 1.983e-04, eta: 0:00:00, time: 0.287, data_time: 0.004, memory: 2919, heatmap_loss: 0.0016, acc_pose: 0.5142, loss: 0.0016\n", + "2022-07-04 15:22:14,264 - mmpose - INFO - Epoch [40][5/5]\tlr: 1.993e-04, eta: 0:00:00, time: 0.264, data_time: 0.005, memory: 2919, heatmap_loss: 0.0018, acc_pose: 0.6176, loss: 0.0018\n", + "2022-07-04 15:22:14,347 - mmpose - INFO - Saving checkpoint at 40 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 24/24, 39.0 task/s, elapsed: 1s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-04 15:22:16,325 - mmpose - INFO - Epoch(val) [40][2]\tAP: 0.2285, AP .5: 0.7025, AP .75: 0.1388, AP (M): 0.2216, AP (L): 0.2426, AR: 0.3000, AR .5: 0.7500, AR .75: 0.2500, AR (M): 0.3000, AR (L): 0.3000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.228\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.702\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.139\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.222\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.243\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.300\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.750\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.250\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.300\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.300\n" + ] + } + ], + "source": [ + "train_model(model, datasets, cfg, distributed=False, validate=True, meta=dict())" + ] + }, + { + "cell_type": "markdown", + "id": "30751417-9cc4-4ab7-86c9-d98718e6acdd", + "metadata": {}, + "source": [ + "## 使用训练得到的模型预测（和B1代码一致）" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b0d33dad-0c37-40a7-b06b-d6c315d43690", + "metadata": {}, + "outputs": [], + "source": [ + "from mmpose.apis import inference_top_down_pose_model, init_pose_model, vis_pose_result, process_mmdet_results\n", + "from mmdet.apis import inference_detector, init_detector" + ] + }, + { + "cell_type": "markdown", + "id": "10e41070-d5db-4a95-8fd4-b178b4f642aa", + "metadata": {}, + "source": [ + "### 指定模型`config`配置文件和`checkpoint`权重文件" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fbe95325-d30a-4d57-98c9-71303e2440da", + "metadata": {}, + "outputs": [], + "source": [ + "# 目标检测模型\n", + "det_config = 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py'\n", + "det_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'\n", + "\n", + "# 人体姿态估计模型\n", + "pose_config = 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py'\n", + "pose_checkpoint = 'work_dirs/hrnet_w32_coco_tiny_256x192/latest.pth'\n" + ] + }, + { + "cell_type": "markdown", + "id": "a053326c-e001-42cc-b1cd-e9aef148101d", + "metadata": {}, + "source": [ + "### 初始化模型" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "518eabbe-4dc9-4873-b8cf-fca977a93e56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from local path: work_dirs/hrnet_w32_coco_tiny_256x192/latest.pth\n" + ] + } + ], + "source": [ + "# 目标检测模型\n", + "det_model = init_detector(det_config, det_checkpoint)\n", + "\n", + "# 人体姿态估计模型\n", + "pose_model = init_pose_model(cfg, pose_checkpoint)\n" + ] + }, + { + "cell_type": "markdown", + "id": "58e55de0-dd33-4725-982d-5167cf47f025", + "metadata": {}, + "source": [ + "### 执行预测" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2390de7b-8e43-4496-bd2e-60ea05f98c6f", + "metadata": {}, + "outputs": [], + "source": [ + "# img_path = 'data/TongjiDancer.png'\n", + "img_path = 'tests/data/coco/000000196141.jpg'\n", + "\n", + "mmdet_results = inference_detector(det_model, img_path)\n", + "\n", + "# 提取类别 ID 为 1 的 行人 目标检测框\n", + "person_results = process_mmdet_results(mmdet_results, cat_id=1)\n", + "\n", + "pose_results, returned_outputs = inference_top_down_pose_model(pose_model, img_path, person_results, bbox_thr=0.3, format='xyxy', dataset='TopDownCocoDataset')\n", + "\n", + "vis_result = vis_pose_result(pose_model, img_path, pose_results, dataset='TopDownCocoDataset', show=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0a033e3d-36f8-4199-b5ab-4cd8e4e9a1af", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "show_img_from_array(vis_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1eb7c4eb-4f66-4a49-9fcb-567ef225a9e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv2.imwrite('outputs/B4_pred.jpg', vis_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c271e69b-988f-4199-8685-d6a5757647a1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220C1\343\200\2212D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220C1\343\200\2212D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..65af810 --- /dev/null +++ "b/2022/\343\200\220C1\343\200\2212D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D手掌关键点 预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_hand_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "43a8b4a8-8256-4d01-bc93-d18aae75f177", + "metadata": {}, + "source": [ + "手掌检测模型：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/mmdet_modelzoo.md\n", + "\n", + "扩展阅读：\n", + "\n", + "Mediapipe手部关键点三维实时检测：https://www.bilibili.com/video/BV1x44y127Yu" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.git',\n", + " '.circleci',\n", + " '.dev_scripts',\n", + " '.github',\n", + " '.gitignore',\n", + " '.pre-commit-config.yaml',\n", + " '.pylintrc',\n", + " '.readthedocs.yml',\n", + " 'CITATION.cff',\n", + " 'LICENSE',\n", + " 'MANIFEST.in',\n", + " 'README.md',\n", + " 'README_CN.md',\n", + " 'configs',\n", + " 'demo',\n", + " 'docker',\n", + " 'docs',\n", + " 'mmpose',\n", + " 'model-index.yml',\n", + " 'pytest.ini',\n", + " 'requirements.txt',\n", + " 'requirements',\n", + " 'resources',\n", + " 'setup.cfg',\n", + " 'setup.py',\n", + " 'tests',\n", + " 'tools',\n", + " 'mmpose.egg-info',\n", + " 'checkpoints',\n", + " 'outputs',\n", + " 'data']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "id": "a5c11033-5a43-4b53-9880-df6f847f347d", + "metadata": {}, + "source": [ + "## 用目标检测预测框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aa445dc4-c09f-4c90-9846-62b725457214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth\" to /home/featurize/.cache/torch/hub/checkpoints/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth\n", + "100%|█████████████████████████████████████████| 486M/486M [00:04<00:00, 106MB/s]\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth\" to /home/featurize/.cache/torch/hub/checkpoints/res50_onehand10k_256x256-e67998f6_20200813.pth\n", + "100%|█████████████████████████████████████████| 130M/130M [00:01<00:00, 105MB/s]\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo_with_mmdet.py demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \\\n", + " https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth \\\n", + " configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \\\n", + " --img data/piano.jpeg \\\n", + " --out-img-root outputs/C1/C1_1_hand_img" + ] + }, + { + "cell_type": "markdown", + "id": "05a6d4a0-493b-4c39-b9fb-7b636ee3774d", + "metadata": {}, + "source": [ + "## 用标注框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7e063f0f-1027-4ecf-a431-884b0ac1144d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo.py \\\n", + " configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \\\n", + " --img-root tests/data/onehand10k/ \\\n", + " --json-file tests/data/onehand10k/test_onehand10k.json \\\n", + " --out-img-root outputs/C1/C1_2_img_gt" + ] + }, + { + "cell_type": "markdown", + "id": "bcd3a87f-2ab1-4091-94be-298e20215f21", + "metadata": {}, + "source": [ + "## 视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fafafee2-2819-4457-9ded-d28dde6db5d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>> ] 68/157, 4.7 task/s, elapsed: 14s, ETA: 19s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_with_mmdet.py demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \\\n", + " https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth \\\n", + " configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \\\n", + " --video-path data/play_piano.mp4 \\\n", + " --out-video-root outputs/C1/C1_4_video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21ade942-2e4c-4d12-8a70-6eacc13dc578", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220C2\343\200\2213D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220C2\343\200\2213D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..16dc144 --- /dev/null +++ "b/2022/\343\200\220C2\343\200\2213D\346\211\213\346\216\214\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 3D手掌关键点 预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/3d_hand_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-12\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。\n", + "\n", + "扩展阅读：https://www.bilibili.com/video/BV1x44y127Yu" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "785ad5e4-c80c-4b7c-8bdb-6ee1688f9e92", + "metadata": {}, + "source": [ + "## 用预先标注好的标注框作为`top_down`算法的输入" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16181058-1f53-40d2-a1fd-6d9c9ecc05ac", + "metadata": {}, + "outputs": [], + "source": [ + "!python demo/interhand3d_img_demo.py \\\n", + " configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3d_all_256x256-b9c1cf4c_20210506.pth \\\n", + " --json-file tests/data/interhand2.6m/test_interhand2.6m_data.json \\\n", + " --img-root tests/data/interhand2.6m \\\n", + " --out-img-root outputs/C2/C2_1_3d_hand_img \\\n", + " --rebase-keypoint-height" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06c1aaa6-f264-4aa3-91c5-4a43601ea2dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220C3\343\200\2212D\344\272\272\350\204\270\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220C3\343\200\2212D\344\272\272\350\204\270\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..e2c0c02 --- /dev/null +++ "b/2022/\343\200\220C3\343\200\2212D\344\272\272\350\204\270\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D人脸关键点 预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_face_demo.md\n", + "\n", + "作者：[同济子豪兄](https://space.bilibili.com/1900783) 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "e398145c-bc71-440d-b535-3ff638b8cac2", + "metadata": {}, + "source": [ + "## 扩展阅读\n", + "\n", + "Mediapipe Blaze Face-人脸468个关键点的关键点实时检测：https://www.bilibili.com/video/BV1ei4y1d7zA\n", + "\n", + "摄像头实时在线Demo：https://codepen.io/tommyzihao/pen/dyWEjBN" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "9eb4b66a-4913-4825-87c1-cef1f5c8004d", + "metadata": {}, + "source": [ + "## 安装`face_recognition`（大概3分钟）\n", + "\n", + "人脸识别开源库`face_recognition`介绍：https://zhuanlan.zhihu.com/p/45827914\n", + "\n", + "`face_recognition`主页：https://github.com/ageitgey/face_recognition" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4a1861c1-a176-4550-b655-9bb827d76129", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting face_recognition\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1e/95/f6c9330f54ab07bfa032bf3715c12455a381083125d8880c43cbe76bb3d0/face_recognition-1.3.0-py2.py3-none-any.whl (15 kB)\n", + "Collecting face-recognition-models>=0.3.0\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cf/3b/4fd8c534f6c0d1b80ce0973d01331525538045084c73c153ee6df20224cf/face_recognition_models-0.3.0.tar.gz (100.1 MB)\n", + "\u001b[K |████████████████████████████████| 100.1 MB 24.0 MB/s eta 0:00:01\n", + "\u001b[?25hCollecting dlib>=19.7\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e5/3b/7a8522a5c2ef6ff6252e46b0788b3d2c2280198c49d6ecf3b576b171045f/dlib-19.24.0.tar.gz (3.2 MB)\n", + "\u001b[K |████████████████████████████████| 3.2 MB 92.1 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: Click>=6.0 in /environment/miniconda3/lib/python3.7/site-packages (from face_recognition) (7.1.2)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from face_recognition) (1.21.4)\n", + "Requirement already satisfied: Pillow in /environment/miniconda3/lib/python3.7/site-packages (from face_recognition) (8.4.0)\n", + "Building wheels for collected packages: dlib, face-recognition-models\n", + " Building wheel for dlib (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for dlib: filename=dlib-19.24.0-cp37-cp37m-linux_x86_64.whl size=4863143 sha256=7129df7b5717c13ded3f39086ceaa1a00e467e89acc83092f52bb9819afe4cee\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/16/6b/c6/39a2204a3b959b25c1d8e34cb2ac651cadf58307a71dbfe9bd\n", + " Building wheel for face-recognition-models (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for face-recognition-models: filename=face_recognition_models-0.3.0-py2.py3-none-any.whl size=100566173 sha256=cc5fd1838f50d82c6ae6340778afed3e66794707be3e594c57a9b0bbb18e5199\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/85/e5/4b/ed9642e474ccaecc9f20c5152eeecef59a184b0e93825a4732\n", + "Successfully built dlib face-recognition-models\n", + "Installing collected packages: face-recognition-models, dlib, face-recognition\n", + "Successfully installed dlib-19.24.0 face-recognition-1.3.0 face-recognition-models-0.3.0\n" + ] + } + ], + "source": [ + "!pip install face_recognition" + ] + }, + { + "cell_type": "markdown", + "id": "a5c11033-5a43-4b53-9880-df6f847f347d", + "metadata": {}, + "source": [ + "## 用目标检测预测框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e94c8d92-922e-4f88-8589-5d59b6138628", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth\n", + "100%|███████████████████████████████████████| 37.2M/37.2M [00:00<00:00, 115MB/s]\n" + ] + } + ], + "source": [ + "!python demo/face_img_demo.py \\\n", + " configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \\\n", + " --img data/multi-person.jpeg \\\n", + " --out-img-root outputs/C3/C3_1_2d_face" + ] + }, + { + "cell_type": "markdown", + "id": "05a6d4a0-493b-4c39-b9fb-7b636ee3774d", + "metadata": {}, + "source": [ + "## 用标注框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7e063f0f-1027-4ecf-a431-884b0ac1144d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo.py \\\n", + " configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \\\n", + " --img-root tests/data/aflw/ \\\n", + " --json-file tests/data/aflw/test_aflw.json \\\n", + " --out-img-root outputs/C3/C3_2_2d_face_gt" + ] + }, + { + "cell_type": "markdown", + "id": "bcd3a87f-2ab1-4091-94be-298e20215f21", + "metadata": {}, + "source": [ + "## 视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "965d90f8-cb77-4210-aab7-3c1d58491f76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 59/60, 1.8 task/s, elapsed: 33s, ETA: 1s\n" + ] + } + ], + "source": [ + "!python demo/face_video_demo.py \\\n", + " configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \\\n", + " --video-path data/face_child.mp4 \\\n", + " --out-video-root outputs/C3/C3_4_2d_face_video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21ade942-2e4c-4d12-8a70-6eacc13dc578", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220C4\343\200\2212D\344\272\272\350\204\270+\344\272\272\344\275\223+\346\211\213\346\216\214\345\205\250\350\272\253\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220C4\343\200\2212D\344\272\272\350\204\270+\344\272\272\344\275\223+\346\211\213\346\216\214\345\205\250\350\272\253\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..14704c5 --- /dev/null +++ "b/2022/\343\200\220C4\343\200\2212D\344\272\272\350\204\270+\344\272\272\344\275\223+\346\211\213\346\216\214\345\205\250\350\272\253\345\205\263\351\224\256\347\202\271 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D人脸+人体全身关键点 预训练模型预测\n", + "\n", + "人脸+人体全身关键点检测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_wholebody_pose_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.git',\n", + " '.circleci',\n", + " '.dev_scripts',\n", + " '.github',\n", + " '.gitignore',\n", + " '.pre-commit-config.yaml',\n", + " '.pylintrc',\n", + " '.readthedocs.yml',\n", + " 'CITATION.cff',\n", + " 'LICENSE',\n", + " 'MANIFEST.in',\n", + " 'README.md',\n", + " 'README_CN.md',\n", + " 'configs',\n", + " 'demo',\n", + " 'docker',\n", + " 'docs',\n", + " 'mmpose',\n", + " 'model-index.yml',\n", + " 'pytest.ini',\n", + " 'requirements.txt',\n", + " 'requirements',\n", + " 'resources',\n", + " 'setup.cfg',\n", + " 'setup.py',\n", + " 'tests',\n", + " 'tools',\n", + " 'mmpose.egg-info',\n", + " 'checkpoints',\n", + " 'outputs',\n", + " 'data',\n", + " 'work_dirs']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "id": "a5c11033-5a43-4b53-9880-df6f847f347d", + "metadata": {}, + "source": [ + "## 用目标检测预测框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c4c1abb-7e8e-4e8d-bf96-beabb78fd401", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth\n", + "100%|█████████████████████████████████████████| 243M/243M [00:02<00:00, 116MB/s]\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \\\n", + " --img data/multi-person.jpeg \\\n", + " --out-img-root outputs/C4/C4_1_whole_body_img" + ] + }, + { + "cell_type": "markdown", + "id": "05a6d4a0-493b-4c39-b9fb-7b636ee3774d", + "metadata": {}, + "source": [ + "## 用标注框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7e063f0f-1027-4ecf-a431-884b0ac1144d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo.py \\\n", + " configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \\\n", + " --img-root tests/data/coco/ \\\n", + " --json-file tests/data/coco/test_coco.json \\\n", + " --out-img-root outputs/C4/C4_2_gt_whole_body_img" + ] + }, + { + "cell_type": "markdown", + "id": "bcd3a87f-2ab1-4091-94be-298e20215f21", + "metadata": {}, + "source": [ + "## 视频预测-`top_down`算法" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5120a34d-5515-4328-9ab5-ee2fc2091c3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 192/193, 3.0 task/s, elapsed: 64s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \\\n", + " --video-path data/mother.mp4 \\\n", + " --out-video-root outputs/C4/C4_3_whole_body_video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21ade942-2e4c-4d12-8a70-6eacc13dc578", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220D1\343\200\2212D\344\272\272\344\275\223\345\247\277\346\200\201\350\277\275\350\270\252 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220D1\343\200\2212D\344\272\272\344\275\223\345\247\277\346\200\201\350\277\275\350\270\252 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..e25c0ce --- /dev/null +++ "b/2022/\343\200\220D1\343\200\2212D\344\272\272\344\275\223\345\247\277\346\200\201\350\277\275\350\270\252 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D人体姿态追踪 预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_pose_tracking_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-06\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "ba65cbfc-6255-4668-b344-9985e77e7464", + "metadata": {}, + "source": [ + "## 2D Top-Down Video Human Pose Tracking" + ] + }, + { + "cell_type": "markdown", + "id": "fec858a0-95a8-46b7-944d-c3caba82b8a6", + "metadata": {}, + "source": [ + "mmpose原生追踪算法原理：https://github.com/open-mmlab/mmpose/blob/master/mmpose/apis/inference_tracking.py#L39-L116\n", + "\n", + "（根据iou或者oks算一个贪心的匹配，极其简易的追踪算法）" + ] + }, + { + "cell_type": "markdown", + "id": "bc0789df-4d1d-48e3-8afd-b7600ece0fdc", + "metadata": {}, + "source": [ + "### 单帧视频预测\n", + "\n", + "--bbox-thr 目标检测框阈值，默认为0.3\n", + "\n", + "--kpt-thr 关键点检测阈值，默认为0.3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "27f40fee-da99-443b-a29f-dcfc1386d759", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth\" to /home/featurize/.cache/torch/hub/checkpoints/res50_coco_256x192-ec54d7f3_20200709.pth\n", + "100%|█████████████████████████████████████████| 130M/130M [00:01<00:00, 112MB/s]\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 4.3 task/s, elapsed: 23s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_pose_tracking_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/D1/D1_1_top_down_tracking_single_frame\n" + ] + }, + { + "cell_type": "markdown", + "id": "fae1d269-757c-49ee-be43-2e3b250aecc1", + "metadata": {}, + "source": [ + "### 多帧视频预测（速度会慢一些）\n", + "\n", + "使用`--use-multi-frames`参数，将视频前后多帧画面输入模型用于姿态预测。\n", + "\n", + "使用`--online`参数，仅输入该帧之前的帧，不输入该帧之后的帧。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cac29e0c-b16f-49d3-b0af-8503b075deba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 0.5 task/s, elapsed: 181s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_pose_tracking_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/D1/D1_2_top_down_tracking_multi_frames \\\n", + " --use-multi-frames \\\n", + " --online" + ] + }, + { + "cell_type": "markdown", + "id": "15bf5008-8574-4239-bb3e-104a4a37b13b", + "metadata": {}, + "source": [ + "## 2D Bottom-Up Video Human Pose Tracking" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "83a33877-a956-4c47-8caa-cf49d52eb13c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w32_coco_512x512-bcb8c247_20200816.pth\n", + "100%|█████████████████████████████████████████| 109M/109M [00:01<00:00, 114MB/s]\n", + "[ ] 0/99, elapsed: 0s, ETA:/home/featurize/work/MMPose教程/mmpose/mmpose/core/post_processing/group.py:240: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " y = ind // W\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 0.2 task/s, elapsed: 482s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/bottom_up_pose_tracking_demo.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py \\\n", + " https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/D1/D1_5_bottom_up_tracking_video" + ] + }, + { + "cell_type": "markdown", + "id": "f5b934ae-e3bf-479b-940c-9710df7c86e7", + "metadata": {}, + "source": [ + "## 基于 MMTracking 的 2D Top-Down Video Human Pose Tracking" + ] + }, + { + "cell_type": "markdown", + "id": "4086f085-dcba-49f6-832a-944b1fc2eed5", + "metadata": {}, + "source": [ + "### 安装MMTracking\n", + "\n", + "MMTracking教程：https://www.bilibili.com/video/BV1s44y1g75J" + ] + }, + { + "cell_type": "markdown", + "id": "2004e9ba-7811-479e-8d48-c4fe871c66b9", + "metadata": {}, + "source": [ + "### 单帧视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1587b92d-6ee2-4ad5-9ef2-f4159d20f606", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "/home/featurize/work/MMPose教程/mmtracking/mmtrack/models/mot/tracktor.py:28: UserWarning: DeprecationWarning: pretrains is deprecated, please use \"init_cfg\" instead\n", + " warnings.warn('DeprecationWarning: pretrains is deprecated, '\n", + "2022-07-06 16:11:03,055 - mmtrack - INFO - initialize FasterRCNN with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth'}\n", + "2022-07-06 16:11:03,055 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\n", + "2022-07-06 16:11:03,056 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\n", + "Downloading: \"https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\" to /home/featurize/.cache/torch/hub/checkpoints/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\n", + "100%|█████████████████████████████████████████| 158M/158M [00:01<00:00, 114MB/s]\n", + "2022-07-06 16:11:04,659 - mmtrack - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth'}\n", + "2022-07-06 16:11:04,660 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth\n", + "2022-07-06 16:11:04,660 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth\n", + "Downloading: \"https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth\" to /home/featurize/.cache/torch/hub/checkpoints/reid_r50_6e_mot17-4bf6b63d.pth\n", + "100%|███████████████████████████████████████| 98.6M/98.6M [00:00<00:00, 111MB/s]\n", + "Warning: The model doesn't have classes\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 1.8 task/s, elapsed: 56s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_pose_tracking_demo_with_mmtracking.py \\\n", + " demo/mmtracking_cfg/tracktor_faster-rcnn_r50_fpn_4e_mot17-private.py \\\n", + " configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/D1/D1_3_mmtracking_single_frame" + ] + }, + { + "cell_type": "markdown", + "id": "b8da0d73-61bd-4c85-8f62-571ade81053c", + "metadata": {}, + "source": [ + "### 多帧视频预测" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "79d7d3b2-14a7-42c9-bc98-2ca1a91f423b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "/home/featurize/work/MMPose教程/mmtracking/mmtrack/models/mot/tracktor.py:28: UserWarning: DeprecationWarning: pretrains is deprecated, please use \"init_cfg\" instead\n", + " warnings.warn('DeprecationWarning: pretrains is deprecated, '\n", + "2022-07-06 16:12:15,434 - mmtrack - INFO - initialize FasterRCNN with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth'}\n", + "2022-07-06 16:12:15,434 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\n", + "2022-07-06 16:12:15,434 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-ffa52ae7.pth\n", + "2022-07-06 16:12:15,605 - mmtrack - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth'}\n", + "2022-07-06 16:12:15,606 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth\n", + "2022-07-06 16:12:15,606 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth\n", + "Warning: The model doesn't have classes\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\" to /home/featurize/.cache/torch/hub/checkpoints/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth\n", + "100%|█████████████████████████████████████████| 273M/273M [00:02<00:00, 116MB/s]\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 99/99, 0.6 task/s, elapsed: 178s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_pose_tracking_demo_with_mmtracking.py \\\n", + " demo/mmtracking_cfg/tracktor_faster-rcnn_r50_fpn_4e_mot17-private.py \\\n", + " configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth \\\n", + " --video-path data/mot_people_short.mp4 \\\n", + " --out-video-root outputs/D1/D1_4_mmtracking_multi_frames \\\n", + " --use-multi-frames \\\n", + " --online" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b4c092a-c0d5-42e8-b5c1-ec8f697e18fb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\343\200\220D2\343\200\2212D\345\212\250\347\211\251\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" "b/2022/\343\200\220D2\343\200\2212D\345\212\250\347\211\251\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..5e45c3c --- /dev/null +++ "b/2022/\343\200\220D2\343\200\2212D\345\212\250\347\211\251\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213 \351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213.ipynb" @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63f1dd0a-8685-40fd-b5d7-3218f97ac899", + "metadata": {}, + "source": [ + "# 2D Animal Pose预训练模型预测\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/2d_animal_demo.md\n", + "\n", + "作者：同济子豪兄 2022-06-10\n", + "\n", + "如果报错`CUDA out of memory.`则重启前面几个代码的`kernel`即可。" + ] + }, + { + "cell_type": "markdown", + "id": "4ff424e2-8d49-4f7c-8e81-24a957d1b7b9", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c24d56aa-5528-4561-bb1c-33bcffe2c852", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "dc37c003-6a05-452b-82da-45f45fcba04d", + "metadata": {}, + "source": [ + "## 用标注框作为`top_down`算法的输入框输入" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "22c1623b-6d6f-465f-8b03-382fd9317c5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth\" to /home/featurize/.cache/torch/hub/checkpoints/res50_macaque_256x192-98f1dd3a_20210407.pth\n", + "100%|█████████████████████████████████████████| 130M/130M [00:01<00:00, 107MB/s]\n" + ] + } + ], + "source": [ + "!python demo/top_down_img_demo.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res50_macaque_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth \\\n", + " --img-root tests/data/macaque/ \\\n", + " --json-file tests/data/macaque/test_macaque.json \\\n", + " --out-img-root outputs/D2/D2_1_macaque_gt_img" + ] + }, + { + "cell_type": "markdown", + "id": "70374980-1f64-4b3b-9805-690b489de44f", + "metadata": {}, + "source": [ + "## 视频预测" + ] + }, + { + "cell_type": "markdown", + "id": "3fc842f0-a879-4336-8092-4d57993b362d", + "metadata": {}, + "source": [ + "使用全图作为输入" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5badef9b-a40c-4aee-b61b-7284c2c36a5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth\" to /home/featurize/.cache/torch/hub/checkpoints/res152_fly_192x192-fcafbd5a_20210407.pth\n", + "100%|█████████████████████████████████████████| 263M/263M [00:02<00:00, 114MB/s]\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 120/120, 22.9 task/s, elapsed: 5s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_full_frame_without_det.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth \\\n", + " --video-path data/fly.mp4 \\\n", + " --out-video-root outputs/D2/D2_2_fly_video\n" + ] + }, + { + "cell_type": "markdown", + "id": "1bf48470-9106-446d-be77-976f376553f5", + "metadata": {}, + "source": [ + "## 马" + ] + }, + { + "cell_type": "markdown", + "id": "de2a211e-fb2d-4b2d-bd38-e390f3edcf57", + "metadata": {}, + "source": [ + "\n", + "\n", + "使用目标检测预测框作为输入\n", + "\n", + "MS COCO数据集中的80个类别中，包含10类动物\n", + "\n", + "15: 'bird', 16: 'cat', 17: 'dog', 18: 'horse', 19: 'sheep', 20: 'cow', 21: 'elephant', 22: 'bear', 23: 'zebra', 24: 'giraffe'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e4ed2baf-4880-4dec-8422-bb179c925cc3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\n", + "Downloading: \"https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\" to /home/featurize/.cache/torch/hub/checkpoints/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth\n", + "100%|█████████████████████████████████████████| 160M/160M [00:01<00:00, 114MB/s]\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth\" to /home/featurize/.cache/torch/hub/checkpoints/res50_horse10_256x256_split1-3a3dc37e_20210405.pth\n", + "100%|█████████████████████████████████████████| 130M/130M [00:01<00:00, 115MB/s]\n", + "Running inference...\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 140/145, 4.9 task/s, elapsed: 29s, ETA: 1s\n" + ] + } + ], + "source": [ + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split1.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth \\\n", + " --video-path data/horse1.mp4 \\\n", + " --out-video-root outputs/D2/D2_3_horse_video \\\n", + " --bbox-thr 0.1 \\\n", + " --kpt-thr 0.4 \\\n", + " --det-cat-id 18 \\\n", + " --radius 5 \\\n", + " --thickness 3" + ] + }, + { + "cell_type": "markdown", + "id": "4c7d9383-b0a3-4f88-b06c-e778b6c76a41", + "metadata": {}, + "source": [ + "## 其它动物\n", + "\n", + "MMDetection 目标检测模型：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/mmdet_modelzoo.md\n", + "\n", + "MMPose 动物关键点检测模型：https://mmpose.readthedocs.io/en/latest/topics/animal.html\n", + "\n", + "> 如遇报错`urllib.error.HTTPError: HTTP Error 403: Forbidden`，重新运行一遍即可" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "359328fc-d431-480d-af78-10f364cb41ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing model...\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth\n", + "Downloading: \"https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth\" to /home/featurize/.cache/torch/hub/checkpoints/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth\n", + " 90%|█████████████████████████████████████ | 439M/486M [00:04<00:00, 100MB/s]" + ] + } + ], + "source": [ + "# 猕猴\n", + "!python demo/top_down_pose_tracking_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \\\n", + " https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res152_macaque_256x192.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res152_macaque_256x192-c42abc02_20210407.pth \\\n", + " --video-path data/macaque1.mp4 \\\n", + " --out-video-root outputs/D2/D2_4_macaque \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.3 \\\n", + " --radius 10 \\\n", + " --thickness 3 \\\n", + " --smooth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e7a5082-e4c5-46be-959c-5460088be378", + "metadata": {}, + "outputs": [], + "source": [ + "# 猫\n", + "# 数据：https://drive.google.com/file/d/1l_h8NJaUCOgs9Y4iomNvIBQ_a88why42/view?usp=sharing\n", + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \\\n", + " --video-path data/cat1.mp4 \\\n", + " --out-video-root outputs/D2/D2_5_cat_vid \\\n", + " --det-cat-id 16 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.3 \\\n", + " --radius 5 \\\n", + " --thickness 3 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0d3b3c4", + "metadata": {}, + "outputs": [], + "source": [ + "# 狗\n", + "# 数据：https://drive.google.com/file/d/120XkZpN3Rs8CM-AysrzfMsILjgInlXJM/view?usp=sharing\n", + "!python demo/top_down_video_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \\\n", + " --video-path data/dog1.mp4 \\\n", + " --out-video-root outputs/D2/D2_6_dog_vid \\\n", + " --det-cat-id 17 \\\n", + " --bbox-thr 0.4 \\\n", + " --kpt-thr 0.3 \\\n", + " --radius 5 \\\n", + " --thickness 3 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eca0c19f", + "metadata": {}, + "outputs": [], + "source": [ + "# 羚羊 & 豹\n", + "!python demo/top_down_img_demo.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.pth \\\n", + " --img-root tests/data/ap10k/ --json-file tests/data/ap10k/test_ap10k.json \\\n", + " --out-img-root outputs/D2/D2_7_ap10k_gt_img " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b71460d", + "metadata": {}, + "outputs": [], + "source": [ + "# 蝗虫\n", + "!python demo/top_down_img_demo.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res50_locust_160x160.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_locust_160x160-9efca22b_20210407.pth \\\n", + " --img-root tests/data/locust/ --json-file tests/data/locust/test_locust.json \\\n", + " --out-img-root outputs/D2/D2_8_locust_gt_img " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d97605d", + "metadata": {}, + "outputs": [], + "source": [ + "# 斑马\n", + "!python demo/top_down_img_demo.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/zebra/res50_zebra_160x160.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_zebra_160x160-5a104833_20210407.pth \\\n", + " --img-root tests/data/zebra/ --json-file tests/data/zebra/test_zebra.json \\\n", + " --out-img-root outputs/D2/D2_9_zebra_gt_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3947b083", + "metadata": {}, + "outputs": [], + "source": [ + "# 老虎\n", + "!python demo/top_down_img_demo.py \\\n", + " configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py \\\n", + " https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256-546c4594_20210414.pth \\\n", + " --img-root tests/data/atrw/ --json-file tests/data/atrw/test_atrw.json \\\n", + " --out-img-root outputs/D2/D2_10_atrw_gt_img" + ] + } + ], + "metadata": { + 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@@ -0,0 +1,459 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1cae7fa", + "metadata": {}, + "source": [ + "# 在本地安装 MMPose 环境\n", + "\n", + "本教程以 Macbook Pro 为例\n", + "\n", + "同济子豪兄 2022-6-21" + ] + }, + { + "cell_type": "markdown", + "id": "681f110d", + "metadata": {}, + "source": [ + "## 安装 mmcv" + ] + }, + { + "cell_type": "markdown", + "id": "6efe947e", + "metadata": {}, + "source": [ + "在 mac 上安装 mmcv -full：https://openmmlab.feishu.cn/docs/doccnJd89XfYFB9Q3x8oJFGYG2b" + ] + }, + { + "cell_type": "markdown", + "id": "1d7679a1", + "metadata": {}, + "source": [ + "## 进入openmmlab虚拟环境（如有）" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96fca4b9", + "metadata": {}, + "outputs": [], + "source": [ + "!conda activate open-mmlab" + ] + }, + { + "cell_type": "markdown", + "id": "b770e0f3", + "metadata": {}, + "source": [ + "## 安装mmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "960f9f32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import mmcv" + ] + }, + { + "cell_type": "markdown", + "id": "9a2d97ba", + "metadata": {}, + "source": [ + "## 从Github下载最新版 mmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "49c29691", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmpose'...\n", + "remote: Enumerating objects: 19322, done.\u001b[K\n", + "remote: Counting objects: 100% (398/398), done.\u001b[K\n", + "remote: Compressing objects: 100% (258/258), done.\u001b[K\n", + "remote: Total 19322 (delta 190), reused 284 (delta 139), pack-reused 18924\u001b[K\n", + "Receiving objects: 100% (19322/19322), 25.22 MiB | 371.00 KiB/s, done.\n", + "Resolving deltas: 100% (13648/13648), done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/open-mmlab/mmpose.git" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2059e6ed", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入 mmpose 目录\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ff0dc2f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n", + "Ignoring dataclasses: markers 'python_version == \"3.6\"' don't match your environment\n", + "Collecting poseval@ git+https://github.com/svenkreiss/poseval.git\n", + " Cloning https://github.com/svenkreiss/poseval.git to /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-install-zv1dz49i/poseval_b8acdf5f38fe4d71b8e09c5a0fbaee76\n", + " Running command git clone --filter=blob:none -q https://github.com/svenkreiss/poseval.git /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-install-zv1dz49i/poseval_b8acdf5f38fe4d71b8e09c5a0fbaee76\n", + " Resolved https://github.com/svenkreiss/poseval.git to commit 3128c5cbcf90946e5164ff438ad651e113e64613\n", + " Running command git submodule update --init --recursive -q\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: numpy in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from -r requirements/build.txt (line 2)) (1.21.6)\n", + "Requirement already satisfied: torch>=1.3 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from -r requirements/build.txt (line 3)) (1.10.0)\n", + "Requirement already satisfied: chumpy in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from -r requirements/runtime.txt (line 1)) (0.70)\n", + "Requirement already satisfied: json_tricks in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from -r requirements/runtime.txt (line 3)) (3.15.5)\n", + "Requirement already satisfied: matplotlib in 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mmpose==0.28.0) (1.7.3)\n", + "Requirement already satisfied: torchvision in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (0.11.1)\n", + "Requirement already satisfied: xtcocotools>=1.12 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (1.12)\n", + "Requirement already satisfied: setuptools>=18.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==0.28.0) (59.4.0)\n", + "Requirement already satisfied: cython>=0.27.3 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==0.28.0) (0.29.25)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (1.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (4.28.3)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (0.11.0)\n", + "Requirement already satisfied: setuptools-scm>=4 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (6.3.2)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (3.0.6)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (21.3)\n", + "Requirement already satisfied: six>=1.11.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from chumpy->mmpose==0.28.0) (1.16.0)\n", + "Requirement already satisfied: torch in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from torchvision->mmpose==0.28.0) (1.10.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmpose==0.28.0) (1.2.2)\n", + "Requirement already satisfied: typing_extensions in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from torch->torchvision->mmpose==0.28.0) (4.0.1)\n", + "Installing collected packages: mmpose\n", + " Running setup.py develop for mmpose\n", + "Successfully installed mmpose-0.28.0\n" + ] + } + ], + "source": [ + "# Mac电脑\n", + "!CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e ." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b26657f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using pip 21.3.1 from /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/pip (python 3.7)\n", + "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n", + "Obtaining file:///Users/tommy/Desktop/%E4%B8%8A%E6%B5%B7%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%AE%9E%E9%AA%8C%E5%AE%A4/mmpose/%E6%91%84%E5%83%8F%E5%A4%B4%E5%AE%9E%E6%97%B6demo/mmpose\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info\n", + " writing /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/PKG-INFO\n", + " writing dependency_links to /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/dependency_links.txt\n", + " writing requirements to /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/requires.txt\n", + " writing top-level names to /private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/top_level.txt\n", + " writing manifest file '/private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " adding license file 'LICENSE'\n", + " writing manifest file '/private/var/folders/ly/mdyk3b_d1r39hm00y4s1yk1m0000gn/T/pip-pip-egg-info-089cvix8/mmpose.egg-info/SOURCES.txt'\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: chumpy in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (0.70)\n", + "Requirement already satisfied: json_tricks in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (3.15.5)\n", + "Requirement already satisfied: matplotlib in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (3.5.0)\n", + "Requirement already satisfied: munkres in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (1.1.4)\n", + "Requirement already satisfied: numpy in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (1.21.6)\n", + "Requirement already satisfied: opencv-python in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (4.5.4.60)\n", + "Requirement already satisfied: pillow in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (8.4.0)\n", + "Requirement already satisfied: scipy in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from mmpose==0.28.0) (1.7.3)\n", + "Requirement already satisfied: torchvision in 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satisfied: setuptools-scm>=4 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (6.3.2)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (3.0.6)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (0.11.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (1.3.2)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from matplotlib->mmpose==0.28.0) (21.3)\n", + "Requirement already satisfied: six>=1.11.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from chumpy->mmpose==0.28.0) (1.16.0)\n", + "Requirement already satisfied: torch in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from torchvision->mmpose==0.28.0) (1.10.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmpose==0.28.0) (1.2.2)\n", + "Requirement already satisfied: typing_extensions in /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages (from torch->torchvision->mmpose==0.28.0) (4.0.1)\n", + "Installing collected packages: mmpose\n", + " Attempting uninstall: mmpose\n", + " Found existing installation: mmpose 0.28.0\n", + " Uninstalling mmpose-0.28.0:\n", + " Removing file or directory /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmpose.egg-link\n", + " Removing pth entries from /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/easy-install.pth:\n", + " Removing entry: /Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose\n", + " Successfully uninstalled mmpose-0.28.0\n", + " Running setup.py develop for mmpose\n", + " Running command /Users/tommy/opt/anaconda3/envs/open-mmlab/bin/python3.7 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose/setup.py'\"'\"'; __file__='\"'\"'/Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n", + " running develop\n", + " running egg_info\n", + " writing mmpose.egg-info/PKG-INFO\n", + " writing dependency_links to mmpose.egg-info/dependency_links.txt\n", + " writing requirements to mmpose.egg-info/requires.txt\n", + " writing top-level names to mmpose.egg-info/top_level.txt\n", + " reading manifest template 'MANIFEST.in'\n", + " adding license file 'LICENSE'\n", + " writing manifest file 'mmpose.egg-info/SOURCES.txt'\n", + " running build_ext\n", + " Creating /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmpose.egg-link (link to .)\n", + " Adding mmpose 0.28.0 to easy-install.pth file\n", + "\n", + " Installed /Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose\n", + " /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n", + " EasyInstallDeprecationWarning,\n", + " /Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n", + " setuptools.SetuptoolsDeprecationWarning,\n", + "Successfully installed mmpose-0.28.0\n" + ] + } + ], + "source": [ + "!pip install -v -e ." + ] + }, + { + "cell_type": "markdown", + "id": "879abc8d", + "metadata": {}, + "source": [ + "## 从Github下载最新版 mmpose-webcam-demo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1736f835", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmpose-webcam-demo'...\n", + "remote: Enumerating objects: 132, done.\u001b[K\n", + "remote: Counting objects: 100% (132/132), done.\u001b[K\n", + "remote: Compressing objects: 100% (78/78), done.\u001b[K\n", + "remote: Total 132 (delta 63), reused 117 (delta 50), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (132/132), 88.37 KiB | 655.00 KiB/s, done.\n", + "Resolving deltas: 100% (63/63), done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/open-mmlab/mmpose-webcam-demo.git" + ] + }, + { + "cell_type": "markdown", + "id": "ccadb0d3", + "metadata": {}, + "source": [ + "## 验证安装成功" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f8960c9c", + "metadata": {}, + "outputs": [], + "source": [ + "import mmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "06ea0b77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.28.0'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mmpose.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a764e3db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220B\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\270\200\357\274\211.ipynb" "b/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220B\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\270\200\357\274\211.ipynb" new file mode 100644 index 0000000..8c977b6 --- /dev/null +++ "b/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220B\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\270\200\357\274\211.ipynb" @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f64a5d48", + "metadata": {}, + "source": [ + "# MMPose摄像头实时demo\n", + "\n", + "同济子豪兄 2022-6-22" + ] + }, + { + "cell_type": "markdown", + "id": "9ab5ece2", + "metadata": {}, + "source": [ + "参考文档：https://github.com/open-mmlab/mmpose/blob/master/demo/docs/webcam_demo.md" + ] + }, + { + "cell_type": "markdown", + "id": "1c8e471f", + "metadata": {}, + "source": [ + "## 进入 MMPose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e8fda0f7", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "08e94c80", + "metadata": {}, + "source": [ + "## 测试摄像头" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "028683ab", + "metadata": {}, + "outputs": [], + "source": [ + "# 运行后按 q 键退出 \n", + "!python demo/webcam_demo.py --config demo/webcam_cfg/test_camera.py" + ] + }, + { + "cell_type": "markdown", + "id": "bb290494", + "metadata": {}, + "source": [ + "## 墨镜、大眼睛特效" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff2546df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose/mmpose/core/post_processing/smoother.py:176: UserWarning: Smoother received empty result.\n", + " warnings.warn('Smoother received empty result.')\n" + ] + } + ], + "source": [ + "!python3 demo/webcam_demo.py --cpu\n", + "\n", + "# !python3 demo/webcam_demo.py --cuda\n", + "\n", + "# 或运行\n", + "# !python3 demo/webcam_demo.py --config demo/webcam_cfg/pose_estimation.py" + ] + }, + { + "cell_type": "markdown", + "id": "503d7817", + "metadata": {}, + "source": [ + "## 人体姿态追踪" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8642f459", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "!python3 demo/webcam_demo.py --config demo/webcam_cfg/pose_tracking.py --cpu" + ] + }, + { + "cell_type": "markdown", + "id": "ff3072e7", + "metadata": {}, + "source": [ + "## 手势识别" + ] + }, + { + "cell_type": "markdown", + "id": "213d698a", + "metadata": {}, + "source": [ + "NVGesture数据集的 25 类手势\n", + "\n", + "论文：https://research.nvidia.com/sites/default/files/pubs/2016-06_Online-Detection-and/NVIDIA_R3DCNN_cvpr2016.pdf\n", + "\n", + "![手势识别类别](img/nvgesture.png)\n", + "\n", + "0: 'five fingers move right',\n", + "\n", + "1: 'five fingers move left',\n", + "\n", + "2: 'five fingers move up',\n", + "\n", + "3: 'five fingers move down',\n", + "\n", + "4: 'two fingers move right',\n", + "\n", + "5: 'two fingers move left',\n", + "\n", + "6: 'two fingers move up',\n", + "\n", + "7: 'two fingers move down',\n", + "\n", + "8: 'click',\n", + "\n", + "9: 'beckoned',\n", + "\n", + "10: 'stretch hand',\n", + "\n", + "11: 'shake hand',\n", + "\n", + "12: 'one',\n", + "\n", + "13: 'two',\n", + "\n", + "14: 'three',\n", + "\n", + "15: 'lift up',\n", + "\n", + "16: 'press down',\n", + "\n", + "17: 'push',\n", + "\n", + "18: 'shrink',\n", + "\n", + "19: 'levorotation',\n", + "\n", + "20: 'dextrorotation',\n", + "\n", + "21: 'two fingers prod',\n", + "\n", + "22: 'grab',\n", + "\n", + "23: 'thumbs up',\n", + "\n", + "24: 'OK'\n", + "\n", + "详细配置文件见`configs/_base_/datasets/nvgesture.py`\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e7bc4d3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmpose/mmdet_pretrained/ssdlite_mobilenetv2_scratch_600e_onehand-4f9f8686_20220523.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/gesture/mtut/i3d_nvgesture_bbox_112x112_fps15-363b5956_20220530.pth\n", + "The model and loaded state dict do not match exactly\n", + "\n", + "unexpected key in source state_dict: backbone.depth.conv3d_1a_7x7.conv3d.weight, backbone.depth.conv3d_1a_7x7.batch3d.weight, backbone.depth.conv3d_1a_7x7.batch3d.bias, backbone.depth.conv3d_1a_7x7.batch3d.running_mean, backbone.depth.conv3d_1a_7x7.batch3d.running_var, backbone.depth.conv3d_1a_7x7.batch3d.num_batches_tracked, backbone.depth.conv3d_2b_1x1.conv3d.weight, backbone.depth.conv3d_2b_1x1.batch3d.weight, backbone.depth.conv3d_2b_1x1.batch3d.bias, backbone.depth.conv3d_2b_1x1.batch3d.running_mean, backbone.depth.conv3d_2b_1x1.batch3d.running_var, backbone.depth.conv3d_2b_1x1.batch3d.num_batches_tracked, backbone.depth.conv3d_2c_3x3.conv3d.weight, backbone.depth.conv3d_2c_3x3.batch3d.weight, backbone.depth.conv3d_2c_3x3.batch3d.bias, backbone.depth.conv3d_2c_3x3.batch3d.running_mean, backbone.depth.conv3d_2c_3x3.batch3d.running_var, backbone.depth.conv3d_2c_3x3.batch3d.num_batches_tracked, backbone.depth.mixed_3b.branch_0.conv3d.weight, backbone.depth.mixed_3b.branch_0.batch3d.weight, backbone.depth.mixed_3b.branch_0.batch3d.bias, backbone.depth.mixed_3b.branch_0.batch3d.running_mean, backbone.depth.mixed_3b.branch_0.batch3d.running_var, backbone.depth.mixed_3b.branch_0.batch3d.num_batches_tracked, backbone.depth.mixed_3b.branch_1.0.conv3d.weight, backbone.depth.mixed_3b.branch_1.0.batch3d.weight, backbone.depth.mixed_3b.branch_1.0.batch3d.bias, backbone.depth.mixed_3b.branch_1.0.batch3d.running_mean, backbone.depth.mixed_3b.branch_1.0.batch3d.running_var, backbone.depth.mixed_3b.branch_1.0.batch3d.num_batches_tracked, backbone.depth.mixed_3b.branch_1.1.conv3d.weight, backbone.depth.mixed_3b.branch_1.1.batch3d.weight, backbone.depth.mixed_3b.branch_1.1.batch3d.bias, backbone.depth.mixed_3b.branch_1.1.batch3d.running_mean, backbone.depth.mixed_3b.branch_1.1.batch3d.running_var, backbone.depth.mixed_3b.branch_1.1.batch3d.num_batches_tracked, backbone.depth.mixed_3b.branch_2.0.conv3d.weight, backbone.depth.mixed_3b.branch_2.0.batch3d.weight, backbone.depth.mixed_3b.branch_2.0.batch3d.bias, backbone.depth.mixed_3b.branch_2.0.batch3d.running_mean, backbone.depth.mixed_3b.branch_2.0.batch3d.running_var, backbone.depth.mixed_3b.branch_2.0.batch3d.num_batches_tracked, backbone.depth.mixed_3b.branch_2.1.conv3d.weight, backbone.depth.mixed_3b.branch_2.1.batch3d.weight, backbone.depth.mixed_3b.branch_2.1.batch3d.bias, backbone.depth.mixed_3b.branch_2.1.batch3d.running_mean, 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backbone.depth.mixed_4f.branch_2.1.batch3d.running_var, backbone.depth.mixed_4f.branch_2.1.batch3d.num_batches_tracked, backbone.depth.mixed_4f.branch_3.1.conv3d.weight, backbone.depth.mixed_4f.branch_3.1.batch3d.weight, backbone.depth.mixed_4f.branch_3.1.batch3d.bias, backbone.depth.mixed_4f.branch_3.1.batch3d.running_mean, backbone.depth.mixed_4f.branch_3.1.batch3d.running_var, backbone.depth.mixed_4f.branch_3.1.batch3d.num_batches_tracked, backbone.depth.mixed_5b.branch_0.conv3d.weight, backbone.depth.mixed_5b.branch_0.batch3d.weight, backbone.depth.mixed_5b.branch_0.batch3d.bias, backbone.depth.mixed_5b.branch_0.batch3d.running_mean, backbone.depth.mixed_5b.branch_0.batch3d.running_var, backbone.depth.mixed_5b.branch_0.batch3d.num_batches_tracked, backbone.depth.mixed_5b.branch_1.0.conv3d.weight, backbone.depth.mixed_5b.branch_1.0.batch3d.weight, backbone.depth.mixed_5b.branch_1.0.batch3d.bias, backbone.depth.mixed_5b.branch_1.0.batch3d.running_mean, 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backbone.depth.mixed_5b.branch_2.1.batch3d.running_var, backbone.depth.mixed_5b.branch_2.1.batch3d.num_batches_tracked, backbone.depth.mixed_5b.branch_3.1.conv3d.weight, backbone.depth.mixed_5b.branch_3.1.batch3d.weight, backbone.depth.mixed_5b.branch_3.1.batch3d.bias, backbone.depth.mixed_5b.branch_3.1.batch3d.running_mean, backbone.depth.mixed_5b.branch_3.1.batch3d.running_var, backbone.depth.mixed_5b.branch_3.1.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_0.conv3d.weight, backbone.depth.mixed_5c.branch_0.batch3d.weight, backbone.depth.mixed_5c.branch_0.batch3d.bias, backbone.depth.mixed_5c.branch_0.batch3d.running_mean, backbone.depth.mixed_5c.branch_0.batch3d.running_var, backbone.depth.mixed_5c.branch_0.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_1.0.conv3d.weight, backbone.depth.mixed_5c.branch_1.0.batch3d.weight, backbone.depth.mixed_5c.branch_1.0.batch3d.bias, backbone.depth.mixed_5c.branch_1.0.batch3d.running_mean, backbone.depth.mixed_5c.branch_1.0.batch3d.running_var, backbone.depth.mixed_5c.branch_1.0.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_1.1.conv3d.weight, backbone.depth.mixed_5c.branch_1.1.batch3d.weight, backbone.depth.mixed_5c.branch_1.1.batch3d.bias, backbone.depth.mixed_5c.branch_1.1.batch3d.running_mean, backbone.depth.mixed_5c.branch_1.1.batch3d.running_var, backbone.depth.mixed_5c.branch_1.1.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_2.0.conv3d.weight, backbone.depth.mixed_5c.branch_2.0.batch3d.weight, backbone.depth.mixed_5c.branch_2.0.batch3d.bias, backbone.depth.mixed_5c.branch_2.0.batch3d.running_mean, backbone.depth.mixed_5c.branch_2.0.batch3d.running_var, backbone.depth.mixed_5c.branch_2.0.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_2.1.conv3d.weight, backbone.depth.mixed_5c.branch_2.1.batch3d.weight, backbone.depth.mixed_5c.branch_2.1.batch3d.bias, backbone.depth.mixed_5c.branch_2.1.batch3d.running_mean, backbone.depth.mixed_5c.branch_2.1.batch3d.running_var, backbone.depth.mixed_5c.branch_2.1.batch3d.num_batches_tracked, backbone.depth.mixed_5c.branch_3.1.conv3d.weight, backbone.depth.mixed_5c.branch_3.1.batch3d.weight, backbone.depth.mixed_5c.branch_3.1.batch3d.bias, backbone.depth.mixed_5c.branch_3.1.batch3d.running_mean, backbone.depth.mixed_5c.branch_3.1.batch3d.running_var, backbone.depth.mixed_5c.branch_3.1.batch3d.num_batches_tracked, cls_head.output_conv.depth.weight, cls_head.output_conv.depth.bias\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "!python3 demo/webcam_demo.py --config demo/webcam_cfg/gesture_recognition.py --cpu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49e0e531", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220C\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\272\214\357\274\211.ipynb" "b/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220C\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\272\214\357\274\211.ipynb" new file mode 100644 index 0000000..6bbfbb7 --- /dev/null +++ "b/2022/\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo/\343\200\220C\343\200\221MMPose\346\221\204\345\203\217\345\244\264\345\256\236\346\227\266demo\357\274\210\344\272\214\357\274\211.ipynb" @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd99676d", + "metadata": {}, + "source": [ + "# MMPose摄像头实时demo\n", + "\n", + "同济子豪兄 2022-7-6\n", + "\n", + "参考文档：https://github.com/open-mmlab/mmpose-webcam-demo" + ] + }, + { + "cell_type": "markdown", + "id": "b06e72e5", + "metadata": {}, + "source": [ + "## 进入 mmpose-webcam-demo 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e01076a8", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose-webcam-demo')" + ] + }, + { + "cell_type": "markdown", + "id": "b869e196", + "metadata": {}, + "source": [ + "## 人体+人脸+人手关键点检测" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82bef8e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "!python3 run.py --config configs/examples/pose_estimation/pose_estimation.py --cpu" + ] + }, + { + "cell_type": "markdown", + "id": "7d318b60", + "metadata": {}, + "source": [ + "## 冰墩墩" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ad9a7827", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/Users/tommy/Desktop/上海人工智能实验室/mmpose/摄像头实时demo/mmpose/mmpose/core/post_processing/smoother.py:176: UserWarning: Smoother received empty result.\n", + " warnings.warn('Smoother received empty result.')\n" + ] + } + ], + "source": [ + "!python3 run.py --config configs/examples/bing_dwen_dwen/bing_dwen_dwen.py --cpu" + ] + }, + { + "cell_type": "markdown", + "id": "237be081", + "metadata": {}, + "source": [ + "## 新年特效" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "17437f5b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "!python3 run.py --config configs/examples/new_year/new_year.py --cpu" + ] + }, + { + "cell_type": "markdown", + "id": "141bc389", + "metadata": {}, + "source": [ + "## 情人节特效" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcbd7285", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/Users/tommy/Desktop/上海人工智能实验室/mmpose/mmpose/mmpose/core/post_processing/smoother.py:176: UserWarning: Smoother received empty result.\n", + " warnings.warn('Smoother received empty result.')\n" + ] + } + ], + "source": [ + "!python3 run.py --config configs/examples/valentine_magic/valentine_magic.py --cpu" + ] + }, + { + "cell_type": "markdown", + "id": "122d6cf1", + "metadata": {}, + "source": [ + "## 换脸\n", + "\n", + "注意，本代码仅供教学科普娱乐，请勿用于作恶。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f5461ac5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load checkpoint from http path: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth\n", + "load checkpoint from http path: https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark-e2158108_20211205.pth\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tommy/opt/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "!python3 run.py --config configs/examples/face_swap/face_swap.py --cpu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad558047", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220A1\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" "b/2023/0404/\343\200\220A1\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" new file mode 100644 index 0000000..5daca45 --- /dev/null +++ "b/2023/0404/\343\200\220A1\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMPose.ipynb" @@ -0,0 +1,829 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bc3d1961-9081-49c9-be56-ad0c748decf1", + "metadata": {}, + "source": [ + "# 安装配置MMPose" + ] + }, + { + "cell_type": "markdown", + "id": "0fc8ebd8-db6e-4a17-895f-2f87fc98ca79", + "metadata": {}, + "source": [ + "按照顺序逐行运行本代码，即可安装配置 MMPose 环境\n", + "\n", + "推荐代码运行[云GPU环境](https://featurize.cn?s=d7ce99f842414bfcaea5662a97581bd1)：GPU RTX 3060、CUDA v11.2\n", + "\n", + "作者：同济子豪兄 2023-3-31" + ] + }, + { + "cell_type": "markdown", + "id": "63d0dc47-4601-49a3-8d25-12b4d24fa6f0", + "metadata": {}, + "source": [ + "> 提示：以下代码运行时，若长时间运行卡着不动，可重启 kernel 后重新运行一遍" + ] + }, + { + "cell_type": "markdown", + "id": "6fc1c686-1267-4503-b9e4-bcb188a7f974", + "metadata": { + "tags": [] + }, + "source": [ + "## 安装Pytorch" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2fef57fc-d5f0-4cd0-a134-1f025c15439f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.pytorch.org/whl/cu113/torch_stable.html\n", + "Collecting install\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4d/c8/8cbca135f9e167810756ea2bc34b028501936675fcbd7dadccf752fa4622/install-1.3.5-py3-none-any.whl (3.2 kB)\n", + "Collecting torch==1.10.1+cu113\n", + " Downloading https://download.pytorch.org/whl/cu113/torch-1.10.1%2Bcu113-cp37-cp37m-linux_x86_64.whl (1821.5 MB)\n", + "\u001b[K |██████▉ | 388.3 MB 124.0 MB/s eta 0:00:12" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[K |█████████████▍ | 760.2 MB 113.5 MB/s eta 0:00:10" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[K |████████████████████ | 1133.0 MB 104.9 MB/s eta 0:00:07" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[K |██████████████████████████▌ | 1508.2 MB 105.2 MB/s eta 0:00:03" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[K |████████████████████████████████| 1821.5 MB 100.0 MB/s eta 0:00:01\n", + "\u001b[?25hCollecting torchvision==0.11.2+cu113\n", + " Downloading https://download.pytorch.org/whl/cu113/torchvision-0.11.2%2Bcu113-cp37-cp37m-linux_x86_64.whl (24.6 MB)\n", + "\u001b[K |████████████████████████████████| 24.6 MB 74.2 MB/s eta 0:00:01\n", + "\u001b[?25hCollecting torchaudio==0.10.1+cu113\n", + " Downloading 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0.11.1+cu113\n", + " Uninstalling torchvision-0.11.1+cu113:\n", + " Successfully uninstalled torchvision-0.11.1+cu113\n", + " Attempting uninstall: torchaudio\n", + " Found existing installation: torchaudio 0.10.0+cu113\n", + " Uninstalling torchaudio-0.10.0+cu113:\n", + " Successfully uninstalled torchaudio-0.10.0+cu113\n", + "Successfully installed install-1.3.5 torch-1.10.1+cu113 torchaudio-0.10.1+cu113 torchvision-0.11.2+cu113\n" + ] + } + ], + "source": [ + "# 安装Pytorch?\n", + "!pip3 install install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n" + ] + }, + { + "cell_type": "markdown", + "id": "054b64e4-4796-4136-b45e-3664b281cf30", + "metadata": {}, + "source": [ + "## 用MIM安装MMCV" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "83b1c9e3-0d69-423d-a8a2-172d18dfdb33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting openmim\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ef/2f/dc7ce077629d1234f9cf7f17896cdcd49be0791da99aea673cced49b9700/openmim-0.3.6-py2.py3-none-any.whl (51 kB)\n", + "\u001b[K |████████████████████████████████| 51 kB 3.0 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: pandas in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (1.3.4)\n", + "Requirement already satisfied: pip>=19.3 in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (21.1.3)\n", + "Collecting rich\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6b/f0/79df8eaa345ece1d33d8dfeec46ea166028da37b314dc44ead18c058a126/rich-13.3.2-py3-none-any.whl (238 kB)\n", + "\u001b[K |████████████████████████████████| 238 kB 90.2 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: Click in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (7.1.2)\n", + 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" Successfully uninstalled Pygments-2.10.0\n", + "Successfully installed markdown-it-py-2.2.0 mdurl-0.1.2 model-index-0.1.11 openmim-0.3.6 ordered-set-4.1.0 pygments-2.14.0 rich-13.3.2\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Collecting mmengine\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/08/7d/baf7c6bf400c63fa06be595624b364684e7280341bd9b58c66ecdd93d8aa/mmengine-0.7.0-py3-none-any.whl (365 kB)\n", + "\u001b[K |████████████████████████████████| 365 kB 61.7 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (0.31.0)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (13.3.2)\n", + "Requirement already satisfied: opencv-python>=3 in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (4.5.4.60)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (3.5.0)\n", + "Collecting addict\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: termcolor in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (1.1.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (1.21.4)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (6.0)\n", + "Requirement already satisfied: pillow>=6.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (8.4.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) 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(0.1.2)\n", + "Installing collected packages: addict, mmengine\n", + "Successfully installed addict-2.4.0 mmengine-0.7.0\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Collecting mmcv==2.0.0rc3\n", + " Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/mmcv-2.0.0rc3-cp37-cp37m-manylinux1_x86_64.whl (57.9 MB)\n", + "\u001b[K |████████████████████████████████| 57.9 MB 16.9 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (0.31.0)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (6.0)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (21.3)\n", + "Requirement already satisfied: numpy in 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(1.16.0)\n", + "Requirement already satisfied: scipy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (1.7.3)\n", + "Collecting pycocotools\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ef/c6/90220be3b39fbc4cbd203775ca47dd8dc97fae06fbd2b500637395621b7c/pycocotools-2.0.6.tar.gz (24 kB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (1.21.4)\n", + "Collecting terminaltables\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c4/fb/ea621e0a19733e01fe4005d46087d383693c0f4a8f824b47d8d4122c87e0/terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from 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filename=pycocotools-2.0.6-cp37-cp37m-linux_x86_64.whl size=373932 sha256=4ad02106e33be27c805c24b811b27d53fa547adbcf9147c6bec3e3991943b248\n", + " Stored in directory: /home/featurize/.cache/pip/wheels/f8/94/70/046149e666bd5812b7de6b87a28dcef238f7162f4108e0b3d8\n", + "Successfully built pycocotools\n", + "Installing collected packages: terminaltables, pycocotools, mmcv, mmdet\n", + " Attempting uninstall: mmcv\n", + " Found existing installation: mmcv 2.0.0rc3\n", + " Uninstalling mmcv-2.0.0rc3:\n", + " Successfully uninstalled mmcv-2.0.0rc3\n", + "Successfully installed mmcv-2.0.0rc4 mmdet-3.0.0rc6 pycocotools-2.0.6 terminaltables-3.1.10\n" + ] + } + ], + "source": [ + "!pip install -U openmim\n", + "!mim install mmengine\n", + "!mim install 'mmcv==2.0.0rc3'\n", + "!mim install \"mmdet>=3.0.0rc6\"" + ] + }, + { + "cell_type": "markdown", + "id": "38c55520-071b-41de-91f4-3ec78e16bf27", + "metadata": {}, + "source": [ + "## 安装其它工具包" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb4b3373-f117-46a9-ab43-e67753eb7c61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: opencv-python in /environment/miniconda3/lib/python3.7/site-packages (4.5.4.60)\n", + "Requirement already satisfied: pillow in /environment/miniconda3/lib/python3.7/site-packages (8.4.0)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (3.5.0)\n", + "Collecting seaborn\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8f/2e/17bbb83fbf102687bb2aa3d808add39da820a7698159302a1a69bb82e01c/seaborn-0.12.2-py3-none-any.whl (293 kB)\n", + "\u001b[K |████████████████████████████████| 293 kB 66.9 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: tqdm in /environment/miniconda3/lib/python3.7/site-packages (4.61.2)\n", + "Requirement already satisfied: pycocotools in /environment/miniconda3/lib/python3.7/site-packages (2.0.6)\n", + "Requirement already satisfied: numpy>=1.14.5 in /environment/miniconda3/lib/python3.7/site-packages (from opencv-python) (1.21.4)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (4.28.3)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (3.0.6)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (6.3.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (1.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (21.3)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib) (52.0.0.post20210125)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib) (1.2.2)\n", + "Requirement already satisfied: pandas>=0.25 in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (1.3.4)\n", + "Requirement already satisfied: typing_extensions in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (4.0.1)\n", + "Requirement already satisfied: pytz>=2017.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas>=0.25->seaborn) (2021.3)\n", + "Installing collected packages: seaborn\n", + "Successfully installed seaborn-0.12.2\n" + ] + } + ], + "source": [ + "!pip install opencv-python pillow matplotlib seaborn tqdm pycocotools -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + ] + }, + { + "cell_type": "markdown", + "id": "0defb2f8-d5ab-4ee4-a66f-85cc17c221de", + "metadata": { + "tags": [] + }, + "source": [ + "## 下载 MMPose" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "89026868-58b1-4755-aca3-ded98451e906", + "metadata": {}, + "outputs": [], + "source": [ + "# 删掉原有的 mmpose 文件夹（如有）\n", + "!rm -rf mmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "09c51fee-d040-46cf-8d86-c0248ab7fe84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "正克隆到 'mmpose'...\n", + "remote: Enumerating objects: 25835, done.\u001b[K\n", + "remote: Counting objects: 100% (554/554), done.\u001b[K\n", + "remote: Compressing objects: 100% (309/309), done.\u001b[K\n", + "remote: Total 25835 (delta 285), reused 450 (delta 234), pack-reused 25281\u001b[K\n", + "接收对象中: 100% (25835/25835), 27.58 MiB | 15.25 MiB/s, 完成.\n", + "处理 delta 中: 100% (18340/18340), 完成.\n", + "正在更新文件: 100% (1458/1458), 完成.\n" + ] + } + ], + "source": [ + "# 从 github 上下载最新的 mmpose 源代码\n", + "!git clone https://github.com/open-mmlab/mmpose.git -b dev-1.x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b6a71270-f87b-4cf3-87f0-771f45f38c28", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入主目录\n", + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "f1cd1922-e20a-459a-bf45-e826d8245d88", + "metadata": {}, + "source": [ + "## 安装 MMPose" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21e78bae-1d33-478d-8b72-5e8584f6cadf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Obtaining file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmpose\n", + "Requirement already satisfied: chumpy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (0.70)\n", + "Requirement already satisfied: json_tricks in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (3.16.1)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (3.5.0)\n", + "Requirement already satisfied: munkres in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (1.1.4)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (1.21.4)\n", + "Requirement already satisfied: opencv-python in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (4.5.4.60)\n", + "Requirement already satisfied: pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (8.4.0)\n", + "Requirement already satisfied: scipy in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (1.7.3)\n", + "Requirement already satisfied: torchvision in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (0.11.2+cu113)\n", + "Requirement already satisfied: xtcocotools>=1.12 in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (1.13)\n", + "Requirement already satisfied: mmcv<2.1.0,>=2.0.0rc1 in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (2.0.0rc4)\n", + "Requirement already satisfied: mmdet<3.1.0,>=3.0.0rc6 in /home/featurize/work/关键点检测/mmdetection (from mmpose==1.0.0rc1) (3.0.0rc6)\n", + "Requirement already satisfied: mmengine<1.0.0,>=0.4.0 in /environment/miniconda3/lib/python3.7/site-packages (from mmpose==1.0.0rc1) (0.7.0)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc1->mmpose==1.0.0rc1) (21.3)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc1->mmpose==1.0.0rc1) (6.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc1->mmpose==1.0.0rc1) (0.31.0)\n", + "Requirement already satisfied: addict in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc1->mmpose==1.0.0rc1) (2.4.0)\n", + "Requirement already satisfied: pycocotools in /environment/miniconda3/lib/python3.7/site-packages (from mmdet<3.1.0,>=3.0.0rc6->mmpose==1.0.0rc1) (2.0.6)\n", + "Requirement already satisfied: shapely in /environment/miniconda3/lib/python3.7/site-packages (from mmdet<3.1.0,>=3.0.0rc6->mmpose==1.0.0rc1) (2.0.1)\n", + "Requirement already satisfied: six in /environment/miniconda3/lib/python3.7/site-packages (from mmdet<3.1.0,>=3.0.0rc6->mmpose==1.0.0rc1) (1.16.0)\n", + "Requirement already satisfied: terminaltables in /environment/miniconda3/lib/python3.7/site-packages (from mmdet<3.1.0,>=3.0.0rc6->mmpose==1.0.0rc1) (3.1.10)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (13.3.2)\n", + "Requirement already satisfied: termcolor in /environment/miniconda3/lib/python3.7/site-packages (from mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (1.1.0)\n", + "Requirement already satisfied: setuptools>=18.0 in /environment/miniconda3/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==1.0.0rc1) (52.0.0.post20210125)\n", + "Requirement already satisfied: cython>=0.27.3 in /environment/miniconda3/lib/python3.7/site-packages (from xtcocotools>=1.12->mmpose==1.0.0rc1) (0.29.33)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (2.8.2)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (0.11.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (1.3.2)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (3.0.6)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (6.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmpose==1.0.0rc1) (4.28.3)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmpose==1.0.0rc1) (1.2.2)\n", + "Requirement already satisfied: typing-extensions<5.0,>=4.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (4.0.1)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (2.14.0)\n", + "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (2.2.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /environment/miniconda3/lib/python3.7/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->mmengine<1.0.0,>=0.4.0->mmpose==1.0.0rc1) (0.1.2)\n", + "Requirement already satisfied: torch==1.10.1 in /environment/miniconda3/lib/python3.7/site-packages (from torchvision->mmpose==1.0.0rc1) (1.10.1+cu113)\n", + "Installing collected packages: mmpose\n", + " Running setup.py develop for mmpose\n", + "Successfully installed mmpose-1.0.0rc1\n" + ] + } + ], + "source": [ + "!mim install -e ." + ] + }, + { + "cell_type": "markdown", + "id": "a828bfe4-82e5-497a-9bda-cb9837df2cb3", + "metadata": {}, + "source": [ + "## 下载预训练模型权重文件和视频素材" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5aac1d0c-dbf5-478c-ab80-7e2aa0ab0f01", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# 创建 checkpoint 文件夹，用于存放预训练模型权重文件\n", + "os.mkdir('checkpoint')\n", + "\n", + "# 创建 outputs 文件夹，用于存放预测结果\n", + "os.mkdir('outputs')\n", + "\n", + "# 创建 data 文件夹，用于存放图片和视频素材\n", + "os.mkdir('data')\n", + "\n", + "os.mkdir('data/test')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bae3b8b0-07a2-4a79-83cf-42276ea1c9ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 11:05:20-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/videos/cxk.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 5535528 (5.3M) [video/mp4]\n", + "正在保存至: “data/test/cxk.mp4”\n", + "\n", + "cxk.mp4 100%[===================>] 5.28M 12.3MB/s 用时 0.4s \n", + "\n", + "2023-04-04 11:05:20 (12.3 MB/s) - 已保存 “data/test/cxk.mp4” [5535528/5535528])\n", + "\n", + "--2023-04-04 11:05:21-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/two-girls.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 44217475 (42M) [video/mp4]\n", + "正在保存至: “data/test/two-girls.mp4”\n", + "\n", + "data/test/two-girls 100%[===================>] 42.17M 26.6MB/s 用时 1.6s \n", + "\n", + "2023-04-04 11:05:22 (26.6 MB/s) - 已保存 “data/test/two-girls.mp4” [44217475/44217475])\n", + "\n", + "--2023-04-04 11:05:23-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/fly.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 167194 (163K) [video/mp4]\n", + "正在保存至: “data/test/fly.mp4”\n", + "\n", + "data/test/fly.mp4 100%[===================>] 163.28K --.-KB/s 用时 0.1s \n", + "\n", + "2023-04-04 11:05:23 (1.09 MB/s) - 已保存 “data/test/fly.mp4” [167194/167194])\n", + "\n", + "--2023-04-04 11:05:23-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/solo_dance.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 4751457 (4.5M) [video/mp4]\n", + "正在保存至: “data/test/solo_dance.mp4”\n", + "\n", + "data/test/solo_danc 100%[===================>] 4.53M 12.1MB/s 用时 0.4s \n", + "\n", + "2023-04-04 11:05:24 (12.1 MB/s) - 已保存 “data/test/solo_dance.mp4” [4751457/4751457])\n", + "\n", + "--2023-04-04 11:05:24-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/two_dancers.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 3264055 (3.1M) [video/mp4]\n", + "正在保存至: “data/test/two_dancers.mp4”\n", + "\n", + "data/test/two_dance 100%[===================>] 3.11M 8.23MB/s 用时 0.4s \n", + "\n", + "2023-04-04 11:05:24 (8.23 MB/s) - 已保存 “data/test/two_dancers.mp4” [3264055/3264055])\n", + "\n", + "--2023-04-04 11:05:25-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/play_piano.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 27143412 (26M) [video/mp4]\n", + "正在保存至: “data/test/play_piano.mp4”\n", + "\n", + "data/test/play_pian 100%[===================>] 25.89M 25.2MB/s 用时 1.0s \n", + "\n", + "2023-04-04 11:05:26 (25.2 MB/s) - 已保存 “data/test/play_piano.mp4” [27143412/27143412])\n", + "\n", + "--2023-04-04 11:05:26-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/piano.jpeg\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 167767 (164K) [image/jpeg]\n", + "正在保存至: “data/test/piano.jpeg”\n", + "\n", + "data/test/piano.jpe 100%[===================>] 163.83K 1.06MB/s 用时 0.2s \n", + "\n", + "2023-04-04 11:05:26 (1.06 MB/s) - 已保存 “data/test/piano.jpeg” [167767/167767])\n", + "\n", + "--2023-04-04 11:05:27-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/face_child.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 815897 (797K) [video/mp4]\n", + "正在保存至: “data/test/face_child.mp4”\n", + "\n", + "data/test/face_chil 100%[===================>] 796.77K 3.11MB/s 用时 0.3s \n", + "\n", + "2023-04-04 11:05:27 (3.11 MB/s) - 已保存 “data/test/face_child.mp4” [815897/815897])\n", + "\n", + "--2023-04-04 11:05:27-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/mother.mp4\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 19442142 (19M) [video/mp4]\n", + "正在保存至: “data/test/mother.mp4”\n", + "\n", + "data/test/mother.mp 100%[===================>] 18.54M 18.7MB/s 用时 1.0s \n", + "\n", + "2023-04-04 11:05:29 (18.7 MB/s) - 已保存 “data/test/mother.mp4” [19442142/19442142])\n", + "\n", + "--2023-04-04 11:05:29-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/multi-person.jpeg\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 604280 (590K) [image/jpeg]\n", + "正在保存至: “data/test/multi-person.jpeg”\n", + "\n", + "data/test/multi-per 100%[===================>] 590.12K 2.48MB/s 用时 0.2s \n", + "\n", + "2023-04-04 11:05:30 (2.48 MB/s) - 已保存 “data/test/multi-person.jpeg” [604280/604280])\n", + "\n" + ] + } + ], + "source": [ + "# 单人-唱跳篮球\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/videos/cxk.mp4 -P data/test\n", + "\n", + "# 两个女生跳舞视频，来源：https://mixkit.co/free-stock-video/two-girls-having-fun-in-a-retro-restaurant-42298/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/two-girls.mp4 -O data/test/two-girls.mp4\n", + "\n", + "# 小虫子视频，来源：https://user-images.githubusercontent.com/87690686/165095600-f68e0d42-830d-4c22-8940-c90c9f3bb817.mp4\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/fly.mp4 -O data/test/fly.mp4\n", + "\n", + "# 单人跳舞视频\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/solo_dance.mp4 -O data/test/solo_dance.mp4\n", + "# 两个跳舞的人，视频来源：https://www.youtube.com/watch?v=fP_IZKfc4vo\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/two_dancers.mp4 -O data/test/two_dancers.mp4\n", + "\n", + "# 弹钢琴视频，来源：https://mixkit.co/free-stock-video/hands-of-a-pianist-performing-a-song-on-a-piano-41667/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/play_piano.mp4 -O data/test/play_piano.mp4\n", + "\n", + "# 弹钢琴图片，来源：https://www.pexels.com/zh-cn/photo/6671953/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/piano.jpeg -O data/test/piano.jpeg\n", + "\n", + "# 孩子的笑脸视频，来源：https://mixkit.co/free-stock-video/teacher-and-students-waving-with-painted-hands-36029/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/face_child.mp4 -O data/test/face_child.mp4\n", + "\n", + "# 妈妈和女儿跳舞，视频来源：https://mixkit.co/free-stock-video/mother-and-daughters-in-a-kitchen-dancing-4565/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/videos/mother.mp4 -O data/test/mother.mp4\n", + "\n", + "# 多人，图片来源：https://www.pexels.com/zh-cn/photo/2168292/\n", + "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/images/multi-person.jpeg -O data/test/multi-person.jpeg\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa97e1da-f245-4d32-892d-dec07a6b2c16", + "metadata": {}, + "source": [ + "## 检查安装成功" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "424fca02-13cc-493f-8833-ec63900f02d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pytorch 版本 1.10.1+cu113\n", + "CUDA 是否可用 True\n" + ] + } + ], + "source": [ + "# 检查 Pytorch\n", + "import torch, torchvision\n", + "print('Pytorch 版本', torch.__version__)\n", + "print('CUDA 是否可用',torch.cuda.is_available())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "96a6e738-d9d3-4e9f-a053-15bf2f29d43b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MMCV版本 2.0.0rc4\n", + "CUDA版本 11.3\n", + "编译器版本 GCC 9.3\n" + ] + } + ], + "source": [ + "# 检查 mmcv\n", + "import mmcv\n", + "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n", + "print('MMCV版本', mmcv.__version__)\n", + "print('CUDA版本', get_compiling_cuda_version())\n", + "print('编译器版本', get_compiler_version())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dae8b28c-fa1f-4911-ba27-50ed745e9679", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mmpose版本 1.0.0rc1\n" + ] + } + ], + "source": [ + "# 检查 mmpose\n", + "import mmpose\n", + "print('mmpose版本', mmpose.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "6f89f196-6f70-46a5-9715-ee4755717c13", + "metadata": {}, + "source": [ + "没有报错，即证明安装成功。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90954e26-49ee-42ad-b2ff-7514e8542db4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220A2\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMDetection.ipynb" "b/2023/0404/\343\200\220A2\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMDetection.ipynb" new file mode 100644 index 0000000..7a9ff78 --- /dev/null +++ "b/2023/0404/\343\200\220A2\343\200\221\345\256\211\350\243\205\351\205\215\347\275\256MMDetection.ipynb" @@ -0,0 +1,1724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bc3d1961-9081-49c9-be56-ad0c748decf1", + "metadata": {}, + "source": [ + "# 安装配置MMDetection" + ] + }, + { + "cell_type": "markdown", + "id": "0fc8ebd8-db6e-4a17-895f-2f87fc98ca79", + "metadata": {}, + "source": [ + "按照顺序逐行运行本代码，即可安装配置 MMDetection 环境\n", + "\n", + "推荐代码运行[云GPU环境](https://featurize.cn?s=d7ce99f842414bfcaea5662a97581bd1)：GPU RTX 3060、CUDA v11.2\n", + "\n", + "作者：同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "63d0dc47-4601-49a3-8d25-12b4d24fa6f0", + "metadata": {}, + "source": [ + "> 提示：以下代码运行时，若长时间运行卡着不动，可重启 kernel 后重新运行一遍" + ] + }, + { + "cell_type": "markdown", + "id": "6fc1c686-1267-4503-b9e4-bcb188a7f974", + "metadata": { + "tags": [] + }, + "source": [ + "## 安装Pytorch" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2fef57fc-d5f0-4cd0-a134-1f025c15439f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.pytorch.org/whl/cu113/torch_stable.html\n", + "Requirement already satisfied: install in /environment/miniconda3/lib/python3.7/site-packages (1.3.5)\n", + "Requirement already satisfied: torch==1.10.1+cu113 in /environment/miniconda3/lib/python3.7/site-packages (1.10.1+cu113)\n", + "Requirement already satisfied: torchvision==0.11.2+cu113 in /environment/miniconda3/lib/python3.7/site-packages (0.11.2+cu113)\n", + "Requirement already satisfied: torchaudio==0.10.1+cu113 in /environment/miniconda3/lib/python3.7/site-packages (0.10.1+cu113)\n", + "Requirement already satisfied: typing-extensions in /environment/miniconda3/lib/python3.7/site-packages (from torch==1.10.1+cu113) (4.0.1)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from torchvision==0.11.2+cu113) (1.21.4)\n", + "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /environment/miniconda3/lib/python3.7/site-packages (from torchvision==0.11.2+cu113) (8.4.0)\n" + ] + } + ], + "source": [ + "# 安装Pytorch?\n", + "!pip3 install install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n" + ] + }, + { + "cell_type": "markdown", + "id": "054b64e4-4796-4136-b45e-3664b281cf30", + "metadata": {}, + "source": [ + "## 用MIM安装MMCV" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "83b1c9e3-0d69-423d-a8a2-172d18dfdb33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: openmim in /environment/miniconda3/lib/python3.7/site-packages (0.3.6)\n", + "Requirement already satisfied: requests in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (2.24.0)\n", + "Requirement already satisfied: tabulate in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (0.8.7)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (13.3.2)\n", + "Requirement already satisfied: model-index in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (0.1.11)\n", + "Requirement already satisfied: pip>=19.3 in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (21.1.3)\n", + "Requirement already satisfied: pandas in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (1.3.4)\n", + "Requirement already satisfied: colorama in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (0.4.4)\n", + "Requirement already satisfied: Click in /environment/miniconda3/lib/python3.7/site-packages (from openmim) (7.1.2)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from model-index->openmim) (6.0)\n", + "Requirement already satisfied: ordered-set in /environment/miniconda3/lib/python3.7/site-packages (from model-index->openmim) (4.1.0)\n", + "Requirement already satisfied: markdown in /environment/miniconda3/lib/python3.7/site-packages (from model-index->openmim) (3.3.6)\n", + "Requirement already satisfied: importlib-metadata>=4.4 in /environment/miniconda3/lib/python3.7/site-packages (from markdown->model-index->openmim) (4.8.2)\n", + "Requirement already satisfied: zipp>=0.5 in /environment/miniconda3/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown->model-index->openmim) (3.6.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.4 in /environment/miniconda3/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown->model-index->openmim) (4.0.1)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas->openmim) (2.8.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas->openmim) (2021.3)\n", + "Requirement already satisfied: numpy>=1.17.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas->openmim) (1.21.4)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->openmim) (1.16.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /environment/miniconda3/lib/python3.7/site-packages (from requests->openmim) (2021.5.30)\n", + "Requirement already satisfied: idna<3,>=2.5 in /environment/miniconda3/lib/python3.7/site-packages (from requests->openmim) (2.10)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /environment/miniconda3/lib/python3.7/site-packages (from requests->openmim) (1.25.11)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /environment/miniconda3/lib/python3.7/site-packages (from requests->openmim) (3.0.4)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->openmim) (2.14.0)\n", + "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->openmim) (2.2.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /environment/miniconda3/lib/python3.7/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->openmim) (0.1.2)\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Collecting mmcv-full\n", + " Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/mmcv_full-1.7.1-cp37-cp37m-manylinux1_x86_64.whl (58.2 MB)\n", + "\u001b[K |████████████████████████████████| 58.2 MB 11.4 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: Pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (8.4.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (1.21.4)\n", + "Requirement already satisfied: opencv-python>=3 in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (4.5.4.60)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (6.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (0.31.0)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (21.3)\n", + "Requirement already satisfied: addict in /environment/miniconda3/lib/python3.7/site-packages (from mmcv-full) (2.4.0)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /environment/miniconda3/lib/python3.7/site-packages (from packaging->mmcv-full) (3.0.6)\n", + "Installing collected packages: mmcv-full\n", + "Successfully installed mmcv-full-1.7.1\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Requirement already satisfied: mmengine in /environment/miniconda3/lib/python3.7/site-packages (0.7.0)\n", + "Requirement already satisfied: opencv-python>=3 in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (4.5.4.60)\n", + "Requirement already satisfied: termcolor in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (1.1.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (1.21.4)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (13.3.2)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (6.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (0.31.0)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (3.5.0)\n", + "Requirement already satisfied: addict in /environment/miniconda3/lib/python3.7/site-packages (from mmengine) (2.4.0)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (0.11.0)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (6.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (4.28.3)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (21.3)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (1.3.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine) (8.4.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in 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in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (21.3)\n", + "Requirement already satisfied: Pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (8.4.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (0.31.0)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (6.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (1.21.4)\n", + "Requirement already satisfied: mmengine in /environment/miniconda3/lib/python3.7/site-packages (from mmcv==2.0.0rc3) (0.7.0)\n", + "Requirement already satisfied: termcolor in /environment/miniconda3/lib/python3.7/site-packages (from mmengine->mmcv==2.0.0rc3) (1.1.0)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from mmengine->mmcv==2.0.0rc3) (13.3.2)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmengine->mmcv==2.0.0rc3) (3.5.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (3.0.6)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (1.3.2)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (4.28.3)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (2.8.2)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmengine->mmcv==2.0.0rc3) (6.3.2)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib->mmengine->mmcv==2.0.0rc3) (1.16.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmengine->mmcv==2.0.0rc3) (1.2.2)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmengine->mmcv==2.0.0rc3) (52.0.0.post20210125)\n", + "Requirement already satisfied: typing-extensions<5.0,>=4.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine->mmcv==2.0.0rc3) (4.0.1)\n", + "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine->mmcv==2.0.0rc3) (2.2.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine->mmcv==2.0.0rc3) (2.14.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /environment/miniconda3/lib/python3.7/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->mmengine->mmcv==2.0.0rc3) (0.1.2)\n", + "Installing collected packages: mmcv\n", + " Attempting uninstall: mmcv\n", + " Found existing installation: mmcv 2.0.0rc4\n", + " Uninstalling mmcv-2.0.0rc4:\n", + " Successfully uninstalled mmcv-2.0.0rc4\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "mmdet 3.0.0rc6 requires mmcv<2.1.0,>=2.0.0rc4, but you have mmcv 2.0.0rc3 which is incompatible.\u001b[0m\n", + "Successfully installed mmcv-2.0.0rc3\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html\n", + "Requirement already satisfied: mmdet>=3.0.0rc6 in /environment/miniconda3/lib/python3.7/site-packages (3.0.0rc6)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (1.21.4)\n", + "Requirement already satisfied: six in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (1.16.0)\n", + "Requirement already satisfied: pycocotools in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (2.0.6)\n", + "Requirement already satisfied: scipy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (1.7.3)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (3.5.0)\n", + "Requirement already satisfied: terminaltables in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (3.1.10)\n", + "Collecting mmcv<2.1.0,>=2.0.0rc4\n", + " Using cached https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/mmcv-2.0.0rc4-cp37-cp37m-manylinux1_x86_64.whl (59.4 MB)\n", + "Requirement already satisfied: mmengine<1.0.0,>=0.4.0 in /environment/miniconda3/lib/python3.7/site-packages (from mmdet>=3.0.0rc6) (0.7.0)\n", + "Requirement already satisfied: yapf in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (0.31.0)\n", + "Requirement already satisfied: packaging in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (21.3)\n", + "Requirement already satisfied: addict in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (2.4.0)\n", + "Requirement already satisfied: Pillow in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (8.4.0)\n", + "Requirement already satisfied: opencv-python>=3 in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (4.5.4.60)\n", + "Requirement already satisfied: pyyaml in /environment/miniconda3/lib/python3.7/site-packages (from mmcv<2.1.0,>=2.0.0rc4->mmdet>=3.0.0rc6) (6.0)\n", + "Requirement already satisfied: termcolor in /environment/miniconda3/lib/python3.7/site-packages (from mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (1.1.0)\n", + "Requirement already satisfied: rich in /environment/miniconda3/lib/python3.7/site-packages (from mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (13.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (4.28.3)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (6.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (2.8.2)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (3.0.6)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (1.3.2)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet>=3.0.0rc6) (0.11.0)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet>=3.0.0rc6) (1.2.2)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet>=3.0.0rc6) (52.0.0.post20210125)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (2.14.0)\n", + "Requirement already satisfied: typing-extensions<5.0,>=4.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (4.0.1)\n", + "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from rich->mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (2.2.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /environment/miniconda3/lib/python3.7/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->mmengine<1.0.0,>=0.4.0->mmdet>=3.0.0rc6) (0.1.2)\n", + "Installing collected packages: mmcv\n", + " Attempting uninstall: mmcv\n", + " Found existing installation: mmcv 2.0.0rc3\n", + " Uninstalling mmcv-2.0.0rc3:\n", + " Successfully uninstalled mmcv-2.0.0rc3\n", + "Successfully installed mmcv-2.0.0rc4\n" + ] + } + ], + "source": [ + "!pip install -U openmim\n", + "!mim install mmcv-full\n", + "!mim install mmengine\n", + "!mim install 'mmcv==2.0.0rc3'\n", + "!mim install \"mmdet>=3.0.0rc6\"" + ] + }, + { + "cell_type": "markdown", + "id": "38c55520-071b-41de-91f4-3ec78e16bf27", + "metadata": {}, + "source": [ + "## 安装其它工具包" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eb4b3373-f117-46a9-ab43-e67753eb7c61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: opencv-python in /environment/miniconda3/lib/python3.7/site-packages (4.5.4.60)\n", + "Requirement already satisfied: pillow in /environment/miniconda3/lib/python3.7/site-packages (8.4.0)\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (3.5.0)\n", + "Requirement already satisfied: seaborn in /environment/miniconda3/lib/python3.7/site-packages (0.12.2)\n", + "Requirement already satisfied: tqdm in /environment/miniconda3/lib/python3.7/site-packages (4.61.2)\n", + "Requirement already satisfied: pycocotools in /environment/miniconda3/lib/python3.7/site-packages (2.0.6)\n", + "Requirement already satisfied: numpy>=1.14.5 in /environment/miniconda3/lib/python3.7/site-packages (from opencv-python) (1.21.4)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (6.3.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (4.28.3)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (3.0.6)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (1.3.2)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (21.3)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: six>=1.5 in /environment/miniconda3/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib) (52.0.0.post20210125)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib) (1.2.2)\n", + "Requirement already satisfied: typing_extensions in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (4.0.1)\n", + "Requirement already satisfied: pandas>=0.25 in /environment/miniconda3/lib/python3.7/site-packages (from seaborn) (1.3.4)\n", + "Requirement already satisfied: pytz>=2017.3 in /environment/miniconda3/lib/python3.7/site-packages (from pandas>=0.25->seaborn) (2021.3)\n" + ] + } + ], + "source": [ + "!pip install opencv-python pillow matplotlib seaborn tqdm pycocotools -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + ] + }, + { + "cell_type": "markdown", + "id": "0defb2f8-d5ab-4ee4-a66f-85cc17c221de", + "metadata": { + "tags": [] + }, + "source": [ + "## 下载 MMDetection" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "89026868-58b1-4755-aca3-ded98451e906", + "metadata": {}, + "outputs": [], + "source": [ + "# 删掉原有的 mmdetection 文件夹（如有）\n", + "!rm -rf mmdetection" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cacbcda5-9b7a-4baa-979a-418a1f4ca658", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "正克隆到 'mmdetection'...\n", + "remote: Enumerating objects: 35150, done.\u001b[K\n", + "remote: Counting objects: 100% (81/81), done.\u001b[K\n", + "remote: Compressing objects: 100% (76/76), done.\u001b[K\n", + "remote: Total 35150 (delta 21), reused 41 (delta 5), pack-reused 35069\u001b[K\n", + "接收对象中: 100% (35150/35150), 47.10 MiB | 17.87 MiB/s, 完成.\n", + "处理 delta 中: 100% (24781/24781), 完成.\n", + "正在更新文件: 100% (1831/1831), 完成.\n" + ] + } + ], + "source": [ + "# 从 github 上下载最新的 mmdetection 源代码\n", + "!git clone https://github.com/open-mmlab/mmdetection.git -b dev-3.x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b6a71270-f87b-4cf3-87f0-771f45f38c28", + "metadata": {}, + "outputs": [], + "source": [ + "# 进入主目录\n", + "import os\n", + "os.chdir('mmdetection')" + ] + }, + { + "cell_type": "markdown", + "id": "f1cd1922-e20a-459a-bf45-e826d8245d88", + "metadata": {}, + "source": [ + "## 安装 MMPose" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21e78bae-1d33-478d-8b72-5e8584f6cadf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using pip 21.1.3 from /environment/miniconda3/lib/python3.7/site-packages/pip (python 3.7)\n", + "Non-user install because site-packages writeable\n", + "Created temporary directory: /tmp/pip-ephem-wheel-cache-3ehktuz4\n", + "Created temporary directory: /tmp/pip-req-tracker-tu4x1bba\n", + "Initialized build tracking at /tmp/pip-req-tracker-tu4x1bba\n", + "Created build tracker: /tmp/pip-req-tracker-tu4x1bba\n", + "Entered build tracker: /tmp/pip-req-tracker-tu4x1bba\n", + "Created temporary directory: /tmp/pip-install-hezcz704\n", + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Obtaining file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmdetection\n", + " Added file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmdetection to build tracker '/tmp/pip-req-tracker-tu4x1bba'\n", + " Running setup.py (path:/home/featurize/work/关键点检测/mmdetection/setup.py) egg_info for package from file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmdetection\n", + " Created temporary directory: /tmp/pip-pip-egg-info-tjnayvn7\n", + " Running command python setup.py egg_info\n", + " running egg_info\n", + " creating /tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info\n", + " writing /tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/PKG-INFO\n", + " writing dependency_links to /tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/dependency_links.txt\n", + " writing requirements to /tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/requires.txt\n", + " writing top-level names to /tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/top_level.txt\n", + " writing manifest file '/tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no files found matching 'mmdet/VERSION'\n", + " warning: no files found matching 'mmdet/.mim/model-index.yml'\n", + " warning: no files found matching 'mmdet/.mim/demo/*/*'\n", + " warning: no files found matching '*.py' under directory 'mmdet/.mim/configs'\n", + " warning: no files found matching '*.yml' under directory 'mmdet/.mim/configs'\n", + " warning: no files found matching '*.sh' under directory 'mmdet/.mim/tools'\n", + " warning: no files found matching '*.py' under directory 'mmdet/.mim/tools'\n", + " writing manifest file '/tmp/pip-pip-egg-info-tjnayvn7/mmdet.egg-info/SOURCES.txt'\n", + " Source in /home/featurize/work/关键点检测/mmdetection has version 3.0.0rc6, which satisfies requirement mmdet==3.0.0rc6 from file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmdetection\n", + " Removed mmdet==3.0.0rc6 from file:///home/featurize/work/%E5%85%B3%E9%94%AE%E7%82%B9%E6%A3%80%E6%B5%8B/mmdetection from build tracker '/tmp/pip-req-tracker-tu4x1bba'\n", + "Requirement already satisfied: matplotlib in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (3.5.0)\n", + "Requirement already satisfied: numpy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (1.21.4)\n", + "Requirement already satisfied: pycocotools in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (2.0.6)\n", + "Requirement already satisfied: scipy in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (1.7.3)\n", + "1 location(s) to search for versions of shapely:\n", + "* https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\n", + "Fetching project page and analyzing links: https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\n", + "Getting page https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\n", + "Found index url https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Looking up \"https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\" in the cache\n", + "Request header has \"max_age\" as 0, cache bypassed\n", + "Starting new HTTPS connection (1): pypi.tuna.tsinghua.edu.cn:443\n", + "https://pypi.tuna.tsinghua.edu.cn:443 \"GET /simple/shapely/ HTTP/1.1\" 200 None\n", + "Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\"\n", + "Caching due to etag\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/cc/59/bdb97d8477c857a5d6ddc6cfd3a49e68d7b9e5c0ee9526e3537457178fa6/Shapely-1.0-py2.4.egg#sha256=bc2c1939844202b6759212aa0c9be201a0fe2e5c87b8ef6add5d3f61cece8b13 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a5/0c/fa64b023a7fec5231b74dc3146565ea4656091e37f8e6bdad42d31714a29/Shapely-1.0.1.tar.gz#sha256=ae46bfd4cfc01eee99c96a9f3e1817328e08c24295cb6e67382c488d8f3f89b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.1\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/22/cc/681c1b1554664f12144a4c706e4eef8f412f9b877b8cb8d9ec83ce34a97b/Shapely-1.0.1.win32.exe#sha256=d80f8b220d1c1e165a80b60d3e79a4fe860c17e75106a4e0a233d77e0150c232 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/30/70/89d1f92f25cbc8d706b9f4d2f6b1b8d672183083a970d11be927dbe9fcd3/Shapely-1.0.11.tar.gz#sha256=dff4c23824faf85ba923a13b92ed3f6c360d0600456e9447620c4f573dda9b53 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.11\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/52/35/ef6607754c213ff1b1c322d753dbe18fd268584bfcc66934db3adf1aa602/Shapely-1.0.11.win32.exe#sha256=cf16e36b5914b7179732f75207feb69c8a4ee22c5121e10775c0f85ad8965416 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/30/f4/4ca3984266afbdf84181328fe0e1931efd093af78f8a07561f889e14de5a/Shapely-1.0.12.tar.gz#sha256=5ae389b5b61426bd0d85ad07e674117b08d7f59ef81f6f2197531f29644169d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.12\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/fd/41/1e5e0a5122b3571dd8f37deda3cd349b636bef31580a2d9cb232569b6826/Shapely-1.0.12.win32.exe#sha256=e28df6cf7f411f0c472bb9c779365ec05e291cc45003c31d198a381c97922e66 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/6c/39/6b7bebdf2ea9e1be51cb90bc5c66c5824af00b6d03df17c955adbbb87ed3/Shapely-1.0.13.tar.gz#sha256=d50e2e15baf8a93874575b143aba6f6a8b786277bb2351ba092bc28a8842e3e8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.13\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3c/cb/b10d375504dade89ba74ab73aa036847a351fc357cfe01e3e1a88c6ecf7f/Shapely-1.0.14.tar.gz#sha256=97abc20183385e4c444f605130f9e4d2a384a6626f3a7bcef9c7e3f6372bf46c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.14\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/14/3f/98d27ccf56994fc731973f23edc1f0524db2fa339619420369427f518905/Shapely-1.0.14.win32.exe#sha256=285a3685cb355c98633b72a154010a238a5b8fde9ba168201dbf71dbc827499e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3c/34/befe50b94f592eaa70e1a06426c1c561ead27352587de16ad25df11f552b/Shapely-1.0.15.tar.gz#sha256=d4f85757e29b472c1140b86d041fd207f923e04bd48e3b729f790063b256246e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.15\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d1/08/82952d443e478cfe3d6994c77fb9fdb29e3dae3d0afc04c41d578dfa59c3/Shapely-1.0.2.tar.gz#sha256=5282eac78f3fd44ad4445e561a2a287192575012bece271bf022fef6c83efcac (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/d4/30/56f07c600b8899d574c8b324efcfa851698463b5d047a00f542f9b167439/Shapely-1.0.2.win32.exe#sha256=b1009abcd9122b627ad6c064749196d81cceae67ae947bbd6cd199011b4b8e62 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/0a/ea/6c2f34ddc9c13abb361a10e16752b5a9fd75f0e5a66587b834090e490ae0/Shapely-1.0.3.tar.gz#sha256=bb929db49065271a452d91ac955cb3c97e89b65cf52a64c4cc084687afd8f82d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.3\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/3c/24/0c641c3c1d7ee9f9db0e0ad5a02ff35439f0603dd67d780a716c83db4239/Shapely-1.0.3.win32.exe#sha256=b305855656b5df98839cb358ef161026c41a3358cc10a4ce241bebd61638ed12 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/9d/a3/9c732d5a1e8405452617ea64a9433e5006b9ab6af5369b7a10cce5842707/Shapely-1.0.4.tar.gz#sha256=81e537a757d93fd22d5ded2c604fbae01abc9796fa543302424db02e0da7a8c6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.4\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/b7/ee/321ea804cf4d908c7fccd3eabb33188cc237888bb1cfa7212a9233599356/Shapely-1.0.4.win32.exe#sha256=bab71b05abf8b9dfcb5b1dde3f4f422aefb15a0619cf481903d582fa3b6f4680 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/32/af/d208f3ae131dd53388b74ff08efbce97161e87e2aa6f0580ad3c18180803/Shapely-1.0.5.tar.gz#sha256=b3662ddf5d88ecfbbdbb20d5dc14013c94ad2eae34681d9e00bf58a3ad638e70 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.5\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a5/f6/2906bbd06b8ce42de89fd3989f4bf85bbcd3a1b2b0a2d0568422f6b0ca14/Shapely-1.0.6.tar.gz#sha256=e508379185ce4f269dc99d6b1810f2e1e4fe88f73bf4a96a1759fb75679fafe6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.6\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/5f/b3/135bd85cec8055dc83b5d184ae7a8eab41635b5e2d6fba3ab5843b845142/Shapely-1.0.6.win32.exe#sha256=e328fd88c86a73e1fa547909ee1f4cfb38fefb394d37f23576f851e8634f66d2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1f/f5/00d6a71d672f09c5979a78c6605b8c307a13c95c026068c617863b1bfbff/Shapely-1.0.7.tar.gz#sha256=d2aaa545369b1b146e019e174c0e785adba9f9a7b1fa1b261a4393be74af3c98 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0.7\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/54/ce/ca152d9dc67aa5fe788782362e1d6eefde72279ff5e58223009880267cf8/Shapely-1.0.7.win32.exe#sha256=c56013538c2bdb16ea879d377d0174a2282fe97ee29677d5caa66d8f7130c6cc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/53/7a/4cf617dbb3d8c5a6ff7cbb5e2dab0f2b8d5faec31d59d406d95dbc00b240/Shapely-1.0.8.win32.exe#sha256=ed702404da018f0635cefab18991ed34e849b039a80d4a656ae3d3f07f4cdced (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/37/c3/0dc90feb44cbd2be3d8f53b2732dd04e01acf6a3616564f7648ef3c3da97/Shapely-1.0.tar.gz#sha256=b8048a36452d3e19500787ac664c90e642fcfaec899e989388140b4e528470c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/f4/7d/a4dfffa881df36a6eba14010c2f2e7f835e4462f725637e70f488fb45944/Shapely-1.0.win32.exe#sha256=6437682943f30ff71e6be3d65190c565bf4a07df5d5f8f823cd416f6db8ce5e6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/aa/7b/604665d0552f5ffc44a99d3bb71208ebca52e799b36b03d6cf4026aba9a4/Shapely-1.0a7-py2.4.egg#sha256=25e23fa21c42a1c4cd71133c8819c83728a0c6f76e5c5746cd607068faaf9507 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/94/50/235fb07d3723e7efe18d3d01c8fdc0514c2f4a3f6bcfbaa4d39da717e138/Shapely-1.0a7.tar.gz#sha256=99fd241ea70768cf0755f79b6f3ac8201163f9f3c7df6614cc96531ec22ed6a6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0a7\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/93/39/5f64cf9e0a5784f067ce868e1ed043e82732afee2b8b3d716737bd1556ef/Shapely-1.0b1-py2.4.egg#sha256=f8dbc507f07dccaa0b0df2d5ca83a39a7eb2956bf9565240cc1b363f6e47bb9a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1e/1b/36acd19e86e71cc3f10a096660e7ddd84d9d71ce8d10909c5495341684a8/Shapely-1.0b1.tar.gz#sha256=06893b1533032ee3f7924aeb8f8acf0bdd1a41857c3cef0028ccbda5e94eafb2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0b1\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/3c/fd/17359973d394e18d28199e238706438250e9b3a510c148133f297259292b/Shapely-1.0b2-py2.4.egg#sha256=0a386f0b9e207335c5e81ec13eddc8adc3a7a6678f313a581475bf5089ab4990 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/43/bd/03e59c263ad3ecdd1346e2cac6685ae1f4be8a18707c6e17dfb9a72fa752/Shapely-1.0b2.tar.gz#sha256=fc2ee9633b3d5506d0e5f5c1e07e079497efadeb6c9df1c0842c6992d610e85c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0b2\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/1c/d2/ae5acee4bae2cce6c9f560f5a706c60fa32be70f372698bf6069c1c75983/Shapely-1.0b3-py2.4.egg#sha256=e44f6059cc1cf8df0b9c27cc005e8a00bdbb182b1c7c8509ee153aeb91950161 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d3/a2/a72bdf2a48428f80d78eda9b5c90709d6dc169a51e956cf185a538075809/Shapely-1.0b3.tar.gz#sha256=3e2f1426ebe7bcb2c83b24939597f098d1cb98dadc47c74b0d195f07b2432137 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0b3\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/7f/c6/a118f2367bdc75dd789e0c55f8476b71db53db048c0a9b8a876d149ae16f/Shapely-1.0b4-py2.4.egg#sha256=e0a0b2e4d087e68651ddb9dbdb108e47fcd0253a7d17036671ee88641f992767 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d7/43/04b4ef0c39f0be3832ecc140cfb3b14fe5d967fcbf6c8f74fdded2194093/Shapely-1.0b4.tar.gz#sha256=65d9fe9aa4066dcff1ff9f3b49b49146690d836848cba73317ec666d68f737da (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0b4\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/68/46/716179017060373eec9992737c9737119ec4c0187ccff3b479110d7bc211/Shapely-1.0rc1-py2.4.egg#sha256=5ad9badfecc1d464328ce2b71f1e83490088b397c8eb9faa7be250e7be59d0b4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/11/13/984249041caca7b4f9f3cbc6b725f2e62ebfeae97ee2d05f383c2cf9284f/Shapely-1.0rc1.tar.gz#sha256=33a05265515bc707d1ed7d8c8ca60a1557900e55d566ec958456dff04a8ccad9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0rc1\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/c0/78/3251a67835a802da94e54b6f4f43059adc8037357af2136c67a34716434f/Shapely-1.0rc1.win32.exe#sha256=32e30fda09e7e9e6b4ad14d80575b040bfdfe713f1fdd169ed52f4c7bde21c4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/b4/d2/ecf4eaf6b4bf972fb0e5a6634e0ded6b054445f88f14f7a7bbbccb57eee2/Shapely-1.0rc2-py2.4.egg#sha256=b9ef3ce6330b593b40e80cc27cdaa56a48b354a22901bf61a6fc87f85e362644 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/48/50/7f44945a851d22863dbfb79cff657b6ad554621a4b37bb85a20bcacdf11b/Shapely-1.0rc2.tar.gz#sha256=34870014cc12a219dee70e52bdd031998065e485d68f3ec9324bcd7486c4665c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.0rc2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/54/f9/14fd6138110852706e2fa5d61933263303e3291cfaf3be78c0aa0dc4f7c6/Shapely-1.0rc2.win32.exe#sha256=c1ff4e94a08fd034362da1be969a2f2551c8d0108850b086cb4f556c7eaceea0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a3/02/a32b60e338de0e370dea4ebc17a8530dcc4eb26f1c7f353b08f64f7c044e/Shapely-1.2.1.tar.gz#sha256=810965249eca972edaed5b33ff381aef1e934b73054cd175b918dd8cff92a56b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.1\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/b4/ba/b9c06fa04711e12d504155ea3371e8be314226657d4229c40c488e47376c/Shapely-1.2.1.win-amd64.exe#sha256=8bfaedb30677b369fea4e39ace7919777b6a716a22d2d2911453090cf13ea5ed (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/d5/91/1c453f7a876aead20bb956ff8fb08d798e0ff8bfe49df18b4be753fdf3c9/Shapely-1.2.1.win32.exe#sha256=fd1e9f46943bba1fe565a66e9bc852be5726e9eea88d077196f7bea16e9ed54f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/b3/01/aca38d60b971d15d44cc480db5ddde07345a509a40bb68663fc5e54bd7e4/Shapely-1.2.10-py2.5-win32.egg#sha256=ffa6d3b2dfba2059ddaf3691f6a7894fd66ee66059e0fb0d58d92ebf1600065b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/1a/b9/95a5bd19468fd34747ee56e5782117b264cd2022eecf647d83c5425a8e44/Shapely-1.2.10-py2.6-win-amd64.egg#sha256=0e5a16066630067daf222dc88c804e8175d756a44eb7dff6d861107f2441c7b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/e7/b6/f7d407b6f4948b1293024f000b5ed22b8f0d908caf5c067c2341231691c3/Shapely-1.2.10-py2.6-win32.egg#sha256=b39083fb0cf242f8ec7a9e910e1834759411ae01e8f8a88c4fa9c65808f398eb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/62/d0/a1f82120f5e6046b9af95c30add5ed7ebbf5c6abe9cab3986f7093d0ac3d/Shapely-1.2.10-py2.7-win-amd64.egg#sha256=976ca4a2d1f1dd1304362b2386b972ddc4e8cbeff54934d9cd28872b93bf8a52 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/b9/83/9c4d97a521fd7462c4dc41ba5b428ed321f62a7c29695af4294d8e7dea01/Shapely-1.2.10-py2.7-win32.egg#sha256=f66705f416528530576f20e281e0ded556054974fd288408b3846ec808b4ca2d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link 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https://pypi.tuna.tsinghua.edu.cn/packages/e4/58/81a95c29fca93efff80e7242236d79e097d533b64aafdbd7dda7d82a6990/Shapely-1.2.12.win32-py2.6.exe#sha256=6e67658de866e7311dcb7e63980fd26d0a347f04a19540c33004144d4432c1b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/46/ae/8eb6da30a41031ba5dd2f93c49f5a4bedd72820ad789aa72ae9e32cb61a1/Shapely-1.2.12.win32-py2.7.exe#sha256=2ba70144d7e718f0915b96d680fce6c4e1105168cb9bde8486962cfbc71d657a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/58/d8/0c89399163664a1e9489a5746771c5b923b41237605270fc678ccb0c96aa/Shapely-1.2.13-py2.5-win32.egg#sha256=8c17ed451dd168f5bcac43faa658594bb1f5a9bd17c4b29fb60905ecc56d728d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/ec/9e/75457724aac5421f8b2832c9071677f4214ea37561b2fec130a4b7c64378/Shapely-1.2.13-py2.6-win-amd64.egg#sha256=8566be83c336f4b362e702898db470e0fd2f0071dfc9864771fefd5ea9bdbf1e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/15/ee/23682b2b74219c0514604d48383dfe88a9bff535747cc3724624235bcfe8/Shapely-1.2.13-py2.6-win32.egg#sha256=449c178eb33f5bde75efc75b1164b8595cb991d0df5243f7f2f0a4c2a39ed149 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/89/c9/861c202436e691c506157aed97c1a0360c123226f79878e7f2fe6ac8da6f/Shapely-1.2.13-py2.7-win-amd64.egg#sha256=ce0bf09465cde06c7c69a5486d4daa3fa165e62eaf6f551feeb039e59f88b91f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/66/be/20f55411c930a775c9621923a49aeda5be97df863710aae45b196624f576/Shapely-1.2.13-py2.7-win32.egg#sha256=78b07d65cad4ba291488ad5214550f0e132f50f972ad64016fe7e4300df210b5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/21/45/54a3f69a58388c86c3ad0f7625bbbbf4b2508232017f85246fbdfe8496e5/Shapely-1.2.13.tar.gz#sha256=64310a22d2f1c1d5f85de0f8ac08cf360ce1eac5cd78a84e8e4185cc92128c86 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.13\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/24/b5/78f53cbbe151f92ad7e1e093a3eeb28fd3c658b896c9dc65af8c05e6bd16/Shapely-1.2.13.win-amd64-py2.6.exe#sha256=ae2cdb82bc1244c03c9d1a913fa61056bff47ae25aad0d54064c69168ad394e8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/23/fa/59edda28018ffe3f1d062a6120fd2751b733cefa820ee77388613ddc8d1f/Shapely-1.2.13.win-amd64-py2.7.exe#sha256=e265c8966923ed83e615fccac5a22960223f9a9bdfea3a5fed0ca292cc17d42d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/4f/6e/ae5886558d67b181b0482b4ff94625a63e4d8bcd9ca97375122449214e7d/Shapely-1.2.13.win32-py2.5.exe#sha256=f89a3b320ef3bd09f3e79c8314bc73876d133f278c1c7b7ba139ee71cf3eccb0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/7b/5e/ee0ce4b7146ebcd285cf93ccf50b1d019179ee99a01b302c443f5b03db0d/Shapely-1.2.13.win32-py2.6.exe#sha256=330a3a1466b97088f6ab0310ff80d2bac396fe1e1178fd83b6bea741978412b4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: 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https://pypi.tuna.tsinghua.edu.cn/packages/e3/c1/2f66b52bd2f6214adea55d0704ca894807c35ac23646a710dfd74336f29c/Shapely-1.2.14-py2.6-win32.egg#sha256=7c6d37f9dd5335a2e9bbee66733ebd58ea839a0d4d0690789e6beeaa1b526abc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/31/7c/c3f424b4c412cf87e5b8957fe4c86319b5df5484139fe17e02b847a0108d/Shapely-1.2.14-py2.7-win-amd64.egg#sha256=9e20fc7535d71ace9ea3ff92455a05a4d15624e5962a81248373aa7e74cfe757 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/6c/33/a0b4a616384497c39ef61d912ac9d6286e4ef1f72241c50a68e5642da9cf/Shapely-1.2.14-py2.7-win32.egg#sha256=bf1d664ce7f189bf17aec2b0850339dba12727071b06323351dcce00823815eb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/59/d7/b55171d4baa1ac3a4ca3db03406a48d505bf26816756672dbbbce5b317f8/Shapely-1.2.14.tar.gz#sha256=dc417f9ffafb79de989a8fb1c7a0a10dc5029f085abd831964d7e40c7ea78846 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.14\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/50/11/ed0df42e4810b99d02e2490c93a1b62f03b2641c586c466eeb332e75c9c5/Shapely-1.2.14.win-amd64-py2.6.exe#sha256=5825980ea1201b3ab1a9aa6f4073185d629b9ffde4e5216283955ffb6ec6ebb5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/d7/c5/013a5f3fa2401c18147abac5d2cfdd4050c54e35cf8957a088cbe405c091/Shapely-1.2.14.win-amd64-py2.7.exe#sha256=df9ed4ee9ae27c30a8ea92d5fc50d33f9244ff83a6f9297edf37ad0cff241bca (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: 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https://pypi.tuna.tsinghua.edu.cn/packages/3c/d7/7155b1df080817d3cb5c4c132829c353d6b0d0bb603ea220b6c932de26b7/Shapely-1.2.15.tar.gz#sha256=0e63eadefea1976a098127d82e3cdf046b7cbcc4acf1a09e069d1bc650eddeb5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.15\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/6a/a8/dbe1b513b42b67b2e43aed3a3d9c2012218de8d52abbe09ca655048507d7/Shapely-1.2.16.tar.gz#sha256=06296b4f59c6399a30f86d345d661e005ed4b521a02ee4de0d39a3fc5497adef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.16\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7f/34/81b56cb8bdaf01c8e1b1db392a02191849dac183c97c137c590de5e32c8b/Shapely-1.2.17.tar.gz#sha256=e16828bd52ba8206d4cbf7ac14825cd5b88f0c4857d7da5f37d861eb4cfa91cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.17\n", + " Skipping link: unsupported archive format: .exe: 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https://pypi.tuna.tsinghua.edu.cn/packages/63/00/f2d6141ab433f0e1b1af75334058be7401b4c23cd61789cd117f60823561/Shapely-1.2.18-py2.7-win-amd64.egg#sha256=eb21bb2fc717968356b3390a5a503ab6d5f71547cb74dc905b0e2a8f9abaf0a3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/a6/8e/a18cfc9860f1e2a07b26e59196753339b99b4904f0ef062440093c8227d5/Shapely-1.2.18-py2.7-win32.egg#sha256=a81082c4e9f5e57ed59479adb55cd5b603a034fb1616a58f55ba8d7fb43a5617 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7e/51/d8f73b778a629f1efe700fc86ecaa1d96e1ed25d6ab249940053079a7cb1/Shapely-1.2.18.tar.gz#sha256=b3307b008e1cf96923f0e41a54b39e4ac316cdfa038b26ccbfc47a99cb69573c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.18\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/36/cf/5d56a2a34b37640deb2f2a6cc6af7d768d9166e854d6e32cc6d101d614b6/Shapely-1.2.18.win-amd64-py2.6.exe#sha256=82eb4a104145dc8dc59af03f5fac7a01c583a72415b66fa3460b5b678875253b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/0c/59/bd73445fe318a6d0222a3458f0bafe65fd1bae72bf47186f5482b32b44fc/Shapely-1.2.18.win-amd64-py2.7.exe#sha256=e97cfb2537bc20c2e5168bad92c152c171039e93cfdd2461ffe7e876405f2be7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/65/c8/3b4326b4dbfa0454eabe9ab91d6516cde7960e24efbb385e5d74310296e8/Shapely-1.2.18.win32-py2.6.exe#sha256=b54d467a41e2b49d93aef3e397c70b3f8b89b016533f97066daf3afa7fd1e1db (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/ca/ff/d420b265e041e45e8543d9f2df3a340470ecf6420b535a99adb92e69331b/Shapely-1.2.18.win32-py2.7.exe#sha256=7e8ac06549aa9732ab929e6c54122c374bbf14fb810197be2dd5fb4d9a8b829a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7a/08/f93b0dc41b464682b72ed63013679826a61d800bb0cb76494ed795f05910/Shapely-1.2.19.tar.gz#sha256=1ad866433e3a232330089c9beaa83366d41ec5a3494eed0cebba3734c479d822 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.19\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/6c/ff/762e64ef5507fddde194537f254e868f9f78638bd1efbfc5b71ab9dd82fd/Shapely-1.2.2.tar.gz#sha256=2560d8537d542078e3547d8f0b628f7d0e9c699928c5e9c7b9558158121ae118 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/3b/fb/c0c0badd7d80d95f51190cfb3c95ffd3b458c6f76a48f194dd88eec281ae/Shapely-1.2.2.win-amd64.exe#sha256=259f3ed18f58427b880c7f5197fbc2f618bf7128086e3fdb3058bf357d8b6df4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/18/64/40478c55736369981f9cb89cc90bf1063410c8d232554b95a4effed78c38/Shapely-1.2.2.win32.exe#sha256=ff77f79935cb195a239bd87d4c8f08f54819cd444e19e5b06415f6495071c62b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/49/02/0d1ce74f001ca365bc66604abea891819bca64c8b2bbbd15bf219dc8fc74/Shapely-1.2.3.tar.gz#sha256=b8f0b239a50bb86425fa3b2bf21b1792894d705eb16a0f04741c8b16401598c8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.3\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/55/12/e7f6db17c7cddf23180c5ffd826f298c8e871ed66e55ffb856dc5868e655/Shapely-1.2.3.win-amd64.exe#sha256=1e8607fb3567fd0a28b3473b22bc298732299cb013f9540cf7cd158a4e966cc1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/cc/c0/c0ecfc1e43efd89d1c7d5a6381f68b6e14e402acc340496d268549e43d2a/Shapely-1.2.3.win32.exe#sha256=27b3602d4f81787230485958632d34b10b7f294ab836eb5015696f9d7021e5a7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/4f/79/05a1867173c02aff9d006db428f9343a68028d660985e640f284da577297/Shapely-1.2.4.tar.gz#sha256=a5291c2f82afef20ee8f7c17b37b3a264736aa301e83934e6fba4a069d462f2a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.4\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/c7/99/23f38952cf3f616eaded05892af6ea479d6d1aa877e0b497296429da7cee/Shapely-1.2.5.tar.gz#sha256=ee021b7c0b0e0a084340c26f46de76fbc2d81d9828451a3b2ee37fa75a7500ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.5\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/f2/8e/b3568a2f61726512ce759a45cf66759d8fb68b047b307132a071bc3a08b0/Shapely-1.2.5.win-amd64.exe#sha256=65e059e01f7f298080aef0da3b77d0a73995d92e66fbcc5c0215b1ecac6dd039 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/0e/d3/0af3de05445d47c991e69515ca1494ece4b965ca990c6a06f2d31fe2a898/Shapely-1.2.5.win32.exe#sha256=c37038a3a6b86957c164a8a6faa2cabcd870a71619dceddbb6b0828096f91455 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e3/95/fd58ac7f7b926d518655e70701b2e8ea6d068821a34b068dc5ae9d00cdd5/Shapely-1.2.6.tar.gz#sha256=57fd66356c19e007632d47caf0ff9ce4864482e64a8e720a26d942cad983d6c6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.6\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/a0/63/952e2fa74c0c73c2f519703ac032dc36a0f3a8bbe7ce67a97d1d7ebe031a/Shapely-1.2.6.win-amd64.exe#sha256=5e14a69357cc9a3c275310f1deca29953e0173ca9d03d46140bc0fd2479ea010 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/3b/5f/87cb94dcd4d45116c46bb77b7680852095c97236187534b7871a1443698e/Shapely-1.2.6.win32.exe#sha256=98c8e965f0139056c9f7b28582c430b72c60c1912bb5914b7435e2b48cba3e06 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/93/65/d8511cbb623afe98fc1333e4b146b3bc284cccc014b8e948f38b258b9ee3/Shapely-1.2.7-py2.5-win32.egg#sha256=34fe71f8a8b17895dc9c5aaf85944365cb07b93b673cc7e9a34e01dd2e511edb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/bd/de/e269610503c14da1ac6aa7bdbf9471ec30b3d9e143bebc60ee5cc9e3069d/Shapely-1.2.7-py2.6-win32.egg#sha256=88e58155cb35d7f6bb2d11b1d54d4047ebc9f82f80d7cfeb7218d9d2289c1497 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/f3/0b/703262433622c92c15704581f6542965f57a5fd5d82781fe37ee78e18991/Shapely-1.2.7-py2.7-win32.egg#sha256=73a833c6495006e957a903b6f341e7e7bc29c5918469b5719d3addd4e5a427f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/da/3a/28e88bd71c9bd7f0b29f50971efe915cc041f1d2268eca8de195d78965c0/Shapely-1.2.7.tar.gz#sha256=fa17bae8eae090dcf98e96fb2fc426a4c8da142fbb38a2d8703ed53b524c839a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.7\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/7e/20/ce5497b34a7aa9099fcfa77dce8e0625db9d77babb10196c983399ea14de/Shapely-1.2.7.win-amd64.exe#sha256=2df2f9194d4df3611d704304e1e5796b5621669fa6a1053aea53832059defed9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/4c/53/7b3dafca771a51c89096646553cd2abdf326a581bdd897ce06e84db7f338/Shapely-1.2.7.win32.exe#sha256=7a5a86bd88109a06ccefe3d4fa3d1eacd824edad8f4c15dd1cad9b28e489e083 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/7d/6f/9184dfe384160fc4457a7b6c8a145ef38f7f40021198284eb73d29063fa6/Shapely-1.2.8-py2.5-win32.egg#sha256=a0ce6910781248d0cc3121f45b38835d836302ed379691e003dc50f314540b1d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/5d/6b/13610d28b6c02019523b85f3a6d710cd04d9810f7bf1f53f607b0857d237/Shapely-1.2.8-py2.6-win-amd64.egg#sha256=3cbcf79bf97c235840dd08c4f7a4eda9272fa556a381bb10b415caa1092a2dab (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/05/f9/c33f003c9c6050f1e3917bf0972ecf13510c2282ba652c6a884d76e26602/Shapely-1.2.8-py2.6-win32.egg#sha256=14195687b97efa9176c21983ea1ad15399affbdb259efc6303362257f05d5d19 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/32/b6/e07e33a6c0213f008480c29084bb1dd95af8911bb9b93a6d403b2c34c92f/Shapely-1.2.8-py2.7-win-amd64.egg#sha256=9c6e17f30218b8d2cc8405a8cb9019edbf68d812c9bc1c10808050d940ecb0b5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/e1/ad/a57f83b28eb403cff7cf37ac30e1a9034d83ad8503a2ed1e16656a931efc/Shapely-1.2.8-py2.7-win32.egg#sha256=477296ca782fc09fb4e28a39bdeac16ccf767d49ec68cabc8cb799d0e324ff80 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d3/4a/3222f64252d384a0ca41463b8ac266d9ace6562d67bbc0975004cb4ff735/Shapely-1.2.8.tar.gz#sha256=04a693118c46df72a884e3712bda6ccbcd1bb7d2e4f83b5315992c54304833ab (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.8\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/68/f8/97489fbe8f6d6979cd282466c1b9a65034db37e60d099ffea7bb33c3c8af/Shapely-1.2.8.win-amd64.exe#sha256=5d6e6b92dc1e392ae096fcced3f1e56d972a6418d27b42608083e4f554cee7ff (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/be/47/c0446f97e8e9dbce5950d7f81b3090a3403f4b4c32c62f51132f6cd315d0/Shapely-1.2.8.win32.exe#sha256=a8eeb43d88ddddf0590ea0253a9fa60e9299835a7e0c81dfa14bbfa5155900fd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/f9/be/aeb2bd40ee23b213816945894c2a12bf11cce3032faf111d3a62fcb71d8a/Shapely-1.2.9-py2.5-win32.egg#sha256=e6439bdf30595a0a14f46b4c482859436530323031d34d81e539235ea0b4538f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/6e/ea/c0bf6f7cf122b40ab6339eaaa5f08a85080d524b80e38f71dbf8c72e251c/Shapely-1.2.9-py2.6-win-amd64.egg#sha256=aa195e8b18fd1f487f43bd1ce75f951b376ea944bce46602372e1d20d2eb350d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/52/80/a49794ac69d49cd1031dbb517d7679d234a3ba8eac4adfd70a89003a4c0d/Shapely-1.2.9-py2.6-win32.egg#sha256=3bc688237172d65bbe58901e064453037f04b5ae05a74d9ac95253b8b0127b50 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/ff/c8/d0fb178df594a3f8704c8a04250ada8b4b5c7a6b3f68e8c6fa5718ed671e/Shapely-1.2.9-py2.7-win-amd64.egg#sha256=bffb5f0286ae68390bb4c0986d2a5043773a4c31eb5e006228e2f0f83b1cc8df (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .egg: https://pypi.tuna.tsinghua.edu.cn/packages/33/9e/abd848c003622f362d3bad92f6ea356b6c8d47b629c0c007bf355f670e3c/Shapely-1.2.9-py2.7-win32.egg#sha256=8e82ca099418ae061fdf16619f5e5c6f352fe61557738cfff7bb983cb6a4b95f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d3/57/404aa35c0ca2e50dae9b93be4754506cb03226434644a82384f1b2355ffe/Shapely-1.2.9.tar.gz#sha256=e53490a8fbc9f10db6ed55e8f586d2474402e3c01af406e8d7ce000eccea3a0a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2.9\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/7e/c2/a8c3066a881a1cd88236966dc1cc4f088282fa3e4611055ae1f0ffdd69d0/Shapely-1.2.9.win-amd64.exe#sha256=79d278c648770f0945f774f00b9072ea38fc702d1ac86d136465cd9674a2b83a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/92/c5/c5fabcdb17779bbf6b036de5e8b5907ab94d05d1ff804bf7431cee204090/Shapely-1.2.9.win32.exe#sha256=11c7db73cc87466d63cc5ee3a6256103ef56ed4795fd7a4bf7c02164ab83b3de (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/c0/ae/d52b293bbe740763326dc58050f4a5dfef31a3c67f4c3620874ff387c2f7/Shapely-1.2.tar.gz#sha256=6c519abad5bf769e8fa9db1c3cc241ae02f1bec4119cf46fcb65eb4005e0a028 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/98/f6/ca3507396fdd6e8fcdb35fc5bd44549538fed309da02d6e9eeea7ea3fa82/Shapely-1.2.win-amd64.exe#sha256=26a8edfe800fb742d28a4b3f35ad5bcad1fc6c8f10e06e5ba2763d47438e055f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/d8/a6/849eb8f7197515945dd18e44f0ac091883b6d9a2aef032317ffc1ebad0fd/Shapely-1.2.win32.exe#sha256=56b78e356bc5fc73d45c405163d0b779ce08bcff3e5b0969aa57b3d5ca6fd6cc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/92/8f/dffaa96fee14662c0ff5678e46e7161b9ed1445e8c4c2b69b7a1c7903034/Shapely-1.2b1.tar.gz#sha256=d858ff8bd3cf816fc5abaf771a329a538de7efc34ef93f0a575c6b9e31abc2d7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1a/4d/3efd7bbb829339c241120c10f0b5bf572fce77bc431546e613dd5a7918b9/Shapely-1.2b2.tar.gz#sha256=1f5129c22704f4b33ffdcfb507a2d003b11767ecb781de6adfed119c47396206 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/87/80/42a8aa8e6e7b7ab45066fe2ffa74eef9a48fcba8687248fec1ea420ff9ed/Shapely-1.2b3.tar.gz#sha256=cad646287cec4fb2ac1ba3281c4f7e4175cc6dc7a3029bccc98c647e5ddda3a4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b3\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8f/79/af98e7510419385fb44062c666016bc0fab0c2b705f6b459b036dc16ae17/Shapely-1.2b4.tar.gz#sha256=71adfc4e0f297ced6a202af72a82d9886d49ffb1a1708682aa4036053f2f976f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b4\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/0b/20/f955d6017d03fa5060a2cf1311c95eda749c0943df6d00fa507420bb85e4/Shapely-1.2b5.tar.gz#sha256=130b7bb3cd082190ca086544fe2a8483c1b6edebef2e47022564c2b9ab4e8919 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b5\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/09/a7/17d2c96c9f00d28f80d6d25b128c6a7a11ebbbf4a3ddfa2d9ce4bf9abfde/Shapely-1.2b6.tar.gz#sha256=4c80495e927e3b7d29e3f3eee669b931dc57d51e8dd6b56ac7590d8991de8058 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b6\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/88/a6/fa3adb0d5c5b35fd27204d07ba7bfede921a841733b05ae651f1404a9d46/Shapely-1.2b6.win32-py2.4.exe#sha256=74988dccce2d58254132fe2fb392295738e0f558612e499f40084bdd1b410052 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/e9/d3/6b9d1e8cc3ce8f7e8eedb6425d25072a1dbd47ca41b752cd29681fd259aa/Shapely-1.2b6.win32-py2.5.exe#sha256=b2a92081329c3ae202f2b4cc5d710940a7e78b1c202af32469f4f52ecf9d2802 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/2d/d6/355d54c6e28a43a8690ade31f44758f3b6141c1f57cf5d12934e41e4ed0d/Shapely-1.2b6.win32-py2.6.exe#sha256=5ad6221b000fdab61257df838f28e6151ccadb1baab59fe9a99315e104d2c539 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/38/bf/f58fa23f8b7f748a29dc19a7200676d74f2f104fafed36d8551124b9a265/Shapely-1.2b7.tar.gz#sha256=3b18fcb6e61f3bae26ffb25fc440f175cc296f71bd2b9fce3f52483f1df00f84 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2b7\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/98/00/725eb9358fd4c3a4ef96ab633fdf7102a085ffc27b0a49581909f9f1772a/Shapely-1.2b7.win32-py2.5.exe#sha256=0bd0e9a46217fdb8bc389adca90c888cff8a300afe4f82eca0d933441a1a0e04 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/70/f0/f2d6f590d9fb230af802676b56884b383d199b0ba54b32d9aee941263c4b/Shapely-1.2b7.win32-py2.6.exe#sha256=242dbcd2c7b3d0e56c936b076d6c2039b415860817c62ee0e1eb51116f4f73c4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/46/af/a94c926466b689966312e4a7adce8086d2eb7c47a0ad67750e0381d64e59/Shapely-1.2rc1.tar.gz#sha256=841e8a184b81e8789e0c408686865027e4be3226a148447e3637098629f3023c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2rc1\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/4c/41/cfb1771f10c63d4b17f0c2b9fe2754158d563f3a7a263bd0572d893f4714/Shapely-1.2rc1.win32.exe#sha256=cff7bbc41cd5c2e206cebd2adfcf72e44a8318bb6db7f981ba6dc401f247f550 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8c/c6/feb187d456e779ef9dcc77f3418df761046212a39c5dc48c74e5552bfa30/Shapely-1.2rc2.tar.gz#sha256=3eba3a928e62b42eb7c839121cfb82045537f90ba9117fc84f582cb35ea34050 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.2rc2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/b0/40/9af4fb0b732d79110a4d8c179e286b1f966bb674e028b37cd01737f6df0f/Shapely-1.2rc2.win32.exe#sha256=366df88f74a6ccf5c35808de8e9f6f139099942e16da40261e6ea1e5ed1ff0fb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/39/e9/be27410ca360e6b1df146ece940d5fc56631b07b9b2502fcd07734d536b5/Shapely-1.3.0.tar.gz#sha256=297df004aec27e38ed93987e93ff5f8187cd0299394fe8b0b9028566015a9bb5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.3.0\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/8c/64/69130e2c64bf40f00708e4cb6a124be9b6f1b68d62ba75ce63829c26fa18/Shapely-1.3.0.win-amd64-py2.7.exe#sha256=e2be9499f23cf6f99da303404643432a5f7acaac168b866b28069444c62b7361 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/25/98/2551910dae8ef87b5e81af1196e0b653afcdb0016c9b9bd3918bcd14e597/Shapely-1.3.0.win-amd64-py3.3.exe#sha256=f6d7d31f618b4990275c37bd2fd48151f8436a0a87239732f19db35674ccde8f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/db/e7/9a1e9f72924668727073dd07f2988128ae266be5a4bfeddc4144614e9e14/Shapely-1.3.0.win32-py2.7.exe#sha256=a134b0bd558a39dc48cd86c3dd35a4d71e5e33366029a1052d22119d254248d3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/15/1c/68d3b53923cdc74de906a28075c2bd21f5b6c2c52f8cee64f8503fe88ce3/Shapely-1.3.0.win32-py3.3.exe#sha256=65229d8ecbb2004e0359f36d1ab832acb964743a7da0c3a8047e45d07693dbb8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1c/8f/da4292f95921943ac795c3a225dc7646b65ed2f4321f18759cdd185b1707/Shapely-1.3.1.tar.gz#sha256=f458f7033e6bb811893e6c4305b28535fc09a1a10bb0d1f35bd0dc2bd5613a25 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.3.1\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/bc/52/c374fd1d881e1eb5a6cc8168c88fc130d87d17ff21482d6793daee3fc2c3/Shapely-1.3.1.win-amd64-py2.7.exe#sha256=e0422c0c96886c366c2ab33108556838bad588d4df22ad895125ebf70fc84326 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/8f/5d/2ce448d05dbf6febc24fca6e4667817c8b3cd4ef1609526b91dec6367d7b/Shapely-1.3.1.win-amd64-py3.4.exe#sha256=eca69f897b83b9c725bbcca174ecbd48ca1d7547cc778a52c42cdfb7975fa8d0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/ae/c9/3b8fb551d686d1894c4f2f3dfc2608f7607751aad8b1d9a68fef7c4354c1/Shapely-1.3.1.win32-py2.7.exe#sha256=6ac1eda2fb25fb9610e8beba8a816ccbd0161c9a5fdc5c150031add64b50e4cf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/0f/e6/4785603f3a3dea892e379db090e5f1d4de4f594b92c79c0c7bbcf96e7319/Shapely-1.3.1.win32-py3.4.exe#sha256=ca707ed2a18daa05ea721b37974f15b9ba08b8039fa2d95ac2445978b734e26d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/49/6e/80054002de41698b525ce520af0ee0a259df23006d63c7af661939cf526f/Shapely-1.3.2.tar.gz#sha256=9f0de51c71373fcee4962f79f4813d67ea1e711f4394d7636c929a4019c5c2ff (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.3.2\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/27/73/c45395dec8016c898792f81d5e9306719e43760d51fab43017a796ecd476/Shapely-1.3.2.win-amd64-py2.7.exe#sha256=8ae7222b306443bd0d6c64e7705f5e8a00888671c9b156b4283e7f7319b0e157 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/1e/e6/0d9df14143e0552bbc1fc4f516800e0c970aea5ba60460e8e74bbbc73cd5/Shapely-1.3.2.win-amd64-py3.4.exe#sha256=e56cfc461b3b8ea69e3be73dd631e103b6299a267fae933092cb493a61ad177d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/d2/ab/d053767df4001941b43e6f78265d292d97ce52123e1ef54091d567a42bf4/Shapely-1.3.2.win32-py2.7.exe#sha256=f55c043e9b018dbf451fe96fc1a7e25cd274b380b59892eee99b440f738f0e84 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/68/d4/2cdbdffe3c5718e8d510b8b19ac5f5f9192fe7b9d89457f47ea1a28547fb/Shapely-1.3.2.win32-py3.4.exe#sha256=79d693eecaa72777d8e6dde3998023d1bd4431de5869e4ead0113951ec102a60 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/cd/cf1e6e7d39128e041a7acdd55b9c99918dc1717e0d3323d3fa4935193ec3/Shapely-1.3.3-cp27-none-macosx_10_9_x86_64.whl#sha256=46b7adf8154ebb67f5586d51db644bd253b42a182aba022034185c175d12f0fd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/16/fb7ffea010536619144c4a98fb1788dd53b9e32969c450c3b4064b372a28/Shapely-1.3.3-cp27-none-win32.whl#sha256=7830c6b5113efb7f0e46b9db6ab5cea9c585eba50e77ef42457532efa67c62e2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ce/da/d29b3ad71988075826cd1e6495f75e7c1dcf12e2327906e78aaf639e2ff1/Shapely-1.3.3-cp27-none-win_amd64.whl#sha256=9bcaa2cb09b4dcbf38349d27c745ebeb6b25e3c46cf0c0cc7142743bdb98fa14 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/23/f1/ebe2036669c3cb1f4240f1165337a1f691ac368c47193292f9bf74bbd02f/Shapely-1.3.3-cp34-cp34m-macosx_10_9_x86_64.whl#sha256=758467e38a02592698bbbe5cced3d382e6ef7eecef8dc0fafd6e532400df70c4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8f/a2/06856b0870bb45b0ed440d6c212b81192628f7743243f28be6a78d2af7a3/Shapely-1.3.3-cp34-none-win32.whl#sha256=0a3d601e0ede8db7676f7f1c3867e7a4636da25625e2551779403af34d0efd65 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/da/7b1f74b2e82c44accf1aff86f6bd6d08643fec387a7504e320980a0f53b8/Shapely-1.3.3-cp34-none-win_amd64.whl#sha256=389752f6b85089525b4da90a1c1b5154d836dc91f3b2a9ed140c2cfa040c9fda (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/24/3b/da89d18aea5f7dcabd5bdef8b46cea1df8c0022d5183b4c16476af66308d/Shapely-1.3.3.tar.gz#sha256=c4fbfadfa56f4e441af6220c16b92c8140cf7651b2f8848f0984e168914ed4d8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.3.3\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/79/41/c9d4dba302158c06b6ccf2533dfdf09c69a748cd8274dc2d1bbac388c1dc/Shapely-1.3.3.win-amd64-py2.7.exe#sha256=75cfeabf6f10bcbe37207dd22c7a135e027f8dd3b07e5e12e39a35e845eb9133 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/ed/41/29dc2ca130f5e62e93ab70afaaf15d34a045e7bfd1ce489069e5f021a1e8/Shapely-1.3.3.win-amd64-py3.4.exe#sha256=23a7cd7e85d82aa69c7fae695cb095259270ff0befbb96f1d6c77fbabd78f997 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/28/4b/05ed2c6f0e18f27394181fa0826fc513fd50aee31e2385b36a7778596d39/Shapely-1.3.3.win32-py2.7.exe#sha256=f330054f54a88d3792fb357011ccd254be3c290e3e49ea2793e59eba148d986e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/b5/88/7946571c3c04c84b17e5227d766cf5e1cc23fecbf98160e9cf48a034bcb5/Shapely-1.3.3.win32-py3.4.exe#sha256=fcf6adc335654463ee9bba3b29c12b465abfb95cebec6e1913c3bad097d01186 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/44/0b/f277f90782dbc7873bf615c1384aca80636898f8f3bca75281c35ae726ba/Shapely-1.4.0-cp27-none-macosx_10_9_x86_64.whl#sha256=38b1837b3a919fe2f6871548897c61503c7a2d2512227d0371ac02bfa32d6fc6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/cf/bb188048b937391806c4b88e61f93cb68f29476bdc63d562ce5d93fa090c/Shapely-1.4.0-cp27-none-win32.whl#sha256=44b0a492e4adac9825b680f82d9fa866b3988170e59ff1a8b60de2d0b4c25142 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e5/f6/2ea6b5609da7b331b09d3bed3681dc51f6d50924323ec0de3c70ed41e992/Shapely-1.4.0-cp27-none-win_amd64.whl#sha256=58a94d0f85dd2c9f7a8514c99f0f327c608c18ae36ffef449205cd572153805a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/40/32/b96fa71ccad5475d1eaafb9402acbb695dc14b0923bb5f88720c78aa7013/Shapely-1.4.0-cp34-none-win32.whl#sha256=b4ee9c3d8012f92caddff36f449bbc1ded2ec4a01dbb32840fd5746c0a563c4d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a1/7b/533be1809701416d90ca1711ff07fdec34d8a36ec2f0f76dbcaf0cd87f4a/Shapely-1.4.0-cp34-none-win_amd64.whl#sha256=8b88fbff2fc948b188010a513813141cf0f6646a255016e699b6b0b64f982aa5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/3c/60/f0e3dec8e8cd6f7126c9678a05b23e301dfefe83aaf259b45c31a2185a0f/Shapely-1.4.0.tar.gz#sha256=b88336d1001b31d6d096008d984f89927db5c6b3a318d22189ea1ff448172b1e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.4.0\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/9b/9f/1a9c26b8e8a2f74fe3bc97c560ce80cecef09291776f7d8e6d8172d8632e/Shapely-1.4.0.win-amd64-py2.7.exe#sha256=370f1e45a662bab5bc6fde3c2c220b999d288c6772c64a33985f4b428c1a3f0c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/73/f4/61ae7814edef3d9711b9ecae3cc4ad76c4be049151dc315525d308f80e5d/Shapely-1.4.0.win-amd64-py3.4.exe#sha256=a4e4ac8c71b0326922d4773814b0b2114b5e74175f5ecf36c9b8135fccbc8771 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/f6/f6/bfdb255e7986368dbe69e6cd95db156511b11bc435821e384680048a9fc4/Shapely-1.4.0.win32-py2.7.exe#sha256=d97b7c54afc562f20bfee47340de25ee83a3e446de868266f4d329104c324792 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: unsupported archive format: .exe: https://pypi.tuna.tsinghua.edu.cn/packages/55/74/5b29cf0ae8ee1453689f9a5e8aef324c8a43d099cf5bf806ba5b7c972a7e/Shapely-1.4.0.win32-py3.4.exe#sha256=1a6d4c80d926eec374fc4991611f9bb1e528f95b04e4f336aa47ec0220206f7d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/09/a5/e8e85bd174533d16d6f7ff1a10f0b9a81dda464541d91a0e7ec80e5644b5/Shapely-1.4.1.tar.gz#sha256=6e9914d1109c9b965cb4b582fe676a0230793fb6ccc5ae5e6ef996bbc000ada5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.4.1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/07/43/f25dbe54da9ec15675afdefbc06be2e5aba931f78c7cfb7f735fe8a93f65/Shapely-1.4.2.tar.gz#sha256=24dbc5b4c496a8c8980e46d1ed1fcd5d7ac1c450cd82ac33c00baacbc06f1fb2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.4.2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/5d/dd/14774f9968b73dcc34fd931d7e21b61db0f9bc2a41ccb2f443d35719d7f5/Shapely-1.4.3.tar.gz#sha256=fb9372170965a2b08fb7bc05613ffdc99980be206adf9d27e24d4da381a7beb2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.4.3\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d9/4c/99e368fe3bb4a59539e7dbcf3bedd175af5213698b13325fe51ddac8e790/Shapely-1.4.4.tar.gz#sha256=1ed727933fc905abcef654f672e90c35236d1f98b4e515fcbe6105dd866f1dd7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.4.4\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e0/03/9077388f24ff6ea1ca31ab69c87cb07ca960eee3600e39a48ea60b9a453e/Shapely-1.5.0.tar.gz#sha256=619a58859d49244ed25da6654f8c972f452f911bb16b91020184b4e1cf4ae71e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.0\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/fa/aa/d25c7a58513cd8ebc5042e90acbe63251009ea1630b3221a1e163ecbee49/Shapely-1.5.1.tar.gz#sha256=f1082bf6a3615ad88bba10fb20301e91c21b6a499d95312f42e61a388938a5e4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.1\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/72/53/851e44050a68c0f282ddf2789c4fcae3cbae0f058e5c677efec669917616/Shapely-1.5.10-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=b4c5283a6e6524184bc4fec100e1f22371bbaa2e41f66423ca110a9d759738fe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/87/61/08186057cc795b44e33bc52cb1fc58cb64d25eb7ab88eb62b455bd62f39f/Shapely-1.5.10-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=556898e81153451fc82eae80b0b8652e723a65289610db92318f04a63c44826e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a5/43/161b5040e745b396918c6465daaf22aaf5820dee802e685c69cd152470f8/Shapely-1.5.10.tar.gz#sha256=878da9a4f9220d7f0394a4d3f5a12defbd9d541f9f1f3e5eb74878edbee42a66 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.10\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c5/5a/5535e0561fbd564e1329dbef76efbb1ec814040b2efa7dad8d6108a50e26/Shapely-1.5.11-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=ccbd97e3c34f8d3099765c4a67ca529719162c0e6d26f03a3eef386873545593 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/89/e3e20c6ab5da6246b3f3cf21bb9e269d1909f68be686fbe2d5d4b9003853/Shapely-1.5.11-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=ef1fce75b60ed9c0665906408d6d7e5432de91d3e76be5646a781c2e9d97068e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/50/00/c2b76fe79a975408b66e6563f7bfda9079ebd63f501fda0cc7af713cdbb1/Shapely-1.5.11.tar.gz#sha256=500d56e4a93fd7024dd75718f48c8a06ff6d121193f5f92f7ee54a9771ae6851 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.11\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/71/eb/8aca3c0066c2e6dd505bdd3c947db30278bc2a5442b936676b02b9dee088/Shapely-1.5.12-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=320fbd0438a83f9fdbd0e77d97567772361923cece7ff8e27e32f03b0c7a7fd8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d4/a8/3348b707c365c49f1d23c76cd483cd5bdadc5e73cba34c83c4d6c01712dc/Shapely-1.5.12-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=a10e67d19c277f323c1ed582be1a3fe4d026eb98ed9d091a0f1b7fb959604a66 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/62/06/ac45106f509001b880dc1e2ade29edbac21078dc36c4f3ceb924bbea79d7/Shapely-1.5.12.tar.gz#sha256=dcb88e52146f29344fe7d05cbc1a50a20ce7d65659ea610b99c944ec622971b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.12\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7a/4f/7ed80e83a7670b57dac58e20df3c4dc44e6eb497cb7ccd349841941e927b/Shapely-1.5.13-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=e911c1ff06a6816ba00c8d9ef4e6a0fd9ccf8794a27a9b5dbdfe95f83199b477 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c3/87/84904053611c965947fc0e0a9dd02bdf2e32f9b4f46164d6e8c254578db7/Shapely-1.5.13-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=351624b5e3a097c13a8b91bdcaf3bad43014a0d4a3c522c15272f8054c55f0fe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/f2/7b3f37d0cc30e47489957d734b9a1d956ccb8cb4d59fdbc96ff8b0f0d704/Shapely-1.5.13-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=4ef6ad7b98a842f5a9db6288f65389a35338a559693f1312cd1146ff20b8dcb3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/ad/f9/4640d50324635fbdc7b109f8ef37de5f04456b89ed175cf2f71ae05efd8f/Shapely-1.5.13.tar.gz#sha256=68f8efb43112e8ef1f7e56e2c9eef64e0cbc1c19528c627696fb07345075a348 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.13\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b7/43/db9618bb1a56d67b7d7f36bf6ec89ce43561923da6a399d0bb707dfc5423/Shapely-1.5.14-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=1d7f44b66b653e0b0e0d195913abc9a4fba37a3187a0bc8c023efe0af01dd4e0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/20/e8/40089f2b5131ed9f34574a2116fb4917659809f098dde0d76d25e4a29c26/Shapely-1.5.14-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=fa114d2a3e7f287ccc53b079dd47ed8e040d78e5a77d9977c607ef4546df3828 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2e/a4/b94392e1de7d6f9ababefe03627e0d7cce946a068c2c89172672805e3ede/Shapely-1.5.14-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=58774fd665ee793210a9fd5ae4645b73a8c615fe6e997f6248e6c04ae51b1fb4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b5/27/bd2d8e73067fb949b02982a93b9edee295b0f4f6bd3c17693fbac8dc21a4/Shapely-1.5.14.tar.gz#sha256=0e2f49f09abc66575c9bfb7908b7a21f402efc581d212416936476b5818f7f56 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.14\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fc/c6/9bbe693a91e37e5ead0331f2bb1321c4f4814727b438ebc0e0e80f5e4b60/Shapely-1.5.15-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=63b0df9511d931abd023da951a2c69a8ae0cb716bbc51e8fd5b9c94f5356c397 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/89/db/d5f54796f08587ffb7f4fb6ed8878dda71464a2b2d08affa0ebdf5d66b86/Shapely-1.5.15-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=de54c6b44195b966200bee2db8a2e6ec1ea32492ded0fe33116bc5a88a96ddb6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/86/22/fcb2cfb0c73a17479e86bfbd69261370716b1d061e245641fe37dec3c54d/Shapely-1.5.15-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=e0a86731c4ddac397cb00fc2f8559b84f9be5edfa571fb1f38d04145376b7832 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/dc/44/84893410afc713e44a82a8f900de3ea63a80fa460beb3f9a9fed2ded0140/Shapely-1.5.15.tar.gz#sha256=e75d9461ad21ab0750b3e582a9af23de47c47c3aef43fa7f7ac0b63bc7ead851 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.15\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7f/94/5fab89ce9286a53925221028328df67295fe7d9167fd7c706d810068e930/Shapely-1.5.16-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=b2e8fdced5f1fb5e1dc8e8478e501ab7a1d6c5da17948cbade4a1c2aa033ec6c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a3/62/565b5a54a5f8fa68e9340ce398c8f6a9714a3554f453e33a185d176c4d40/Shapely-1.5.16-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=dc152fa8c60bd0a6cf6218cc31215fd22297d32158ba13e05e74d0fbae90a0aa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/b3/23b1e78a77be7d9e6fec8c4baa3cfd823ab4f12fb528fc8146cb7d0230c0/Shapely-1.5.16-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=efc26ac3348e3a82bb13cadb8e0826f8cc81f8b92fff2c454cf7d7fc5ecea253 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/f2/0d/80b8cded75b1ba22ef8f3ef5e156e014f6fd3025f74314e1a22bc3687741/Shapely-1.5.16.tar.gz#sha256=29ad6593909e04a337a4c57d1a7fa234e4e02d53e549e3019a2d0789e6c930fd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.16\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d8/46/f1ae09ec27a01c11648f91659fb79c04e00e288ecbfacc432e744695ece5/Shapely-1.5.17-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=287eaf9dbeb91b6c31e7e8deec4502b7b2c09ca9b7a1e3d9cadff1618274429f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5a/06/c5acd5bedcbd3f38e3f851f5a861a708d69a3841b04ba0b77e92b5ececdc/Shapely-1.5.17-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=0a3d8ef71988b47050df043c8d0e0b5da731a777e563a4c85b72489ee1f9b1c0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a0/b7/8213285f9670dd1d71fbb3f1f2910ac0ba4b3208d770ff94428969bac246/Shapely-1.5.17-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=9c2156fe9426e10d3250c164e301a4b66489d6bbcd35d145e32a88ed37037ae7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bc/e8/de9d17c019db9d9a9e2d33306828ab37be5a523dc32437a54e1af954b3a8/Shapely-1.5.17.post1-cp27-cp27m-macosx_10_6_intel.whl#sha256=7617bde4aba2219e4e9b1376bf99417976fe323d31c3baa14d94229d99d8dbcf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/87/18/85929d2091db9792f5048799e3a4d0b5f7b73b9a98b7486043c768a756e9/Shapely-1.5.17.post1-cp27-cp27m-manylinux1_x86_64.whl#sha256=c4392e276a11dcddd8b1622d6826142abf014a899b4126af3b203e468fa079f0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e1/c4/5152744ed5bb80f44ff43c8db1fc98590281b52569c52a58f348d4cf1e61/Shapely-1.5.17.post1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=a8a6935ac880fcbfcfa9c6334f190011558ba0201bec4959c8264a67885d99ce (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/a9/3252ff40b6e8ce6067071bc408b351cbb178780071acae8f7bad8ce79166/Shapely-1.5.17.post1-cp33-cp33m-manylinux1_x86_64.whl#sha256=09175ad6afe4262ae951e6c60261ce7a61936b67155d5cbdef49c0976b1ed271 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/39/c6/8723ee7af1528b1a5d041a2b157aa93dce82be927c2e98b199cae53d5567/Shapely-1.5.17.post1-cp34-cp34m-macosx_10_6_intel.whl#sha256=57377599ec8e602b91d8474b0b20e2693be97f0f2960a2e3729c905faa2928c6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/79/0e/b1b535b6f6378fd06a0e898276cb77a7e00e4b429a99214eb38a32ed939c/Shapely-1.5.17.post1-cp34-cp34m-manylinux1_x86_64.whl#sha256=769482bd2907862497ad3ec672a840634483d832361af3eb7613cdf19291692c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9a/39/f84ce2b5cb4434e4ab256179c486818dd4e908607774922a8535bc61c50b/Shapely-1.5.17.post1-cp35-cp35m-macosx_10_6_intel.whl#sha256=b717b7ec0965117a6fdb5401ca2aa292e6af16af6b31e84479cf2d7f1dee250b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/12/ef/fe206c69468b699adb3ff2e0fa555c69f20456a7a82fe52509148d81f672/Shapely-1.5.17.post1-cp35-cp35m-manylinux1_x86_64.whl#sha256=7abe5ecc0663370ab43e959b3f2a0d36adfbc3535a5ab4ad84949cecf570d448 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8d/12/6079549471d2d5ac2b20274ab3bd7c896658914340d1242080b0a2e08990/Shapely-1.5.17.post1-cp36-cp36m-macosx_10_6_intel.whl#sha256=164931afbb85d74c0d5c9af126f3bdd45a89a6c6203b00b75ba83e9ef529c956 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e6/23/03ea2c965fe5ded97c0dd97c2cd659f1afb5c21f388ec68012d6d981cb7c/Shapely-1.5.17.tar.gz#sha256=31fee47d9208078a19f40a451c0c99c085d16343e66cbd0dd5af0af6f48cfc3a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.17\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/50/85/94db21515f6420cd8f29884f1dc0583f9bd861cc97b7455e7ca2306f6288/Shapely-1.5.2-cp27-none-macosx_10_6_intel.whl#sha256=433f823ee77ffe539f2ffea57fc0fd191a4263790e75f49a6bc9edbe0ce16191 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/e9/96/ffe51273dbf83ebd4c5a1e515ea90d567e6ecbf828a473be3c456d9c29f3/Shapely-1.5.2.tar.gz#sha256=fc1681759f3c2c2fde1b277db9936fefd9a179b5e3cd159f035e97d4df888647 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.2\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b4/bc/004abb54b1826e94f0030e5c946a6b5e92cedcf4030c725b26eff187ddc8/Shapely-1.5.3-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=45fdffb0db04172c8bcffde11433ac3d3b8a77376b3005081cd8305a04c2754a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cc/4f/819b850efba2f0cf845200f582c62661adf8157c4ec56885971ea59e9999/Shapely-1.5.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=941ffb4dc92bca1be5db7792d2a52fb07f42bccc44c19069fff092d583925cc1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a3/1e/e01e55f0659806e296dd1dc688a9a5875ace3edc2713566546c7c7d1e894/Shapely-1.5.3.tar.gz#sha256=4ceec6ed8803e1e93564fc41b6c36a947908ba7108c9f3302375382b5d654acb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.3\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a8/84/28ee5c2e76b6da5ca36a82918a306431d6367aeed996abcac502268def29/Shapely-1.5.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=835c26ff7b832885c1de6b585cbbefffc9fe8062e5ab2af1d4b90d43c2b71fae (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/35/f6/1ae5fad106d8c2916916fe663ee654fc659679c3e001a0af0172cf2c774e/Shapely-1.5.4-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=774394a48ca48d8030771090c0c24b3e5de50d4ba7d74a07dec3028307eb4fd2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/9a/f8/afceb96d6e458ea4822c909ff2cafeb82ac94670a180b04cc61cf4fecb9a/Shapely-1.5.4.tar.gz#sha256=374d7a1cff415288aada4d4e941501ca383bdc8b16ddfe174c3d7ec1e6612644 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.4\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/57/23/da976e45c7c49bfbfc765d064026ed68830af1fab45c83548c36f6b9e488/Shapely-1.5.5-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=50ab618c74af652bebd4c4773338a46f2ace28b07ac66608093b057fc63cf81e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f8/ba/bd9e544ff34ff2f576341c87afa16adbd24e4cdb871ec701f35b43af4aab/Shapely-1.5.5-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=c49883bbd0a076e6dda92065aa07c501e34e4bac519ab859c8f4e5f66e97651b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a4/82/5ff987ee1017ac3c33d63abb30cdd4daef7332ec9097f313d7a9fefef9fc/Shapely-1.5.5.tar.gz#sha256=b97c9ff01deb618b6e184b1e11bbad3c96cf5fafd66cdb7168aaec40a7bcc5d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.5\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ed/d6/758877e201a1e6e6e38d0ff77c7efaa0ef30fc859f329e5c6e3fd16acaf9/Shapely-1.5.6-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=e5437cc6bcbeb8a87a86965d3230db183b483dc3b57a99314d15867e0c355a5c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/55/5a1da6a9b935335f991761bab235660f5937320db6da3f0ed255c6ab0737/Shapely-1.5.6-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=842b628c9270da3b1428dec4bbbd912b93f58668e72dc82e65ea0c198e271f9e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b7/64/737463fa2e2e364df4570070a042b0b12a58ea6bde6bd79e0200b796b170/Shapely-1.5.6.tar.gz#sha256=bd0c1c7b41bf598965b27dcefc6184aae83865c53444889fb89fa2ca8d98b5b2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.6\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f0/62/8e484fa7046047913b942019f13d3a9a56bd33ae03c95a19620a25868e98/Shapely-1.5.7-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=3314b885db6e2d1981941a2863a6ef7ef5677781d73b1698cc171a363f407119 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b6/e0/1819d59c6a95d5125ec22df58308f34cfe28cca1d38dc36469723efc37be/Shapely-1.5.7-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=4e0b4ce5af949cdf6ab17c43dc76e66c0935fbb363ad341627311f3d97d06074 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a6/c8/ce82bcc524ecab2cfc52f549f1c4af8a986bc417a92a0e6a04256b6d8e19/Shapely-1.5.7.tar.gz#sha256=f20e5bd2a6f036c793e902164d63ec169afc34cb0fa489827b8a642619778d1c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.7\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/e0/d7461d511af5e41fc8d7e60b52d256acc778a4b28f08c2734a81476b6381/Shapely-1.5.8-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=f0dd5903a207590912c5eb8d4ccfb7b222d0225763abd43c2f806053a8db005b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/da/0d/9923032b6e93809c6f68c30cac3aeb7e3553b58592785588beb3f5650ff4/Shapely-1.5.8-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=ffa0760895e2d0ae24d4fc2291980d9f61501f3bfc3eb07b05ed190e24a7656d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7a/1a/22773bf859c1a39cccab62245837f341730b9524352de7ed644c5b9b094e/Shapely-1.5.8.tar.gz#sha256=d068876c275a5ecd3e33522ceab9335efb5f79feb4ae172664f6885e105d3d87 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.8\n", + " Skipping link: none of the wheel's tags (cp27-none-macosx_10_10_intel, cp27-none-macosx_10_10_x86_64, cp27-none-macosx_10_6_intel, cp27-none-macosx_10_9_intel, cp27-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ef/5e/ed1df0c66e7b0cbed887c7e888ba882fcd1152048617264f617a5783782c/Shapely-1.5.9-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=51bc4b4fc1b4757f434c844e5d81210a67d224e4c60de61f68200f374cdf47e1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/26/e7/2fb4a2ad58f43949e5486cb841a5e3f49ef2fde07943076cd5fed9ca3002/Shapely-1.5.9-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=2df59af586f8468aa51a6e9b987d50e71e410222029d20fb92422ae09439655d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/61/e5/483bfc99db8ed583f62fd6cacb9f29ad7eb3d6b4b729eff6e49d52ce07ee/Shapely-1.5.9.tar.gz#sha256=51b0eb6434b63226fed742c2fe9730102f7a34a5206b6581853f991768b5cc2e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.5.9\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/74/9aa8bb2fd51ace9e7ae345068e0cd3f809f234ceb56f35640b537c81d7b3/Shapely-1.6.0-cp27-cp27m-manylinux1_x86_64.whl#sha256=f0c678396993af4ed8725b0b13d200e6a02c1d50329670d8b2236ae2140faf0d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/90/2d/7dee74709a2c2edfc49265ae67da57a14ad20d0d5127cb66536d62ee14f9/Shapely-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl#sha256=1eda3c9ed6f221b2e384cba6a7ec6d9115ea8ce03a365c801e7bf5f7d6b576fe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/61/3d/c37efee2659675f5548e20a3d1e5971ec81fa81bc341a8e2b91d1201d2bc/Shapely-1.6.0-cp34-cp34m-manylinux1_x86_64.whl#sha256=c4e1e7f945bddb3711c59ce1eae5c506066c573fe1c0ee27264483db7bf2fdee (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/90/95ba8f485f82163a2ca889617b641a0555918935548e348e431e1275e801/Shapely-1.6.0-cp35-cp35m-manylinux1_x86_64.whl#sha256=88c4b952f2134ac3ff661b4679ee0e0748b41b42f7c9c7dc81072a546ba004bd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2e/e5/74d9f023426f7bfdf08dd745cabc5035e360c4d7c2aed8620211258962a0/Shapely-1.6.0-cp36-cp36m-manylinux1_x86_64.whl#sha256=878dda54c4dbd08fa1e5fb31707ae60d6120faabc1d7eb490be636d5f21bb706 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8b/18/d01334652244d5b9e3964b305ae9ce3e6d747345ec0767c4e0ea482d077a/Shapely-1.6.0.tar.gz#sha256=34d07cb277a5acf9ca5583a5b926678c58fb512d4fb4fcccbf3992e995b7ef60 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.0\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5a/e3/a1bb7f6082143b42f5226fccf74ddd151c51d90d45f9c5a07be7def7c54b/Shapely-1.6.1-cp27-cp27m-manylinux1_x86_64.whl#sha256=e85f51c099f2bf7696e49622270849522a1594e5ce6630df0f81392212daa03d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/c1/60bfc70ef87a362f9063e7199967a6197c3f29d9df16909cb40b2dffe0ac/Shapely-1.6.1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=36769d69e3146d516cbe279bca01e26ad4d0a79ab9ec3c9a694bf9884aff3b3c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/ef/fc0a4c8883976c4d7e339c184646e6be327c39c736f838001031705cfe0f/Shapely-1.6.1-cp34-cp34m-manylinux1_x86_64.whl#sha256=e1c9affb405368c6b9a061b4271beb657862e6753f2344359b683d730c9c65d3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/d1/782ee8be4827fc0d8a6507b597624880bf0f3dd0c48bf869ce5618c23ef7/Shapely-1.6.1-cp35-cp35m-manylinux1_x86_64.whl#sha256=911471f7ce425aa3ea41e9f0aa673abe715284dccec8d963fdf4ea6bd02757b4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/54/46664005f26d9ede91c498596b841a563dc41e75a8e8b01ada36443305ec/Shapely-1.6.1-cp36-cp36m-manylinux1_x86_64.whl#sha256=c8191497cb9465c7b40bd85e5544f226e39be6004e6673e1fdcb4511ce1f2a42 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/97/33/d299b42241bf80fce900c6da3a8f4e40f909e2c936e690f749c2ac2fb05d/Shapely-1.6.1.tar.gz#sha256=5ae137eb95ff615be399a285cf447913f845b0224e03d1167f638867eaccd0c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/57/04/d4a2ea964add23a7ac2a29d9c36f9bf636124530fce9ee362e6bbcc347db/Shapely-1.6.2-cp27-cp27m-manylinux1_x86_64.whl#sha256=e1835c39b4af8735af59dbd2ae8ca85b534737a9663bc74ad6542589be851324 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b3/65/8fec1782409c457af7143817501a50a8d20fb1a4ad5004f2bc71a3397277/Shapely-1.6.2-cp27-cp27mu-manylinux1_x86_64.whl#sha256=53c3dab7a44a60fdb89969bbd50ef98097136a5ce4dae07d1d64fca2c6d8f4aa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f7/cf/0a2d7aab3e49823637a05561962f0aba945514344cddf8cd0b65c1b5689d/Shapely-1.6.2-cp34-cp34m-manylinux1_x86_64.whl#sha256=253d411e178e72721e54066a99310af8895413338d07d29aa532203e96678acd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d5/ca/4e1bdf21f8406901eb607abefb6c0a66f81aa665a43c313cbbbee493ac6f/Shapely-1.6.2-cp35-cp35m-manylinux1_x86_64.whl#sha256=0513590d79ced7dc8a7f7847045aa1e71e79c77b876714142a5c1de8058117c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/89/22/d82e9f5f884971e5491120cefe1bec366d9da7b43ddf0c7fa3aad542befb/Shapely-1.6.2-cp36-cp36m-manylinux1_x86_64.whl#sha256=c42ab21f30c5f193c66faa7c74e389745b5f9de2796c2589f9759819614e808b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/38/7b/ff289af5e07091da4b3927414fe4f3c6ae0efbb0666928b05bcfb2859d7e/Shapely-1.6.2.post1-cp27-cp27m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=3ed74fff92d3af2eda981ce642dafd3fc56b25bea1bb85837fba45477e259a64 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/79/31/f671372a0e819689f18c7177e2a21c56f21853484c8e61ec804723f4c5ed/Shapely-1.6.2.post1-cp27-cp27m-manylinux1_x86_64.whl#sha256=7c414fbefc95469fa605300affddee88afeefc43d1b2a3260f97a4740c868827 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4c/9b/589c0dfaa2cbdf35ab627efeffb8d25317150d4e99f3584f8e7565bacf06/Shapely-1.6.2.post1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=4670ed530652cc33ea0a9b9bce03e10d8da9ad6ea40e660fd42b59e422f06f25 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8f/1f/f206c991a780b8642f8a9892339a0142a8be582444b58ede14017705fc3a/Shapely-1.6.2.post1-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=508d13c91b7f05599a2071f9067cc6df947dedda5086325bf405ca024733caf4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a7/1c/bc51204addb2141fe59701479893e7cc95f17f3bc29c9572fe348af9321c/Shapely-1.6.2.post1-cp34-cp34m-manylinux1_x86_64.whl#sha256=864354ae9b6ebe41c01d800847b194162dc723fcd7d967b426e12fd4742cac13 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/83/38/67f9826a0e6ac8e3dbf9207b1e30f6e74f042e8a4ff0a958fb113ef81d12/Shapely-1.6.2.post1-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=669e0616dc5322c8b32fbcc8c90bcbfeb89d46b53cba2511e5c22408eebd9e54 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/20/91/b1a0f20ce5819afd16d5bd9c582c55070817399171834b46a090552d60af/Shapely-1.6.2.post1-cp35-cp35m-manylinux1_x86_64.whl#sha256=299e4adf7f3fe59f34e1f73dcae263425a857707072e1d73a2fd5c88e8b3c9c8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_intel, cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a1/06/f73966fd138b8a2593a791d2a51fc14591be0b20b11d0be330305e0024b3/Shapely-1.6.2.post1-cp36-cp36m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=f17ec6b620c34d25785bbe8c057bf8e297af8862f834ff44ee7a98c331b9d514 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c3/79/01cf5825c16c03c3e10a47d168df7dead3aac4f14a10ebfffd4b87e82311/Shapely-1.6.2.post1-cp36-cp36m-manylinux1_x86_64.whl#sha256=f8b135098a7f63dcc751da0414b1ecf88a288c2427684e7ea25a1ceee2ed86b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/60/c4/392aff1cb73f20d3cf4bb01a9eeb3eb5db7a167c094d69beca402b9a5846/Shapely-1.6.2.post1.tar.gz#sha256=07fba518e76b3276558f62a5829bdfa476f790cdef752383ccdc8c66b04b0899 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.2.post1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/bf/66/302e857b29457ab683ba8f476a385dca1212b6958ed105c2b195d3f87fdb/Shapely-1.6.2.tar.gz#sha256=0ed0db2ddc10e092de30c2ddae28cc8bd2543601b6af83877b76c3280f2537c6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.2\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cf/20/7c311274274907aa26ac5fdb68716d48e7177e373e2b3f7c5ae481e4c4ce/Shapely-1.6.3-cp27-cp27m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=db8e5e512824c58084092a644a6256b404f5e6f24b48f635ed86bc9c6e6299fa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3a/f5/8dc68a23e2a770fc263727947b8a76d3b0c1d92a637e7bd9087c752696db/Shapely-1.6.3-cp27-cp27m-manylinux1_x86_64.whl#sha256=32e2e45994c3d757fa09e40538aa59d1d070375599ac149ba30d80ce2dd2c5af (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1b/1f/dfbfd3b77e0441040b6d91373f98bd87aa0c80b387ab6e7bc2cdbbb88d14/Shapely-1.6.3-cp27-cp27mu-manylinux1_x86_64.whl#sha256=f0edcea8ae2f3cc17ea5f7bbe466250681498045b6e99bb570e619eb8bbc82f7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ba/0d/42786a8c54cb8961c96700ef252628e35eabdf3a7e94fa997b9c2ca7ba4e/Shapely-1.6.3-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=13ce19e4b7ea872a1deec007e797bf16df9cc5b8df0e7b4c7b5ef8e1efc9209f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9e/03/acc336d42070932cc6a0f08134e987273556b74bf0a7ef7be1e03f350f6a/Shapely-1.6.3-cp34-cp34m-manylinux1_x86_64.whl#sha256=ed3938fdd088bfb2b536d80cea503abcb997f0aece3f13ccfcdb83e78b18e65e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/fb/efbd526321a24d052eff77a93cd9686b790d1314930d75b664d2ffadda05/Shapely-1.6.3-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=3f25d057edef86ece556ef94e2473bfdc17ea32714a0b6c3f77435e8f94bb8b0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/17/a9/cd1f080ad9d6177e5f1bde7ae80ad9806c892e5eae8114a98b82aa6ffa5f/Shapely-1.6.3-cp35-cp35m-manylinux1_x86_64.whl#sha256=d25fe725780029cf9300d637b4b54b1b19f3a752a8cb6d209fc756ea72ebfc86 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_intel, cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/9d/88a1f169f1c79043247c0346643a099a80da79099f3dfd1d2f07da13990d/Shapely-1.6.3-cp36-cp36m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=87d424d35bbc1e92860f9f31849966a4be418839c4ea8e5a26459f72b1f10f5f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/70/89/15016679ee1a327a798aacb3b9de709bfdeef679c10fc3e786202256049c/Shapely-1.6.3-cp36-cp36m-manylinux1_x86_64.whl#sha256=6fb5f62c319381b42e067e5bd6cbb6ad5a69c1f8915158554a8bc9dca6a65f1a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/c0/10/1457c46e20b509108a32a5776141d78d410161dae8ab8da74efe67c530eb/Shapely-1.6.3.tar.gz#sha256=14152f111c7711fc6756fd538ec12fc8cdde7419f869b244922f71f61b2a6c6b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.3\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/05/54/f5477df6b71a584416175348560fbdfaf5aa3b8c4999e753da330a324532/Shapely-1.6.4-cp27-cp27m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=f8befeeef0563f6af22117d0546e69429e8871331f2224f9f3d0b25be99cf2b9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/77/7e/c773a8e29bb9c6bd079001e03c2fd2880a92fde7c36a175aa59ef8d8345c/Shapely-1.6.4-cp27-cp27m-manylinux1_x86_64.whl#sha256=68d90a451c8a0681ac40ab3f2881dd55d9a22a5d8b0bce07c3052b48a2754f40 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/79/6f/6e3cde1856d31ba6c5800d50b5ee8dd1d0274722c1ba4dddac19e443a5ac/Shapely-1.6.4-cp27-cp27mu-manylinux1_x86_64.whl#sha256=af3319aecc99ca919e7ce38db228e6561bf7fd7d6bd878af5a412a3af3d7bbc0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/77/451608115f19213ac761ed3c4759dfe927ecfefffaa414857fc02c1d8c43/Shapely-1.6.4-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=d5328b8d30c8bc2a57660ab060e0995a976b90f6e6d0eb4d67099ffdf953f65d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c5/a2/01b495bb3d8170d8e213fd412a6ca9e33019e2871521e6368445ca197204/Shapely-1.6.4-cp34-cp34m-manylinux1_x86_64.whl#sha256=a8ba01527de33f998513beba60de1a4b42c19ef06c50ec5811879d9e2bdcc8d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/04/9773ab5ee7f94eaada7b70f788061d4226c9255c0d446c7ee928d944dd51/Shapely-1.6.4-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=bd6e3a7ec81b2ae069bf1e2a1db3ac796b39448e533e1d3bb35e25ad05bab374 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9a/70/fbd030a0a1d47fb83b3cfde81f8072e3a0e6e9dbd60ba777b5f2470ea8ac/Shapely-1.6.4-cp35-cp35m-manylinux1_x86_64.whl#sha256=3d5dcc5745f0df07be533e5aa0cac1091779bf93b92e399a1c00ed877ba6b39b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_intel, cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/3a/6bc95ad3b4bf8aac2f602ba3981b0ebecb77587c701f68e2ae82e676bbf2/Shapely-1.6.4-cp36-cp36m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=8250ce31153cc89fd80704f422348ea8a5e727021da763db84e81f5dca428351 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/f2/9f5f3cb42ce722aef3341cbbe7ed54aed6e198c75b5f08fcc1265eb3499d/Shapely-1.6.4-cp36-cp36m-manylinux1_x86_64.whl#sha256=cbff48e1d3115c0831ef64584784f56dbfd94e4f0fcb7e67645f0b5ac7d42d74 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/77/0c39df45039dc362f2017617199bd441895a9c54b28660b51e00d31a903a/Shapely-1.6.4.post1-cp27-cp27m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=13c82c9110e630039163bdcdb9969db4fadd145bcf633fc678fa762b01686e6d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/38/94d973796ef3b480e59b7520b1d2df1e8464d0325f1140ac54e13871b16b/Shapely-1.6.4.post1-cp27-cp27m-manylinux1_x86_64.whl#sha256=cf02668c8633a41782b5244e04d5d0689847ac535cdf2fea66e4cac7dd772895 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a5/cf/0d51a15861d944da8463d8c1aecf39cfc6852ea9ecd092046824e9b237b3/Shapely-1.6.4.post1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=049e7732c58f8ee143899f9efbf714d8e7e3578443883c95614cfd4891d36502 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1b/db/553bac9564e8ea6b015d752b21a7730af9d109fc076c04986e7ae601e2a5/Shapely-1.6.4.post1-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=c1f4d74e4c4a5681e77fbee5a7b0599a91ca39a8e260f2839a9a764382994f7f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ea/99/4b272599ef25c4d25122c1ef91338ed9c5c28ea4e68ed4f5cf6fab03a980/Shapely-1.6.4.post1-cp34-cp34m-manylinux1_x86_64.whl#sha256=41850cbfc79de117e3ccbc025b2eb7ee9170c29e65910003828e560ff076fc0d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7f/95/2e37df96576ca2bcfbc00892910b80b8ce9991c94117ffa83701e2cbf054/Shapely-1.6.4.post1-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=a0db70e3384f1227bc40e22e9d77db3a54e02b54b48cf6b0c22ddfc0b688542d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c3/09/5ef0a1f7057077c0b0715c7a7a906c50933e2480655c1740723cc8cca331/Shapely-1.6.4.post1-cp35-cp35m-manylinux1_x86_64.whl#sha256=592a7821c00ef4e0ae07e952366ad537313ccb7488665dd54b87e6f8eb31ef80 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_intel, cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c8/7f/a47c3899460e8b99130048b398ab6e16945ed7f71df60ec7bf816c74128a/Shapely-1.6.4.post1-cp36-cp36m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=2222eba7f461f4b3f406b569727561f300ecd57e31e01b49ffd327dc4cbda632 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7d/b0/1b678d064ce9f27453ef9f3993750c10fe7847102b5ef0d850b5457b58c3/Shapely-1.6.4.post1-cp36-cp36m-manylinux1_x86_64.whl#sha256=e848bf533f0d6cbc336b4cf01dd1aa7873174ba7304a620baa7d663d1d0ce879 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7d/3c/0f09841db07aabf9cc387662be646f181d07ed196e6f60ce8be5f4a8f0bd/Shapely-1.6.4.post1.tar.gz#sha256=30df7572d311514802df8dc0e229d1660bc4cbdcf027a8281e79c5fc2fcf02f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.4.post1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/73/44/feb112a6fe682bfbdbe707d470fb46a958bb3923c2d4ea74acbbd852936d/Shapely-1.6.4.post2-cp27-cp27m-macosx_10_9_x86_64.whl#sha256=3ca69d4b12e2b05b549465822744b6a3a1095d8488cc27b2728a06d3c07d0eee (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/66/6e/3a0a5a1c39319c9e413847ea3844808a8fd222201202a712f85b917ad257/Shapely-1.6.4.post2-cp27-cp27m-manylinux1_x86_64.whl#sha256=714b6680215554731389a1bbdae4cec61741aa4726921fa2b2b96a6f578a2534 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/d1/b8e1b089a8ddd6df74be583d70373eac55c725c6197c115efbd3c3e1509f/Shapely-1.6.4.post2-cp27-cp27mu-manylinux1_x86_64.whl#sha256=5d22a1a705c2f70f61ccadc696e33d922c1a92e00df8e1d58a6ade14dd7e3b4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b1/60/61bf2fd9c5af72c329b3275b97a24a2c797ef3e0db1e20be5235a87e0267/Shapely-1.6.4.post2-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=34e7c6f41fb27906ccdf2514ee44a5774b90b39a256b6511a6a57d11ffe64999 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/aa/aa/977b46331b6ee860cf156297f6b95ec12ae3ae9ad454c71e577a73dfd4db/Shapely-1.6.4.post2-cp34-cp34m-manylinux1_x86_64.whl#sha256=3e9388f29bd81fcd4fa5c35125e1fbd4975ee36971a87a90c093f032d0e9de24 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f6/28/88276eec5e899eca331664a036686d2711e637c4f791045c168ba8214b96/Shapely-1.6.4.post2-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=0378964902f89b8dbc332e5bdfa08e0bc2f7ab39fecaeb17fbb2a7699a44fe71 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a2/6c/966fa320a88fc685c956af08135855fa84a1589631256abebf73721c26ed/Shapely-1.6.4.post2-cp35-cp35m-manylinux1_x86_64.whl#sha256=523c94403047eb6cacd7fc1863ebef06e26c04d8a4e7f8f182d49cd206fe787e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cf/f8/110690b7c44b9418f573e351084c23d33d782f84416bdb179f1b0f9f401c/Shapely-1.6.4.post2-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=ba58b21b9cf3c33725f7f530febff9ed6a6846f9d0bf8a120fc74683ff919f89 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/38/b6/b53f19062afd49bb5abd049aeed36f13bf8d57ef8f3fa07a5203531a0252/Shapely-1.6.4.post2-cp36-cp36m-manylinux1_x86_64.whl#sha256=ebb4d2bee7fac3f6c891fcdafaa17f72ab9c6480f6d00de0b2dc9a5137dfe342 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/32/9d1cbfdc94864282be7745a7f7679ca16beb12ec5d71efd12a3f6cc0690a/Shapely-1.6.4.post2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=7dfe1528650c3f0dc82f41a74cf4f72018288db9bfb75dcd08f6f04233ec7e78 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/97/36/1af447160f713d72f35dd7e749788367b7a13285c4af2fbd675128aa4e99/Shapely-1.6.4.post2-cp37-cp37m-manylinux1_x86_64.whl#sha256=3ef28e3f20a1c37f5b99ea8cf8dcb58e2f1a8762d65ed2d21fd92bf1d4811182 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.4.post2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a2/fb/7a7af9ef7a35d16fa23b127abee272cfc483ca89029b73e92e93cdf36e6b/Shapely-1.6.4.post2.tar.gz#sha256=c4b87bb61fc3de59fc1f85e71a79b0c709dc68364d9584473697aad4aa13240f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.4.post2\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/55/60/d04f6cf9834125f1d205cd43d14ebcf58d2cbc74d6e702d1ea59b0bbe2dd/Shapely-1.6.4.tar.gz#sha256=b10bc4199cfefcf1c0e5d932eac89369550320ca4bdf40559328d85f1ca4f655 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6.4\n", + " Skipping link: none of the wheel's tags (cp26-cp26m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/73/f445e7b48d1fedf424a8c5306a164aa06c3da67ea07de5bab61d0bec83a7/Shapely-1.6.dev0-cp26-cp26m-manylinux1_x86_64.whl#sha256=d0a7981bcf22342588dde3d0988840278b4b3cbffc3fa49c57f7de674b7c9d53 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp26-cp26mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/6c/80a7c0bbd64e7600032a190b506271d8f2b2d5e21d15ecc69905f6de78f1/Shapely-1.6.dev0-cp26-cp26mu-manylinux1_x86_64.whl#sha256=4bdf648d35458088aad5298d8c3ee948c15c4df6abf0e357a9d8546886c309e4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ca/8d/1e31231313f35873c342fbfc9deed924cdca5fdb0cae1c83fb9fa5f4a2a2/Shapely-1.6.dev0-cp27-cp27m-manylinux1_x86_64.whl#sha256=3f6038e0978b9c3ae867a7b2ff0ba991264dbc7ed8b088556f92ce104d5445f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/f4/ec9b0bbbcdd496ce358d443d8e46c7cede23ac1efa869df9d515d24e169d/Shapely-1.6.dev0-cp27-cp27mu-manylinux1_x86_64.whl#sha256=7345a7148b6e5d31dd522080fab3f1f00dc1165d4a3761fe38cbb0dba9060ab0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/69/6d/89453314dbde2028e15b6e5f795e893b8131ff8ddf822bc397cda59e0bf7/Shapely-1.6.dev0-cp33-cp33m-manylinux1_x86_64.whl#sha256=b9c17bb811431491b051c1cba633e3aef3ff0d73589f0143d96fed80b7b472a2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4d/47/b374799744dd4fcb446d612eb2b9a0383269d7aa3cd56b8726602e4fbf5b/Shapely-1.6.dev0-cp34-cp34m-manylinux1_x86_64.whl#sha256=288baca60a5b7fb374072f447be71f3cc1191d749da101039747b7ac022677c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/4b/52c94467df0b6e3646f74744858f747f0efd4ec92dfd8bc473a369e95dda/Shapely-1.6.dev0-cp35-cp35m-manylinux1_x86_64.whl#sha256=7211fb1e4c5e51db92ad138666a67c9c1e5164fe0ab44f040c087a28a59e041a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp26-cp26m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e3/c3/82f37bb19b86eefe4b2f6128cef98f5a57e3391cdecbf4bc93318de30a5c/Shapely-1.6a1-cp26-cp26m-manylinux1_x86_64.whl#sha256=2e68b8ed1f59edb9377e64aa6da77c8bfa25fb45df93638179d7a1880d87637a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp26-cp26mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/74/ce/f1ae6f39a5bf4e4531fcb88396257d3136c45a5c610fcc3bef3bafa655f4/Shapely-1.6a1-cp26-cp26mu-manylinux1_x86_64.whl#sha256=4f1eb31c25363a85d17f2c1949098406186035a75fd30976cb22e827eb9644bd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/38/7d8af1478ebeb697d691e8346abad7a0b1e14659e219dd0e89c4f53f5fce/Shapely-1.6a1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=ab5cb1aa8c7569e14006b77440eb468479169d1e47a38ad86cbe7a38e1f432de (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d1/ea/db9acbc471d40dbecd5e9f6a1a2be9fcf7daf2fe95ebc24154e621f0229d/Shapely-1.6a1-cp27-cp27m-manylinux1_x86_64.whl#sha256=7d9a315fc159b3a15d4cacaf4ce55a019620ea45be3a435523afc2f3575a6575 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/eb/df/876d3711800fcf0c8505c39542b20c9da3b97e7fc8d6a5de886bca956ab8/Shapely-1.6a1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=c4fefa33a6b4884b0fc2245ba98080d2f7a18516510bb9c2e19f8b460ad44e94 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/9b/b17b38e667584f878662545cbe775ee8c9111acb931bd832f9e1367a4f9c/Shapely-1.6a1-cp33-cp33m-manylinux1_x86_64.whl#sha256=914d66d575dcbd264d5f318aa74727606a55c37db81e72647dcb87e6e1a500e0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a8/4a/e054138173a59d2d48ce235ff16bc9a09be513415ba545d1db9afdfb54b9/Shapely-1.6a1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=ad641c142ac659aac2f77d57fff3abc31dcab8cb511f8594f80d02f0de096b66 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/60/e74581485c97099a8d2706f85ce4e1b8c7f742619d78e8bc2e45852bb38a/Shapely-1.6a1-cp34-cp34m-manylinux1_x86_64.whl#sha256=19669cfd8db6612ae45100064934e1f5ce31a8f74731f8ed00ad27771d85c8b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/de/9c/366bac1db078340af8595976c38238ae7044f300bf26c98340926d60deb3/Shapely-1.6a1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=843c94b2f4d1bcac47b3deb1317a111339d4507d9d8762ecc8d8959799108dfa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4e/8e/d6070f0c157cc3d05898dedda23ed59912508bc1ed992289670e0187cd04/Shapely-1.6a1-cp35-cp35m-manylinux1_x86_64.whl#sha256=153e17997da350c99eb695a09e4245dc5559899c188a0943b22eadf17586ff88 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/83/3b/99d5d0e266c3bf5f361c76ff046a539eaf96a4e9ca1beaef5121dcaf5cdc/Shapely-1.6a1.tar.gz#sha256=db509c7500ac4e48c60000ca1002cc82e51d0ff0dc00d020f236ad9bb4467f98 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6a1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/85/5ce81748b3633431f22f5ee7e86c9b211c095442a135959e3863d212a174/Shapely-1.6a2-cp27-cp27m-macosx_10_6_intel.whl#sha256=9af1de9a7934c5c659cdf065db301d32dcd425d2b9b69c1ca70874aebcd35861 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4b/08/ee47f1478d5a3781ae4db04947f4d12c2facbc2b3e9db3b504fe470d7133/Shapely-1.6a2-cp27-cp27m-manylinux1_x86_64.whl#sha256=2da1660729a95ba7cee8025819c40c14175378a72adb6172b57b6965723bde18 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a3/9f/b222ee7924f1f44207c7e025bb5bb76d6176dbe49ede3e61b23ae3e536e2/Shapely-1.6a2-cp27-cp27mu-manylinux1_x86_64.whl#sha256=39affae698be7bb34465c7d6c66911974b174d56be1596dde0b2212e4df2da2d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/58/50/d03969c06c75a9e88a9cc7da958d8401636e8c80dc972cfc2722bf0ebe59/Shapely-1.6a2-cp33-cp33m-manylinux1_x86_64.whl#sha256=f53edc742d1279f6e8b0777433eb7d6dc17880fbcd1a7332722e96aa2796d50f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/10/0f16eb739e070bf43bc87a4b8b5ddb3d0da650680e40852f88fce9de7eab/Shapely-1.6a2-cp34-cp34m-macosx_10_6_intel.whl#sha256=d1f0ce7ae49aec82e4696b48bcbcdd78a1c51a11d628e78225b5237a6701303f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ca/6b/83264bbdc99c494aac61b2f4cac55551c48f60ef85063467d3303d429d5e/Shapely-1.6a2-cp34-cp34m-manylinux1_x86_64.whl#sha256=1d615797154f96b7d73381e50e3777a27fba473235497b906aba9e206b667272 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/97/78/64f5b6c2c25da0541162469cebdf44b7c8be7e777cf90b7b266fd0b05190/Shapely-1.6a2-cp35-cp35m-macosx_10_6_intel.whl#sha256=b1973d6d0daf7e0ab4c8acbc976b9cb4e51dfd3a2d29349720e560bba14e1003 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7d/d1/b9680ea0f9c6cfe4453e9535e902a792071d3dcc5d4d9f842628dadc552d/Shapely-1.6a2-cp35-cp35m-manylinux1_x86_64.whl#sha256=38ed3801ddeaafa814280cc54215694186b47e99fa0a0ccb56c5f8770ddd5157 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7a/9a/d34408e9a80ba4874f5b48c192c5f9d84441c17b95038b30da19c7581a39/Shapely-1.6a2.tar.gz#sha256=fd224a2d86a718b4210d687f33f782dcc3518cb696d7688d8ec300d02da0cc4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6a2\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/fc/b24f2860e01214d40748c091662c34d077fb19a60377f8c8cfb0aaf70e24/Shapely-1.6b1-cp27-cp27m-macosx_10_6_intel.whl#sha256=870a51ef5aa71d590a5732fc3744358df1032d27f41cc30c157b97aa76e11129 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/90/2c/ef7c812d085a9ccf4a26f39a200cd3c6f636ebe2705586a1a6d8aaf5e464/Shapely-1.6b1-cp27-cp27m-manylinux1_x86_64.whl#sha256=4b28424b1d8b0eaa521db15a33a52c6d5b24336359fc3bec9d78cd4a41d602b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e4/91/b0ab0de971b784508831b4ab211e8c250c452becdc7f8509dc0336cdadbd/Shapely-1.6b1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=e92098baf285df751892f1f94c0f35ecca208dc9a7fca7ecd0e548886e240db7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5c/7b/16b04db71baaa876cbfccfd65ca71c3f8901db9f2a367153f3c818426f04/Shapely-1.6b1-cp33-cp33m-manylinux1_x86_64.whl#sha256=9b7cf3d29714820331ba85af837ab26050fa10bf0db41966a59a0b880b977d2c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/70/05/d7a76e6e7716cc6a6157e68d5a79d1738d323c43dceffd0b42e0c0e68a9f/Shapely-1.6b1-cp34-cp34m-macosx_10_6_intel.whl#sha256=d04827d1fa60d6633a4145152421e0f7e2b2f9ef69bdf92636c9b89c0d7b5ac8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/42/7b/54fb3fa8e3d9ef3c7c13789e1636fad2144e0aca86db4ea2183f7f1fb963/Shapely-1.6b1-cp34-cp34m-manylinux1_x86_64.whl#sha256=b5efe4cbc613b1d6b08a162105ea1a313fd7716e7575c822bb385fc70c2ad14c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/88/33/595a84b6e7df4713892d6397a8f31af5d4806448eedf3410cef18e763060/Shapely-1.6b1-cp35-cp35m-macosx_10_6_intel.whl#sha256=c11979808425e0ab29ff882adb2397a07482642b96c56fc8aba866bad265b1dc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3f/75/d1d2f0b33fc88f943b2f4edc9329c88a4ee31453264947cd9dfad7c75700/Shapely-1.6b1-cp35-cp35m-manylinux1_x86_64.whl#sha256=bbc5a4109e9c07fa107bf0a1bc996188c7ad1c3c721b72537d7f59d804e137bb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/39/36/55d4f8842a93930df9f179986f6cae9087bb994c4d791e449aa3cfb98f20/Shapely-1.6b1.tar.gz#sha256=2b01abcdf67babb964b3accbda49a596c28fda8f85beb0044a94acdc10a66271 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6b1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/af/d505d48c700cd268b639d141a4f90426d4a2195dd2fec80cc5ef21438641/Shapely-1.6b2-cp27-cp27m-macosx_10_6_intel.whl#sha256=d0890f4b6991bff3e41265171c75912c4a585238cd11f9c0c648503dfe044193 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/30/a31aa8efe750b6c8865e0ca02e7755bf481adad0a9ac017d1aa0fc4aed09/Shapely-1.6b2-cp27-cp27m-manylinux1_x86_64.whl#sha256=af15e4cc7c6fda38bbd27a4bbc57788fb9675a99e9a9a3c7d61d1efb9bb4ffea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/55/9e/e25497e73e7324ffb60d7edd9f8cecebbc64301af21384ac63c62b2a003b/Shapely-1.6b2-cp27-cp27mu-manylinux1_x86_64.whl#sha256=6b69d403819e5791fc66cc90dea9ee26ee05986b03201e900ad16bc4524bef1e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b5/a4/44e35ff7baabf7eda8444c699e934cddeda1037f198348a20060481fa73d/Shapely-1.6b2-cp33-cp33m-manylinux1_x86_64.whl#sha256=0dc4d896ad1e95e4af0964fc82f699cdf37de2ecb24a8684802cf12afe243ff3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/05/25/80696d4e1c0e3f7596cae279b8cea3692e63d23a7a8ede32e0998433127b/Shapely-1.6b2-cp34-cp34m-macosx_10_6_intel.whl#sha256=ed1f2f344124e0c29e9e1a1b0fb96dbdc03c841f5133721bd945d159771808a9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ac/0a/38fd730426fb1734ac8d6ba80b24457fb5626fc7941705f5276424a8ac34/Shapely-1.6b2-cp34-cp34m-manylinux1_x86_64.whl#sha256=6e044ea1d8a2463ccd632cc2374fe1a81a1fe90080b5425b0b0115a15b9abb40 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e2/6e/2d547912eddeaa8f19f8359ec90e594eb3c45317d5b21a6774b12c9eae70/Shapely-1.6b2-cp35-cp35m-macosx_10_6_intel.whl#sha256=9629bfd4d1ac0cc7878ceaefdb0a3b4934675035faef5b27b338cb7efcbbbe7e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/88/82/43c71330b0248627cd7509b8ca4d681ddc97c708ea32bb2d71554a9ee0d7/Shapely-1.6b2-cp35-cp35m-manylinux1_x86_64.whl#sha256=b11dcb9caf5f798bd1116842b435581670c65c468e7d1040d6993f5310fda4b8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/45/35/8997278c9bf25514a781580b39e96f3c0c728d56a0003699caccd1960ab0/Shapely-1.6b2.tar.gz#sha256=ee5bcb012173f1f0188a30eb4322b4df0a1a87abaa587e1a2a91607b6819c431 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6b2\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/14/55/e3688b135baf6fc7ddece230366aaa4ba6287c9fc4428fe38051ade21fc3/Shapely-1.6b3-cp27-cp27m-macosx_10_6_intel.whl#sha256=32b2ac0fe63d134dc7449d674296de1b349ba1fea01a594de07b91633bfd1d80 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b2/16/a9d60d9a94d0137372240cd9f84d1aa7d4bf065bb3999503aeae6f02af82/Shapely-1.6b3-cp27-cp27m-manylinux1_x86_64.whl#sha256=f6af81d6496c698a385656c402d5202e83c5f14e17c89d4d2fcceb671253f814 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/13/8776790847cb84e3daacdd5f47cedf788333d9656d5eceed3e6e56a934bc/Shapely-1.6b3-cp27-cp27mu-manylinux1_x86_64.whl#sha256=e889eefc1dce156e80c9f7faecec39a90ec89433dd71e14af811279a955d1411 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9d/72/c095916ecdb36bbf9cf5be3f69a38104526e409456b73734ca47134d8595/Shapely-1.6b3-cp33-cp33m-manylinux1_x86_64.whl#sha256=e77730d9753a74e08f3556057d02f521aaf39439e31259822a416b4ec5412a88 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4f/8b/0266cbd049e35c4a653e274d4a09ab62cfc912e472969a09e0b2efe2b6d4/Shapely-1.6b3-cp34-cp34m-macosx_10_6_intel.whl#sha256=8df2b2729c08b8bc7c81b8bc73ea6e146664aeaa479d0ab4bd6a7db09226dde2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7c/2e/fa06cf3f12451c9580f38536ee285da149daff7b915750c1fb4c23b50964/Shapely-1.6b3-cp34-cp34m-manylinux1_x86_64.whl#sha256=d0bae158b47e0fe82f6576b6e21e973f991b70933a9ec8ec4d4f7379b32f9a88 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f2/f6/550fb511256bd3b3068038c1408b68cbd4877ab73ae87a14f4b430a185a2/Shapely-1.6b3-cp35-cp35m-macosx_10_6_intel.whl#sha256=77d844956f3471e85c517c6e6af563ffaef8d70e501645546a97242ad1dcc8a5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3a/bc/354341ef60f9dc097022ab2f8bb7f4bb17c8e7635e79a74e6c65a0e71446/Shapely-1.6b3-cp35-cp35m-manylinux1_x86_64.whl#sha256=858ef18029729543853f8e2bf2e212c448a0b0410e6fea2cfb051dc7f5a894ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0d/87/004bfca558bd4fd144e5dfab74f51f56b98fa00ce94dbb2adf42d0fb5559/Shapely-1.6b3-cp36-cp36m-macosx_10_6_intel.whl#sha256=cac42ba8a4e980a5c5f12252c965e9d82e8c675f699565967dfefe157f304fe0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/92/a7/24920a6966edab16b64022ca25c9c45dc209cd767297a556b21cfe639955/Shapely-1.6b3.tar.gz#sha256=ff0f30508a2680041907eca657da01fb0602bcc417b33de5e323eed35fff1e6b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6b3\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d9/69/420a986c5e3b4c78306073f50a78cf13f6f3b7695a7421e3cf7efbabd630/Shapely-1.6b4-cp27-cp27m-macosx_10_6_intel.whl#sha256=e382586fe14478afd7d43e62efd8a7807ea15be97726f8d7ef0f9abdd48c6e02 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/48/99/d260df27c7b6b21a6f6462e952b35aea04ff48fdc18155471e7d3c40f0e7/Shapely-1.6b4-cp27-cp27m-manylinux1_x86_64.whl#sha256=6fabe2f18c461fa727f87edd86c010f51dee76c478c41a2b9100b8c4628abfd6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/67/637b4eaeddf2ccf7b1ea4cffcfdd79fe0fed46660b4d740e8b86b4f1e5c5/Shapely-1.6b4-cp27-cp27mu-manylinux1_x86_64.whl#sha256=f71a793cd4fdc454916a1767a05aa6a5cc65e2c176674e97628b0792652df7ae (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp33-cp33m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3f/af/542f0aa1c031622a375b76a1b99ca39bc4daeaa1313efe9bbbf06dbe5efe/Shapely-1.6b4-cp33-cp33m-manylinux1_x86_64.whl#sha256=f811cadb265f789d4be11e82dda5ba791bd1e2b3fefc7a541351a583160b1e0f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a5/2e/ac1dbbb51a6fa4b164549fed6e7fce754ef18466a6abb8141891d60fdca4/Shapely-1.6b4-cp34-cp34m-macosx_10_6_intel.whl#sha256=0f7e0aa38adee5b5cec9ac0001abe6e6c81c89f843c9ee337408db1f4d4627b5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/3c/65ed90498f56f8446c73748e57c40c7e96ef2a75c4922506df7025191e48/Shapely-1.6b4-cp34-cp34m-manylinux1_x86_64.whl#sha256=b2bfb1f9e878ac86d06d991a47a50c6ad949807915bb4c93b22c576bfa1d3d96 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d6/ef/61adcd63b0676c999108e17911f4b18cff69f13435842895c5574a1ecb05/Shapely-1.6b4-cp35-cp35m-macosx_10_6_intel.whl#sha256=9aac306f0293902735c9a076a3f54c5ce973d8c7a8ba2e18ad26d7aa7648e56a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/80/23c9755642aba38c4759cdfc1d08a0aaa46c606172bbaed98a997fb6b16a/Shapely-1.6b4-cp35-cp35m-manylinux1_x86_64.whl#sha256=8215f111cbe329f92faf8c1ba906a50da20c821216598646f068ad61aee50fb7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d0/a5/7a6410801991a8a67a6c319d41a5ebaee2d18e473a952f0fca2e5e4ee9ed/Shapely-1.6b4-cp36-cp36m-manylinux1_x86_64.whl#sha256=314dfdf61aa4b86f700b7f11e716717cedc192ceede96d720549ed57091d60dd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/9c/51/45a7f07b2149558f38f449c78b72b44935e8fba40a653fdf71e05dbf3484/Shapely-1.6b4.tar.gz#sha256=e46fbd8a5f62b2867ff72cc0eba9a8476c12245b49ec2c3cbd75ee8b256fff72 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6b4\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e5/90/9ea47ae4561b46d10f12505c045090f84877ba271e6bd616739c375f269d/Shapely-1.6b5-cp27-cp27m-manylinux1_x86_64.whl#sha256=6d21d94f2553acc5355afafd5fb457553dd6dda1bb501601ae12cc6c2123b730 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/39/66/473b20508b78661700583e3275986f1dedcb0a7127b1b9fe0a2ba3cd769d/Shapely-1.6b5-cp27-cp27mu-manylinux1_x86_64.whl#sha256=3a6a58a6947521c7baa91e7afe45e276536af1a201dc7d54c8846bba909914fd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/12/bd/2c54b78311d85c5996349960b9fcdd351ebe1cf692ff8df1d6fc3efa7bab/Shapely-1.6b5-cp34-cp34m-manylinux1_x86_64.whl#sha256=46a57b5b21a9c14dcf92ce6a7c7e44541a1e677899f5bf7f3a363f9a789b2e87 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8c/df/cf9506232abc50010bf15944f8e2e0a5a924108c7a634c736bf2741cb3d8/Shapely-1.6b5-cp35-cp35m-manylinux1_x86_64.whl#sha256=e8fed43fc737e161daf88e84f4ea197f54c8ad6e61cd303b00b0c545ab4e68e9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cc/3c/0b4b1670ffbc58f9f3f55d5157f31ded7a8deac49d52f14c91c750fd66ae/Shapely-1.6b5-cp36-cp36m-manylinux1_x86_64.whl#sha256=b2f3790174356aace3274d666e2c76f636ac67e5edd8d44f11d30be9eb5a2d4a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/88/6d/a20c6975414426e208f777a46a7cde173061dbef165c16b0e3098ce43938/Shapely-1.6b5.tar.gz#sha256=0ebc6a5784098dda95f9bced95c3608f20d37b98d139af21e263a014fc141fde (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.6b5\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/48/1f/d5d9003ab5ef3a44c9c87cbc0622fafd6af545c0de7eb1fc3a8432807f20/Shapely-1.7.0-cp27-cp27m-macosx_10_9_x86_64.whl#sha256=11090bd5b5f11d54e1924a11198226971dab6f392c2e5a3c74514857f764b971 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4f/d8/8fb04cee0dfce158e1842d6203e1893b7540b32985a1a15f718f6715f380/Shapely-1.7.0-cp27-cp27mu-manylinux1_x86_64.whl#sha256=7554b1acd64a34d78189ab2f691bac967e0d9b38a4f345044552f9dcf3f92149 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/58/98/a24402957bf4823e4ef99017282d84daf323f439d1c2a28d468a9d484f6b/Shapely-1.7.0-cp35-cp35m-macosx_10_6_intel.whl#sha256=a6c07b3b87455d107b0e4097889e9aba80a0812abf32a322a133af819b85d68a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/d6/22d6dd3547b1020c5478598c8d6d8fbb15d422cf0a78f54a4c2e1161b6ba/Shapely-1.7.0-cp35-cp35m-manylinux1_x86_64.whl#sha256=b845a97f8366cc4aca197c3b04304cc301d9250518123155732da6a0e0575b49 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/77/eb/940c19719bb304f14db997ee97c2717665d76a716c671510c7098011cadb/Shapely-1.7.0-cp35-cp35m-win32.whl#sha256=50f96eb9993b6d841aac0addb84ea5f9da81c3fa97e1ec67c11964c8bb4fa0a5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ae/b3/7d5f50bf620a6295cc42ad9ae11e37a82a47606ab91e71ab568274b21b88/Shapely-1.7.0-cp35-cp35m-win_amd64.whl#sha256=640e8a82b5f69ccd14e7520dd66d1247cf362096586e663ef9b8098cc0cb272b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f9/e2/7f31b776bb2f49eceb2834a97ecbfdb62ddcfac345428df1c85743ecfbb5/Shapely-1.7.0-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=7e9b01e89712fd988f931721fa36298e06a02eedf87fe7a7fd704d08f74c00f1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/20/fa/c96d3461fda99ed8e82ff0b219ac2c8384694b4e640a611a1a8390ecd415/Shapely-1.7.0-cp36-cp36m-manylinux1_x86_64.whl#sha256=1af407c58e7898a511ad01dc6e7c2099493071d939340553686b27513db6478e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/67/7ddf55fb0a2188176c30c8ef944600c870b111637b26e80d781aab2e3dd0/Shapely-1.7.0-cp36-cp36m-win32.whl#sha256=2a2d37105c1d6d936f829de6c1c4ec8d43484d7b8bae8493bdd4267140dce650 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b8/0b/e5b073f74d8c752e85d11769572f8af134d1d3c9234a8fae0a7c0d1e224a/Shapely-1.7.0-cp36-cp36m-win_amd64.whl#sha256=4acbd566544c33bbc58c7dd264638ff3b91a57d9b162693c37520ea60d13668d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8d/29/1cafd5ec68237611cfabdddc0ca966f3030d7c930db1a61a90cf008456c7/Shapely-1.7.0-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=ae9a2da2b30c0b42029337854f78c71c28d285d254efd5f3be3700d997bfd18e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/82/43/3d04326a1e7c1d7dd8791004443b6b0ef425afda7bb2ce669c6d293e4ba0/Shapely-1.7.0-cp37-cp37m-manylinux1_x86_64.whl#sha256=29be7767a32df19e2186288cee63e539b386a35139524dc22eeceb244d0b092b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7.0\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b6/08/18ad6429aff555428290397c81b70271550f7285b32d495492ec295bf53b/Shapely-1.7.0-cp37-cp37m-win32.whl#sha256=9c62a9f7adceaa3110f2ec359c70dddd1640191609e91029e4d307e63fc8a5af (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ea/55/61a5d274a210585b5d0c3dac81a82952a4baa7903e3642228d7a465fc340/Shapely-1.7.0-cp37-cp37m-win_amd64.whl#sha256=234c5424d61d8b263d6d20045f5f32437819627ca57c1ea0c08368013b49824b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/c8/216a54e5622f662794837c86414398cb283672f74d9b6f7f0c23d4fa4801/Shapely-1.7.0-cp38-cp38-macosx_10_9_x86_64.whl#sha256=cc0fb1851b59473d2fa2f257f1e35740875af3f402c4575b4115028234e6f2eb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/da/93/da69f1f278c02b4dfcf27b33e36bed43d2a6c8213d57b9a21840af4a407a/Shapely-1.7.0-cp38-cp38-manylinux1_x86_64.whl#sha256=2154b9f25c5f13785cb05ce80b2c86e542bc69671193743f29c9f4c791c35db3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/87/f9/cd8c264b4243b8c1d490359a1f11e1de1fc7adba67b3731677a75fa90298/Shapely-1.7.0-cp38-cp38-win32.whl#sha256=f7eb83fb36755edcbeb76fb367104efdf980307536c38ef610cb2e1a321defe0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d5/34/2ba705629c3c7a6afbf3247d044d5e795b100aa5ac9593975edeb8be374c/Shapely-1.7.0-cp38-cp38-win_amd64.whl#sha256=3793b09cbd86fe297193b365cbaf58b2f7d1ddeb273213185b2ddbab360e54ae (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/44/ec/4eddbf9d17a917c51fb4ad159aa7137f506681e91ab559cf87d120e1d78d/Shapely-1.7.0.tar.gz#sha256=e21a9fe1a416463ff11ae037766fe410526c95700b9e545372475d2361cc951e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7.0\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7e/e9/eb2a2e07e2ee9a434c0dde5bb6c5ae78eef82f4aebdc45e2525de96dd775/Shapely-1.7.1-1-cp39-cp39-manylinux1_x86_64.whl#sha256=46da0ea527da9cf9503e66c18bab6981c5556859e518fe71578b47126e54ca93 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/ac/b0fefd3584551ebc2cb337ce14e781ff80583d17abbccf7595b6c9281eeb/Shapely-1.7.1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=4c10f317e379cc404f8fc510cd9982d5d3e7ba13a9cfd39aa251d894c6366798 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f9/5a/d0d3548f1cb698325c44ea8da7fca14c3aea943b5c6199aaaf6f58818a8f/Shapely-1.7.1-cp35-cp35m-macosx_10_6_intel.whl#sha256=17df66e87d0fe0193910aeaa938c99f0b04f67b430edb8adae01e7be557b141b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cf/00/4ccaedf7c2805c93aef21116477283b3beb9b6c5a761b08447d51c9f3e0f/Shapely-1.7.1-cp35-cp35m-manylinux1_x86_64.whl#sha256=da38ed3d65b8091447dc3717e5218cc336d20303b77b0634b261bc5c1aa2bae8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c1/98/c6b8846b9e452d355f2bfe342b140c2d374adef912b21f37e7504bbae324/Shapely-1.7.1-cp35-cp35m-win32.whl#sha256=8e7659dd994792a0aad8fb80439f59055a21163e236faf2f9823beb63a380e19 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8c/0f/697f75dcf382293d5cfcbbe15c28d81085446d3c20c0a707d0f42bc5ed04/Shapely-1.7.1-cp35-cp35m-win_amd64.whl#sha256=791477edb422692e7dc351c5ed6530eb0e949a31b45569946619a0d9cd5f53cb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/1d/4e487dd6c37efb01d107e0bbc48deed299348cf3b1a98ff110c2af923b3c/Shapely-1.7.1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=e3afccf0437edc108eef1e2bb9cc4c7073e7705924eb4cd0bf7715cd1ef0ce1b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9d/18/557d4f55453fe00f59807b111cc7b39ce53594e13ada88e16738fb4ff7fb/Shapely-1.7.1-cp36-cp36m-manylinux1_x86_64.whl#sha256=8f15b6ce67dcc05b61f19c689b60f3fe58550ba994290ff8332f711f5aaa9840 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bf/3b/2bfa648c733fc233aed12893b20eee2e81c3b8361c34637855667afe5c4a/Shapely-1.7.1-cp36-cp36m-win32.whl#sha256=60e5b2282619249dbe8dc5266d781cc7d7fb1b27fa49f8241f2167672ad26719 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0a/e2/1b7aba5ce9804998581efeab455d8a1a902f992f20d2e15dd4ab0a7db4db/Shapely-1.7.1-cp36-cp36m-win_amd64.whl#sha256=de618e67b64a51a0768d26a9963ecd7d338a2cf6e9e7582d2385f88ad005b3d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/21/23/c7e6882fbb9239d54b6794a7f1a78125a53f172751bdbdaf17463101f464/Shapely-1.7.1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=182716ffb500d114b5d1b75d7fd9d14b7d3414cef3c38c0490534cc9ce20981a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/98/f8/db4d3426a1aba9d5dfcc83ed5a3e2935d2b1deb73d350642931791a61c37/Shapely-1.7.1-cp37-cp37m-manylinux1_x86_64.whl#sha256=4f3c59f6dbf86a9fc293546de492f5e07344e045f9333f3a753f2dda903c45d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7.1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a2/ac/3f05fb517e1b6a5f0770d71572c09f748bbe4b39bebcb09d91c5141ba02c/Shapely-1.7.1-cp37-cp37m-win32.whl#sha256=6871acba8fbe744efa4f9f34e726d070bfbf9bffb356a8f6d64557846324232b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/00/b6/71bd30edeab05060e8fbfb16bf2a074364b037615cd823945e0052d26484/Shapely-1.7.1-cp37-cp37m-win_amd64.whl#sha256=35be1c5d869966569d3dfd4ec31832d7c780e9df760e1fe52131105685941891 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/76/79/a7bffb8eadfdfc98a7d87746e0e4e1043f3c43207e11888154236f8f76ca/Shapely-1.7.1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=052eb5b9ba756808a7825e8a8020fb146ec489dd5c919e7d139014775411e688 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/a0/7c1d7398f8c176b8d50b70de2608b7128611c1dc15ae5c627a0fda225eb0/Shapely-1.7.1-cp38-cp38-manylinux1_x86_64.whl#sha256=90a3e2ae0d6d7d50ff2370ba168fbd416a53e7d8448410758c5d6a5920646c1d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ab/f4/9938e6d19fdb8ece7be7f6b155e4e46dd24a2e2c43bf34d67bebeb919b2f/Shapely-1.7.1-cp38-cp38-win32.whl#sha256=a3774516c8a83abfd1ddffb8b6ec1b0935d7fe6ea0ff5c31a18bfdae567b4eba (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/7a/3b196a683a641cfa4fe741ad57b60a98c382ad3c72815947448ae00fe32d/Shapely-1.7.1-cp38-cp38-win_amd64.whl#sha256=6593026cd3f5daaea12bcc51ae5c979318070fefee210e7990cb8ac2364e79a1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/62/69cca822e9ecf3f5eebae2105bccc04ed1818508df4f2b93f8464492f155/Shapely-1.7.1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=617bf046a6861d7c6b44d2d9cb9e2311548638e684c2cd071d8945f24a926263 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e5/52/3e2e11453b6b98cb78883aac249bf497a20d9e0c1c71ecf2f98165def72d/Shapely-1.7.1-cp39-cp39-manylinux1_x86_64.whl#sha256=b40cc7bb089ae4aa9ddba1db900b4cd1bce3925d2a4b5837b639e49de054784f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/27/4a/9b8c5a0bc6da4f1b8573a82640ea045a90774d089a7f4d666649d56450e2/Shapely-1.7.1-cp39-cp39-win32.whl#sha256=2df5260d0f2983309776cb41bfa85c464ec07018d88c0ecfca23d40bfadae2f1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f7/4c/bcbbe5d6fe5b7b12669b0c6e74479939f793531a08fe20e8eeddd5544986/Shapely-1.7.1-cp39-cp39-win_amd64.whl#sha256=a5c3a50d823c192f32615a2a6920e8c046b09e07a58eba220407335a9cd2e8ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/42/f3/0e1bc2c4f15e05e30c6b99322b9ddaa2babb3f43bc7df2698efdc1553439/Shapely-1.7.1.tar.gz#sha256=1641724c1055459a7e2b8bbe47ba25bdc89554582e62aec23cb3f3ca25f9b129 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7.1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/33/08/cce3aced9f939a805b341600563e037381275d82e87222a2ab433c53e56a/Shapely-1.7a1-cp27-cp27m-macosx_10_9_x86_64.whl#sha256=56b8184ef9cf2e2e1dd09ccfe341028af08ea57254524c9458e7f115655385af (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d8/c3/3753cbb6bfa3245174ccc913a47fe96faba68e95789956e8b7620727dfbb/Shapely-1.7a1-cp27-cp27m-manylinux1_x86_64.whl#sha256=ffe14cf22da9c95aa87a287ddb96202e3cbb4ec1ec862050d9e4b114307fa206 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/13/ba/dc749f53496976490d7ebdeb6751e974efe12ffa1691b5e11cdd2a470a30/Shapely-1.7a1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=eb4f295b1ff558857d8061ff7716b1e10ec3c24b5b784bccb51dc87e6fd3ad07 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ba/df/81a9fd0c931e945fabfc37ffd60caa757705a1efb66aec54626bcc5175d1/Shapely-1.7a1-cp34-cp34m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=f87c677c0b176827167d1ebad37bba36a9e6baf61f608ff8ef4b9d9ff002c3c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/02/2e/3055d2235a8370e6340f3f2c682a96c2425be15601b54eb14702e8ac53f8/Shapely-1.7a1-cp34-cp34m-manylinux1_x86_64.whl#sha256=7e06705e0a20e10f0ce35b233b32b57f6b77044e58e2ad4023d6e64f6c3719a7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7d/3f/e9a03196eb6250151562d88a632371cac402a151706790616cd4d2f75cbf/Shapely-1.7a1-cp35-cp35m-macosx_10_9_intel.macosx_10_9_x86_64.whl#sha256=e3c3eb85f7d4308ccbfcdd23513bfe201b193673c98400219b9a480b903b3033 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/86/cb/145748158b021de148cea91665b2d6cebce24850a7d7f75754716cbbf855/Shapely-1.7a1-cp35-cp35m-manylinux1_x86_64.whl#sha256=9e45485c49fd9ee81a81be756e648a0c1c125e770e3ed42845350d75a46723ad (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/82/044f9886b03208baeeee392da4e5bbc16aafecbf4e5c5050758161c97c18/Shapely-1.7a1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=7268fd767dc88ef083a528a1e8977a358c7a56cb349aae9e4c36913cfba30857 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/63/9fe78dc96c51af628e6975a40e7110f72905946605ea6317042070c24d85/Shapely-1.7a1-cp36-cp36m-manylinux1_x86_64.whl#sha256=99dc867fe6519c1af1840cceea8bcf5dd1ece077207bdcb19072cdb4fbda8584 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bf/0a/62fe5b41bfce9b14cebcb839e84b47d678da0edf52ae959a48351a0c3a76/Shapely-1.7a1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=045e991636787c22bf3e18b57cdaa200681acc0e5db0720123643909d99ad32b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/24/df/dcbbc24a283a2ab74546cf33cc91cf110ac8c52fd86a981c4ead68f79401/Shapely-1.7a1-cp37-cp37m-manylinux1_x86_64.whl#sha256=937502b7f7bfea39910e30617a30d74ce1b6585895b3d8a2a4602c223a0dd73c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/87/36/9e7c49196c724c853bb4ca92c95155f08559a24f341bb09a864c4c311061/Shapely-1.7a1.tar.gz#sha256=2e8398aacf67cfdfcd64154738c809fea52008afefb4704103f43face369230d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a1\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_10_intel, cp27-cp27m-macosx_10_10_x86_64, cp27-cp27m-macosx_10_6_intel, cp27-cp27m-macosx_10_9_intel, cp27-cp27m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0b/9a/dfd0cc23543131de95dade8f3825b2a28834f8cd7a533d476f391ad0c54b/Shapely-1.7a2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=2482052bf690abf07429ae080ce98992c9ffca61ae9d1625abb75ef1dc537ad0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f7/f6/3b171ed9826bca21bd545cbbe83befcd241b4438f407bd04d16d7ce9346e/Shapely-1.7a2-cp27-cp27mu-manylinux1_x86_64.whl#sha256=0acc128a75085ceb8d06ff1f977c0543b066318588b9efee4c0058969c294ddf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-macosx_10_10_intel, cp34-cp34m-macosx_10_10_x86_64, cp34-cp34m-macosx_10_6_intel, cp34-cp34m-macosx_10_9_intel, cp34-cp34m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9e/be/be5cc187b55fc3cae627aeeb8252c40c9cccbb40f54340876e835875b635/Shapely-1.7a2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=d215b086761c86117e605429452af0875149bf61cbc7e3cc62f490e65aba9600 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp34-cp34m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ef/93/38f731868f37640f3e3f739ca4cb7e22026bc5b13197d63956d1f7383ec8/Shapely-1.7a2-cp34-cp34m-manylinux1_x86_64.whl#sha256=3e2d7a4f273942542faa1984c2f078250eece2a82e70d0c71252f26d3d2cc203 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_10_intel, cp35-cp35m-macosx_10_10_x86_64, cp35-cp35m-macosx_10_6_intel, cp35-cp35m-macosx_10_9_intel, cp35-cp35m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/99/d4/27cd9627b9a09651deb4ba1d31160230f18780eb0f960be1cf3689f6bdcd/Shapely-1.7a2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=61070b237faf24e87ecc15549d8ca00c216273a03a83f1e5a5704a77a16517c5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/34/77/73bb186321cb8e26bf77c9fef8f24bac80de39df5300ec35a8de1034df49/Shapely-1.7a2-cp35-cp35m-manylinux1_x86_64.whl#sha256=673b61161c8e396a569eabf67cbaf63628f13848ea7f7103a260fd19a4da154c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_10_intel, cp36-cp36m-macosx_10_10_x86_64, cp36-cp36m-macosx_10_6_intel, cp36-cp36m-macosx_10_9_intel, cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f1/06/e14724b43e2393a1b16e89c4afbd517467c5eb64de8ed47cb1be7e422805/Shapely-1.7a2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=2a5cd243945e1a9ba4659804c0d626cc59f8434d9d86652a2f00554d7657914e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/98/ac/e3918a9b305a36f8861641cc59cc2af93790f0da6da4df827953ebc22375/Shapely-1.7a2-cp36-cp36m-manylinux1_x86_64.whl#sha256=66aab79043fbf176f09e53c6fd9939d61a23ca97ed010722b95957025437f474 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_10_intel, cp37-cp37m-macosx_10_10_x86_64, cp37-cp37m-macosx_10_6_intel, cp37-cp37m-macosx_10_9_intel, cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7c/7c/8ac1b85758fe1980a4f3887d83986e39da9d4fce2d8f6814485391554284/Shapely-1.7a2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#sha256=d90f957f8643045b24f8953ec6a1f1edfca4baf359338b027e5976f3796746b2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/c8/f8/608eb49aca336d07bb9841abe50693bdb06fc16cde367886a570f37e4e82/Shapely-1.7a2-cp37-cp37m-manylinux1_x86_64.whl#sha256=5e3760ca3614181e0c8ce2f98b371392a86078fba40431c8c3696d36ea4184ec (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a2\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/73/eb/90c3ba3ea31887c1adcd72576c3fee98f4163165882e207bc6c36105a07a/Shapely-1.7a2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=9beb242abc5b24b01cc32cb2029ae848c7fcf25a082fc3e41b9fd64f5e2cbefa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/75/c47461d10cc9257d3aea7ebdfece05a7f711176776a32ec6882f4f7eee61/Shapely-1.7a2-cp38-cp38-manylinux1_x86_64.whl#sha256=967690e8e858223cfb7c1e01b8de5612e65f95694809f5b860228f8b47cb351f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/2d/48/01422c10a798ba34822ae665c2adbfc1fe1681ff1ebd7c92f7c3cfa268a7/Shapely-1.7a2.tar.gz#sha256=911284f46bd7e449ae77ef6e7a97b2f9acca48f683265608fc2e0bf937d864bb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a2\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a9/61/709e59cd38a6fcd983ce6caa542625e973b510ceef4a8c072bb0c89948af/Shapely-1.7a3-cp27-cp27m-macosx_10_6_intel.whl#sha256=efaf0f468d1fefd659acf2d2484a6b075e0b794b265f5b68cb31e0606c8a1b43 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e3/25/2c15ca0ae98daad696ec72f2e6d4989c346292d81e25a360bdc662f06a35/Shapely-1.7a3-cp27-cp27mu-manylinux1_x86_64.whl#sha256=b61bd807d1c97de0b72af98db9354c67474460d7dc5482203438570ff9a263ae (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a3/8c/11f02499eff6fdc15f435f7b4439f02c91d62cc55844eea379bd3101b9c4/Shapely-1.7a3-cp35-cp35m-macosx_10_6_intel.whl#sha256=3822c72eb6f4f89854b67c717dfeefad383b7597fe7d4fef1614ef93c73d57f4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0a/18/ce205e24c1cb3ce3375cd7d714609c4f6da53f2e31481aa5da84343d1105/Shapely-1.7a3-cp35-cp35m-manylinux1_x86_64.whl#sha256=b6059c54e70d757f36af629e7d0286d986ab93875c18cd41bbf02ac6dae98a65 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/94/e6/44313b9276a3e19b2591b1c52400d4d4f8405256984ee7130460e69dd13e/Shapely-1.7a3-cp36-cp36m-macosx_10_6_intel.whl#sha256=612bc75e87b3bbc35bea016b129ca23b4b02c4dc1ee7a1dfd0637d3783a50d08 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9f/09/b5dd34c194d5197758331a56d86662915830c3eaace88c47ac75b090c692/Shapely-1.7a3-cp36-cp36m-manylinux1_x86_64.whl#sha256=52a487f59872e6764c47b0c8319661f50bc2b3845537d80a9cf83002f6e81ca4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/57/bf/640dde37e2649973b117b07ac3f457f01554aa77d69cf46c6458968ddcd2/Shapely-1.7a3-cp37-cp37m-macosx_10_6_intel.whl#sha256=03fcb11fb5ef76a1ed4de8fcbc29a95f2c5ad5cce89180d7c45906f0d87f11ef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/ff/7c/74ff982d4b465b79513dab2cf808e8fc4608785425a3e51a43c1688c4af2/Shapely-1.7a3-cp37-cp37m-manylinux1_x86_64.whl#sha256=dd7fd64b0da18a5eddff1abfa9e39fa1861c6d7ca7e959bdecf0036965084cf3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a3\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ef/af/1bf541d0b33788c1f6db2bfa032fa3a68081823dcfa8a12bba25a5cf9c4f/Shapely-1.7a3-cp38-cp38-macosx_10_9_x86_64.whl#sha256=9e1be3a71e4ce9937469d0129a38461c5e87724bd74474b75430faddc7c646b0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/17/2b/aba011e40a2842ab5b95d98f2bbb84570c99ed25fdd851f557cc6d36429a/Shapely-1.7a3-cp38-cp38-manylinux1_x86_64.whl#sha256=54ceaeacf0302b4211cfdb6671fdc98f32dfd2055cd0b306593f5252d97b914e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/17/60/b0622bb74dd6fd41b9cc82e0ed477075317d09f4e99f008e5b54a400fb03/Shapely-1.7a3.tar.gz#sha256=98ca7fc3a95c5f83bd9d80b84fce3e55def8174dc1a84075d1d67ac989821f51 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7a3\n", + " Skipping link: none of the wheel's tags (cp27-cp27m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/73/72/e0a66f9602f6bf424ceb1e99b26d3591b81bdda9a9a899a8f40b0bba184e/Shapely-1.7b1-cp27-cp27m-macosx_10_6_intel.whl#sha256=d41d17cf3cb927fb702efe5ddd86c74612b852211cb4b73f2f3f46606c50f815 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp27-cp27mu-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8c/9c/9c4c40a2ad40578eaf878ae5f6c942087c11b66823d0f105c8fd5a6ef7d3/Shapely-1.7b1-cp27-cp27mu-manylinux1_x86_64.whl#sha256=66c38bca0fc966730f82cebebbb676b756ad7069f52f5d3d11e98d8d30bd8fc5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/73/f7/c6025737730e07d4ea18b843dfbd665639869de363d79f608b7afef41304/Shapely-1.7b1-cp35-cp35m-macosx_10_6_intel.whl#sha256=318cb97e683418925f08ead1562916705171594daa5e555ea6cb8a9cd77192a5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/2d/225e32eb651147a1b6b27f1104fe183211193df35872015fd2b7e8cde681/Shapely-1.7b1-cp35-cp35m-manylinux1_x86_64.whl#sha256=0ca90e62e6713736e37d1a61050b1d2c78fbb6c6fd6a627d9174827891f8d6f4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/95/ac/5d66f215e869237fef538688887aae663ffb38ba52a631015589b8760c03/Shapely-1.7b1-cp35-cp35m-win32.whl#sha256=249b3f3d1740a657a4dccc98500276036b3beace28d89a952cf0d38fd9c867ce (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp35-cp35m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/be/70f63c4b4276fcb0f3c366de66818c73140344196e2c8920ec5ffeed8cad/Shapely-1.7b1-cp35-cp35m-win_amd64.whl#sha256=c0147e36d6c734b1fb850b79162bf405b24bf07ef02b2bf74b39eacac39a5463 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/b9/4b0d8f12889893a6126a0371e2e00f4e84da8ab25a356da846e2e714e51b/Shapely-1.7b1-cp36-cp36m-macosx_10_6_intel.whl#sha256=615c5726cb305ebbb9a404eb290e59923011755d433ad67a0ecc4d814e3dc159 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/00/114a1f4377608c316794f3fe966c2b8c0f2cdf1c5b43f98d7e7f9a2f47a5/Shapely-1.7b1-cp36-cp36m-manylinux1_x86_64.whl#sha256=e4332463bee6d523b36d96c481e6ea8baff2295142692408980e0f99f06978b3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e4/ab/283444acba4044e1fc5dc73fe6969a40394a3009c605e47b4b32726c19df/Shapely-1.7b1-cp36-cp36m-win32.whl#sha256=71da106057d356c98f68fdadef3b9228b8fa7407e46bcb65e6446f076b41c177 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/33/1c/e379158638c4ee82a37d282c5642bad0f7a6942496378a51ea913d51d0e1/Shapely-1.7b1-cp36-cp36m-win_amd64.whl#sha256=9ce5131366f73afafba2680305f55ca726196a412b26dc9cbd36afaf061e7a8e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_6_intel) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/94/9b9e2ebde9767842430a669fa464ea6087700a4814b92df7080950001e77/Shapely-1.7b1-cp37-cp37m-macosx_10_6_intel.whl#sha256=9f5804e1cc9cc4a0a37ed4f61a76cc447ecabcb94775d2d553662bc239fa7915 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/42/27/18e18b4db9d23152d2011ad46ced2df7d17f15d994c2604fd683808b74e4/Shapely-1.7b1-cp37-cp37m-manylinux1_x86_64.whl#sha256=4fa6645a6f7033dd6b598dd285e8522cb908d603fe9ce1ed397022c5e05b9ab4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7b1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f4/c3/cedf8dc3ecc009710ddfd04ab75d5cef492d36ca47e111d2e711f7eb33c1/Shapely-1.7b1-cp37-cp37m-win32.whl#sha256=218dc92b1b2ae8534ec61715bbe6afb29356832179276fdf62cb1b395bb65b77 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8b/94/15269dfe9bcd515f84fc4326c22f29b1c50af6f6f7fdc6651c098b02441c/Shapely-1.7b1-cp37-cp37m-win_amd64.whl#sha256=4a668fd01b1391f1af07ebc3ca178b48558dfe67dbb4b384b5ac201a9625216c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/6a/f24e6e90d07e82ecc0e16de805c1f2680f3c6e2498869a18abe54516b5fe/Shapely-1.7b1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=1b8083c80eaed3ccc7fc60c0e410ccd9d588b6cde41df206bb38c3f179dfa8d3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/71/83/cff0933c52a5f6f75b90359e50d94fd0491556d81eed2190022bc07ec1d3/Shapely-1.7b1-cp38-cp38-manylinux1_x86_64.whl#sha256=418328d29803467624b9d8b39ee74775d85c21e7d581fde3270aa563d744415b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c5/8f/4c870d83f103884a7466cf610942cf149c6e6f38bb7994c155ee076513c8/Shapely-1.7b1-cp38-cp38-win32.whl#sha256=77004ca76309df060cec8bddce48fea334f8a4e163306df6aa4e75b0edf0edbe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2d/57/063d165d5cf8df1e1c135eac1edbdef5045861dbbbf8e7a479ff0d736600/Shapely-1.7b1-cp38-cp38-win_amd64.whl#sha256=2d2e577afe31ebe73fc9aa1dc23af299b248aca7929ea26051d2540c8386de86 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/81/3a/359fe8686e868093232a15ddd4ce1df2a0b93845489b7924cd92e39d7d5c/Shapely-1.7b1.tar.gz#sha256=369ac0653143c9cfc4436f19f7662780e4044a535cee3c971f5f5f7b64a44685 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/), version: 1.7b1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/39/563e6598e74db4e2ac184e980b9c71cbccc2caaa7e40b3df6e63c863fd57/Shapely-1.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=1c5632cedea6d815b61eb4c264da1c3f24a8ce2ceba2f74e30fba340ca230563 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/24/5e84be7dedaffc2d5a94c1266fc2420813f629500da4d244b6096448a59e/Shapely-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d4ce1f18a0c9bb6b483c73bd7a0eb3a5e90676bcc29b9c27120236e662195c9d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/09/15/d193dd7a4ab0a5e2bebe66ba20ec6adff7823ae8f2853561ed8ca905c9f2/Shapely-1.8.0-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=4e8cdffeec6d0c47ed1eb215ec4e80c024ac05be6ded982061c1e1188034f22f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64, cp36-cp36m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fd/cd/0de59a3ec816f3f57d9c2ab547cc1d5b9f28a1e3acf1805b541ba5701511/Shapely-1.8.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=83d10f8b47a7568fc90063f72da62cda201dc92ecadf80cc00c015babc48e11f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/44/1b/c022bf33611a119cec23e369b5d7164c0370500d79eafe9ef8f16010006f/Shapely-1.8.0-cp36-cp36m-win32.whl#sha256=78b3a46dadd47c27e658d5e8d9006b4b1eb9b7ab947b450059225dcee799a18f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c0/08/19a61bc62349d9bcde304de074b863fc271c9a2a8d6d5cf908c49e3d47ad/Shapely-1.8.0-cp36-cp36m-win_amd64.whl#sha256=0e640d6da59172d679270f0dfd88128b6ae7c57df864a030dd858ff924c307fc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/84/e8/7a9dd29aae5cb6955fcd789caeeec3c9fa88afc4d4db2b1367c80dc63e17/Shapely-1.8.0-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=68bdf463f7a609fbed42bbded18fa74c82a5741251984a5597d070060f4286f4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/ae/20/33ce377bd24d122a4d54e22ae2c445b9b1be8240edb50040b40add950cd9/Shapely-1.8.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=2c3cc87e66cbffd00ce0457c03969b64935752824bf43a1cd61f21cf606997d6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.0\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/4f/09825157d3a5225b97e5e66ee6f09be90f47cea8e75b956cca00aaed1d50/Shapely-1.8.0-cp37-cp37m-win32.whl#sha256=bd84d993a0e8e07f5ebb4c67794d5392fdd23ce59a7ccc121900f2080f57989a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3f/e7/b40214fe8ccf92b59724adbf0684fb5203da2578a49fcfde00d7bb9364f9/Shapely-1.8.0-cp37-cp37m-win_amd64.whl#sha256=796b15a483ac37c2dc757654186d0e064a42fb6f43cb9d1ff65d81cd0c92a84e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/4f/d96da54dadc06b3202df977f3721cc72f9f322f0ec5f15dd0baabd5ab365/Shapely-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl#sha256=622f62d2b2da81dd40841a56db0f78bcf9f9af7a83c7d5f5dc9bcb234aa650ba (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/61/28/bb389be30535f8fad595865fd1de4c488cec08d12af5c4a80afab7af4f82/Shapely-1.8.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=26b43b69dfeb8a8cb27aacf5597134baf12337845c2bacb01809540c20d3d904 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d4/2c/5eaba235b0b8276f8955e913207ba5f9e8d0b12f757107163a628ecf064c/Shapely-1.8.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=cfb9d72d255af1a484e3859f4c9bb737950faf1d16c21d2b949ffc4ba5f46147 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/26/360e334e07a1bf985f0673a31f0f733abea13c0c641781ffa4ada8eabee5/Shapely-1.8.0-cp38-cp38-win32.whl#sha256=f304243b1f4d7bca9b3c9fdeec6565171e1b611fb4a3d6c93efc870c8a75958c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/83/e0/44dd69025120d289484e95480dabb9e84afe27c07d8764423ce70312af58/Shapely-1.8.0-cp38-cp38-win_amd64.whl#sha256=8917a91430126165cfa4bc2b4cf168121e37ff0c8657134e7398c597ca1fe934 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9a/de/384e6c12f37285637f1599c4d7ef9a25741bc15f053eaa5654d658843b7c/Shapely-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl#sha256=19b54cd840883fd71cce98fd94916d1731eed8a32c115eb082b3ed24e631be02 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0c/f6/4c5bbab26f76d88567a420aea60e9d929496d61b90ad07b46683f0fc51be/Shapely-1.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=13cbb959863cec32d48e2cffdc4bb81828bc3b0fa4256c9b2b32edac5021a0e4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f0/b0/7a44fd270e194deb681315ff0f18eb0b28aaadf9c4096b33cd5970837671/Shapely-1.8.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=7e1aebf4f1b2fbef40152fd531216387fcf6fe4ff2d777268381979b63c7c779 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/dc/07/ce87bc7a479a23cc77b7fc2d9ec65eb8e74c506c2dea4287dc4c331bfa40/Shapely-1.8.0-cp39-cp39-win32.whl#sha256=83145eda2e582c2046d1ecc6a0d7dbfe97f492434311124f65ea60f4e87a6b65 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/02/d4/d6a2a6a6f833a9ccfb7962a82d4f8040f2bc52649cec4588c21762656200/Shapely-1.8.0-cp39-cp39-win_amd64.whl#sha256=9b54ebd8fa4b78320f6d87032fe91363c7c1bf0f8d4a30eb93bca6413f787fd5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1c/0c/454c80f71bd5ece52fb06d2905bf956b9122f4be539d5ae5df4b10dd3e14/Shapely-1.8.0.tar.gz#sha256=f5307ee14ba4199f8bbcf6532ca33064661c1433960c432c84f0daa73b47ef9c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.0\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d0/96/20875269a2db25a6be8079a6c4b3775f037c216aceb9b67ad7edb877d476/Shapely-1.8.1-cp310-cp310-macosx_11_0_arm64.whl#sha256=ce175b5c36752dd741d6461f3fe5981b933bceb901b13f2fe343b1177d8f8bbf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fd/0a/a0be45583dba8d03f1b2e8257cf36aa7d6920064634f01536c1be027663e/Shapely-1.8.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=3cd8d178d906238100f73811af12167b7cf84dad43347cc1b212e698bc9456fa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4f/87/370d4f2db2bfd38ea0055f8df77dd2fe58f0631a85adf26057050cf96606/Shapely-1.8.1-cp310-cp310-win_amd64.whl#sha256=1abbc91140828e24341f100ab34023c28e52bd21c821abe3ba110687a8f41353 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8b/f6/560206ac6e7c46c967c0644ba7938077cbb1cc46e72c9ad1946b14e74f99/Shapely-1.8.1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=509fad9b937e0cf6f1a3b5a8ea0d2645a5a4e80075fa701a7314c7bd10901a86 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b2/aa/203f6ec19fcb7016e982bb27e6eb76f7ac4fdd5f221fcef42418cb80b6c7/Shapely-1.8.1-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=72298336132910c6975e21a09e4862cfd1770263c48db7f4c45d438486dac655 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/c1/a522d47beea65e11e658c6b3a4f00da039b064e109782685d97f059d61fb/Shapely-1.8.1-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=cbbcb9d2bf2c446d3998d08a63d00d792c38ae980542e10b2fe71ff18da66719 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8a/e5/c296b68825aea5f7f8ff53468ff8146ea286755c0ae86303e5cdc7f90bb2/Shapely-1.8.1-cp36-cp36m-win32.whl#sha256=31025abab41a999bc05bc0b7b8e2a44d16bc0840df45ecea546a3b7fb4eaaa56 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/55/12/802e890966777d3d1949f2d9d8326244dba3f9c67235e16ff7273b02f4ee/Shapely-1.8.1-cp36-cp36m-win_amd64.whl#sha256=b7f016241884f40adb71eddd0001ddf8809757068c8692a00231297916a45c78 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/e4/da7dc85a41bef471194c95c70c7c5bdb1862fa63f0af833e59f4e89c799f/Shapely-1.8.1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=385023865b6ff0f04fd0fa2957b63f8631117dbf7550c2c3ba9bd0d6e659d182 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a8/dd/a730e1d2017e1d06df7aeed7a1579500f4ca44ca38f0fe659eb6a8885757/Shapely-1.8.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=d29b5e65a7452aab0b35076532cf0561cea43e3544af266731addf5da6fc06f6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/9b/c6/22f2ca71d1cc44e36ccba8b8f2ea98d888d5c27bdcfacbada1128b1b1cc1/Shapely-1.8.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=c14b011f3266db8edacc4700db9f2b6d6d16663253976fa560f0fa8ba86bd37c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/2e/788f6f01714d86b9890aba506c4414c289afd3f034cf8e32c8ca4359a125/Shapely-1.8.1-cp37-cp37m-win32.whl#sha256=f869bb3b2eba23548248ef650ac522a9a9eda1c91b4acf759ecbfb372afe0326 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2d/54/4e6530bc01c5c2e57f55952ba4a87ac7ae4fa165e189e54c279fe398eeb0/Shapely-1.8.1-cp37-cp37m-win_amd64.whl#sha256=f9e9bbd7d333b772d5a1e1f7896b25788845f6baf0747a7966eb130506829644 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/64/bb/4139abd3d8b7ea56ddb5b179c32e0c3abcec7ef26dc0301fa9f2e60c0fd6/Shapely-1.8.1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=4cee7d1a1fa0322114c255f6aaf56cf641f53b658088f78ed344e6e097f9d62b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1f/bb/5e15144e1968c706d43f6a3130b5120230ff17face7c3516ffbc1b49057b/Shapely-1.8.1-cp38-cp38-macosx_11_0_arm64.whl#sha256=f8dd33ec28cc48f2fab49e8222cfa58c6d31f83cff0e8e69507296c1b11ebae8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c2/ae/495847370308204268e2c079e3c7f78e417e6ebc6b3ba6d33147f76a41fb/Shapely-1.8.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=66638d89c31af0dea2ef63c4fe6ce608c8300841bdebaaad5585dcc38c6d3ba2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/80/f8ec2a0143757ecda540cb73e4942830a5620a5975779333516a5fbe22ae/Shapely-1.8.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=7301f0e3a8a0f350d489f5224042c9d49e0c919b98dfbd8faca4baf6c32aa853 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/3e/299896e0b0e7764f95b1207ea82def2d1ef823ceb745427974eb1dcb40f9/Shapely-1.8.1-cp38-cp38-win32.whl#sha256=816777089d3a9ed0150f175a5ea5a6d5643879994f326ed5cf9ab416437640d2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2d/94/57fdf391c6e7197dccb3dfec39d14f85024b28c0b6502ee4908b49a0a4d7/Shapely-1.8.1-cp38-cp38-win_amd64.whl#sha256=58836e57bcc6650b2f9cca1620a1bf41bcccd7481a4b551ecd02589e85693f23 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/4a/d0d715f9cdaa7b61ccc14c72d98676d4381c67f78c9b6be9cfe76b2065c1/Shapely-1.8.1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=cd709c431f8cc0ee7a92c999a3890945b38213deb97a83e3247e25546843381a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/87/a6/35bda75081007dce430a94e8b07bb7726db032d0b0d75f7e5068b8ad42f4/Shapely-1.8.1-cp39-cp39-macosx_11_0_arm64.whl#sha256=bdad1df6319d750002886ef9022c4e16fd310e99d861679bae1c4cb5fda4cf2a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/85/30/2ea4ada50b6056321385cd73fc318dc2ac477ea17c8a17b824a41203a24f/Shapely-1.8.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=228eea8904c2e67a807080e6369fcbb81e16875f7adfc0676fbc07689e673f57 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d4/7e/c7a9960bdf91c6e9582e6447c11f28e35ee63f22749c3c919cd9c515ec34/Shapely-1.8.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=21becf92face52023c967339a4360339b3fd2a81481accb6dd946dbe97875964 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/58/f5/d1d0d83c6c67997f3e02288837d75f4c3e8676c9e94ee7b553e15d8b29d8/Shapely-1.8.1-cp39-cp39-win32.whl#sha256=d71b588d1d8f2489ae8023fbb542d45f77be5d441c4ee7802a3642e3ab064fa2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/3a/fe78da3a4ea87820dd5d05abbde6ed29a7fe5e1d0b33375ea3916fa21727/Shapely-1.8.1-cp39-cp39-win_amd64.whl#sha256=2ce5f14eb108dbb53f16e7820bc0ae08dd5725999e521d69efd3ca1da89d04ad (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/89/1b/26185b606d14d99d38974fb318903ba5a36f3011055d12ed9a86074b3972/Shapely-1.8.1.post1-cp310-cp310-macosx_11_0_arm64.whl#sha256=0ca96a3314b7a38a3bb385531469de1fcf2b2c2979ec2aa4f37b4c70632cf1ad (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8e/ed/103b2b58a5f7ea4c48147d223eb293e9b2e6fbf3fbcef1d5f250f4053f8f/Shapely-1.8.1.post1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=493902923fdd135316161a4ece5294ba3ce81accaa54540d2af3b93f7231143a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fc/d0/12dd05ee7c8b5734c4b3d3fd4ed9fbde4680efeb823b577a4cefeafbc2c1/Shapely-1.8.1.post1-cp310-cp310-win_amd64.whl#sha256=b82fc74d5efb11a71283c4ed69b4e036997cc70db4b73c646207ddf0476ade44 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3b/b6/e198b562f4c9de1d41de2200988fb57e23e052c5590827e137eae0d2d4dc/Shapely-1.8.1.post1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=89bc5f3abc1ccbc7682c2e1664153c4f8f125fa9c24bff4abca48685739d5636 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/97/2d/5c31d430d598c143d1b6352068e5c9bbf62c726f53413628f05f55b21e1f/Shapely-1.8.1.post1-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=44cb895b1710f7559c28d69dfa08cafe4f58cd4b7a87091a55bdf6711ad9ad66 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e4/e3/32411af8b7385fb3fb08c09c3afe1fa581bb71b551abd411e460ffdc3cd3/Shapely-1.8.1.post1-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=437fff3b6274be26ffa3e450de711ee01e436324b5a405952add2146227e3eb5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c7/d5/244f1773fd1f52bf6e40a78d8205f4c0fe23bc22822868d5f6925c995547/Shapely-1.8.1.post1-cp36-cp36m-win32.whl#sha256=dc0f46212f84c57d13189fc33cf61e13eee292704d7652e931e4b51c54b0c73c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1a/7c/30e9207842d79b45bbd9640846142b10c5ccc74db8bf24e39e03639a7de2/Shapely-1.8.1.post1-cp36-cp36m-win_amd64.whl#sha256=9248aad099ecf228fbdd877b0c668823dd83c48798cf04d49a1be75167e3a7ce (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ab/a3/969af2bfa6197979d257630aa2e134e450b661417d9c82a503edeeb5e534/Shapely-1.8.1.post1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=bab5ff7c576588acccd665ecce2a0fe7b47d4ce0398f2d5c1e5b2e27d09398d2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/19/304e2bdf24e1df57d4ce4fb36d572b53b0c8b11d9830d6c0677f6d35e07e/Shapely-1.8.1.post1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=2381ce0aff67d569eb509bcc051264aa5fbdc1fdd54f4c09963d0e09f16a8f1b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/9d/4d/4b0d86ed737acb29c5e627a91449470a9fb914f32640db3f1cb7ba5bc19e/Shapely-1.8.1.post1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=b4d35e72022b2dbf152d476b0362596011c674ff68be9fc8f2e68e71d86502ca (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.1.post1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1a/db/d77bfc1569cbc2d3ba3d239ca28aac4ac1032a1d9ad4f52c21a646e8964b/Shapely-1.8.1.post1-cp37-cp37m-win32.whl#sha256=5a420e7112b55a1587412a5b03ebf59e302ddd354da68516d3721718f6b8a7c5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/62/db/2e97416ef149560ebf7f9f2111da2bd81523cba426b2d658698b5133fd3e/Shapely-1.8.1.post1-cp37-cp37m-win_amd64.whl#sha256=c4c366e18edf91196a399f8f0f046f93516002e6d8af0b57c23e7c7d91944b16 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f9/8a/1c09dacc44d6204446b8e9d7ebe5e12f8c4d50fd8c344def29235b35450e/Shapely-1.8.1.post1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=2020fda37c708d44a613c020cea09e81e476f96866f348afc2601e66c0e71db1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/de/d4/8265a194b6167b7da4cd07d42a307b595f1f6768df6a0ecbda2b547fd239/Shapely-1.8.1.post1-cp38-cp38-macosx_11_0_arm64.whl#sha256=69d5352fb977655c85d2f40a05ae24fc5053cccee77d0a8b1f773e54804e723e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0e/2a/b66983efe4150c13e8f203e85ea30cd8220d44e1aa3550702a6fb8508b83/Shapely-1.8.1.post1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=83f3c8191d30ae0e3dd557434c48ca591d75342d5a3f42fc5148ec42796be624 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/a4/1ff0bdd1dcfa05c899e77324ecf91df1fb4d49b92deaaf4818ab354b752c/Shapely-1.8.1.post1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=3e792635e92c9aacd1452a589a4fa2970114b6a9b1165e09655481f6e58970f5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cb/ae/700b4646a0c509032e654bd0e493e4c398e901c45021dc5a2ffc78585a0d/Shapely-1.8.1.post1-cp38-cp38-win32.whl#sha256=8cf7331f61780506976fe2175e069d898e1b04ace73be21aad55c3ee92e58e3a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/17/0f/dbafa939c9e805c3ea694dbeee4aa3b71c2bbbbb330216dfca66a64ed426/Shapely-1.8.1.post1-cp38-cp38-win_amd64.whl#sha256=f109064bdb0753a6bac6238538cfeeb4a09739e2d556036b343b2eabeb9520b2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ab/39/cf7142fcc8f78ade79c537df97cf43c39d78ddaf8326ff9e56c14cd320f9/Shapely-1.8.1.post1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=aea1e87450adffba3d04ccbaa790df719bb7aa23b05ac797ad16be236a5d0db8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5a/a0/4cdfed996996340e720f0e21dbe4805737d27a0be55a3729eaf9b41718d2/Shapely-1.8.1.post1-cp39-cp39-macosx_11_0_arm64.whl#sha256=3a3602ba2e7715ddd5d4114173dec83d3181bfb2497e8589676c284aa739fd67 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/66/8b/5366c7ebd0e0b6dd2d67b7553b59c571dea695b27643b3f6982f28dd3744/Shapely-1.8.1.post1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=679789d774cfe09ca05118cab78c0a6a42985b3ed23bc93606272a4509b4df28 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/43/ab/d860199f4c9b9fc004f607701da4fed9690d99e8283baeb88c912ebd9da4/Shapely-1.8.1.post1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=363df36370f28fdc7789857929f6ff27e659f64087b4c89f7a47ed43bd3bfe4d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3b/e6/228b4eace24ebbca55d6b558e3e8422fc234ba59fc392cc48ff1c0eb25be/Shapely-1.8.1.post1-cp39-cp39-win32.whl#sha256=bc6063875182515d3888180cc4cbdbaa6443e4a4386c4bb25499e9875b75dcac (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/3a/57d73dc0896e8ef69bc38893838297381856669a1e19b4eaebf09a21e521/Shapely-1.8.1.post1-cp39-cp39-win_amd64.whl#sha256=54aeb2a57978ce731fd52289d0e1deee7c232d41aed53091f38776378f644184 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/30/36/a0a03a29924479c28ed85ab0f21661a98783142fa0c2c75dfdef8c9fe228/Shapely-1.8.1.post1.tar.gz#sha256=93ff06ff05fbe2be843b93c7b1ad8292e56e665ba01b4708f75ae8a757972e9f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.1.post1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/96/d6/7a079ff26207f2357b152f21a5d53a1e8f6b3205bf9a90fb9a52560e0dcf/Shapely-1.8.1.tar.gz#sha256=0956a3aced40c31a957a52aa1935467334926844a6776b469acb0760a5e6aba8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b2/46/01a366508f6bc40c89abed3f60137172058a343db36d5ba974ba65693d88/Shapely-1.8.2-cp310-cp310-macosx_10_9_universal2.whl#sha256=7c9e3400b716c51ba43eea1678c28272580114e009b6c78cdd00c44df3e325fa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/48/d9/a8ae8c498ae438b7e6d1bada0ad748ccc685f8c692e4cf69735aef036342/Shapely-1.8.2-cp310-cp310-macosx_10_9_x86_64.whl#sha256=ce0b5c5f7acbccf98b3460eecaa40e9b18272b2a734f74fcddf1d7696e047e95 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/32/7c/5891db019c99bfd4cc37691b0b866810572fcfc3e759613c9c9d14aea6e5/Shapely-1.8.2-cp310-cp310-macosx_11_0_arm64.whl#sha256=3a40bf497b57a6625b83996aed10ce2233bca0e5471b8af771b186d681433ac5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fd/21/fd3c79f904d903a249c62632ae973c8c8c6d12e7bc8a75290552e94d915b/Shapely-1.8.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=6bdc7728f1e5df430d8c588661f79f1eed4a2728c8b689e12707cfec217f68f8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4d/b6/f916613c5fd0e6aad45c4ef7834a9986690f444d34d8777f31689cefb96c/Shapely-1.8.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=a60861b5ca2c488ebcdc706eca94d325c26d1567921c74acc83df5e6913590c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3c/4c/3654bcb0856d60ce1d6fdfca092204fd4408026cbdc13bfa4ea08d908201/Shapely-1.8.2-cp310-cp310-win32.whl#sha256=840be3f27a1152851c54b968f2e12d718c9f13b7acd51c482e58a70f60f29e31 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b9/02/5d57a68279b0d63c309a1a75ab4caf52b3b72e0e76a8804b20fe9f333029/Shapely-1.8.2-cp310-cp310-win_amd64.whl#sha256=c60f3758212ec480675b820b13035dda8af8f7cc560d2cc67999b2717fb8faef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4b/57/14a7a60ae057b7b8dfd9c159e5632445912608c6c44b5dc357b0088d7aa1/Shapely-1.8.2-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=56413f7d32c70b63f239eb0865b24c0c61029e38757de456cc4ab3c416559a0b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/43/8d/285cb2ebf55fe126bba787dc68dd1b3a764c82b21703655cfa4e0f57c10c/Shapely-1.8.2-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=256bdf8080bb7bb504d47b2c76919ecebab9708cc1b26266b3ec32b42448f642 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/41/3f/069106156ba92071c676e30a57563a462287a7392aba18221d6399647588/Shapely-1.8.2-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=c0a0d7752b145343838bd36ed09382d85f5befe426832d7384c5b051c147acbd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/97/c6/d5bdf62b8aae8e11803b182fb06c5e768df30c7a56a65d5b8cc93383a9f5/Shapely-1.8.2-cp36-cp36m-win32.whl#sha256=62056e64b12b6d483d79f8e34bf058d2fe734d51c9227c1713705399434eff3b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d2/a3/82551edf7b91a48b89c2e3702b560f54c1bcdfef8030c8afaab34e188b1b/Shapely-1.8.2-cp36-cp36m-win_amd64.whl#sha256=8e3ed52a081da58eb4a885c157c594876633dbd4eb283f13ba5bf39c82322d76 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9b/08/ba9690f4d6af0c4cc62b3fd47829c4cdb7df11647cdd0786ea32a0509fbf/Shapely-1.8.2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=7c8eda45085ccdd7f9805ea4a93fdd5eb0b6039a61d5f0cefb960487e6dc17a1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e7/16/5a93fa453ceef88a58629abda8c85f6b488a1cb5603558bea6503414f94b/Shapely-1.8.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=beee3949ddf381735049cfa6532fb234d5d20a5be910c4f2fb7c7295fd7960e3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/d1/ec/3038263d69a0065d3ab6944ae839f5f00896efd29b13ae62d73c00345b95/Shapely-1.8.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=e07b0bd2a0e61a8afd4d1c1bd23f3550b711f01274ffb53de99358fd781eefd8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.2\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b5/79/c603a68b24c332e6f9678d499ff573bc7ad2e239f164b53c2a8521bfd191/Shapely-1.8.2-cp37-cp37m-win32.whl#sha256=78966332a89813b237de357a03f612fd451a871fe6e26c12b6b71645fe8eee39 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d9/e1/4cd1e83771105804fc2e26e1eba9f113d2c77888878650b3405bf52d09c3/Shapely-1.8.2-cp37-cp37m-win_amd64.whl#sha256=8fe641f1f61b3d43dd61b5a85d2ef023e6e19bf8f204a5160a1cb1ec645cbc09 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/30/1a/8c1d57ffb105a321c60e4fc9c7c99331a087c09afa6db22315f215397e0c/Shapely-1.8.2-cp38-cp38-macosx_10_9_universal2.whl#sha256=cec89a5617c0137f4678282e983c3d63bf838fb00cdf318cc555b4d8409f7130 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/17/766d3ad21c5bdb3bc0a22a9c25f824e9ae4b600aadb6633bb22450003bff/Shapely-1.8.2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=68c8e18dc9dc8a198c3addc8c9596f64137101f566f04b96ecfca0b214cb8b12 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7e/cb/fcff1baf15e9f192203620d416aa8cc415c3c0b25100e2bb67b86aa3fbc5/Shapely-1.8.2-cp38-cp38-macosx_11_0_arm64.whl#sha256=f12695662c3ad1e6031b3de98f191963d0f09de6d1a4988acd907405644032ba (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/22/1a/e558a27b264ca721a346665eb3fe2822d87e34fa82fb99140b3567978776/Shapely-1.8.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=15a856fbb588ad5d042784e00918c662902776452008c771ecba2ff615cd197a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6e/a4/2a290df16e3d69d9414397bb83b1ece43484366e56112e0340e121c1e910/Shapely-1.8.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=d74de394684d66e25e780b0359fda85be7766af85940fa2dfad728b1a815c71f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/87/2a/9cdcad8b799d2a6cbd2e49823529228e5051bc56d0ade7234c835ad0c308/Shapely-1.8.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=d3f3fac625690f01f35af665649e993f15f924e740b5c0ac0376900655815521 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2b/e3/d99e35c835cd32efc9ed93cc5493c3899d168d2d138ed69ad6e8e1e52690/Shapely-1.8.2-cp38-cp38-win32.whl#sha256=1d95842cc6bbbeab673061b63e70b07be9a375c15a60f4098f8fbd29f43af1b4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9c/7a/f515690844afdefee85d56c889f8b3d8f838424167dbe71f70260ea5b324/Shapely-1.8.2-cp38-cp38-win_amd64.whl#sha256=a58e1f362f2091743e5e13212f5d5d16251a4bb63dd0ed587c652d3be9620d3a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a3/f4/2bbadf36349cc6a702ffb4e8441136550566921cad9cb784da1b1b96f98d/Shapely-1.8.2-cp39-cp39-macosx_10_9_universal2.whl#sha256=5254240eefc44139ab0d128faf671635d8bdd9c23955ee063d4d6b8f20073ae0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/99/9b3cbfa88ea3726fe42080fb7cbaaf6c4f60a5c2fb5e66f1a2fd534b8880/Shapely-1.8.2-cp39-cp39-macosx_10_9_x86_64.whl#sha256=75042e8039c79dd01f102bb288beace9dc2f49fc44a2dea875f9b697aa8cd30d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4c/df/c8eb05a08f529d6a674f30e4526fd00428970629e982586684dfaff97631/Shapely-1.8.2-cp39-cp39-macosx_11_0_arm64.whl#sha256=0c0fd457ce477b1dced507a72f1e2084c9191bfcb8a1e09886990ebd02acf024 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/05/d5/b9c7c2523c79727dfec88c6bbcbf37948f03a3498b41d6b27f1c02ee89e8/Shapely-1.8.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=6fcb28836ae93809de1dde73c03c9c24bab0ba2b2bf419ddb2aeb72c96d110e9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cb/4a/7ce95bddece98e7e2baa383cd2a7cfe019b207b35b9a0282355657842872/Shapely-1.8.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=44d2832c1b706bf43101fda92831a083467cc4b4923a7ed17319ab599c1025d8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e2/bb/6569cf334ff294504fa3aa9683e27ab4f2da9fdc67593e32d6b25a687773/Shapely-1.8.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=137f1369630408024a62ff79a437a5657e6c5b76b9cd352dde704b425acdb298 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/29/d1/4c27e539f2a8656f5f521ccdfee19a2b71f31d37369e0617e2fe99c6b87f/Shapely-1.8.2-cp39-cp39-win32.whl#sha256=2e02da2e988e74d61f15c720f9f613fab51942aae2dfeacdcb78eadece00e1f3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/35/0c/dbd7fbf21e38a6042419ac3d36acb3ecafd5b65e5849c3112aa692a82171/Shapely-1.8.2-cp39-cp39-win_amd64.whl#sha256=3423299254deec075e79fb7dc7909d702104e4167149de7f45510c3a6342eeea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/93/3c/cda77e57a08c49569de5bd90376c547bcb981420100adcb0f3770ed681b1/Shapely-1.8.2.tar.gz#sha256=572af9d5006fd5e3213e37ee548912b0341fb26724d6dc8a4e3950c10197ebb6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.2\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ce/54/ca74be4edbe97b8640345fec3931596b34155efb9bc58404a001bdb68618/Shapely-1.8.3-cp310-cp310-macosx_10_9_universal2.whl#sha256=06a48662b5dc22d0be1306286b589bfbed2515d735f2ca1f1c93ed614e95d27f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/af/2c/8237a9b38224ba0d66ad67e6f398778ca9adacd8feddfa1ba7ca335081a0/Shapely-1.8.3-cp310-cp310-macosx_10_9_x86_64.whl#sha256=62953e3863d3332b847d5ea4a1be8a664d3ace6a234b76aff59c127282de1fe4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ac/78/bc66798ead58abe4fd86a198a9cc7eab87588dbdd007f33676cc5c7e8c7a/Shapely-1.8.3-cp310-cp310-macosx_11_0_arm64.whl#sha256=386add5cc173dde2bed1e4b14008bfe29822cb528c2243677d272a5a86a6b3cc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e1/33/fe6315e73e7225a61cb1b646fed9ea5c6524ba34eb02d267b151b0f55ba2/Shapely-1.8.3-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=661fc1008bd093532802501fe00f974ab1fda44da8572c036675ed0df0b5bce0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0b/32/3d431dbc7aa2090c169efeb088fad0f1787b6ea9fcfdc159df5d187e9dff/Shapely-1.8.3-cp310-cp310-win32.whl#sha256=93335508fd37235cf1848ae3d4e872db098f80602304b48e3550f39c2a2ce275 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/35/c4/b892625f8ccadef50d525f61060cc480c5ca5468a3f740d399d9972b1237/Shapely-1.8.3-cp310-cp310-win_amd64.whl#sha256=73f1d1032dbca99f48710f5c4553f2ef0962f64ca214ea1737711e71dcdb16ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ec/af/51c5590f71bc3c4c5d6891722591978f48eaa6994fe4c6a804f870adab3b/Shapely-1.8.3-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=b76ada0996b0ec7a342ec702c8ec617c9ffa2083713b6b33e772cf759e2e42c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4a/30/bea8620cc979275c102916064c323eb6b156a0209df77f135a22a490f493/Shapely-1.8.3-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=98aa7731e953ed360e99fa5dfcc5d5a6839c27a7e276a87fb76a67fa8bad8d57 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/22/5c/4dca67693aab3e50be327764e0bf2c9f3b6f5d77f35604ffcb713db4c8ed/Shapely-1.8.3-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=cd0b27adc5931a44e96264e4a976bc62e0f8efa4bfe296ee8ea47320ceee7020 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ed/03/e14c83d640ab8c82fee4d21c07cfd74e11ccf328f470ff2e135966ab9eb7/Shapely-1.8.3-cp36-cp36m-win32.whl#sha256=249409d73623b79e549b0e40a7b24eb6144c07d181411e1551af453c5c4ddfb5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/53/d8d0d51a3966ed1c44d6e067de01c8064298ffe55a4bf24763d31cb18b19/Shapely-1.8.3-cp36-cp36m-win_amd64.whl#sha256=193e3a165ae34240869a6f25acada5331c301eb42d0e7314bbf206294fd1663f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5c/fd/aecb88ff7ae3a431ee1261306d8df63bc5714f65acb8f8eca8c8ddf77047/Shapely-1.8.3-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=00ef33f12b1457bd3593da9e5f3ea2f2c3156d43fa93bda143d69370c3fe2880 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f0/ae/68b4708727165eedb9a1d77ce625d3c85d26f538e6ed78a015adb8cad708/Shapely-1.8.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=8c539bf3128b7c90dda645dfefa242e8438b9097831a1a70c623a533e4e63476 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/51/54/744d4d95f57dcdf62dc2118e9d838f42f5c1c6726b89663624adbf7ee532/Shapely-1.8.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=4f19d5ddd7ac9cb1ca125e54ee01a7c9456ef357158f7a3632d8fd202cb8a0e9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.3\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/23/26/edbe037319e5d08c21c3a835baeb90088c154786337c238531e22a050c12/Shapely-1.8.3-cp37-cp37m-win32.whl#sha256=57b23ea2b51e58fd19508f73398b5b46db07719543e6836dd30b2da3efa88e1c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/38/e7/0880b859a5e54f4db66cd297ea44996887deb678c41c6cc8d259bcb1d948/Shapely-1.8.3-cp37-cp37m-win_amd64.whl#sha256=4e4ae56ce3c774302ae3d4c67a061b8786f3709cef8321d9b8554adf40578d39 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5c/46/a939f0e222ab57b1d5d4a513b623a79167ce6661ec99d28e640319a51c22/Shapely-1.8.3-cp38-cp38-macosx_10_9_universal2.whl#sha256=1d0b91bc876266d7243a61c0e4575efd804e955851c58cc08d3406304767298e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ed/ad/738243d2dc4adfcf5e70f5a2de98a907771b1e80df1d8395cc29b2b90afe/Shapely-1.8.3-cp38-cp38-macosx_10_9_x86_64.whl#sha256=6d57ea35d4e6851cd619fb344fb9e99e27f6cfd7e253de4d34581f647dbdff3d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/68/a4/9f198b2caf825c5355292982fb9c879c9729a7db5a773b69196d33f52faa/Shapely-1.8.3-cp38-cp38-macosx_11_0_arm64.whl#sha256=e3c0fcfa1d7d89ff3518b7377058b2860311dbf6a9058bb529e04c8f0a5cc873 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2a/4d/c1a2af86620130f71d09f049dc50377c07bf6d7afda6611674ff7bd46c7d/Shapely-1.8.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=284b7d51b3c5de5c2672a8094b21e2ab7e05b68731c0525814266cd02d3cc517 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fe/05/dacfedf723f0d6014e14b0bdfb1c304bcb517711a145c644d6f58edb7e1a/Shapely-1.8.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=f7fedc245dbc87597d08d517fc122f7d603d4ca911290a2b003a07be780a7eef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3e/c4/06fe72b43ae3014675d33b8da039a10679da518c8e585b91762f91fffbaa/Shapely-1.8.3-cp38-cp38-win32.whl#sha256=9f4dc39c96f912d06f0110bc155077b0eb81cd3510b6c404904ce60a8133a2fb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c9/26/af50c736572864c5f2dfac6e816a16c194ad694cfabb6b4855752a305281/Shapely-1.8.3-cp38-cp38-win_amd64.whl#sha256=ab43aee210b760a116dcbb31e651ae14877fe95f4eacba2655a7844b2d5e1c84 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/23/17/f484b0685bc6d05e3a6b650ecdd5bcc9f494750948bb14733865e4929abd/Shapely-1.8.3-cp39-cp39-macosx_10_9_universal2.whl#sha256=2abf47e94382d47e75746c2a8744c061b8ceaea39279c5cda78c02347fd891c8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fd/1b/1ef14078c0d966b0644664bb49b56237583ba2b5db5504eb17d3930d26ed/Shapely-1.8.3-cp39-cp39-macosx_10_9_x86_64.whl#sha256=c759afa287fac3021256f3e96fa9b119610680ecb64f0cd6155445d1c19d70d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/20/f9/008d21a522015c876ee95ab631687471f38766897fe4541317def210f7e7/Shapely-1.8.3-cp39-cp39-macosx_11_0_arm64.whl#sha256=2d0d5e6fb45a245cb3dbd6a1a29e4cf5b7fb1461010a976e91cc05386fd16a01 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/51/ced42fa6aeabfca20b3f0980acf59b4a14a2c6336273f97cd2eeba1f705e/Shapely-1.8.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=8b3def5b57ff4b44adb0cb1fce8eed6a11e7b1be058f1db0cd7fea6774d1878d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d6/5f/871f8057f975d5ec176e11f1d71823fc428df83527517e2b44a1a4b0a878/Shapely-1.8.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=5e154847b7ba20d0a592101760a37b6742033433acbe14410b5d170a1ebda630 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a5/f9/9cbead73be0c736a586223937d7eafe76464bc20b446be191c98f5bb1b45/Shapely-1.8.3-cp39-cp39-win32.whl#sha256=ebd29bacb4fac45eb8d906608eab9cda8abfc4287503419eebb20f32f6660ac1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/69/11/0fc9d48cf3efb38c9f6b589eaa2b8d09b52497849c655bc30bc9bbd194ba/Shapely-1.8.3-cp39-cp39-win_amd64.whl#sha256=4a4d608e6a4523bb08b7f5e7815f21c5683ea773af9642d61dfe7ec033d6dce9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/35/6d/ef38757796e756377be74bcae96c6283b21365afc96796c9d2b6ff6ddbe7/Shapely-1.8.3.tar.gz#sha256=1ce9da186d48efc50130af96d62ffb4d2e175235143d804ef395aad156d45bb3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.3\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f9/07/8ad2ec849870619a4186f59e5dda8992987245ffb5666c84e6e9805ef9cb/Shapely-1.8.4-cp310-cp310-macosx_10_9_universal2.whl#sha256=6702a5df484ca92bbd1494b5945dd7d6b8f6caab13ca9f6240e64034a114fa13 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/a8/02347c450b5507b9a7a2c15ad6a029613149468fc55ac2100ab215666159/Shapely-1.8.4-cp310-cp310-macosx_10_9_x86_64.whl#sha256=79da29fde8ad2ca791b324f2cc3e75093573f69488ade7b524f79d781b042699 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ba/d0/75cd36c81a1aa26dd6d37cf7ae1c79b0bc2d379e0f32c359bcb00b14e5c3/Shapely-1.8.4-cp310-cp310-macosx_11_0_arm64.whl#sha256=eac2d08c0a02dccffd7f836901ea1d1b0f8e7ff3878b2c7a45443f0a34e7f087 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/60/20/c6321e3da92a4ad9495727c9aa8c423e47295572ccd35ece82d1ac013e28/Shapely-1.8.4-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=007f0d51d045307dc3addd1c318d18f450c565c8ea96ea41304e020ca34d85b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0e/7d/89d8027dff40d81e818fed6bac2d5153a08417137e9a2dadab70528316b7/Shapely-1.8.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=04f416aa8ca9480b5cd74d2184fe43d4196a5941046661f7be27fe5c10f89ede (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/dc/4f/99450eed96f614c3ad3469df9dfa27cb4646150b48e97d71e7a0c51cdc9f/Shapely-1.8.4-cp310-cp310-win32.whl#sha256=f6801a33897fb54ce39d5e841214192ecf95f4ddf8458d17e196a314fefe43bb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/b5/46d13799804b5fe9ede2eca8b19023410e29b78baca0bff727671a1df8c1/Shapely-1.8.4-cp310-cp310-win_amd64.whl#sha256=e018163500109ab4c9ad51d018ba28abb1aed5b0451476859e189fbb00c46c7b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/9e/e3cd89e000f72c61c363bb6ff6f3bad9a0230486e2bd83ce9d2330e1bcc5/Shapely-1.8.4-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=687520cf1db1fac2970cca5eb2ea037c1862b2e6938a514f9f6106c9d4ac0445 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/59/e6e2c55a8742745a109917d930c94b41b8c3457bbcffe53c2bcd0b93b361/Shapely-1.8.4-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=471ce47f3b221731b3a8fb90c24dd5899140ca892bb78c5df49b340a73da5bd2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/55/b70c0e751fa4132a02396cfc902affdc37b9ef2a69391e31d3a8dabefaf3/Shapely-1.8.4-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=bb371511269d8320652b980edb044f9c45c87df12ecce00c4bb1d0662d53bdb4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/02/95/1628969c168d0c3ae16fbfff3a2235ab978d44a978884c221dd8989c0788/Shapely-1.8.4-cp36-cp36m-win32.whl#sha256=20157b20f32eac57a56b5ef5a5a0ffb5288e1554e0172bc9452d3de190965709 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1c/e1/bc51e1e281c048442b68342a6dc38a592ddbb4bc9e9ef861acf4996333ef/Shapely-1.8.4-cp36-cp36m-win_amd64.whl#sha256=be731cf35cfd54091d62cd63a4c4d87a97db68c2224408ec6ef28c6333d74501 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/47/a9/7c644e71638bff2bbaa87f05ee62fdf6b4552a55e74bbf731d6210b6d436/Shapely-1.8.4-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=95a864b83857de736499d171785b8e71df97e8cef62d4e36b34f057b5a4dc98c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fd/6d/090ce5c2b4842406edbac06b757c5875652f347213d075a9a578e33ec349/Shapely-1.8.4-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=4c10d55a2dfab648d9aeca1818f986e505f29be2763edd0910b50c76d73db085 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a2/73/07f507b539527579c026ed5f411c35e287685ce794c4dfb7edf6675cdc24/Shapely-1.8.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=a2cc137d525a2e54557df2f70f7b9d52749840e1d877cf500a8f7f0f77170552 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.4\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/99/54/074fb64e060c45d74dca1b44a947097cd463af3d9b36cb89776196f0c492/Shapely-1.8.4-cp37-cp37m-win32.whl#sha256=6c399712b98fef80ef53748a572b229788650b0af535e6d4c5a3168aabbc0013 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ca/4d/85b912d7947261e6b3218b01946bcd7c2efb2bbafaf517c6fe1dc3bb8399/Shapely-1.8.4-cp37-cp37m-win_amd64.whl#sha256=4f14ea7f041412ff5b277d5424e76638921ba771c43b21b20706abc7900d5ce9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/aa/8e40d875294d2632600a4d16fda31aefa9872583931f6b70400e23982989/Shapely-1.8.4-cp38-cp38-macosx_10_9_universal2.whl#sha256=1d431ac2bb75e7c59a75820719b2f0f494720d821cb68eeb2487812d1d7bc287 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/d5/78b23fc2a119624e995be3ef91f934d8c43cb2086dbde48d76048e135b9b/Shapely-1.8.4-cp38-cp38-macosx_10_9_x86_64.whl#sha256=2a6e2fb40415cecf67dff1a13844d27a11c09604839b5cfbbb41b80cf97a625c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a3/9e/85c2a7d1e67ca57d8e0c82f0348ce986493a872a7d56a5008838c9ec390c/Shapely-1.8.4-cp38-cp38-macosx_11_0_arm64.whl#sha256=1f071175777f87d9220c24e4576dcf972b14f93dffd05a1d72ee0555dfa2a799 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/39/f0/b91792dc30e0dee56b065c3ef4fb31e12f322d96355c4793dc0804e5bc27/Shapely-1.8.4-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=7855ac13c5a951bcef1f3834d1affeeacea42a4abd2c0f46b341229b350f2406 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c2/85/fee6e7e24a3f4c32590e7515622cbd1a96960a752b414289e5e5de934a13/Shapely-1.8.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=d7a6fd1329f75e290b858e9faeef15ae76d7ea05a02648fe216fec3c3bed4eb0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/47/30f603f01c251ac720aed49f5ad7966f3fe69b20a5fec90f2eceb3cc791c/Shapely-1.8.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=20c40085835fbd5b12566b9b0a6d718b0b6a4d308ff1fff5b19d7cf29f75cc77 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/3d/15cda66c03cbdce7c49a5f930b5762515701e6b1dba1be71f3812d1d42ee/Shapely-1.8.4-cp38-cp38-win32.whl#sha256=41e1395bb3865e42ca3dec857669ed3ab90806925fce38c47d7f92bd4276f7cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/62/30/2606442b36e9f1f97f64130ff124ae3570b4b9b3f5ee6700934c1dabfeca/Shapely-1.8.4-cp38-cp38-win_amd64.whl#sha256=34765b0495c6297adb95d7de8fc62790f8eaf8e7fb96260dd644cf11d37b3d21 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/34/5e/56cf4659dbd960bd430477814936108b46c037f7a311e47216212ef21a9c/Shapely-1.8.4-cp39-cp39-macosx_10_9_universal2.whl#sha256=53d453f40e5b1265b8806ac7e5f3ce775b758e5c42c24239e3d8de6e861b7699 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/f9/bf6cc34b02b3c7d3777c5e47fa57d2f69387613bbd8decdf3577a9d83a83/Shapely-1.8.4-cp39-cp39-macosx_10_9_x86_64.whl#sha256=5f3bf1d985dc8367f480f68f07770f57a5fe54477e98237c6f328db79568f1e2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8e/60/8186995a96c5a27c9b79c015bbdd89c6d9a6df9988f86f3f1027ecd4a56a/Shapely-1.8.4-cp39-cp39-macosx_11_0_arm64.whl#sha256=033b9eaf50c9de4c87b0d1ffa532edcf7420b70a329c630431da50071be939d9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b7/5b/07374512bbdc9b4460373c5f84174cfbabaa8996866c2b402524faf5c79c/Shapely-1.8.4-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=b1756c28a48a61e5581720171a89d69ae303d5faffc58efef0dab498e16a50f1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/12/5af915b2bc0470f4a652ef9978557a0fa8b705b122ccaec6626029e1674c/Shapely-1.8.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=a352f00637dda1354c549b602d9dcc69a7048d5d64dcdaf3b5e702d0bf5faad2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f8/d3/42f538086829e3a897e317ce341c4f65e798224a9d9161e74c37cf6af688/Shapely-1.8.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=b70463ef505f509809b92ffb1202890a1236ce9f21666020de289fed911fdeaf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cd/e3/ecd448e098d4e6d73cafbca6919d6caf3577c2950e360f9b834092c7cc82/Shapely-1.8.4-cp39-cp39-win32.whl#sha256=5b77a7fd5bbf051a640d25db85fc062d245ef03cd80081321b6b87213a8b0892 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/72/c3/0d52812db25ca71203f6203891277e6ee4f0244b21a36c062123ece15f36/Shapely-1.8.4-cp39-cp39-win_amd64.whl#sha256=5d629bcf68b45dfdfd85cc0dc37f5325d4ce9341b235f16969c1a76599476e84 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b5/9a/625d4fc91ef85873801a16700840786117df4c016162a4532c998a7fe6bc/Shapely-1.8.4.tar.gz#sha256=a195e51caafa218291f2cbaa3fef69fd3353c93ec4b65b2a4722c4cf40c3198c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.4\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/60/d9/94258e5c59297cd765d5a51e30d7ffe515dfd7e5110062308bcb7e7abfbc/Shapely-1.8.5-cp310-cp310-macosx_10_9_universal2.whl#sha256=dbc8b2ed8e7655c0b33abbb5c8c74013699fe29d2ca15f354b6e1abd29f0ed7a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/99/daf6b08244ccba6350dddc0b5d886a9d3b564c27d2464752490bfd3fc7f1/Shapely-1.8.5-cp310-cp310-macosx_10_9_x86_64.whl#sha256=1c6198da94fc993049fc2d31bd183f4f4de4f33f70be8437a9807ae8788c069c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/97/dd/dcfd3382e495043474525b3c211ada8b41904253175b4b88392e7379d407/Shapely-1.8.5-cp310-cp310-macosx_11_0_arm64.whl#sha256=a9a660dae5780fac8bdccaa2c68ca9ddcddb5d55330be47869fa9a8f55cb9580 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_i686, cp310-cp310-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b9/35/e74b6f406bfdc96c38abe644436b43b23d0fa472fee7d24151c48f79f67d/Shapely-1.8.5-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=6baf62a648c8745de3fb69e96a5ef93b1854159dd9c85527978e68fc2b062f76 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4a/d6/41faefe5ef0b126f557fe50ceecb5159055f15918df028ef32046094be41/Shapely-1.8.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=81c1286a1e46d1224bec8c1dba8f174b286710df796b4256198e64d1e987dfc9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/00/72/26998b1de89351455782eddc81bb2785af2f89f3f056b8989620d9744ca5/Shapely-1.8.5-cp310-cp310-win32.whl#sha256=999952cf8ed7a033debf7d2715d7029e7f1a1144eb1bdefabe0d64aa4c9910cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2b/62/7a1daeff99807a9c43c38ed493658b03056117c97d59fedf2be7ab9c3f34/Shapely-1.8.5-cp310-cp310-win_amd64.whl#sha256=7a840e7d96a01e6da6725a002dab662a1dbd1f5d53a6433f7fdbaf2a1322576b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1c/fe/afcb3b9b65c2e0bd28468511a702bdb5a8d703cd8729961b7cbeb229a14c/Shapely-1.8.5-cp311-cp311-macosx_10_9_universal2.whl#sha256=27238d6890f409b0744c4d740413698bbb94d8cf5249406c4668ca28ade8df3b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/69/d3/390b683263eaf6666fffd47b38384adc875878036b01c3059a5169323a0b/Shapely-1.8.5-cp311-cp311-macosx_10_9_x86_64.whl#sha256=1512eb7222f4a00ec4d9c81762bd15ed622fa5e06dd9fe64721db6a5fc3345fe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a7/22/589354aba372264ba6b701bc74ad6fc79aa637f3f7c1f60188d1f932ff3c/Shapely-1.8.5-cp311-cp311-macosx_11_0_arm64.whl#sha256=a0a5621d68ac371162768f1a05a91bcf64ac5f00a517458db5b8d8896c8e4743 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/60/cf6a7d3ec0c9fa52fcc8edce404e4a3969fe784066ab6ec25253714f813f/Shapely-1.8.5-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=8a1545c3e539516e87f5c4a96d5a76c623e385e5defe7555b7b26232043c9c5c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/06/24/b8c47080d89b46efd297aac34d6c530846fa8517376f863224bd2e57bc71/Shapely-1.8.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ba4d98a117ead8b49c2cc8723eb071f04da03d6eb59f340eb51364581bb6c9e0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c2/3b/d6d417554d05fc0749b68a7aa71e595f8feb7734adafe624c64665355146/Shapely-1.8.5-cp311-cp311-win32.whl#sha256=b0b60993adb141b5a1ff7f94fc512f9be69320be6e8757d32c460f25359d51f5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/7f/0d1e132a926d0bccaf054d0b81467cc84a9a1586a86a91d0c934ba7cdc51/Shapely-1.8.5-cp311-cp311-win_amd64.whl#sha256=0d014b1d072f2f5b166c9cf57a58507089570b1839a83e14a308df56d467b809 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3c/32/ba67865cee27d641f72f8789422047541ead097a84ce10343075d9c7aade/Shapely-1.8.5-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=bd55b14fa82d54bf9d7eba4d11c6d9d61f96ab0ffe530c6eb7cd3bb9a6590153 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/dc/15/7522d8c87514829bce9f5aa81d8f0f6fc8fe3e030e719c6d7644fc4a1b4a/Shapely-1.8.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=bd598f7373e1b5f317a3117d388a68b3c165fa15d5aa7de042848a1acbe4c18d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a9/e0/a78a17f76b3da56f53a2b063c35679023c077b3d737092843672d674a9b2/Shapely-1.8.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=1500f7d9fbe47c02298694e282dbd9cd30f6dbd33f0bd2efd0712bf907d9a2b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.5\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/93/f8/4b9bc0c582d1458fd10a830d0748e93389da02980c18c6ae107e1085af96/Shapely-1.8.5-cp37-cp37m-win32.whl#sha256=b1216ec40baf505dba006bfa0a7ced6b2334261925841fa23fafcdb26d9b1f54 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1a/06/785a2a5bb3573056a7aa28f09cbc3ab87dc36978b5dac088a830a886ba74/Shapely-1.8.5-cp37-cp37m-win_amd64.whl#sha256=c1f2f125b785d468c08ee698472716355b54d8cbd69f7f09081893b4e4111e3f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/c1/8a830c643c90f1104f6ba5e5d93ebf5c6a28d6a5ad13cc86dfc2e2c65050/Shapely-1.8.5-cp38-cp38-macosx_10_9_universal2.whl#sha256=eca890915cbebc879c96cfc1b7ff51a09a2326c9f94b6b3e13d6d3cdfc659ed9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3d/a6/0277f01d9564044d3a557e89fde56948d3e8c0d4ae49ef31f6af084003cd/Shapely-1.8.5-cp38-cp38-macosx_10_9_x86_64.whl#sha256=17f1a4f0ba88e8f53975845af312a276e557e7bac8f0572e8b5805705739c892 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e7/f8/77959ce3c2097e9168e38554b27768bca3c6a713442147d7c02716e28da5/Shapely-1.8.5-cp38-cp38-macosx_11_0_arm64.whl#sha256=1aa924b11d53fe817c4e5b14ac0c8e980913b9b5f15e01c23a35e3ee53cccf41 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d6/1d/62477a1319cca5956e891eb291096578476755e08fcbfa820dd66ca230cf/Shapely-1.8.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=045a9a5ba872431c7559020c6761983e4a0c13c9a0eb7fa93e427b4451f2d8cc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e8/e5/cc5f727671b496b89a9ece17a464ef35d5c3c5baf081efc91a50144ef049/Shapely-1.8.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=a43b3ba408f00c6588e174f6fb4dba5739fdee87af2e186ed7e8caece6b91568 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e5/d9/28b69a0a3f6044bac13b103af965fa4659f592fdb93ea54aa228099a8e07/Shapely-1.8.5-cp38-cp38-win32.whl#sha256=72ee40adae74ab93b6cf13d71f7183de8f91b077296a8788e791097703aa279d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/65/56/4ca11ee041b4f05bbbc11765a58931c935d553b49242ce92f619fb912779/Shapely-1.8.5-cp38-cp38-win_amd64.whl#sha256=221c9bd94d286b6941b3f86cbebead24c3f54cbac2cb02fb6746fd768be73b80 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/28/f2/ccea63748ec4d126752be8b68ee9b05f1078b4a36af850605f7abacb0274/Shapely-1.8.5-cp39-cp39-macosx_10_9_universal2.whl#sha256=2c86c9006b266c64bc1b9c0c0ebc20eff8fcce1bc06d4db13bbfbac9e5e2d910 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d0/69/449a2d836ffbd3b7ea3b4b62c263cfea929e9ca45e0c8abce23748419ddb/Shapely-1.8.5-cp39-cp39-macosx_10_9_x86_64.whl#sha256=bffeab5498ac32e014845f675d6c4d7a85cba42cbc76ee794da828faf3c12ba6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2f/b3/5cc39476dbd4e15b46cb60b38547b9977060dc1f96028641f198aa7167f4/Shapely-1.8.5-cp39-cp39-macosx_11_0_arm64.whl#sha256=52f5b61ee2998a75e5bcadb609036bfd626ec472fc456241150bc95a9088c89c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e3/8e/588f760c9dd1aa8c9ee59a7800ab668759463a3b823f5a556a8471c18708/Shapely-1.8.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=57df87c68e58050546fda07963d2f87a7e16db57029eb67239d27732f65f40d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5e/72/d3b6b4c6bfede4fdc1e5712c85babf79d916b185b0265914401728ee7f57/Shapely-1.8.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=a8ad6ebec3b39af4890a59a027ad60e7fece5bfd71ecc998da7b03e5f414d32a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4c/31/f48fbb138ea4758335565c155d12a9cbadcd9e203a698256c47ccb06abeb/Shapely-1.8.5-cp39-cp39-win32.whl#sha256=0e6172d2e9e77aed7b6515bc68eefe623e5be7b865ced4b9892b5205166e2931 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4a/95/bcc9f6e7dccb74f1dc498b5bea492fc53ad89c316bf0e1ea70d11d32cdc4/Shapely-1.8.5-cp39-cp39-win_amd64.whl#sha256=4c9a777bf999eca65ecf9f90821f6c699f72633f1f2fe4ca3fa829e6114fdb45 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7a/6f/9f67f1693087319e3ab45e2fbbc2bf1e81c47413c75a3ffff06a90f6bf44/Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_universal2.whl#sha256=d048f93e42ba578b82758c15d8ae037d08e69d91d9872bca5a1895b118f4e2b0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/40/c2/f4265ebd6f2947f9d391676f998a8fad293ca91ba8704908527428913e9d/Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_x86_64.whl#sha256=99ab0ddc05e44acabdbe657c599fdb9b2d82e86c5493bdae216c0c4018a82dee (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/34/30/5704c57904ea600ef041b960753cd5fc78a315210ae2b927a11d3aba2d6c/Shapely-1.8.5.post1-cp310-cp310-macosx_11_0_arm64.whl#sha256=99a2f0da0109e81e0c101a2b4cd8412f73f5f299e7b5b2deaf64cd2a100ac118 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_i686, cp310-cp310-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/01/74/eaa56c4b230e874458c8d25fddf18d23e2e2625f307db227f38c37b24139/Shapely-1.8.5.post1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=6fe855e7d45685926b6ba00aaeb5eba5862611f7465775dacd527e081a8ced6d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2010_x86_64, cp310-cp310-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/96/3a/c34e35e3181e593c72357353e8fa60052c751eff37c4a6f4e473cbf9aafd/Shapely-1.8.5.post1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=ec14ceca36f67cb48b34d02d7f65a9acae15cd72b48e303531893ba4a960f3ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/78/a67ff51c8167323b91b1b5a687ea5e1b071a9228543eb10a243ccb0e0f2b/Shapely-1.8.5.post1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=d8a2b2a65fa7f97115c1cd989fe9d6f39281ca2a8a014f1d4904c1a6e34d7f25 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/aa/4b/76dae50fcc9433fe512ef51e17f0944c0b2c4f4ff329916f995ae1f95a42/Shapely-1.8.5.post1-cp310-cp310-win32.whl#sha256=21776184516a16bf82a0c3d6d6a312b3cd15a4cabafc61ee01cf2714a82e8396 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/88/43/3a8006855fe0ae3deac065843c2f601c542f841b3c92794619d1ab291b4f/Shapely-1.8.5.post1-cp310-cp310-win_amd64.whl#sha256=a354199219c8d836f280b88f2c5102c81bb044ccea45bd361dc38a79f3873714 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3e/0d/29f313b99579e0c54d2b09a60c8e7f71382a44dd725139dbbce51933868a/Shapely-1.8.5.post1-cp311-cp311-macosx_10_9_universal2.whl#sha256=783bad5f48e2708a0e2f695a34ed382e4162c795cb2f0368b39528ac1d6db7ed (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/55/95/f694322ba2dc37f7956dbf1bfb924ac42979b04c65b5f37631726729acba/Shapely-1.8.5.post1-cp311-cp311-macosx_10_9_x86_64.whl#sha256=a23ef3882d6aa203dd3623a3d55d698f59bfbd9f8a3bfed52c2da05a7f0f8640 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/97/940c9f7c0bc20ca2121ca9fdb4cbf83e5239f672bcd02d3be486e5e3f012/Shapely-1.8.5.post1-cp311-cp311-macosx_11_0_arm64.whl#sha256=ab38f7b5196ace05725e407cb8cab9ff66edb8e6f7bb36a398e8f73f52a7aaa2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/04/65/bc86d90b3cdf99e9851403f0b1558b46eb51217d6786cc5e1ccb22ea5211/Shapely-1.8.5.post1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=8d086591f744be483b34628b391d741e46f2645fe37594319e0a673cc2c26bcf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/8e/68d8278ab04c89dc14b1b1c2bf95b1df18a9b461c6b5f85649f3602d267e/Shapely-1.8.5.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4728666fff8cccc65a07448cae72c75a8773fea061c3f4f139c44adc429b18c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/95/c4/f762a599d63d433012745b3c893a355dabf01e2fd5080c4c60f383ac47f5/Shapely-1.8.5.post1-cp311-cp311-win32.whl#sha256=84010db15eb364a52b74ea8804ef92a6a930dfc1981d17a369444b6ddec66efd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/bf/49e91fcad7bbe562141d6337a7f3c7698da513c9f07264b2c02150cf590a/Shapely-1.8.5.post1-cp311-cp311-win_amd64.whl#sha256=48dcfffb9e225c0481120f4bdf622131c8c95f342b00b158cdbe220edbbe20b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6c/74/3acd45b35aba6120e828e3229378377569a0241678b3f06463991829c313/Shapely-1.8.5.post1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=2fd15397638df291c427a53d641d3e6fd60458128029c8c4f487190473a69a91 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_i686, cp36-cp36m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/3c/7c76e8d66c414d1dac3014c842acf52ebe1ceb513a88685d36f9ff4ffe27/Shapely-1.8.5.post1-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=a74631e511153366c6dbe3229fa93f877e3c87ea8369cd00f1d38c76b0ed9ace (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux2010_x86_64, cp36-cp36m-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/eb/3c/e853d9f7c6ffdbb5db24c3eda4c84cc3229fec52fbd464f53368a2edfe7e/Shapely-1.8.5.post1-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=66bdac74fbd1d3458fa787191a90fa0ae610f09e2a5ec398c36f968cc0ed743f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4b/b3/71e5032e5caee0104d09dff12c3ee1b7890110985acb5dece50673e84112/Shapely-1.8.5.post1-cp36-cp36m-win32.whl#sha256=6d388c0c1bd878ed1af4583695690aa52234b02ed35f93a1c8486ff52a555838 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b6/8f/f1beeb3585a7db42062bb67ce1766b55a7b3e25af1a916a0879bc249b5c9/Shapely-1.8.5.post1-cp36-cp36m-win_amd64.whl#sha256=be9423d5a3577ac2e92c7e758bd8a2b205f5e51a012177a590bc46fc51eb4834 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/49/73/3dda8f4818880e62d3d260150ebae612c2d059216e761675e78240274126/Shapely-1.8.5.post1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=5d7f85c2d35d39ff53c9216bc76b7641c52326f7e09aaad1789a3611a0f812f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2010_i686, cp37-cp37m-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e7/fa/096b96ddc0b5048a6dd968a76c25eccf9d94e3a8699a7823fdb39d4f1785/Shapely-1.8.5.post1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=adcf8a11b98af9375e32bff91de184f33a68dc48b9cb9becad4f132fa25cfa3c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7b/b6/580f795a835f7b93d6e5c4fb125ba4fa81eb0e8489c5ac126ac2669b9521/Shapely-1.8.5.post1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=753ed0e21ab108bd4282405b9b659f2e985e8502b1a72b978eaa51d3496dee19 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.5.post1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/74/65/a898434cb7a69a3ef9554a6f3b3b325e17a5e15887ce88b18b9585e09e84/Shapely-1.8.5.post1-cp37-cp37m-win32.whl#sha256=65b21243d8f6bcd421210daf1fabb9de84de2c04353c5b026173b88d17c1a581 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/9f/d1ac5445a0a25a587ae8dfab76c980862dc5261f209c4a50f5c7a3beaba7/Shapely-1.8.5.post1-cp37-cp37m-win_amd64.whl#sha256=370b574c78dc5af3a198a6da5d9b3d7c04654bd2ef7e80e80a3a0992dfb2d9cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/14/88/523ecb4366850cb94bb7518e07625008e0c20821ddf306bcba36c1bdc5bc/Shapely-1.8.5.post1-cp38-cp38-macosx_10_9_universal2.whl#sha256=532a55ee2a6c52d23d6f7d1567c8f0473635f3b270262c44e1b0c88096827e22 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e4/97/84ee6e6c02ed3b843da53caa66686b1d8594bb8be10525171d2427b4231b/Shapely-1.8.5.post1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=3480657460e939f45a7d359ef0e172a081f249312557fe9aa78c4fd3a362d993 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5b/cc/673c152ebe86a72860526fd26977693ce99c2de921d45ef931a716da0fbf/Shapely-1.8.5.post1-cp38-cp38-macosx_11_0_arm64.whl#sha256=b65f5d530ba91e49ffc7c589255e878d2506a8b96ffce69d3b7c4500a9a9eaf8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_i686, cp38-cp38-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a8/f3/e5743c71d61f3533d49792dd63dbd4237508edd4fb1a3d6a27fe8fb4cd3b/Shapely-1.8.5.post1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=147066da0be41b147a61f8eb805dea3b13709dbc873a431ccd7306e24d712bc0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2010_x86_64, cp38-cp38-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/17/70/c11106764bf0fdc50191136bdc970ec962bd0bb657070a12779e71e28fea/Shapely-1.8.5.post1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=c2822111ddc5bcfb116e6c663e403579d0fe3f147d2a97426011a191c43a7458 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4d/16/ee7ed7a54dbb0bcaa77ca10b0b692e82d1538cd8f663e0f77a46c4dc0435/Shapely-1.8.5.post1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=4b47bb6f9369e8bf3e6dbd33e6a25a47ee02b2874792a529fe04a49bf8bc0df6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/1a/1e6878881c2d91d892767172351e6bfb6f19633acf66f57e43ad9b724611/Shapely-1.8.5.post1-cp38-cp38-win32.whl#sha256=2e0a8c2e55f1be1312b51c92b06462ea89e6bb703fab4b114e7a846d941cfc40 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/22/46/7184845f9064baea7a271cc72b0689821d90572b781fdc5ecdd3aa3aefd3/Shapely-1.8.5.post1-cp38-cp38-win_amd64.whl#sha256=0d885cb0cf670c1c834df3f371de8726efdf711f18e2a75da5cfa82843a7ab65 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/c7/767226767fe15d78198f199c51b91c91f4d963e76a939c4a9a4ff2fc4087/Shapely-1.8.5.post1-cp39-cp39-macosx_10_9_universal2.whl#sha256=0b4ee3132ee90f07d63db3aea316c4c065ed7a26231458dda0874414a09d6ba3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ed/91/0844725ae749f1b9c831d5ca4622a181a3264fbcced217409da537e33818/Shapely-1.8.5.post1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=02dd5d7dc6e46515d88874134dc8fcdc65826bca93c3eecee59d1910c42c1b17 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/17/4f/b461d573cbe5625396c551bd771c83282ea5d4fdb30ee931381a23af09a2/Shapely-1.8.5.post1-cp39-cp39-macosx_11_0_arm64.whl#sha256=c6a9a4a31cd6e86d0fbe8473ceed83d4fe760b19d949fb557ef668defafea0f6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_i686, cp39-cp39-manylinux_2_12_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/5a/1848c3296272866febcfad476c878089299c70594fe3bbf682d084c7ba88/Shapely-1.8.5.post1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl#sha256=38f0fbbcb8ca20c16451c966c1f527cc43968e121c8a048af19ed3e339a921cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2010_x86_64, cp39-cp39-manylinux_2_12_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ec/6d/ff268d09332501c8efccc2cf98e7d2f56659c91a150f43cb84bd0546c3e8/Shapely-1.8.5.post1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=78fb9d929b8ee15cfd424b6c10879ce1907f24e05fb83310fc47d2cd27088e40 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/47/59/3560714f4526d18c44687b7fddd924d5230babff570cfb3af40f466da70c/Shapely-1.8.5.post1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=89164e7a9776a19e29f01369a98529321994e2e4d852b92b7e01d4d9804c55bf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/42/6d/4b56f98700c302b52ce91e4f50704f989998f63ee1603d35bf573bf353d2/Shapely-1.8.5.post1-cp39-cp39-win32.whl#sha256=8e59817b0fe63d34baedaabba8c393c0090f061917d18fc0bcc2f621937a8f73 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/80/7d/0acea9a831cd15b8020ae1442627c55fa312921d4ca19ffae97c0b2ee7c8/Shapely-1.8.5.post1-cp39-cp39-win_amd64.whl#sha256=e9c30b311de2513555ab02464ebb76115d242842b29c412f5a9aa0cac57be9f6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/92/2e/a8bbe3c6b414c3c61c4b639ab16d5b1f9c4c4095817d417b503413e613c0/Shapely-1.8.5.post1.tar.gz#sha256=ef3be705c3eac282a28058e6c6e5503419b250f482320df2172abcbea642c831 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.5.post1\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/21/d0/82ea9573c9ba8e7a5709ee08681249ad25411382f6440f548981286f3b74/Shapely-1.8.5.tar.gz#sha256=e82b6d60ecfb124120c88fe106a478596bbeab142116d7e7f64a364dac902a92 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8.5\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c9/9a/793b0c85fae445493058bbf7b33eb3da727a81cb900911aaf9edbc4d5807/Shapely-1.8a1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=e4ba548e44e134762e4cc4ac9d44d38fbb4bb66a8319ebbf36a80befa3dc0d70 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8b/18/770ae59aa9081be2ba7e777cf39cb0768ce2bf6e60a838edcc5f3bf1b9b3/Shapely-1.8a1-cp36-cp36m-manylinux1_x86_64.whl#sha256=45fb2083fd3f88e168a6482c253f97726e7b986cc4431818de398623b3c22b5e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/91/28/889d93111bff5630c4a3d10c1ad05286f843fb9be4556530f71e21f4d26f/Shapely-1.8a1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=bb9d08fe2fe7af5c7de819a8349ddd1cad50ae15d1da493e326c4f35c6feaceb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/09/13/d9e0eaee038041d98f5ee014374c6ac81a65c9b38272eef6240fa976213c/Shapely-1.8a1-cp37-cp37m-manylinux1_x86_64.whl#sha256=a3357006dec5e5cbf0d5919cd07fa60d93f16b917765a3a531f5f8f60b5ed694 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5), version: 1.8a1\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/60/9d/a7e99a98acd0b0ac2c36c3fef426694d3b2035c00bbf27427cd1262e4a72/Shapely-1.8a1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=90c4aeeeb2501e2f35a0488fe9a87508270fa0870f3420d27417d65ee3f92053 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4f/ea/fe84135e57442f26649a3ac0054a2c105c567543d9d093946c87f33d9616/Shapely-1.8a1-cp38-cp38-manylinux1_x86_64.whl#sha256=91f439c2906691333552810916ba6e0d6777c49f6cccde07fa49378931c83813 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/58/17/b3d0e55d907948d2fd95f56b44e65f27f2593fcb0302aee5caf1f24e433b/Shapely-1.8a1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=0fd0f2b3f8e84044cf587033d233f12fde5c826f17bb78430a393f5574f6840f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9d/92/4e9a73043f7e583cb9dbf11e801625fc576dbf37260db7999889435411b5/Shapely-1.8a1-cp39-cp39-manylinux1_x86_64.whl#sha256=80f331b83e102d343ebcae00f615b996d2eaeee698d9b4249cbc1c7b6263014e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/de/30/e0492acfbb8d5ddd077dff5e5a3f9f85be420d17950b9941cdc2519bd7f0/Shapely-1.8a1.tar.gz#sha256=a748ab74c187060264e7ce838aaac30eead2a791bbea8cc3ab1dfc3cc0b4d067 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.5), version: 1.8a1\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/83/49/097bf0e3ae931ce592742fff7b1d6f40bb6141c4f8bcd154a4ab168be4d1/Shapely-1.8a2-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=127249f414abf3ed9aef31fe4c841f74dafaafc82202fc46f15b66ed81688e5d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64, cp36-cp36m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c3/3f/8327be3943b0b195a0115dc9545c6495039672a3df780272f9d4430e768d/Shapely-1.8a2-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=b7310c2552efc13939302043515c29be4cc08c1febb464e4ee6c20840cbdcfb0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/43/c7/2256194a7af156c52fef8259197a8f8f5627285f46566d8aacb7c3e5604b/Shapely-1.8a2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=447e9e1b73097616e8b5e6beee7ce0db23c2b1840d3a8587fba7876727fbc2de (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/a5/c1/b52f879c27edac00b6cdf1ecd029c3d70f5c998937844a626dd5cf54811c/Shapely-1.8a2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=91746e874682134058cb85243375b0eebdade4f0ea2c14a97fc75ef3cfe07b1f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8a2\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9f/ee/f4bb955cf226ea59527ace97068ad85534d408a648ac20836fca8781e84d/Shapely-1.8a2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=f5162d9d3b95565708cc36f309b322773f4d9c7eb4f3d626f656f0cb3697428d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fc/97/6c699df56c97948f055fe58ce2be10b42c46c852c6fcdd3176054ef4acf3/Shapely-1.8a2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=797c85180494c932acfef6800d026e22d9a2c25cee6f1999ee5a872e1ae3d586 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c4/07/685ca8ee017c1350e60f557fce8541f02cc05620773d3edc5765735928fd/Shapely-1.8a2-cp39-cp39-macosx_10_9_x86_64.whl#sha256=40cfa91dd0c4fb98aff71822104eac0988dc548b1ff74be941606bd2b4f6fde1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/46/6a/4b3023475881522ae3ec0f9e372548658532d126e7db1b3e179940d039a1/Shapely-1.8a2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=00069cc13e98346683fb2e602e9c1f54d102306d7ffa82125bb7c57149351e1d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/2c/7e/0eab63b4418d69559e38131d1001e1908b23891cfa6d5e1e7c41be2fd83d/Shapely-1.8a2.tar.gz#sha256=741b8a532333c912d74b6cb5e33ec68f3aa3ed775b4c6597bbd67547108765dc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8a2\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/dd/a5c532b25975081b8cf4fabbf9288adfb3c5633820d9919fbc4386ec9fef/Shapely-1.8a3-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=259e3f3aae7e6737054d4ce147d3e7a56e5aa4e6a33e53270e71675ff0b5bb73 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64, cp36-cp36m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/b1/7c5999e42bc69bc79a44552809094222d59a7cbf9670f211d75e0fbdeae5/Shapely-1.8a3-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=6b42fd06ff86fec3d621dd2b247fcd1f2f316bb2931826f7d7676ea240ec74bb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/65/7e/dd0b11c29dcea15d21caa0eeefe087651de1398a9bac2a6ce348ab53b342/Shapely-1.8a3-cp36-cp36m-win32.whl#sha256=f5c93e4e144229fd238826c567964ac5a40fa73dc7148d4698e19fabcf2772ce (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/40/84/5d545239ab7fe927bd10e1026c5873fc1e0a7d91fad50d3359f17f649784/Shapely-1.8a3-cp36-cp36m-win_amd64.whl#sha256=5a3e3cf5ab17653519dc53a696856a7f5be711bdd34af4daf3a2cfd2e571d6eb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d1/76/9dd506e8fc7a4f1d36fb3ea4a54700192b7de87cb5bd1b77cb8d95191b52/Shapely-1.8a3-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=dfdefa7af5a89778c44246e8ce02e1d4ce8e11770c9d5959c90eca7061cb637b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/45/02/94cdb5478e23b943ef0869112fcd89f8038509946f2ede2ac634e5332d4d/Shapely-1.8a3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=31262c4f13b067f68e86f244575e18a23a5dae2b6ebbaea3b287dde10f604071 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8a3\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/51/5c/48fa5b60ca7b27326500b1f0d83b863994119fd0c3a34fc42bbaa061a4a7/Shapely-1.8a3-cp37-cp37m-win32.whl#sha256=2f850cc6fb512b28172399121c52a0423b352ffa6e8f95e1f3979255c664e9fe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b7/75/48dc638d671fcfbe7cb6c85a74e77975bd55144444fb6d2d76dfd3b89c06/Shapely-1.8a3-cp37-cp37m-win_amd64.whl#sha256=1d1093ae2b826c437c5283360c21ec34e69278345a5647fad26a64320766074a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/38/2f/92f12515558b31d938a3fa3283eaa6f1a7032ad2ac59234a2c8ee0d15977/Shapely-1.8a3-cp38-cp38-macosx_10_9_x86_64.whl#sha256=9b074db20b5da9f0ca21ae2e1e6f6945187d2447a11e00a5baaf8d028bc6b3c2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/82/a3/e5e7e2da1d39a5dfb18855cdc27a398fe79813caef3a9217f0b673cdaae5/Shapely-1.8a3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=3f4f8f27f7dd0178e7580c7d604fca2378a2342001e926708dfff1bb23233630 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/93/8a/e6c8cb4e385a7439c8008fe91d576c1dd63bd3a1740cd1b50131fd369151/Shapely-1.8a3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=3598a85083c0d557a9a46986157a4ae905c034790903a0c6df25f9477b08e26c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a7/af/3a35277db0be20f01b99ca3a69ceb674b8b49ac6455a5154575eab3f5d84/Shapely-1.8a3-cp38-cp38-win32.whl#sha256=10fabc268e2dfee699f08119c2e40954aaa31dc9a63a23f4d269267da5b33e28 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/18/a95c70348c993b92542efd87e99fd98fc5beb6fb1b451516d40d720020f1/Shapely-1.8a3-cp38-cp38-win_amd64.whl#sha256=066e74afb717c1d1da141ce554c508a5ba331a22a22225a03dcb9b4efb11ab32 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bb/d2/9bf13eeae713d8b978a4182de5b28b6c117252691e219a9e1a16ca677c81/Shapely-1.8a3-cp39-cp39-macosx_10_9_x86_64.whl#sha256=470f0eb60b0d8b0917ca5c7c2804e3c8f5b2eadaf34b801fc7272db74ec25a97 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3a/09/48a4ba95bd914721c6f20ed8e73a73d21be7ac5fc1ed543633e00abafad7/Shapely-1.8a3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=1e5a50673105deea7242c37092b3783db3dd7b4a3c9b7c1ed7f1fba415bb059a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/83/cf/e8575fc22d205990a2d550c10e4e3b7cee8ead075c96ccb19ca4238ea60b/Shapely-1.8a3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4ec40476e74b921dc99ed204412fb9fe06d73c745830f05d3dc364b12132e865 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ed/56/3e3370e5af884d43b35d8f5b0803a01ba10d2fa012e486762efaa368e9fc/Shapely-1.8a3-cp39-cp39-win32.whl#sha256=08a71b2c7226fd23a27b72c6fd735e22e016b1c33313ad8c7dc348547a134a71 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0e/b3/20414477ba817c6d8c5ca9c3f453efe1868ab16618b17c4f56115be466cc/Shapely-1.8a3-cp39-cp39-win_amd64.whl#sha256=74f2c582fdca1ae2e04d43c967ccef50100191916970e1a80749dacfb58fd1f5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/20/06/7ba33e5a4faba8ba519c1b93d7c7c243a0efa7e6a02dc1de6db1f38f8087/Shapely-1.8a3.tar.gz#sha256=6c81659aef6f63f4b3960b77196a0d8322f2c11819dc3f3362b2b3a4b839da20 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8a3\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/83/3e/642eab5f1f06d2537b2e81c6c5055b794e88a2b92d787ad9210418df4944/Shapely-1.8rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=836ea48ae7306ba5c1ca30ae15c7882a62b892093407db75010664482f227ca9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6e/23/e3d2ca40ef49224ec966a35dd650a29e160a28cb5edf3edcee5e02d7a27f/Shapely-1.8rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b9af6fe2c0e144c3ae53742c7eb2fa7b1b2200f285b73ff8d70765b766ef2f5e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c9/4b/3c2050721a25cd21151f2335c5b012d43be72fadeed42f12ef97640e4cf9/Shapely-1.8rc1-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=fbd9cbf42c086c30d906fe78654d33aaa5bdb871e1a13b6a5fa0d808778a2dc7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64, cp36-cp36m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b9/e9/41c3138a0566070271244dd81f46cdbf4fc357c84c648f659d42f6c96b19/Shapely-1.8rc1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4c4b7eea49dd96d78980d289978e8ed108acd728b0f1fc427889e871fdfe5db1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d7/fe/2ca15059649369f5622c219d7b04a74833cb69be963933cd2f2c29d3ff12/Shapely-1.8rc1-cp36-cp36m-win32.whl#sha256=783db18ac7c03f5fddca68c004ce58bcdafb7007883d7d0318d0e7b648177572 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6e/90/b1d34f6d6ab492c79865319ea406b799d8a83bc80ed673cbad5d31d7d661/Shapely-1.8rc1-cp36-cp36m-win_amd64.whl#sha256=b632ba351ea64df50594f37146c1e795b7287f9052f234d259c0be104f74024f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7a/1b/dd3c50787047f2219e5cfb7fb841c15231102d2f036210c75f7551fa2514/Shapely-1.8rc1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=79ebeb09438244140d89591ef549a4b307cf5accd2b450fa7f3694e87de23a4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/5c/7c/d738826f5a2a453a01866e931ba0eb8604419bc530d7beba99610559b2de/Shapely-1.8rc1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=b39bd30e3198872450d91d1d49467aad224998f650acbc2dc9cf90b56ff18913 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8rc1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bc/96/5f74128811eb1604f45b7b845da8251973e09c54ba7f852043c0c82344f0/Shapely-1.8rc1-cp37-cp37m-win32.whl#sha256=6c9d506e48935cb0f9026272636378749209a167449cd2f188b9e31d9e471b25 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/56/31/5899ced3fc1b74fa66553efcd42c2bfb4eeaf70fc69154abf8157bc0b2ec/Shapely-1.8rc1-cp37-cp37m-win_amd64.whl#sha256=ade46b78869396097bc938ca32242692d5a6cad0432c0d5365191b3f759948e4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/dd/74/d7094434a50551da65d635fef8d65dbdeb8ca47309a9aa3741bd90419bb7/Shapely-1.8rc1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=f0eebc6147850331fa2e735c515857cc7c2801dbdd3cba3a85555f106d28c5f0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/12/46/8c6bf4b8ec2de6c9afa6cac10ab81610e8c44553fa86e9da77b15a5784e7/Shapely-1.8rc1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=3766d8c19404eba385658d3ad93a0bf5ba6148dde8c5f3398832ada8b4b98e10 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/69/80382f3327a0956f5f9041dc0cfcdc2363b3feafb8e787def3635a300e9e/Shapely-1.8rc1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=77706afdc250997e7323640c1311f50da084e38c6e821c8a735deb260a99788f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/db/6e/d458846c16b8aa7cfb53864e92b213aefe3651b5245db46a0fb754d1e8ec/Shapely-1.8rc1-cp38-cp38-win32.whl#sha256=5712ea6ab9941b1c2381232a4ed05617da9efcef28bd9f44f650b2c1c51e809f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/85/1a/43a56a201615a6c3f4493b4545442e7f66e95f884bf87483ff1a058b4f7a/Shapely-1.8rc1-cp38-cp38-win_amd64.whl#sha256=37c3cd57596c4a9908e908831a2c697847a2aa03de5ab865ba5e229a84e0bc64 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/86/18/75aef3e82e19943924ddf51bf23e60a0af228ed19db038d80b3f884c21eb/Shapely-1.8rc1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=419a8b37cc4565ed4b0a497d7d089f62c096fd9945249113778b11073b82cd53 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/38/08/1e4548f95be1043569c31421ec5f15414d2d0e29b8ba9c104639d6c37d0f/Shapely-1.8rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=521aae1be987ff314d4637d85c7d1a14bf5e75501443f521d7bc0751f98e478c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e1/47/c042c60fea2649affcb354c737e7b0ceff78055250bf31a51383f01a5dbf/Shapely-1.8rc1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=078b63ca2f49d32624a59790fd2584ca065be57a1e2c8d79003b5495edc64469 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/4c/ebf7909e25a3621cc2c2525139e7ab0f6a4297b10a24d5fa4218f016a4c8/Shapely-1.8rc1-cp39-cp39-win32.whl#sha256=51c917511d9fc26a1dd82b2ab165646089ba49117e52935c9fddea61c3944db1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e1/25/47483d297e0325cd6f605d45d7beaefe66ca6bc1fed1545827e12fb502a0/Shapely-1.8rc1-cp39-cp39-win_amd64.whl#sha256=67745b444619fecf6345d7f34b0ad5b2bae627d7a672a38c0063bbf4eacdad7b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/cd/6a/df15773e3578f3cd0e39188e24ad2f3b5ea623df7678cb3e9d95b407cda1/Shapely-1.8rc1.tar.gz#sha256=d74d554044c0edaa34c15a477dd771f64cd2b43be2e4f4b948f6e41193e12fb1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8rc1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/e4/3294cfa895fb8a0809ff52cb8cbc9325f5d5e7a76be6294255e0e06051d7/Shapely-1.8rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=29641bb277d01c8c488005ad659dd840598518f1583f939ae9f0572358cea7d4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f0/60/8186912922fd65265bbbe31e08cd66fab30c15eefd94ed82d645d0f249ed/Shapely-1.8rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5c502f9b09a148efb3733066d3fc2001afcc938240aa8d383f89e2bab8e27003 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d5/95/884b4b1141849dc6fb8d8a02723571bc3b6d4c2740b20d33662800f3c8f3/Shapely-1.8rc2-cp36-cp36m-macosx_10_9_x86_64.whl#sha256=2d748e895a31dbc7dbf3391aa1e7e85fcf7dc0f32b29b57e4eaa363864b76de7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-manylinux1_x86_64, cp36-cp36m-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4e/77/7d229d278cd8ab0b5e985170ee9c220f8633eecd7a7c6d74215a6708d653/Shapely-1.8rc2-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=5062f867c7fdeed08a5e492c86089c530cedb695005bc858e302b4487a8b6f62 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/18/fc/9fc2404fb5cd508d28571f1e435a3ce9b9e181b8ee8b302a9fe5a9eacccf/Shapely-1.8rc2-cp36-cp36m-win32.whl#sha256=704d8599ad79bf620c11cc8a1a9cb48abbff4b826c7715c6629fd41f15afe124 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp36-cp36m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/13/ce/eacca51d466fad85c2c8e2a7e04b076a7ea987a9b40d41325df2d0c9c59d/Shapely-1.8rc2-cp36-cp36m-win_amd64.whl#sha256=3ed7960f682a1e578f2c1b0a91a166c9656af6a24e63d58f723517a0fdbfe61d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e4/cb/b45bfff52b6a84daefd14ed7dc497bd787182ba8156b1959d8a5fb41b5b1/Shapely-1.8rc2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=e5e7dcfe142367e129feec883901464c6b4f4416112bff41512c6bbbfae4a190 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b8/4b/63eb8b035ada120120d13f7d09ceb8fa7410fc67ec9146867dc9f1870ae4/Shapely-1.8rc2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=9a1e3a7aee88d6fb01ab3245422f5ebeae9b754ddbd21b71a1ccb13e4a5afb0f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8rc2\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/90/34/de0b1f512b62829bf867a345e2cd61f2c003ba2a6df47661873e9f9f64f6/Shapely-1.8rc2-cp37-cp37m-win32.whl#sha256=b6205050c531535528966fcd35f8c2ff7a97736687dcb8c59e8b0fb66bd31f87 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/51/e3/0df9428747706aaa80eed7013988d4d4a965d66439387a14fe8dc95c503d/Shapely-1.8rc2-cp37-cp37m-win_amd64.whl#sha256=f7f105e016a65032a4d9dfeab2963366efa61cfad9418c781cfd3e962b2a3f51 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b6/c1/0820c46a97519dac26abaec59a5dede55632010e1fd01a871c9826e04865/Shapely-1.8rc2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=020cfd1d28d923ad9477e3b0c78cf169257856f0989de9c29a43cb1f3c421032 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8f/9d/3dfff22d02cc81584e44ef460bc723d374180f3d718bafaf48ea9342006d/Shapely-1.8rc2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=37aeacffcb36f01e97e27c069e766525db5fe1b3c79298a968fb69f66477e4dc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux1_x86_64, cp38-cp38-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ea/49/29eee1014a8940ee491344e498dc31a641673e3bbdd36988ea94400e6f83/Shapely-1.8rc2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=d4805110434da54f78efac949e9d5855fc7944fc6758cf2b61549b89fccb769f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d1/b4/3565800e073d2b56edb95e443ed08d5eca7f41f7d82cf36feb3e26c2c80b/Shapely-1.8rc2-cp38-cp38-win32.whl#sha256=c02cc31ceb4d2a6b794398e9375cbf24d89da777fd357c14342fcfec6a376945 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f1/52/71e4fe74a98e3e690a4666e2a9702c5fbcc216aa83839d638fe8126a4014/Shapely-1.8rc2-cp38-cp38-win_amd64.whl#sha256=394bfe3cd1320bf706397afa1662e2e8a4b2025d4ff66c9681765339da93d5ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/88/97/f89e971af7fdb063325772c1b98306750a6bfdb4150da6df52c7f75d83b6/Shapely-1.8rc2-cp39-cp39-macosx_10_9_x86_64.whl#sha256=fb9b092a640a6f261dda06e9acaf0be74323ee8d8393356128f42865aaf3982e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/03/64/617b63c30c91c7b2438dfb8b371507fb9da084c7f9593d0416c1feb1019d/Shapely-1.8rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=7c91c91b458f2eef70edd46e4d2ed3569803ce99ded082d33a8a74c1fdbc5205 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux1_x86_64, cp39-cp39-manylinux_2_5_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f6/b2/fc3b9fd78b3987b7cebb529910c3e907276a2ab40c5bc17b6fcc240a4b83/Shapely-1.8rc2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=411184e3f39ce259abcb6e59ed22092969f61127747f4389dc80b9237c9a319c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/da/31/0e648cd8f27f234a4c68dd1dd95c192ada38cc2f06df60a3394a2a6cef67/Shapely-1.8rc2-cp39-cp39-win32.whl#sha256=7e26656daa442209f228ab03d0ba32f9fa15577bf7abb4c58308eefc59c14906 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d9/cd/2c8656f705dbdada0e4b53a8b23e4989a64d6f61bcb198c54507ef8e90e4/Shapely-1.8rc2-cp39-cp39-win_amd64.whl#sha256=225e3c8c616b53d64eaaed6458334f932b5341c58590bb129e9cff7da260f385 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/01/73/03d423f07793ef7bcc70b3f2359d83fe6c6d55f361c2a897a5ca58a898ff/Shapely-1.8rc2.tar.gz#sha256=2ce020d19c4973aa6aef7495cd5276868ae6f7603428e17f0eae7ebb16e2867d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.6), version: 1.8rc2\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/32/b4/9c8d01e6049666af1cd0f3f5b2935c04fdbdcfd7fca0abe9f52741bd7f97/shapely-2.0.0-cp310-cp310-macosx_10_9_universal2.whl#sha256=7266080d39946395ba4b31fa35b9b7695e0a4e38ccabf0c67e2936caf9f9b054 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a7/1a/1b59663aa6487ad405a737c0de35b3158407d1253e35d787b0808e51b5d9/shapely-2.0.0-cp310-cp310-macosx_10_9_x86_64.whl#sha256=8a7ba97c97d85c1f07c57f9524c45128ef2bf8279061945d78052c78862b357f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e0/3f/06d9cdc5c695438af9d07511466c125282d3d3f65b32fe0e8559d33f33b1/shapely-2.0.0-cp310-cp310-macosx_11_0_arm64.whl#sha256=e4ed31658fd0799eaa3569982aab1a5bc8fcf25ec196606bf137ee4fa984be88 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8b/cb/797c4eb74e3e169cd48f8f5509b4b2c54356ceea95907b3162ce7e7d88ff/shapely-2.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=7b2c41514ba985ea3772eee9b386d620784cccb7a459a270a072f3ef01fdd807 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/06/07/0700e5e33c44bc87e19953244c29f73669cfb6f19868899170f9c7e34554/shapely-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=eab24b60ae96b7375adceb1f120be818c59bd69db0f3540dc89527d8a371d253 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1b/cb/fe31c1ee7b4e776357dcc843ba8077747a467b9dac3a35b44151859aca5b/shapely-2.0.0-cp310-cp310-win32.whl#sha256=d28e19791c9be2ba1cb2fddefa86f73364bdf8334e88dbcd78a8e4494c0af66b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ce/7b/68aae4e5e0662aba2b1a1dff1d3f9508a55cc925cc9aba4bd96996267d5e/shapely-2.0.0-cp310-cp310-win_amd64.whl#sha256=b3d97f3ce6df47ca68c2d64b8c3cfa5c8ccc0fbc81ef8e15ff6004a6426e71b1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/19/2edf55badaa89f28820e5127063784e28ff26251ecf9e3c49600d405348f/shapely-2.0.0-cp311-cp311-macosx_10_9_universal2.whl#sha256=56c0e70749f8c2956493e9333375d2e2264ce25c838fc49c3a2ececbf2d3ba92 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ce/bb/b54bf01a8b0a73f9bb59bed5c6fd0260536c9cc476809871975b897f4e0a/shapely-2.0.0-cp311-cp311-macosx_10_9_x86_64.whl#sha256=292c22ff7806e3a25bc4324295e9204169c61a09165d4c9ee0a9784c1709c85e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9a/52/6030673fadc8ac8db7d6afe7a74118add0bdc7d8b3678b17108e078f2dc5/shapely-2.0.0-cp311-cp311-macosx_11_0_arm64.whl#sha256=40c397d67ba609a163d38b649eee2b06c5f9bdc86d244a8e4cd09c6e2791cf3c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f7/f4/8675870adac3afec23b2e9cdcbb990861acb0ef188db99cec228dc106c32/shapely-2.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=6c71738702cf5c3fc60b3bbe869c321b053ea754f57addded540a71c78c2612e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d5/5d/d9434abd291339aae6a895d9993147220d81704c6fd9db25a84b95e8d6b3/shapely-2.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=73d605fcefd06ee997ba307ef363448d355f3c3e81b3f56ed332eaf6d506e1b5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a6/a6/2acc72d1c6fa357cb434cfffc148fe4ac7655dce7416183c4b50615de92b/shapely-2.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=13a9f978cd287e0fa95f39904a2bb36deddab490e4fab8bf43eba01b7d9eb58f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/82/17/30cc749eb629fb7cbf7fa28fe7ded6935a64b69dc5ab02d0293416987d4b/shapely-2.0.0-cp311-cp311-win32.whl#sha256=ef98fec4a3aca6d33e3b9fdd680fe513cc7d1c6aedc65ada8a3965601d9d4bcf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/48/b0915155ccdf2cf2c69f87a2ff7aa32e1d7b0aac7b2254fb4114bfb10de2/shapely-2.0.0-cp311-cp311-win_amd64.whl#sha256=a9b6651812f2caa23e4d06bc06a2ed34450f82cb1c110c170a25b01bbb090895 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3e/fc/a696bc217d94ee72c8a716ffe15f08b7112f6286a9af49a894b728716f65/shapely-2.0.0-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=e991ad155783cd0830b895ec8f310fde9e79a7b283776b889a751fb1e7c819fc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/50/b0/3ca66b2f6fccb65c5d9b531c263654064a2920b43340c49a41366b270ae1/shapely-2.0.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=550f110940d79931b6a12a17de07f6b158c9586c4b121f885af11458ae5626d7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c1/06/ebae7ee8acec522c39f0bfebd4f438650e06e470a142d16884058d27d9bc/shapely-2.0.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=c47a61b1cd0c5b064c6d912bce7dba78c01f319f65ecccd6e61eecd21861a37a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/22/84/52692d8184cc76ca0b52160e503524bb7313f9219eefacb26a242e9fc1ca/shapely-2.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d486cab823f0a978964ae97ca10564ea2b2ced93e84a2ef0b7b62cbacec9d3d2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0.0\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/29/a7/b214ad92e140959cc5db4fc94b5ec436573a93fd94d13643be69b23f6881/shapely-2.0.0-cp37-cp37m-win32.whl#sha256=de3722c68e49fbde8cb6859695bbb8fb9a4d48bbdf34fcf38b7994d2bd9772e2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c2/99/dd8d32328810cfc757b7c8b0691e0f90a5e2add313b4ef2928de9ace7989/shapely-2.0.0-cp37-cp37m-win_amd64.whl#sha256=99420c89af78f371b96f0e2bad9afdebc6d0707d4275d157101483e4c4049fd6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/35/f1/bab7d299285c945bd748f11b44bc9df385f263555c58195c240df508c3f1/shapely-2.0.0-cp38-cp38-macosx_10_9_universal2.whl#sha256=f96b24da0242791cd6042f6caf074e7a4537a66ca2d1b57d423feb98ba901295 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/72/6d/85249cc7eebef0edb137ff80e3cb12cd0037612e1998c7794a5685308232/shapely-2.0.0-cp38-cp38-macosx_10_9_x86_64.whl#sha256=8b9f780c3b79b4a6501e0e8833b1877841b7b0e0a243e77b529fda8f1030afc2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/61/bb/6e2091c23a76967d3745a34ca55ca8962ad6bf569f844a34edf49c1437b6/shapely-2.0.0-cp38-cp38-macosx_11_0_arm64.whl#sha256=21ba32a6c45b7f8ab7d2d8d5cf339704e2d1dfdf3e2fb465b950a0c9bc894a4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/0c/608397b3ef1b05c71885f8720656c2bb865fe2928ec2b5c04612a65e1e69/shapely-2.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=44198fc188fe4b7dd39ef0fd325395d1d6ab0c29a7bbaa15663a16c362bf6f62 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ca/d4/bc2180d79b977484708bf0262f169d2df09c3da871c0ee9c99ddd8ab4486/shapely-2.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=17d0f89581aa15f7887052a6adf2753f9fe1c3fdbb6116653972e0d43e720e65 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4e/03/f3bcb7d96aef6d56b62e2f25996f161c05f92a45d452165be2007b756e0f/shapely-2.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f69c418f2040c8593e33b1aba8f2acf890804b073b817535b5d291139d152af5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/0a/fd33902c1c2d805d55c436a572117ba211d2c15c3d7962823b93bdad873e/shapely-2.0.0-cp38-cp38-win32.whl#sha256=b1def13ec2a74ebda2210d2fc1c53cecce5a079ec90f341101399427874507f1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/06/fc/23cd69291437319c4f538e0c4a05e62012ee15fc2349b06ed71ce1705a43/shapely-2.0.0-cp38-cp38-win_amd64.whl#sha256=820bee508e4a0e564db22f8b55bb5e6e7f326d8d7c103639c42f5d3f378f4067 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/12/3b/936b385d03d0d3377042f35b5191085368e29545cc0e6df97a3ed6bbf9e4/shapely-2.0.0-cp39-cp39-macosx_10_9_universal2.whl#sha256=eaea9ddee706654026a84aceb9a3156105917bab3de58fcf150343f847478202 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/63/6b/1b4f08fbc15645a639b7f372e478ee133fdc6525e287fa23240c27497b9b/shapely-2.0.0-cp39-cp39-macosx_10_9_x86_64.whl#sha256=a391cae931976fb6d8d15a4f4a92006358e93486454a812dde1d64184041a476 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/b4/2a056a4081c3b603ebe5d8573a94ca125d8f46626cba0b3ad6ae55c86c08/shapely-2.0.0-cp39-cp39-macosx_11_0_arm64.whl#sha256=5fe8649aafe6adcb4d90f7f735f06ca8ca02a16da273d901f1dd02afc0d3618e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ae/6c/470f767a2029964cf2c18d4fb29f7a642e961e9653bb9a7ba1b830d6d7c9/shapely-2.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=2287d0cb592c1814e9f48065888af7ee3f13e090e6f7fa3e208b06a83fb2f6af (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/60/80a95d85f712bea40df97f6275cfd8313227aa6150a8501031c6fae9d4f8/shapely-2.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=c4b99a3456e06dc55482569669ece969cdab311f2ad2a1d5622fc770f68cf3cd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/51/6a/7a34b7ecb659aaafdebb805a62cbdccba8a92d8cd4d2fead50c4659b5d70/shapely-2.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=91bbca0378eb82f0808f0e59150ac0952086f4caaab87ad8515a5e55e896c21e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/66/4e/d96dcbe5fd04fd94c5a17730df6688cc593ba8c894fd21dc57f0d4f0058f/shapely-2.0.0-cp39-cp39-win32.whl#sha256=73771b3f65c2949cce0b310b9b62b8ce069407ceb497a9dd4436f9a4d059f12c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b2/8e/83d9e3bff5c0ff7a0ec7e850c785916e616ab20d8793943f9e1d2a987fab/shapely-2.0.0-cp39-cp39-win_amd64.whl#sha256=5477be8c11bf3109f7b804bb2d57536538b8d0a6118207f1020d71338f1a827c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/61/76/6e635cc4ba33e8c170ef5934dad5c269dc5cb9607e878efb2aba12f78361/shapely-2.0.0.tar.gz#sha256=11f1b1231a6c04213fb1226c6968d1b1b3b369ec42d1e9655066af87631860ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0.0\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/35/da/00737e3cd038d489c257a00829c27b3ff2d3ec264c78540a5d168a06922f/shapely-2.0.1-cp310-cp310-macosx_10_9_universal2.whl#sha256=b06d031bc64149e340448fea25eee01360a58936c89985cf584134171e05863f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1f/2a/dc3353c2431cf53e8d04bb8fba27e584410ca3435c9c85f76d71bf0c0e80/shapely-2.0.1-cp310-cp310-macosx_10_9_x86_64.whl#sha256=9a6ac34c16f4d5d3c174c76c9d7614ec8fe735f8f82b6cc97a46b54f386a86bf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ec/41/d59208743e737184e1b403e95a937aebb022b8459e99efbcd5208fc8be46/shapely-2.0.1-cp310-cp310-macosx_11_0_arm64.whl#sha256=865bc3d7cc0ea63189d11a0b1120d1307ed7a64720a8bfa5be2fde5fc6d0d33f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/69/2e/29633eca429c9e7eca1264df1764a7f179ec9ec186ba25d874342dcb1a47/shapely-2.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=45b4833235b90bc87ee26c6537438fa77559d994d2d3be5190dd2e54d31b2820 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a8/a5/403728b5614b28083f6424dfdefec5fcf58068495fb03bb08532671c642f/shapely-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ce88ec79df55430e37178a191ad8df45cae90b0f6972d46d867bf6ebbb58cc4d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cf/0f/c0456b1382d2a6815727cbd9c0713deca11653b330ba14b2cc165f0b9565/shapely-2.0.1-cp310-cp310-win32.whl#sha256=01224899ff692a62929ef1a3f5fe389043e262698a708ab7569f43a99a48ae82 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/81/8a/7ac076a86b2632f1872284c5e60ed5f2fc26094875a85b35d9fa17b52504/shapely-2.0.1-cp310-cp310-win_amd64.whl#sha256=da71de5bf552d83dcc21b78cc0020e86f8d0feea43e202110973987ffa781c21 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8a/31/ad4881727b700a9320a4139ac3483972901a9fdd17bb6a599aca810dbd85/shapely-2.0.1-cp311-cp311-macosx_10_9_universal2.whl#sha256=502e0a607f1dcc6dee0125aeee886379be5242c854500ea5fd2e7ac076b9ce6d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/e3/92ec80d72de8b881c52e47db1fd2f0519d49b6ad65c4c2a3fcbb9f88a91f/shapely-2.0.1-cp311-cp311-macosx_10_9_x86_64.whl#sha256=7d3bbeefd8a6a1a1017265d2d36f8ff2d79d0162d8c141aa0d37a87063525656 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/18/a6/2e1761f21605e3562b223be7ad82f2edb5c9babdaa8b2d90979112be70f3/shapely-2.0.1-cp311-cp311-macosx_11_0_arm64.whl#sha256=f470a130d6ddb05b810fc1776d918659407f8d025b7f56d2742a596b6dffa6c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/70/21/39c2afae46154f824564d6b5b7213a84d083e243b42b5a1e76de481f91bb/shapely-2.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=4641325e065fd3e07d55677849c9ddfd0cf3ee98f96475126942e746d55b17c8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/7c/76e54fa615a20ceb876e4de6b9f01a56926184bcc2076186c51c27ce39ad/shapely-2.0.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=90cfa4144ff189a3c3de62e2f3669283c98fb760cfa2e82ff70df40f11cadb39 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/04/67/05e96af1c4ee130e12ac228da1ab86f7581809d8f008aa3a9ec19ea22eb2/shapely-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=70a18fc7d6418e5aea76ac55dce33f98e75bd413c6eb39cfed6a1ba36469d7d4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bc/f1/c9db479170a7288d6bd2adcd1892785a3206b7a6f7972e7478714cb39e3c/shapely-2.0.1-cp311-cp311-win32.whl#sha256=09d6c7763b1bee0d0a2b84bb32a4c25c6359ad1ac582a62d8b211e89de986154 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/28/81/0239107ccd32eb30085c99fbf22da686aa72278afcc2c8fdf06fa0fa6320/shapely-2.0.1-cp311-cp311-win_amd64.whl#sha256=d8f55f355be7821dade839df785a49dc9f16d1af363134d07eb11e9207e0b189 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e6/7d/4923f27c340339e1c896c77cafc8ed672c8d381a025bbab6c6ddcba27e8f/shapely-2.0.1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=83a8ec0ee0192b6e3feee9f6a499d1377e9c295af74d7f81ecba5a42a6b195b7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f7/17/8bb86d26173982b81675cf6bcb0941ca144ea569a966d67774460121ba55/shapely-2.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=a529218e72a3dbdc83676198e610485fdfa31178f4be5b519a8ae12ea688db14 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/fb/7ce0aff96d317916ec75889068c9c6bd92268b20839efd270e3d4e7107ab/shapely-2.0.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=91575d97fd67391b85686573d758896ed2fc7476321c9d2e2b0c398b628b961c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c8b0d834b11be97d5ab2b4dceada20ae8e07bcccbc0f55d71df6729965f406ad (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0.1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/91/d3/1f7c41508d42b81b4f454ad20a7f17a73225949805ea638125ac09188d33/shapely-2.0.1-cp37-cp37m-win32.whl#sha256=b4f0711cc83734c6fad94fc8d4ec30f3d52c1787b17d9dca261dc841d4731c64 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e2/87/b8b8d8d57b429b01aa56b7d723075c09f33c988b8091bb6f790c83436909/shapely-2.0.1-cp37-cp37m-win_amd64.whl#sha256=05c51a29336e604c084fb43ae5dbbfa2c0ef9bd6fedeae0a0d02c7b57a56ba46 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bb/9b/c9dc1b43cd4364a247f7e82959f77b7ba63e6fe0b98435e3c98b08ba01d6/shapely-2.0.1-cp38-cp38-macosx_10_9_universal2.whl#sha256=b519cf3726ddb6c67f6a951d1bb1d29691111eaa67ea19ddca4d454fbe35949c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b4/6f/08bb91f043854ec9e73b8d970437b6dca7323e44c63b53d2af6e80a9edba/shapely-2.0.1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=193a398d81c97a62fc3634a1a33798a58fd1dcf4aead254d080b273efbb7e3ff (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/31/05/cbdfaf562ce7a0e0e89b47b3000d3445967c9fca6f906f833faba371053c/shapely-2.0.1-cp38-cp38-macosx_11_0_arm64.whl#sha256=e55698e0ed95a70fe9ff9a23c763acfe0bf335b02df12142f74e4543095e9a9b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/74/c6/2099380d719a7e38cf0643df562b50d458f4960c2c7bb493e2fbe850753a/shapely-2.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=f32a748703e7bf6e92dfa3d2936b2fbfe76f8ce5f756e24f49ef72d17d26ad02 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/82/12/ed1087cd4b9a6bc6f1f77b35078a49991672fbfa7302ea480322615cd909/shapely-2.0.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=1a34a23d6266ca162499e4a22b79159dc0052f4973d16f16f990baa4d29e58b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/94/0f/09e51e71c3a35818abe1ba75f2d2516a5c90b3596989920a0b116768fe32/shapely-2.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d173d24e85e51510e658fb108513d5bc11e3fd2820db6b1bd0522266ddd11f51 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/49/85/ee3d63f3a4affd146ddb01094c69ae74719c4462285e8aad4d3c6ec70a7b/shapely-2.0.1-cp38-cp38-win32.whl#sha256=3cb256ae0c01b17f7bc68ee2ffdd45aebf42af8992484ea55c29a6151abe4386 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2b/cf/c0315a82849de40897ebb09ef441fea4b995570443e4b596b9bc7c8a7fa4/shapely-2.0.1-cp38-cp38-win_amd64.whl#sha256=c7eed1fb3008a8a4a56425334b7eb82651a51f9e9a9c2f72844a2fb394f38a6c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/b4/b0cedcc974f5d3fba51850f81961f48a1246b4c4ddf4cd3faef6829e4173/shapely-2.0.1-cp39-cp39-macosx_10_9_universal2.whl#sha256=ac1dfc397475d1de485e76de0c3c91cc9d79bd39012a84bb0f5e8a199fc17bef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/a4/7e542a209f862f967d7cb8e939eff155f4294a27d17e16441fb8bdd51a2c/shapely-2.0.1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=33403b8896e1d98aaa3a52110d828b18985d740cc9f34f198922018b1e0f8afe (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ea/aa/45fbd031edf3149cb767d8b9f9db45d5faf0324d743c6b8fb0298cc022d0/shapely-2.0.1-cp39-cp39-macosx_11_0_arm64.whl#sha256=2569a4b91caeef54dd5ae9091ae6f63526d8ca0b376b5bb9fd1a3195d047d7d4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/98/e4/2d5b48e13cbcc976f468b995bb8bcfa8e758a8b73fe307af54184989158e/shapely-2.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=a70a614791ff65f5e283feed747e1cc3d9e6c6ba91556e640636bbb0a1e32a71 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0e/da/055d5b854a9a702fed0965d37754b79967ecfd67fe8377264e6a00b521ea/shapely-2.0.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=c43755d2c46b75a7b74ac6226d2cc9fa2a76c3263c5ae70c195c6fb4e7b08e79 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2d/f2/8ec281d357e8bb7d08dc8d727f0e4c8ef3dae7d3fa75c69c8e452bb82d50/shapely-2.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3ad81f292fffbd568ae71828e6c387da7eb5384a79db9b4fde14dd9fdeffca9a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/41/63/088ea482b1f2a046ec0576b173125a6485d0cc7e3868d50e74f4a89039f5/shapely-2.0.1-cp39-cp39-win32.whl#sha256=b50c401b64883e61556a90b89948297f1714dbac29243d17ed9284a47e6dd731 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a7/ae/eab645c4075093584b7a65ab71cb8ff4563a015bd935c9b55dba14b2c1eb/shapely-2.0.1-cp39-cp39-win_amd64.whl#sha256=bca57b683e3d94d0919e2f31e4d70fdfbb7059650ef1b431d9f4e045690edcd5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/10/a7/de139da3ce303101c357a9ba801328cba85cf6ace157da31a4007bca85e4/shapely-2.0.1.tar.gz#sha256=66a6b1a3e72ece97fc85536a281476f9b7794de2e646ca8a4517e2e3c1446893 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0.1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f5/60/be98f4173ed0c7753be66a2f3692f46506951db597ecc33858e253a86db6/shapely-2.0a1-cp310-cp310-macosx_10_9_universal2.whl#sha256=5b826bda9a0aa66cbddc881748b1adbf5dbc34d80ebb07a3b85fb7a75be502e0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1d/95/030c1dc6570770afa22d58492e641a0a97f0b96188d11ebf6310cd7aa85f/shapely-2.0a1-cp310-cp310-macosx_10_9_x86_64.whl#sha256=e8e3f8c7740e481439feaac3563e1af512caf8846aa944718acc58f96d372cd5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ac/45/fa63fc92eb493a8f202b11b19616fab872bc5b80310c2007ac9c284c895e/shapely-2.0a1-cp310-cp310-macosx_11_0_arm64.whl#sha256=f225e0f11007cbdda278bb6086069549962e835123cbc2ad350cf67677d554c7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ee/7c/0bb1f97504965791142bc329626bcd2d1260f6ad735b0774d1620c58d59a/shapely-2.0a1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=94ed56940f44fc7fbcc30b6670b10f90c8a72f9442ac9c92edce1ddefd28549d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/ab/f2e6d73e0dbc89684c8d385aea375d15b0e3fb2fb6dd36057f2e35223168/shapely-2.0a1-cp310-cp310-win32.whl#sha256=704fa966a24c4446948c2550cf2e51e9a9fd5236ccd881d66498d3f7123e9435 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a0/63/bc375616c482d12db41d5cef362f073cabcbe8a31c7731d62d86a0bbc86d/shapely-2.0a1-cp310-cp310-win_amd64.whl#sha256=c37bc4f2fb60d20120b9dda43749e944edb709de952547335267702c00ddaac9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/49/d2/abf761db882f5f0f91495b43fa372b3cedd05da0cd6360ffa601a0f30ffc/shapely-2.0a1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=24476adc318069c3f5357d728e510002d618ee607fd62b3ee013dbbd25b984ae (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/81/d29c235b3861b80f7eaa8134594491cc89168940f4e5098a4a38da649804/shapely-2.0a1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=8ca48109de5c930c21abe0a387fa6c04fb1eb4f73d1baccbdd799312e1cefc64 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/b1/83/235cdb3c9a9f565e490f6b1604f90181d499ad5ef6a988c3c555b240e99f/shapely-2.0a1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=eac897ee012587aecb98f2202ab8e88a34ea0d3575baa148e5ae628c89f5f93d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0a1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d8/f1/f03a61298a5ff0a25aee7fb48d791f67dd49482afa57803d93fd2684486a/shapely-2.0a1-cp37-cp37m-win32.whl#sha256=7442a3464e3a301f42d906c869453aac376ce6a75c10675eafadc0dd454b0ccc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/41/3a/2cd0ff4318a9daf5f43d2c84e89c970adcb28dd80e40918dd3a36a7ade1f/shapely-2.0a1-cp37-cp37m-win_amd64.whl#sha256=f7cca6312429cf2e18dd24605840bc139fafd90b9956bcbc907c43e9b1327c63 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/49/9e/56493a549145b852f3a1adecdb1e2674c8adc5b0ceb4a7323ff0da68502e/shapely-2.0a1-cp38-cp38-macosx_10_9_universal2.whl#sha256=00ebedefa25e731437b94f0c179da4ef43e4f5fd1362f63a6e3b31065a1a2d9e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d2/48/24533afc8589ab9d468d1c0b29e6bbc36d26277fef69e57f7b12ebeeaff2/shapely-2.0a1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=b50ff159b64bf1b028ed3ad14e5d766981625371fb2443d3b62788a6abb2dc1b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b9/4f/ad9dab50587270166843c4ccad78a8eca2507c3417b708a3cb5acd4c3a19/shapely-2.0a1-cp38-cp38-macosx_11_0_arm64.whl#sha256=dda0f6baaab6f9fbb175d692fa22ed3355c20f1fea04b954cedd84eca09e00c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0e/55/410b0006022128e8db3a6068d503adf37a89089c21f093215545ea54c4f7/shapely-2.0a1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=857786c0ffcbf99ecf2fcc5bc95ec83cf8d658aa08387d3e383029a866a30ee2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/82/20d43a3f4b90c144bac6209c379b8b1d370fe02d1c051272d7e330917d66/shapely-2.0a1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=12d221693ed996b5fd55d817ae4322bdc792a91099dc3577ef674bf638c3e514 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/36/97/866fe04570255b73b450789b0d5b45950eaa325c2166c97266a53c0a3a44/shapely-2.0a1-cp38-cp38-win32.whl#sha256=ccd4e243650f373b4085036a513aea556e18a6e99d75483efda28937f3594b19 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a0/e3/bc9dde2f36d4ee2621ca280ce537c583e5ad9c0953991d2adb819b605b96/shapely-2.0a1-cp38-cp38-win_amd64.whl#sha256=b6296c60f049e9e55a20c36e5d925106cd9aafb2b4830e2fcedac51400a2a3e6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/67/ea/591a31ea848c46e2b4e5e622546a5886c9c636c4791ce40d57c009b8191a/shapely-2.0a1-cp39-cp39-macosx_10_9_universal2.whl#sha256=8683341053c63f0732332ba7ffa25658cbbc74d70b5b9d976ff6bbfe67c25c23 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/a5/151d8218f62f6ebf287a28e85fea96ef2d57570eb6d85ec544e7d82bf568/shapely-2.0a1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=8ef6e7a6832072f4dc3feeff49dbb33057f714719c0ac489bc86eb9ba4a0ccf4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/22/46/b2751966a905e09707192d376c9b1b8a593cfbfd7af7f86b2f109fbdd58f/shapely-2.0a1-cp39-cp39-macosx_11_0_arm64.whl#sha256=4882cc2e9a5b6a2c5310bcf530fd64fe045c158d02b4d4ce84c255bb924e41f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f3/92/c262cecac917b6c2fd9713f24da099a38c613504596b053e4e4cc1ddbf7e/shapely-2.0a1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=522811d02cbc7d41ce61b1ced9beefc22088aae9a53cb40bf4c7fc24fb91f8df (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/12/11/32e5cca2120df08b81747a4a189273d58eda555c52c8aead4e83de8e73cd/shapely-2.0a1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=085f1631e3a57b78ce2e6ea8d160e379828768b416641dc81e77f2047f108e1f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/66/ac/0750ff9c1f91bf7531bf787df030b8fe4b02d19735789e9925017d7db44a/shapely-2.0a1-cp39-cp39-win32.whl#sha256=fcec8bf2306f176cda1179dd737085632ec96271d33c2c54f506d8a6b8655a3d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4d/54/998b9017cffcb3d7a73e34da394b4b447820e82f99b62823278708738c74/shapely-2.0a1-cp39-cp39-win_amd64.whl#sha256=502fd501b5ea4f62886a80878da22be9e286608a9ea1238468968518d9af89a0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/48/24/09fa30cb55960e538f120f1c831e4e456c1bae74c86745c5a0d6b22d677e/shapely-2.0a1.tar.gz#sha256=599a030ff2ba2344a2056c74bea888887c2c7206261babf182c92eb73c646c9b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0a1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/69/9b/ba6359e51e68d0e6eeee2eb213f8cafce565eb73b13ee68822d7d2b74d0c/shapely-2.0b1-cp310-cp310-macosx_10_9_universal2.whl#sha256=cdc3d0e45d574b5d25d1aa4bf1ba9c3c1692080414cca43dca6a82c8dc01068e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/13/e8/9b55ae7a811ea50daa0524ff33576a74fad6861b60977400c3f4e60d4d93/shapely-2.0b1-cp310-cp310-macosx_10_9_x86_64.whl#sha256=827c1ee1dd88cd5a65c3ec2e4e874bfcf69628dbf9d65130442ebe46fed3c3f7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/4a/e1709f3515b3d82a4a389906860d0ff2a66e66785a81e1a8481aef76c819/shapely-2.0b1-cp310-cp310-macosx_11_0_arm64.whl#sha256=35e2d6fbde43589a3f8de4a142184c602e733f3a8c95a23388fdc47516e9b6ce (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c7/b5/8b2edf017c4d5ce46d98ae4c64dbefa963c1283314fc608381a7d3d56324/shapely-2.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=2b76d8dd5cfb8c4743c0b492663bf5260ec02618a37be0c8ac5b5abe52c50b33 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/79/5e/dbd99fb80a360b223c22d954fc86f73653dbe62beb723306bc84a85d3bfe/shapely-2.0b1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4218010c1c1a420f2726d06008f3213807d415c9d77b83bb3c524a1e67f8b7df (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/08/1e/158359b1ab9ed242f0475af38c859aafa3e4acf46b4da08232a24b935168/shapely-2.0b1-cp310-cp310-win32.whl#sha256=78f05849f93aa0a64f6cddab338cc7b4bb17a83fff1ab11085b9944cd3eb2f47 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2b/2a/15fcc1c79f03a2b6bc7e78f0f750db4a23c487b70f587c79a297f571af07/shapely-2.0b1-cp310-cp310-win_amd64.whl#sha256=5f0cdbe3623bdcef9f3d11293e3b741fc6dcc738d284de48874f59e6869905c5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d2/f7/8bed321cb9c9c4e680876b6ef6031de11eee2b88eb09018def6a8c9d0cda/shapely-2.0b1-cp311-cp311-macosx_10_9_universal2.whl#sha256=30b0be8d356f7eea3cb434ad446f1a960d4f434f1ec5416457edc336ab5ab5f6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/01/94/411d5d19e8dbeb3d334c850c7635cb6ec42524ffebdd052483d46b03a14b/shapely-2.0b1-cp311-cp311-macosx_10_9_x86_64.whl#sha256=5b949d9c0f65c3df6465f3edeee1bffbe81d4debd57c603d62cca215c1c80df5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/53/4c/f316ff0ec55708a1099c635e7e744cc0027cb5b924cffbe8abf49b86a605/shapely-2.0b1-cp311-cp311-macosx_11_0_arm64.whl#sha256=e3b33121313f762e015e22fbf3a29af28c96f30c16c6d417aec56e8f4f7a65f1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0b/6d/a253c71cc4d459a85d7097719f14237f3fadd635ae4253c28be3b7e6b5ef/shapely-2.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=053ae5e9a190709909ca40bd9e169d9315458d24553af64efa3fa29fd0658d8a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b5/20/e62402d935aa5e445dd7e3d96c319fff400d850f834a1dbd111865a58d77/shapely-2.0b1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=976e21bebc8088a98ed17f704db68c72e18abf92076e8c40512158baca43ae77 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/00/d2/60336cb480fd9f8ae6d13c6b21e8bc642d5d543b4a7213d49330a24c0e78/shapely-2.0b1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=78398ff5ba4f7f7f7906688aebacb89d8fbe57942f2700e2739b2fa56c94ec1d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/49/08/9075a7fc7b7a3048011dfdaf65db135b872125bc67c4b5cff42ef218fc2d/shapely-2.0b1-cp311-cp311-win32.whl#sha256=cd61870ebe17bc42c972ae7c30dd5459da4455e24dff6a2cce264aac18299e90 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/54/09/a8b8bbd64fe67a4f5ceedde05ebf14f7a100e56c8c6434583adf1a46bbaf/shapely-2.0b1-cp311-cp311-win_amd64.whl#sha256=50040535a71661d9a84e482cb9acab4a14f763266e1751ca46baec86f78a3470 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/65/0b/87ac8d221ef7f2edabb719fc2fa7edd4389a4eea9bdb3eaba13a3d1d9d92/shapely-2.0b1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=69c69d670cdf93b8a9a30b366766f2f31d40cd7c3eb9a4b27100bb3a90a6ec0e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/28/17/94211469e3a59843ce61342d089ea8f833b5e20c07f685fe32cb9d81de44/shapely-2.0b1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=bd2a3d95dffe66d3fb4b0db1539a419643ae095820e99277fcc7a616376ca2d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/29/19/f565c580c6754bd30a9777db2f16102f6f67deb4bbf9463b5b78e5e095cd/shapely-2.0b1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=562779f1d63d5f1387ae885f9687414818632e1d6df66bc15c95488d1c6f85f0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/4f/e8/93b39065222834d9bbf6ede35a7e4b85722c048a85148c4cd847bf9c27c6/shapely-2.0b1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=251f3bfb29806fdaf3ae3bb42e0017f49c19c9a94ebd07173bb60c9661332092 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0b1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/90/03/8c4d4faa5c224395e4399cf27e1c3194a37be89da90cf4eb9c0f2de5fc41/shapely-2.0b1-cp37-cp37m-win32.whl#sha256=cba3c9a4acd8d5ec35e42bc275b2f99f947e129400889136615be4da096b1610 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/39/c5/2b151a161c7493597df07a2d0f9be2967fc407921769cb374cdc386a40de/shapely-2.0b1-cp37-cp37m-win_amd64.whl#sha256=4f78ec0a64159935b43a2f3be1a46c7d48124e7b1f97969a2e1a202e0580a68c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/24/70/a3698faf126fead6fe7e81c5f8bdf4f8ef114c7a8316f461498de04afdda/shapely-2.0b1-cp38-cp38-macosx_10_9_universal2.whl#sha256=aead4839e26d6e90144a89d156f04c7d211d5eee10b24209bc4502f4ce7900c4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ba/98/6275a0686d90a0a2f38a43a35a069c1261ef168ee53c487f685a7af391fa/shapely-2.0b1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=d1a9bcf95ae12f74e492c6be19039b9373e9732712d6fd2450e6c31733335ddf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b3/6d/5cdc287dc59d86b78b030256f8bbacc5122a2b248416fb5ca705f677416e/shapely-2.0b1-cp38-cp38-macosx_11_0_arm64.whl#sha256=dc06b1c4206b7be4681e9107b72e2dc987d8f6e2f04f54323565d569af2b3543 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/96/b4/9d9f439ec89ecae9c7b2a14836e4e0767e368af471d018b64aceb8156337/shapely-2.0b1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=ba1a8aa7b56e14b9e8a274962d12d9428be5906bb3773b3552a34efe46672b6f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d9/c2/4f6e2c9b63e3241020526597c3c242b7743d1e9bf43d943d7517e1485803/shapely-2.0b1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=dfa574e7ddc7bb1d6ca7f9b0236ed3f48a5eca4f39c39fd0340a6be33cb92360 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6d/1c/8fbd0d85856891a6c96b11e9fec2ef0588efc8bafc7127cb88d648817ac5/shapely-2.0b1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d31b7e3cd519c20be4798769c9bebab3b10856c4fcb95ad6419eeea150696076 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a5/13/ef9274dd7b1c42fc354630cc67d43a5b8b93a4afdeeba6964704c736b517/shapely-2.0b1-cp38-cp38-win32.whl#sha256=3386378e7f13f38fe7707f3d2afc041b9572c7c0989fd2c8b0cbd335a9ab2b4f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7c/e9/067a08f56e5e0abfa8eea6448711f00ef51c36f413b2b4b87db2beae0f09/shapely-2.0b1-cp38-cp38-win_amd64.whl#sha256=8d59cf622a37a5a066b8c7e3d3e3a21aedc6c610b23c4c7b31c2471f8d2294bd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/92/18/6992c3b85349152047cf252f62894125bb84bc935342ed078dc9bc32f1b8/shapely-2.0b1-cp39-cp39-macosx_10_9_universal2.whl#sha256=2c7340d5775a2d2cea0f70da2169c7ad431d34fd3b4818efb588f52852e9dd1f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ff/05/d2afcddab63bad0c41e1342756fa3a2baf39339c93d48702f6eec12d9ead/shapely-2.0b1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=fe61c7eaf638004d6f5bf380de6aade06811919d8d4a0c19b609e870340e8ebc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b1/8f/1d755d7253f16977d8184425df6f4c2fefcdb1f7eb9532eaa6a2b68e4a43/shapely-2.0b1-cp39-cp39-macosx_11_0_arm64.whl#sha256=d62168810bce3a58a64ca8a5a100f630242c4c510b5fb458d775abb273cb6092 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/90/c9/fa9df1a416ac8e08f4eb9beb01ac8219731b85dfd78230d96510a819fc1c/shapely-2.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=573424da9842875714f5cc6eb06e2fc447c701c18feec4dda2d694158a814952 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bc/7a/68ef76765c26d33fce1a8abaf7d220beb5f6b8e6f124145277c85868ea77/shapely-2.0b1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=72ebacdcf94e19aa9bbb24694d76784d3cf47e1b2a9ba38f05ede4024bdc08bf (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6e/cc/5b709ec40c7ec2b2f12b6a2a4449cbe471535c2cd628fa2a013316cf6725/shapely-2.0b1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=83f9ecfb97c429221d49383f6ccb2f9044a5bc5fd3c1e5c47cccce7f4d172072 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/95/2a/076493512437751669d341df6d43d2dc09fe2f58e9475d113cd9b0969d04/shapely-2.0b1-cp39-cp39-win32.whl#sha256=edba1927b9cfa95fa5f9cf64ddda0da6b19b3ea8f39f15d59431ecd6e2d45823 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/71/01/5937386b687d8e41b5951fb14f9a2d1437252f9890a1f6469bd664da77f9/shapely-2.0b1-cp39-cp39-win_amd64.whl#sha256=f95da1324de69b201e9870f588c261706786869261f5859f68777c3e37537852 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8a/ad/5aa421b192f5458b0f0f8d54ecaf87dbb03f1ca907fd794c8926b79aabbc/shapely-2.0b1.tar.gz#sha256=624dce6c1bcff6f461fc3ce45f6477baa71754b46fcdca40d481b9a3573c871d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0b1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6d/56/16d85bc51a07eb1c37343318df0b61100d7ec0de8bb2240f1c4119acbd6c/shapely-2.0b2-cp310-cp310-macosx_10_9_universal2.whl#sha256=9f93782ca0406846c3e14931c1afa9bc46de63f2eb985589b037746eb7e5de4a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/91/0a/65f49cf6a935aaaf8afbd65d2f70bfebd18173f84cf650b7eb4e133ab4de/shapely-2.0b2-cp310-cp310-macosx_10_9_x86_64.whl#sha256=944a662b3cb5b98e6e15df299ac6b1dc0ebab794030555a2d51e59dce2349af6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/67/b6/c6c1b2c7aba83730412caa8b6fb9556e62e6e59e323112790288fab8e661/shapely-2.0b2-cp310-cp310-macosx_11_0_arm64.whl#sha256=b5dbafbe12f48bf43e631f5164b9bf702344df9d22ae476801c69f8702330065 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6d/48/afc722699fc5d28de6095efead8612ce0a3fb7453d13b42cb20991778dea/shapely-2.0b2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=32cdf2b30e068e77b15d90b10a90a2b4b74da70eea5e8b243c982dfd2fe43d94 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/37/5d/abc72dd92cfd36675657215de8ecff512702c416c5c02daccb0be7170f7e/shapely-2.0b2-cp310-cp310-win32.whl#sha256=4d76e01bd5e0bf2e817f2eeddf4c6e7ed200530c65818a5f8883b8e5cdb5dbf9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6f/38/d46bf33b721f4bfc38c14d2d5850746bc1142c62939dec852750b4a402fc/shapely-2.0b2-cp310-cp310-win_amd64.whl#sha256=b13a61096795186e55dd6f41f240f81141d1938d10fdaf59f387df576dab91c0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8a/2a/1da875e39b3ae1da255f91fb23cdad4a8b2b52bfbdfe027775117082c2d9/shapely-2.0b2-cp311-cp311-macosx_10_9_universal2.whl#sha256=22b820047ddef0e3ead2e01028e5412dc75b8c70c1d60e7f8e972e716119cae1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/84/c6/101277238ab1e6d878d3657f722ecbbb787d6e9e9cac7fd3edbf4a0b96f7/shapely-2.0b2-cp311-cp311-macosx_10_9_x86_64.whl#sha256=d6008c310e6589a8df2ff7f307f7b24dba1f588c04edec905f9a69adc632bb67 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/40/72/69d4cd84eeec70892956ec71e282a2962095c12f06f270f4a162bb8e6338/shapely-2.0b2-cp311-cp311-macosx_11_0_arm64.whl#sha256=0630112087213a9ab34e878be1267cedb2c10a3a20560ab1c8802fa58dd7bb58 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fe/80/df0ac57ca9c6acd4c36b673d14081e9668d9861b17a872d89dca4e7166d9/shapely-2.0b2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=252940416bf6c34ea38b5584f91fcfaa2f651fcbad58e6b4ec2f844880b7c27b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3d/f8/efac3ff0865075faeaa292d31b5d242ad080a3ee7afb4f4afebc0a86400d/shapely-2.0b2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4ef19fb9402e5cb6301d578c6c49859d35d0948c9b4f189ffe7429da1ae03332 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ab/68/e9c04d4f54c8c98b175db0ce63abc0e11324250c50ce9459d1cac5204797/shapely-2.0b2-cp311-cp311-win32.whl#sha256=130536b8b4db588c2b6e3bce4d9e9d7d14e4f40e92dbecb25fbe765ae6530aee (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2c/d4/57159eb11b93af28608f7ff634a6ceab1001f65f166f6a52856db3bb0c1f/shapely-2.0b2-cp311-cp311-win_amd64.whl#sha256=bb95f5f08bcd34f8d810bfd3514c4580881e95f155fe9d8840c2c7e620ce46b0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4c/eb/c78d2b097b140d6f47d406eea03a3a89f4686e8bfaee8551351512833319/shapely-2.0b2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=c758e7458174896b59c41d4c388a0e95d4e4bf6fda60226631182d74bb2bbc4e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/dc/a1/653dbc324778da4572e81d5adec548fbb94f36cc45675b09e9b0e2e2ad07/shapely-2.0b2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=c7f4a90e9437b274c930dd0e2716a737274e1c254e596ec022b7a32a8164f102 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/03/ea/2a59a162876cc0d52353ac72a75bc8496bc7a1f16628f2e37a034eecf2ae/shapely-2.0b2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4ec85206455124a5a9973a277984d64b8c7bf875ec6c368b96262d0012dd81a8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0b2\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2a/be/8801fd8cd25d59f5456e1f3dd5dbd39efc4c57545aa104a005e26fcb02d9/shapely-2.0b2-cp37-cp37m-win32.whl#sha256=d1c23672549c7a87ede495fb6a96ce4746706e54ac7513199fb9279b703667d8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/5e/3fecf68bef7862c28d504564a4665e13065c5fdbd66dcc3b88e0322db87e/shapely-2.0b2-cp37-cp37m-win_amd64.whl#sha256=528f93e933db7f8f98ddcc8fdcf4977f4e4c22e32af9b03846d9464c615c5a33 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2b/5d/32550fee63c285fe3ca4ecd59393bd130d082b638996f2d8c1eedc5b011d/shapely-2.0b2-cp38-cp38-macosx_10_9_universal2.whl#sha256=1e477c7da8e03d1bc15e670784f3295bc81ad7e27ec38458461e9172f0cca084 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5d/03/3aaf52655ef6d3988314d08679c4ff973d87965a3055defd427328727919/shapely-2.0b2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=d96d7c606dc4646d844a7332e873ee26eba874e237a414d7b4d83a11b114a0b5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b1/94/c717e6c27ad0d0de47b82480292875d66eb0fe467e95125b3a0d9144c8a0/shapely-2.0b2-cp38-cp38-macosx_11_0_arm64.whl#sha256=f3afc54b5389a7387d13f1d3ed1e46af3b6a5def1cbb0da02bfcf701750a6743 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ac/18/4cbb54ab5bb710efef01cb41d71a6a93ea1f90e6dcc645a15a26c98ba740/shapely-2.0b2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=a6aca100dae965853b64fcb0940f53e4ddf4a24e3d7d7f4619c8912e128d74c4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5c/b6/fb8fe84b2679796ea44ecfc1185481603bea9b3fdaa8343bff697940ece5/shapely-2.0b2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4f956f5b1ca00cf1e8c79c6e39ba1f653b133d1947239cbba60f87050616effb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0c/e2/0bc481e4a2f173b6b097c0d6f430ac5fac2cbf9342759b0626bb85012027/shapely-2.0b2-cp38-cp38-win32.whl#sha256=4276845b4f585add71b99163cbe080db3a1c1bcfc4843dad1fdd24e49a9f10f3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/96/a4/f313893caae2cb2e09978faad8182ed6b33e7a1b586fab88cd1bd1bd1bb8/shapely-2.0b2-cp38-cp38-win_amd64.whl#sha256=308cb4a4a60ca9b3a067475f6257da533593b831d5c835c90b5f4e860b77dce7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6c/92/1faa1c5760d0cff10b7b2de185e87f7c90f7db8d4ad881fcdf73a47cf45b/shapely-2.0b2-cp39-cp39-macosx_10_9_universal2.whl#sha256=5e8d78668bfed82be6d9ad80cc4585ae5731520821be12a723adbcdfc601a8fa (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/98/dc/e2da2955127f0bbad4d439886f3d5a527530bb366e5e14f297863ca38852/shapely-2.0b2-cp39-cp39-macosx_10_9_x86_64.whl#sha256=190a2b84b115580b513337d5ba5322b5bd310816e7ffcf80c3a3cec42063b13b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e6/ca/c17c2e162a8592829799c8fc304c1df0ab78e63da305fbb7c128e44b5eb8/shapely-2.0b2-cp39-cp39-macosx_11_0_arm64.whl#sha256=c6f3e56ce1ca5c69c0a7d6d33cdaed8df95408a6230d7dd36ba6b4eb297eb9a7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c6/fa/f7969d71373fdede479a07fa8c5de07da2b150fccb8e7ed5fe66b1c7f388/shapely-2.0b2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=a7d75ac5885438ea777bb93b1ef41d2965ec1374f51d0b0891d9089545c8fb85 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c7/76/63a75d564cfcf8fe845be25656b1a9fa95760efa183d2aef4203aea8f015/shapely-2.0b2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=efec2db2368f34f8efd7e5739f900c8f9d5868b089b1dad108ecfcf9f8278cd4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9e/28/7b91c8bdb546396b7d3f84adeb19f144a87de4fc40ed5772113d5c9680b9/shapely-2.0b2-cp39-cp39-win32.whl#sha256=40318da326133a35b0adba5a82ee8a55e0f773811f284afc9051f4daaab96f86 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ee/b0/375c6e30fc17dcbc42798250412d2670b56c0425d3f8cb5319a3688c644c/shapely-2.0b2-cp39-cp39-win_amd64.whl#sha256=8553ebc294a45471d1cd1341af0834032d4ddd32597108f59227f22e3fc0a029 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/24/92/5f91320f08d6cab3ee030506fe53fd57ed4057ac2c0ea431da0d672348f9/shapely-2.0b2.tar.gz#sha256=4345ea7a75f6933325dbe82bdec14b4b4b16b15f6543eb5f5525f363430573d1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0b2\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/fa/38/f99b4f16f56ef930a7efab2926bd7a1b1be00188ab9f89beacee9e82408d/shapely-2.0rc1-cp310-cp310-macosx_10_9_universal2.whl#sha256=0a4b5d4e3e100565c45efa9c3b2b5b1115d78f7e5c930f745e335b9639847266 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f3/73/cfbf1566d565253e42c64bae6838e1a093331183a715446a5fce67ced77d/shapely-2.0rc1-cp310-cp310-macosx_10_9_x86_64.whl#sha256=92bb6a571a5bd854b2da21d04df5675157d637c8ea06334f45972186f2b846f7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7d/03/7c79e9a4eb85250c099137d17839629324db65fcab8fcf9cf8e5cd20a308/shapely-2.0rc1-cp310-cp310-macosx_11_0_arm64.whl#sha256=4ecc8bf234f2f78cdf50cc65970643b46258595bc15bcf87e9ff4861ec8894ed (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/de/cf/9a6d5135b15c14752d0b7af2293827b6b0e770d7905069a3abf2427b7850/shapely-2.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a135f311eaa734fcd79dcee7e5e71483b17c3ba5a6699fe71138b7e1de9252a5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/4e/dd8efb23a9d63f2abd8ecd25720e8e559995c5913674f4137c7a8ef5e278/shapely-2.0rc1-cp310-cp310-win32.whl#sha256=e955afe990343e2d8d8a70e3adaa615e8c47528340ecac258ae31921e29a37ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f1/f6/31ed9fe597d9d4bf7708ba6996daec146b87be55f0c0284e5ea810684464/shapely-2.0rc1-cp310-cp310-win_amd64.whl#sha256=d60550c97dc142b8c14ee3d5142b50b4abb22466feb5b5bbd4bb0113af5689bc (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a6/5c/a376229525f06b7b6c4f18b47d0a9f8f1fb4252719b1c50940f28c961853/shapely-2.0rc1-cp311-cp311-macosx_10_9_universal2.whl#sha256=c524d15c5b31c1d98a8dc38c13964d2b87fc0ac0bbf044333d2a8b87326e170d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/cb/96/c402ca153d13fa5601e8bf7fea12cf403dcec51be09c1ced0dc86f972db0/shapely-2.0rc1-cp311-cp311-macosx_10_9_x86_64.whl#sha256=a45558a2e2bedae10a54d37b0d25c8eb1229d383f940c6204c2c941ca86e51b9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/16/d9/b2894677aaa3e51e681ad910642af15f2953b641f005986ad82255e8cf4f/shapely-2.0rc1-cp311-cp311-macosx_11_0_arm64.whl#sha256=423d652a4fc8ad95a9c4153e0ae13ce9410d0030b8bbc24f6a04909e6d519c07 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ad/7e/6225393ee991f216f09ec14c0c70f8c13624c91a9c55e60d59c9d284cfdd/shapely-2.0rc1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=7a0a35a2b43e21f95257349248eafc72dad85e6e9fd3d41f04b20e5d48249823 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b6/40/1042e7ddb6a7d25180e2946142bda9e65092b53e9ad6647f86fa4aec2615/shapely-2.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=61efe106c48c167ee83c7c11e1adf32a9cd4a2433ad0ff909c94feea85624744 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0c/d0/bd171db4cd36316c949dfc433b31334fffad827abc92864a9493425dbbe3/shapely-2.0rc1-cp311-cp311-win32.whl#sha256=c75da1ee2629fed835a6c562c5c8242cca7922f0faa0f4b6a08313e637ea35ca (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ba/a6/543037e3147d46c54b9c93481a94f1fcd773bd5237d87daf42be3f24c03e/shapely-2.0rc1-cp311-cp311-win_amd64.whl#sha256=22d95232077d01eb16867aa6ecff9f9177bd3a415ecfa00a9f775354805cf9f9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/86/55/db2ae14932ea3d797352faadb0e851cbbcc5f97b31d3babef817bae37d4e/shapely-2.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=a8aea92fdf8bb2c233cd65b8673911ff96b372a897a5e418fd6d84e459db4461 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/be/0a/d66b72a31094a4ef7c339072490a47ec39986adff52028faf5317ea1d087/shapely-2.0rc1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=31eaf3f44c24eaa2a8f1480eaab696bc18f8bfb6ed2a894383b348d58cc0d3c3 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/70/7d/d34d4366f6ece2af0cdb5d19a7559677d1acd80d50fb2c63dc1f19052b08/shapely-2.0rc1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c65c51514a320ceec95c91d54b6cadc14478e6c262ef299e619c23e95ad49ad6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc1\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/2a/48/71cdd2ea9da4b5dc0a616c6f38143cf7844a535713cffa0066ffc073d683/shapely-2.0rc1-cp37-cp37m-win32.whl#sha256=e1ba3df6416d1a064165b6069b3ab59e0aca283de8d5e7ffee20640b9692dc9c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/33/3a/44162b75e786382b70d809aaca6f5f6be0c5338e27293a564ee8642e6059/shapely-2.0rc1-cp37-cp37m-win_amd64.whl#sha256=85effb329fd455500e26c77c00eec1babb75c12aaeddac30778ec3f08994223a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b7/0e/d034f6788baed3f3b3443cb6871b04e5ad55cb303c0e16aed9925942e1db/shapely-2.0rc1-cp38-cp38-macosx_10_9_universal2.whl#sha256=87f7ec1045e6e9da79243ac61c8aae986e135603c8f653d5fbedce0a21e880b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/93/cb/b4700a0a3246228aea37fa25b270bfeaff0cd334107bb471e5abbb0722ae/shapely-2.0rc1-cp38-cp38-macosx_10_9_x86_64.whl#sha256=0329c8cd75093f33a6bae668b466386ee5b2cc9f84e6db50e06af122a54d4971 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/eb/dd/853a81ac3a3ff9023babcf860b1b7864b939ba1693657ca5e82b88ac3fb2/shapely-2.0rc1-cp38-cp38-macosx_11_0_arm64.whl#sha256=ceebebbdd2581be67e48d6d98fb83f2e807b29dcb3ffe51742261f1908c24902 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5f/87/3d4ac768255f8754fd4d7c12d145370d0791c1691f441caf3bb273d0c63f/shapely-2.0rc1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=a58e7309cab4ea55f7c90859e0f32a451bc1d9cc2700fb534a25ae1939ee16a5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4e/26/9d96b2452843e6768ddb3135303013d573557288f07e3e6dc1ff1b3aef8c/shapely-2.0rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=16cc1cea91ae2790cf82d268ede9d1a833ac04ec0d74b2eaeb98060179a9c6ec (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/f8/e3/d552397e12ac54bac53cf8e6f083143ae9e6e9833d3ec1f30f10165f0046/shapely-2.0rc1-cp38-cp38-win32.whl#sha256=aa3d1d463de704e9884e00969a6f5eb45ad9a1d6178bbe35f15cc7dd54f2e19b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/89/26/e21f29f28cdb603b467f38c5b4bfeaa8eac004a4fc47e578611ea9328b09/shapely-2.0rc1-cp38-cp38-win_amd64.whl#sha256=d67f833dd0c320ee666675b9da47278d39f125d56947e6fdaf9f0174199e8602 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/86/17/a6f01db5d87271aac61208ee256e4cf1b46a5f72a52fce7d3cd2f65749de/shapely-2.0rc1-cp39-cp39-macosx_10_9_universal2.whl#sha256=9c5fe972139f4e962ef7be1981a1f61f7a35f624636be74c1d207aecffb092d0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/3b/76/3d8569f46151187037ef8c0606a2e5731a54916ac4ced92ee695ec3bba1a/shapely-2.0rc1-cp39-cp39-macosx_10_9_x86_64.whl#sha256=5a9b22e969567d7c129a66893ebf87b3ef49b95b61e877a0a962992fa7ee6c48 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/5a/7a/887873c7222c7df6d8d48707a6ee5d8c5e2b887c97a1f533f8757282012b/shapely-2.0rc1-cp39-cp39-macosx_11_0_arm64.whl#sha256=8a35ce3fed5b1515708524d0e47890ac12b68a9a1169f4305e6a66bb882239b6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/93/a6/46d0bcc8d4f2b11cdf959e686e6c3eca09b3ed39f4ff1e391078bf2fd77c/shapely-2.0rc1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=f856a9d928e92f2007e75ec2ffaa1275b45d8ecb81a556a4857a513a6eccb257 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/6a/96b02a08e69e6ebb1bce9fd0ba5b766af1e2b37b1d72421435ab0aef9139/shapely-2.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=987621072b8bed9679495145be6fe3deacf3b395be96ab5531df85d185ef44dd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ae/79/5cee3d3ae821d9e66d2ee0206866d7e1b7e09e954b99a4351bc308c3ec90/shapely-2.0rc1-cp39-cp39-win32.whl#sha256=d3f89d358a2e2ed7d4f34135624cc5ebbdaebd877f638c852e0be7eff5b1b096 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/df/55/fdcbdb554f8b2be985ae42865ab3e9925c61889d33f9f3e80a4e9a53c48d/shapely-2.0rc1-cp39-cp39-win_amd64.whl#sha256=d666e929142b1db3b49af223be80e8f8ebf8a519f475f8ab64802e5c5b16d5f2 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/36/1e/115ed63a144bd15a3320372c3a68d41e5c963ea856cb5df33ff57ccf28a4/shapely-2.0rc1.tar.gz#sha256=f09cdfd0d9a2d239afc92c299dabe4510fabb63ca7abf683eb48532fd63e63bd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc1\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ef/28/5683a2850333e8c710c6290769d0d06048a39a221efab0ac7d8c0a843473/shapely-2.0rc2-cp310-cp310-macosx_10_9_universal2.whl#sha256=35adc1aceea668e39141088ff6f29bd7188e9715b478cd5253e04a4e418c3fd9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/67/16/198b2d7f4ebde116a9a4319f7d9033041a69fc618cde976da863f3f2fb46/shapely-2.0rc2-cp310-cp310-macosx_10_9_x86_64.whl#sha256=88f48c53cbba79bccee24d14fdbb18d461411f6467c54e54acfa1190e8eb3c30 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e9/a4/dacc6cab3b2afcc73a565d6169be98b7be66587c946c8081360f307af1da/shapely-2.0rc2-cp310-cp310-macosx_11_0_arm64.whl#sha256=8525ccaeae1a858c08f7585632a0fcbcdfb00956aa26380568c37dca1586e783 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/72/ab558b790b5669095e89679e1085e760210a4424bbf09bd108cd51e70cd8/shapely-2.0rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=6b70d7fbb3617e2f33b24b19313c9df1a87c6534e6535e2dbd0d8c907787487c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e1/7b/77e9c2748de520d1af93a33fbeb34b20d6e1611e47becc171e8fe17937c5/shapely-2.0rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a5c1c85cfbcd04653e085ed471231580e146df825b5200ee1a40269562139365 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/91/98/7597662c5da9bf10b0db8f89e6734c0ddad5b0c8cfe60663bbe8e32fb42a/shapely-2.0rc2-cp310-cp310-win32.whl#sha256=f4473e9525821702c5703d7b5f26aed1b0422f4151ed8e2b38e3dd6fe32449f6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7b/cf/ec13b7abd68f0604f811c8f8353bb3c921506e6f7f7ca91aeac594124270/shapely-2.0rc2-cp310-cp310-win_amd64.whl#sha256=b57fac9cf7a0264f245a0fb7d7a833b0687e0c264eb3e1af60878436e35033d5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e2/86/f044366ebde0956b82f23bc659ba91477a69203b87947cec399b01a96666/shapely-2.0rc2-cp311-cp311-macosx_10_9_universal2.whl#sha256=957f2fe24fbe65366e6941e1cca8414f37e1a54dcace4f7b9d493a9d0225c6bb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/6e/d9/4355da7f3df462422446e12a8aa58bdaa7b3af8fcdcc8319f383890bae79/shapely-2.0rc2-cp311-cp311-macosx_10_9_x86_64.whl#sha256=63fa07b1fd34a35f8595391ca6bad70ac749c657425f086a9976bffca519ad83 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/43/c9/3f1e3bc9b7e92bb0d4c769c15a41db3bc9a3821b12e3ff9be7885e02e07f/shapely-2.0rc2-cp311-cp311-macosx_11_0_arm64.whl#sha256=2f85e35cb7a807e96a9caf4dbe61634acc73e1541c3aa4e827f8c4142c2542b1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ef/45/958775c6cb3b2f74758de927281d8b9e067170e04f76cd81b09a15d36d43/shapely-2.0rc2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=b7afae6c8dc0452b79f0fd2a3194a28fbe68b7f0108562f0ba9b115fcf23fbc0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/7c/92/0db37f18a09e5afa15cbe138e68b09a078a01500340820ebf4c3a26f233c/shapely-2.0rc2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=a0e66e7c090dfd5c05050f0ea3b3f65cb84612d43645184849c8e765caf62fef (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a0/5d/c72ce0230bc285b8d4fc78823d6218d7383d040980b99844f949bf28b689/shapely-2.0rc2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc4ef218a98c517878f84c778dc16d2d0bbe20d2b5f39121feafb3477873b087 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c8/38/2cc9ff372838d71518a18ba4a3f3cceb96953aa8729fea51cde2d9e88d3b/shapely-2.0rc2-cp311-cp311-win32.whl#sha256=c51f05474f1cfcd3287e829c99c7c5126bf357701cad0acc6dcaae6a2ff3b800 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/46/a8/3473ed0d356b3f67b35da96ab595d161548bdd4b881e7cc9649919ca1783/shapely-2.0rc2-cp311-cp311-win_amd64.whl#sha256=40a75f22a67b2f40c143ce88180de5d7cdb5a6997c86a072b90a3c6ba56090d6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/13/41/cf0ea6d0b17360bcece774d3d590d47f3881291c4c2d8f3bf11621119179/shapely-2.0rc2-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=26ace55fa277b6d087d5acbc8cbb050741dcf1b88655ce639cbcf548906b0bb6 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/05/fb/8677e43eb510885dda97a1dd0b0a51cdce9367a44a58e96458ec6684d51d/shapely-2.0rc2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=8f95119519cd6a000e8da39db3a68792693fa2f0b4c60740e6a8a84982496865 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d1/ae/a678397f7e1010a411444693064a7fac55e802141bc2dcb02fb8d3e0106b/shapely-2.0rc2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=d3267d066a7869929b5ffc252475cc05a679762289a778f9c63c0da4f6b29251 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/8c/2d/189afab3fc51280209d3298012a247beda00283f61c56d8f46d833397e21/shapely-2.0rc2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=464cded7f98f821deefbd36b6faa147da993bdcbb59b84f0c840ea6d68cbf7e7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc2\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/91/20/6e0e1b2e89bbdad60f6adb7451ccd83c08292c2e656aa0968ca91f181447/shapely-2.0rc2-cp37-cp37m-win32.whl#sha256=144272cf21ddde309d3c6387e4875eaa4c88debb9b46f7db178e8c1f231de036 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/64/58/1ff9bbe5804b71b0bd72b6fc2c320fe4bedac3185b7a46f88e8daa4d7902/shapely-2.0rc2-cp37-cp37m-win_amd64.whl#sha256=12b6c261df22fa64ae218b7b47b5ec2181e788086f9804ac9342d4844b997361 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/01/90/6c3df4c74a79df055e0c277a020599074f8cf9face6caa896548005b5d65/shapely-2.0rc2-cp38-cp38-macosx_10_9_universal2.whl#sha256=9b69622e7501c6b0e258604ead7218b76725639e9b9167b3a46ef4e902829fe1 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8b/bc/042fed7658e0046dd555b1f9bb10f93e5f0140ae3a0cf152a38affd5255b/shapely-2.0rc2-cp38-cp38-macosx_10_9_x86_64.whl#sha256=e4e99d09e6e29315400c2a3377e6e7229d08e4ab422ae6243e6886c825699f58 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b0/f3/8bbda36726b0f542698b1cb584ac9850c72d242ccd11d0f5b9943e6d3ed4/shapely-2.0rc2-cp38-cp38-macosx_11_0_arm64.whl#sha256=38755619fa99b11c77bfe9d7fbf1b2114ccecf940e909bf972975c684c845d7d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8d/51/ba257637082de60605c364c0373140af2ba63c1f7333378499fbd8f87667/shapely-2.0rc2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=d640a369a6aeb73676262c4fd90192dbbb0e82a5b8305b5d7a854f8fde7d0a7d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/1f/acb93bf851fded8bbd4612a6623a0f6fa93e301f3e1f71dfccf41cb76647/shapely-2.0rc2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=c6a4c2eabb80813bfb3ca3fd173eff6af2a8e61106484512ae30ea506b2b1948 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c7/0d/0e4a9f5402634754f3f1a2d440843a10bff3067a7cf768311cbdf9becfbd/shapely-2.0rc2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f6bca054aab234721fd633bf35b64ee9e1f5a5ba3a1d1916add7b75500af2d68 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/41/37/1f0037c3d98ce8d8324189f44a301d4d1a76b4a64ece2a035addc00507f0/shapely-2.0rc2-cp38-cp38-win32.whl#sha256=5714a531fd54aab9dc4762a84775556b9fee68f751a43c913d8c148f65028417 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d4/c3/afc35023cf28ebfd50d3d94853e8f79f686b9ca7de4f4fa4c99a98c66f1d/shapely-2.0rc2-cp38-cp38-win_amd64.whl#sha256=2e0df7a4b25f2b3f80064419c6b9ac04288827eb1738ad2067453d80b49b6951 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/75/cc/f0d89e75dbf86117821b777736fe50eb1aa9eece9939864e5855cd94e758/shapely-2.0rc2-cp39-cp39-macosx_10_9_universal2.whl#sha256=202daad875632fa64825d3a54cb04bce02c97087062fd6e928fd7498c5d60d1e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8e/6e/11f0011c3aa1562344e8313d622b5eb271e682dcbfd5f8eded3cabf10ae5/shapely-2.0rc2-cp39-cp39-macosx_10_9_x86_64.whl#sha256=8b9f2fcc2d7b85aea8bc812b4e1326a66312eb119e07b6712c4dbce9c0718996 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/98/d1/9f154654a53b2753961188393eebcf4667962060b55995fcac7dcceb3d8c/shapely-2.0rc2-cp39-cp39-macosx_11_0_arm64.whl#sha256=72f70617fee85f714a829bc46d6686e63bcabbcf6a9df0aecbdb0ce01e83837b (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bd/13/304e0af39aa67f528a159249eaaa53219132d9b52e68142ecc3f58cfe7c2/shapely-2.0rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=d72615759282f57ec7a30040df7900abe904cb2cd0ca4754673fe75e78b30e62 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8f/45/31f4ff09d37729985bf510b37d8c95eb8c21ab2100218206962da3e3819c/shapely-2.0rc2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=b708e4a399c7045d89a5ca7cc295a47d2d339712172a2cec7e31a9379c98b6a7 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/18/3d/53b027fe603180ec71eaf3c95ded4086337484eb9a162342db69b8259b1c/shapely-2.0rc2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=146f089ac9194f229760f79411318cbb05b34bc301654a9f14128f380d2fdb64 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8f/c7/3177027c3748e876eedb15ad4761c3dff76b7d04110221f0def3b0bd9e20/shapely-2.0rc2-cp39-cp39-win32.whl#sha256=4c8ae4af4bee8fb1b497cf4af429f201554744386effd271edbd56c6003a7aea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/63/96/d0f3ffddee21b395a1b619a5d4212c37ffd48d47aed941d9c3cf89de8e9f/shapely-2.0rc2-cp39-cp39-win_amd64.whl#sha256=77923b9a2c27a1dfb1395bc5649355197c2d3a47c628747ec343b3e03c692061 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/7f/df/9598a4a7597488dac6e3ad94a041c6d9a82551617b845faf9866c4b7907b/shapely-2.0rc2.tar.gz#sha256=8aa15f1d281b9f6ffc06582415d1449c9a23616508b88b27ecafa60daa161af4 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc2\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/97/b7/fe1f7f57aa1de73c19a13d8c9bfe96374f07807405a6bdfc18175fe5ec5d/shapely-2.0rc3-cp310-cp310-macosx_10_9_universal2.whl#sha256=980e1ccdec1125feb5d95a10942b3e2a181e857718c9ba25975fb4522db16851 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/71/38/ae3ddd50eed8da07b9dc06f0a180b7186b521fd1dcdae93131fc38cf6c88/shapely-2.0rc3-cp310-cp310-macosx_10_9_x86_64.whl#sha256=2ad250083f9f2bb609d898493f084c5c0916f218900083c4607e2112e8bc3c5c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/52/37/fde9644f36dde79fbe95be45afb88573bdf19588db9e4bef411f5c161dc6/shapely-2.0rc3-cp310-cp310-macosx_11_0_arm64.whl#sha256=f3c8d8d41b3999453bde84e7236de441d871a8916b94bacfb6c006241f941ba8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_aarch64, cp310-cp310-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/42/77/7201173099ed0032640dd18e67e29754373356a08c59b43e2c16a2c27fc5/shapely-2.0rc3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=6a209563b9c942be0d3f5677b2741ec31a559f5a065965eca185c2faaaec479f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-manylinux2014_x86_64, cp310-cp310-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1e/8f/27c8dff25c20affe02bc87a229885a4a4614b8e45951c00b1f13e30ef22a/shapely-2.0rc3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8aeb2a441fc7365bf4ad60d3105916a751fea597ff8606d8516fcc25ab4b840f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/de/42/d43ab1a179056c32218bfd56adf86788a206c7e2e6c1e715481964fe98cf/shapely-2.0rc3-cp310-cp310-win32.whl#sha256=e40cca1823d344e3bfc2222a5f69fe0246695ab92501fd2805ab746ace4fd597 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp310-cp310-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/11/57/9134ea28eb3e4d2f2a2089158c4df0281023b25a75817cef70e9697e25cc/shapely-2.0rc3-cp310-cp310-win_amd64.whl#sha256=8de2266ff75246f6829b611ad813672016d879f57c02f20d97c5c512650090f8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/e7/f3/2dbda48b580846a1078cc0f4471d7937686e1bc7ed340fa019e5578720d1/shapely-2.0rc3-cp311-cp311-macosx_10_9_universal2.whl#sha256=728aaa0629217cec2be5b969ec3306bb3882fb55e3515079b472e4c36930623f (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/bc/19/49f8327c62ace74717ee0a7636fa907bc6c369074c92bbc468879f53182e/shapely-2.0rc3-cp311-cp311-macosx_10_9_x86_64.whl#sha256=7d88254abd50ffa2e5f47d2bb87443b9b9071284afa05935b7a0ac4b4ef7d3e0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d3/70/83cbc597de2c96636dac204ced3a9e5a0337dbbadc596f5647e8104c85a3/shapely-2.0rc3-cp311-cp311-macosx_11_0_arm64.whl#sha256=8c4d72367eb9ca2afe42c5b2eeb34a7e7adb32da3f6da3d6d36234622eea4044 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_aarch64, cp311-cp311-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/04/bc/caa609de790420c323b2ec01258cdee407040709d179dcf43c26e4546e21/shapely-2.0rc3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=dcef241683ca7c864d122dcfa8ea61a3bfca1507cf2cdc05e7bff40dbf9a0cbd (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_i686, cp311-cp311-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b4/20/0ab5fcbffbe768615d8e2a851ec82778baf5f61ece5efd5053eafecfacf7/shapely-2.0rc3-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=e38f71102f21e6ca219c0f9051a8dce4d9e9b3db7e1b3483949067cd7969817a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-manylinux2014_x86_64, cp311-cp311-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/4f/17/e6569fb30128788490b04970c9e511536d273dc4893ba21c8c1fe89e3305/shapely-2.0rc3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0e442780015792333ced49c4bf334ad2b17d49ccdd52613aa62bb94a9836ed0e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b5/ba/15e305405d3381c4c4101dd733aa9c731f4a315feb34b617ba4efa65ed30/shapely-2.0rc3-cp311-cp311-win32.whl#sha256=f5894d5c03824f19d4f8a1f3caf42e3a24a45ac1776a3ff3794fff77ee26541d (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp311-cp311-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/63/89/24abf00635005848f26b39cb705b460dfed8f5fe2a2b3321d563eab2388e/shapely-2.0rc3-cp311-cp311-win_amd64.whl#sha256=305ddb2f736ce3b695b965b7051ada608954a472d1b44dbc6748d3e2cd150e56 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/78/64/602cd4302ba756fc5c0d28b8c4fdcc8c9cc14e93b079c24da9ab9f77063d/shapely-2.0rc3-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=2cce2febf234b2e18141cc22c3f69606a8908ebd194e02e5c3c361b8415e4e2e (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_aarch64, cp37-cp37m-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/59/b5/8cedd3cf220bb5e64745f304c92de3a2ec5f0d12039a6c68205b7915aa69/shapely-2.0rc3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=f326cf77a0022dcd44129fa04da12e2a84988934e9869d0f348e7f4f0fd33170 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-manylinux2014_i686, cp37-cp37m-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/0f/fc/b8948eb264bb2fd85aafcf6d211e221208dcb059262a23b5d77bbb8e087f/shapely-2.0rc3-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=bf926319dbb65c89a879d8eaae1ec0d3599b61d84879cfe335cd3a2bc02c66c0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/15/19/f00d46338e1d47dfd1c2c6f8b36f1eed0ea7374ef5f068e0185789c9061d/shapely-2.0rc3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ee3c02219104a65cae197acd495b87a126df8762c769312720bc0f9dd4c52bdb (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc3\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/62/ea/572599c2ed47f3bdac02a3020090fd2485f4857570ff026aabd60bf501bb/shapely-2.0rc3-cp37-cp37m-win32.whl#sha256=59d770258808307d698e541acad42ba3c3bff5457abfe351b44ef78136ee26ea (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp37-cp37m-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/1b/2e/f299465d36827ef4b2acfeedfba188e8cc5e485764397997d3a8c5c1d08d/shapely-2.0rc3-cp37-cp37m-win_amd64.whl#sha256=27972ad1dd94da9f4f494c30afbd80eb55c566766fa4d85d1619452be6e80b01 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/8d/08/3d0d05bf5b5580eff7b119908122016251ead89abe0b09a7dd2d015def53/shapely-2.0rc3-cp38-cp38-macosx_10_9_universal2.whl#sha256=b3daa911e1643d77e1c553f50cde19d49b63a7a173c7fc17cbe38cb26520c532 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/b4/c7/4a72f9f0337f4ad0d99ae0b448c5545f83fc5a2b8b12d4ca688c7e2d034d/shapely-2.0rc3-cp38-cp38-macosx_10_9_x86_64.whl#sha256=187adf21889afcf9f40b52998d11ec1cbe9e1519d14565b106a4e68d550d5ad8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/85/0e/3637c010e5c59dd3c1e2961e5bca3c283c1dbf1b503383184386e58a1070/shapely-2.0rc3-cp38-cp38-macosx_11_0_arm64.whl#sha256=6921d9199f78215720b6b74d247842b70711e4b42cbcce3fd2ab2b943e54c9f0 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_aarch64, cp38-cp38-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/51/ad/36e3587081711f17d05767ee57020de120c5d2b47ad10753b6884eaff16a/shapely-2.0rc3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=e2e5edc65361da5269c5b4582474f3abdb6f34df3d39d1c14801c8da4a49852c (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_i686, cp38-cp38-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/95/a1/550c30873add92a33b5b755df7223b20c549807f40c1f1c6e1caa82c2dab/shapely-2.0rc3-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=7ac091f1a65a5dca9ffcf115de99e213f719b021cc71882326da9d8bd4d5ad60 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-manylinux2014_x86_64, cp38-cp38-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/9d/68/90d8a5a6f823955f9211316e65935766a076ef559e8507c6a95817e664a1/shapely-2.0rc3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=69cd2834c54d871454be42f5e69803ce10062b9571c2b120a2ad459e6bf8d78a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/92/c5/9529e30791a6606bca2d6e23c7f1eccafe58006b79c5432bf8dd332a16ed/shapely-2.0rc3-cp38-cp38-win32.whl#sha256=2aac52a07e1b71255a7810d4152fe30b826ee9184f8ac6aae19cf3b498016645 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp38-cp38-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/76/9c/6842a91668c4594338f104ac8383e22b221fb628f454b518d836e6635a94/shapely-2.0rc3-cp38-cp38-win_amd64.whl#sha256=9070b934063e26a802dbde90545d8a61f0aa347ff0a2100a8d6a5de6ea5389a9 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_universal2) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/d5/80/b9be6d900a31d5c3cda93720343e2598d55ae14a35a0996b848af2fece9b/shapely-2.0rc3-cp39-cp39-macosx_10_9_universal2.whl#sha256=38a823682e2181c061a6721d18f01ed926ca042ce749e7e48e1b1262f8a56a45 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c2/85/7fb14539aa96b630db6ab1ed979045c6d1095a31fa9b7717068594b5ede2/shapely-2.0rc3-cp39-cp39-macosx_10_9_x86_64.whl#sha256=ea1305ab48b8f2379d4fc83552d3e55df55923a6c4e6877c273f8100bad09d09 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-macosx_11_0_arm64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/28/24/748349280dc2ace3b4c490026d01e5706e618351cac49406ad4148881439/shapely-2.0rc3-cp39-cp39-macosx_11_0_arm64.whl#sha256=677ca8f4e23dfe08d502c30d27ffbcb50abc787e50c5f9f5be81d2e5c1b25d07 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_aarch64, cp39-cp39-manylinux_2_17_aarch64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/ac/08/59baf4e9341d2019ec6cbc2c8f7ed6af16b07b10a10fe6506ff249a2d168/shapely-2.0rc3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=ed7de7c3b6e2446c12bd7980d19260324e4b1baba0feff4006b7c46de2749b9a (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_i686, cp39-cp39-manylinux_2_17_i686) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/73/a1/9f7ddef2f75731a3989731c55f2576c147c65195b93645f99b822fb3fe8e/shapely-2.0rc3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl#sha256=991815f87b3944c465566d883a87cdc80d6ec4cfd9023c0244ed74a68fdbb1db (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-manylinux2014_x86_64, cp39-cp39-manylinux_2_17_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/07/62/0fabf0261a61575f62c64ea5b553a0e290c5aba6074f08eae73b132f7230/shapely-2.0rc3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c08188679aeb3ac3a5b84a660b300df0fcea8142f830e3cb472480abeab531d5 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win32) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/c9/35/e25604f18e53e9f790b9e2c887a54ff6da0ffde421a711d7dfb7ae7a0228/shapely-2.0rc3-cp39-cp39-win32.whl#sha256=e07e2d3a1896975a98e5b743b9c05a47dc896ae4bad9022d8943a1d71ff44f48 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Skipping link: none of the wheel's tags (cp39-cp39-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://pypi.tuna.tsinghua.edu.cn/packages/a4/5e/123e9803f41647cf90933bb05f0b76db3cd19ccff8cb92567ac57463987c/shapely-2.0rc3-cp39-cp39-win_amd64.whl#sha256=d1b3c3e46bedf89dde3e0047a8da061379e75ae6d7bdceffe4aeb58ee8b346f8 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7)\n", + " Found link https://pypi.tuna.tsinghua.edu.cn/packages/bd/f5/3170732d23852df2d01c0f0bd394ecb2a7103e3cb95c59f7d1dad52a9ea3/shapely-2.0rc3.tar.gz#sha256=af579ce708dff515dbb213564f9841342a07699307e22e909e3616b71067e891 (from https://pypi.tuna.tsinghua.edu.cn/simple/shapely/) (requires-python:>=3.7), version: 2.0rc3\n", + "Skipping link: not a file: https://pypi.tuna.tsinghua.edu.cn/simple/shapely/\n", + "Given no hashes to check 92 links for project 'shapely': discarding no candidates\n", + "Collecting shapely\n", + " Created temporary directory: /tmp/pip-unpack-2clk3w1p\n", + " Looking up \"https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\" in the cache\n", + " No cache entry available\n", + " https://pypi.tuna.tsinghua.edu.cn:443 \"GET /packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl HTTP/1.1\" 200 2263590\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)\n", + "\u001b[K |████████████████████████████████| 2.3 MB 94.1 MB/s eta 0:00:01 Updating cache with response from \"https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\"\n", + "\u001b[K |████████████████████████████████| 2.3 MB 94.1 MB/s \n", + "\u001b[?25h Added shapely from https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c8b0d834b11be97d5ab2b4dceada20ae8e07bcccbc0f55d71df6729965f406ad (from mmdet==3.0.0rc6) to build tracker '/tmp/pip-req-tracker-tu4x1bba'\n", + " Removed shapely from https://pypi.tuna.tsinghua.edu.cn/packages/1d/a4/931d0780f31f3ea8c4f9ef6464a2825137c5241e6707a5fb03bef760a7eb/shapely-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c8b0d834b11be97d5ab2b4dceada20ae8e07bcccbc0f55d71df6729965f406ad (from mmdet==3.0.0rc6) from build tracker '/tmp/pip-req-tracker-tu4x1bba'\n", + "Requirement already satisfied: six in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (1.16.0)\n", + "Requirement already satisfied: terminaltables in /environment/miniconda3/lib/python3.7/site-packages (from mmdet==3.0.0rc6) (3.1.10)\n", + "Requirement already satisfied: cycler>=0.10 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (0.11.0)\n", + "Requirement already satisfied: packaging>=20.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (21.3)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (4.28.3)\n", + "Requirement already satisfied: setuptools-scm>=4 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (6.3.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (8.4.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (3.0.6)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /environment/miniconda3/lib/python3.7/site-packages (from matplotlib->mmdet==3.0.0rc6) (1.3.2)\n", + "Requirement already satisfied: tomli>=1.0.0 in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet==3.0.0rc6) (1.2.2)\n", + "Requirement already satisfied: setuptools in /environment/miniconda3/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->mmdet==3.0.0rc6) (52.0.0.post20210125)\n", + "Created temporary directory: /tmp/pip-unpack-bm_9zc23\n", + "Installing collected packages: shapely, mmdet\n", + "\n", + " Running setup.py develop for mmdet\n", + " Running command /environment/miniconda3/bin/python -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/home/featurize/work/关键点检测/mmdetection/setup.py'\"'\"'; __file__='\"'\"'/home/featurize/work/关键点检测/mmdetection/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n", + " running develop\n", + " running egg_info\n", + " creating mmdet.egg-info\n", + " writing mmdet.egg-info/PKG-INFO\n", + " writing dependency_links to mmdet.egg-info/dependency_links.txt\n", + " writing requirements to mmdet.egg-info/requires.txt\n", + " writing top-level names to mmdet.egg-info/top_level.txt\n", + " writing manifest file 'mmdet.egg-info/SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " warning: no files found matching 'mmdet/VERSION'\n", + " warning: no files found matching 'mmdet/.mim/demo/*/*'\n", + " writing manifest file 'mmdet.egg-info/SOURCES.txt'\n", + " /environment/miniconda3/lib/python3.7/site-packages/torch/utils/cpp_extension.py:381: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n", + " warnings.warn(msg.format('we could not find ninja.'))\n", + " running build_ext\n", + " Creating /environment/miniconda3/lib/python3.7/site-packages/mmdet.egg-link (link to .)\n", + " mmdet 3.0.0rc6 is already the active version in easy-install.pth\n", + "\n", + " Installed /home/featurize/work/关键点检测/mmdetection\n", + "Successfully installed mmdet-3.0.0rc6 shapely-2.0.1\n", + "Removed build tracker: '/tmp/pip-req-tracker-tu4x1bba'\n" + ] + } + ], + "source": [ + "!pip install -v -e ." + ] + }, + { + "cell_type": "markdown", + "id": "a828bfe4-82e5-497a-9bda-cb9837df2cb3", + "metadata": {}, + "source": [ + "## 下载预训练模型权重文件和视频素材" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5aac1d0c-dbf5-478c-ab80-7e2aa0ab0f01", + "metadata": {}, + "outputs": [ + { + "ename": "FileExistsError", + "evalue": "[Errno 17] File exists: 'checkpoint'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileExistsError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_20793/4244841999.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# 创建 checkpoint 文件夹，用于存放预训练模型权重文件\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'checkpoint'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# 创建 outputs 文件夹，用于存放预测结果\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'checkpoint'" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# 创建 checkpoint 文件夹，用于存放预训练模型权重文件\n", + "os.mkdir('checkpoint')\n", + "\n", + "# 创建 outputs 文件夹，用于存放预测结果\n", + "os.mkdir('outputs')\n", + "\n", + "# 创建 data 文件夹，用于存放图片和视频素材\n", + "os.mkdir('data')" + ] + }, + { + "cell_type": "markdown", + "id": "fa97e1da-f245-4d32-892d-dec07a6b2c16", + "metadata": {}, + "source": [ + "## 检查安装成功" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "424fca02-13cc-493f-8833-ec63900f02d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pytorch 版本 1.10.1+cu113\n", + "CUDA 是否可用 True\n" + ] + } + ], + "source": [ + "# 检查 Pytorch\n", + "import torch, torchvision\n", + "print('Pytorch 版本', torch.__version__)\n", + "print('CUDA 是否可用',torch.cuda.is_available())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "96a6e738-d9d3-4e9f-a053-15bf2f29d43b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MMCV版本 2.0.0rc4\n", + "CUDA版本 11.3\n", + "编译器版本 GCC 9.3\n" + ] + } + ], + "source": [ + "# 检查 mmcv\n", + "import mmcv\n", + "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n", + "print('MMCV版本', mmcv.__version__)\n", + "print('CUDA版本', get_compiling_cuda_version())\n", + "print('编译器版本', get_compiler_version())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dae8b28c-fa1f-4911-ba27-50ed745e9679", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mmdetection版本 3.0.0rc6\n" + ] + } + ], + "source": [ + "# 检查 mmpose\n", + "import mmdet\n", + "print('mmdetection版本', mmdet.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "6f89f196-6f70-46a5-9715-ee4755717c13", + "metadata": {}, + "source": [ + "没有报错，即证明安装成功。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90954e26-49ee-42ad-b2ff-7514e8542db4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220B1\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" "b/2023/0404/\343\200\220B1\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" new file mode 100644 index 0000000..356c29a --- /dev/null +++ "b/2023/0404/\343\200\220B1\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "784ce4a0-b0db-42c8-8c29-0dd3314fe397", + "metadata": {}, + "source": [ + "# MMPose预训练模型预测-命令行\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "23306f50-79b9-44f8-b34c-857b838db0a5", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "83f7c5db-0919-484d-ac50-e2cf666b5fb6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "a16bd452-c4bd-4c74-99e2-5eb5f860e1f9", + "metadata": {}, + "source": [ + "## 图像和视频素材" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ac34dda5-4965-4d23-8aab-82a718cdb5f8", + "metadata": {}, + "outputs": [], + "source": [ + "# 一些素材\n", + "\n", + "# data/test/multi-person.jpeg\n", + "\n", + "# data/test/cxk.mp4\n", + "\n", + "# data/test/two_dancers.mp4" + ] + }, + { + "cell_type": "markdown", + "id": "c1b47d0a-46f2-4378-ada9-df6a73e969e8", + "metadata": {}, + "source": [ + "## 目标检测模型\n", + "\n", + "MMDetection模型库：https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ce447bb1-305c-4eec-88f4-ed74163328bd", + "metadata": {}, + "outputs": [], + "source": [ + "# demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py\n", + "\n", + "# https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth" + ] + }, + { + "cell_type": "markdown", + "id": "916cfe77-ea7e-451a-8f92-50c17e529b86", + "metadata": {}, + "source": [ + "## MMPose模型" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d5d0af0-7f0b-4258-880d-1ef59ba648d9", + "metadata": {}, + "outputs": [], + "source": [ + "# configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py\n", + "\n", + "# https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth" + ] + }, + { + "cell_type": "markdown", + "id": "3ddb98c7-78b3-4faf-9a9a-e61509f64111", + "metadata": {}, + "source": [ + "## RTMPose模型\n", + "\n", + "RTMPose主页：https://github.com/open-mmlab/mmpose/tree/dev-1.x/projects/rtmpose" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f2f1b203-56c7-4c70-83a0-6b62288665bc", + "metadata": {}, + "outputs": [], + "source": [ + "# RTMPose-S\n", + "\n", + "# projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py\n", + "\n", + "# https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b4645375-3290-4841-8024-ae3a2dfce84f", + "metadata": {}, + "outputs": [], + "source": [ + "# RTMPose-L\n", + "\n", + "# projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py\n", + "\n", + "# https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth" + ] + }, + { + "cell_type": "markdown", + "id": "a74faf58-4237-42e6-be61-b0c7e262fc6e", + "metadata": {}, + "source": [ + "## 预测单张图像" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "67499aff-d83e-4fee-8db9-f5d68388fdb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth\n", + "04/04 13:50:57 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "Traceback (most recent call last):\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 249, in \n", + " main()\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 196, in main\n", + " show_interval=0)\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 31, in process_one_image\n", + " det_result = inference_detector(detector, img_path)\n", + " File \"/home/featurize/work/关键点检测/mmdetection/mmdet/apis/inference.py\", line 170, in inference_detector\n", + " data_ = test_pipeline(data_)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/base.py\", line 12, in __call__\n", + " return self.transform(results)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/wrappers.py\", line 87, in transform\n", + " results = t(results) # type: ignore\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/base.py\", line 12, in __call__\n", + " return self.transform(results)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/loading.py\", line 107, in transform\n", + " raise e\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/loading.py\", line 100, in transform\n", + " filename, backend_args=self.backend_args)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmengine/fileio/io.py\", line 181, in get\n", + " return backend.get(filepath)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmengine/fileio/backends/local_backend.py\", line 33, in get\n", + " with open(filepath, 'rb') as f:\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'data/test/multi-person.jpeg'\n" + ] + } + ], + "source": [ + "# HRNet\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \\\n", + " --input data/test/multi-person.jpeg \\\n", + " --output-root outputs/B1_HRNet \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.2 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 8 \\\n", + " --thickness 4 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "93eefc16-24d5-46b9-b401-72534458b3dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth\n", + "04/04 13:51:09 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "Traceback (most recent call last):\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 249, in \n", + " main()\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 196, in main\n", + " show_interval=0)\n", + " File \"demo/topdown_demo_with_mmdet.py\", line 31, in process_one_image\n", + " det_result = inference_detector(detector, img_path)\n", + " File \"/home/featurize/work/关键点检测/mmdetection/mmdet/apis/inference.py\", line 170, in inference_detector\n", + " data_ = test_pipeline(data_)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/base.py\", line 12, in __call__\n", + " return self.transform(results)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/wrappers.py\", line 87, in transform\n", + " results = t(results) # type: ignore\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/base.py\", line 12, in __call__\n", + " return self.transform(results)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/loading.py\", line 107, in transform\n", + " raise e\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmcv/transforms/loading.py\", line 100, in transform\n", + " filename, backend_args=self.backend_args)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmengine/fileio/io.py\", line 181, in get\n", + " return backend.get(filepath)\n", + " File \"/environment/miniconda3/lib/python3.7/site-packages/mmengine/fileio/backends/local_backend.py\", line 33, in get\n", + " with open(filepath, 'rb') as f:\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'data/test/multi-person.jpeg'\n" + ] + } + ], + "source": [ + "# RTMPose\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py \\\n", + " https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth \\\n", + " --input data/test/multi-person.jpeg \\\n", + " --output-root outputs/B1_RTM \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.5 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 8 \\\n", + " --thickness 4 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "markdown", + "id": "34c88527-125b-46da-9d89-9c07fccad9a5", + "metadata": {}, + "source": [ + "## 预测视频：直接将`--input`换成视频路径即可" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "371d4c31-eb0f-4469-a5ec-cf73989c357d", + "metadata": {}, + "outputs": [], + "source": [ + "# HRNet\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py \\\n", + " https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \\\n", + " --input data/test/two_dancers.mp4 \\\n", + " --output-root outputs/B1_HRNet \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.2 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 8 \\\n", + " --thickness 4 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "778ee737-44fc-4fc6-a31f-16aff9008a8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "04/04 11:07:56 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n", + "04/04 11:07:56 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"HardDiskBackend\" is the alias of \"LocalBackend\" and the former will be deprecated in future.\n", + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth\n", + "04/04 11:08:04 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "[ ] 0/563, elapsed: 0s, ETA:/home/featurize/work/关键点检测/mmpose/mmpose/models/heads/coord_cls_heads/rtmcc_head.py:217: UserWarning: The predicted simcc values are normalized for visualization. This may cause discrepancy between the keypoint scores and the 1D heatmaps.\n", + " warnings.warn('The predicted simcc values are normalized for '\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>> ] 554/563, 1.0 task/s, elapsed: 532s, ETA: 9s" + ] + } + ], + "source": [ + "# RTMPose\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \\\n", + " https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \\\n", + " projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py \\\n", + " https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth \\\n", + " --input data/test/two_dancers.mp4 \\\n", + " --output-root outputs/B1_RTM \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.5 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 8 \\\n", + " --thickness 4 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f18b27e-1e97-45f7-b2e6-57215bfa6de2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220B2\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" "b/2023/0404/\343\200\220B2\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" new file mode 100644 index 0000000..2e5a829 --- /dev/null +++ "b/2023/0404/\343\200\220B2\343\200\221MMPose\351\242\204\350\256\255\347\273\203\346\250\241\345\236\213\351\242\204\346\265\213-Python API.ipynb" @@ -0,0 +1,772 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "89e1c71a-b8b1-40f8-b857-dbf8d2259442", + "metadata": {}, + "source": [ + "# MMPose预训练模型预测-Python API\n", + "\n", + "同济子豪兄 2023-4-1" + ] + }, + { + "cell_type": "markdown", + "id": "dcd38377-f35f-41bb-b6bf-105e9b01eb97", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d54de94c-0c85-44dc-96ca-0d777b170508", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "0f65d026-2350-4411-8524-18b96be471e5", + "metadata": {}, + "source": [ + "## 导入工具包" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0b7dd322-d621-4f2c-9275-70619886714d", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np\n", + "from PIL import Image\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import torch\n", + "\n", + "import mmcv\n", + "from mmcv import imread\n", + "import mmengine\n", + "from mmengine.registry import init_default_scope\n", + "\n", + "from mmpose.apis import inference_topdown\n", + "from mmpose.apis import init_model as init_pose_estimator\n", + "from mmpose.evaluation.functional import nms\n", + "from mmpose.registry import VISUALIZERS\n", + "from mmpose.structures import merge_data_samples\n", + "\n", + "from mmdet.apis import inference_detector, init_detector" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3b28f126-b9a4-4275-9ca6-52cec0df26ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "device cuda:0\n" + ] + } + ], + "source": [ + "# 有 GPU 就用 GPU，没有就用 CPU\n", + "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", + "print('device', device)" + ] + }, + { + "cell_type": "markdown", + "id": "659e2160-f36d-4c84-be8c-d990a8e4b1b0", + "metadata": {}, + "source": [ + "## 载入待测图像" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8569873e-c1b5-4010-8718-3711b998c181", + "metadata": {}, + "outputs": [], + "source": [ + "img_path = 'data/test/multi-person.jpeg'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "678fd5fb-bcba-4029-a262-a7314a3fb27a", + "metadata": {}, + "outputs": [], + "source": [ + "# Image.open(img_path)" + ] + }, + { + "cell_type": "markdown", + "id": "d8f5acb9-3e04-4738-a1c1-104842c9bf82", + "metadata": {}, + "source": [ + "## 构建目标检测模型" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b28ef07-c02d-49cb-808d-e68fcbaa9851", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "04/03 13:57:38 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n", + "04/03 13:57:38 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"HardDiskBackend\" is the alias of \"LocalBackend\" and the former will be deprecated in future.\n" + ] + } + ], + "source": [ + "detector = init_detector(\n", + " 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py',\n", + " 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth',\n", + " device=device\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7e49b519-5ef5-43e0-ade9-cba00363caa6", + "metadata": {}, + "source": [ + "## 构建人体姿态估计模型" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8eb2de0b-9644-4812-85ea-5874ee16448e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth\n" + ] + } + ], + "source": [ + "pose_estimator = init_pose_estimator(\n", + " 'configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py',\n", + " 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth',\n", + " device=device,\n", + " cfg_options={'model': {'test_cfg': {'output_heatmaps': True}}}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1afacbe1-31cd-4cfb-8e35-8eb5cd9bf046", + "metadata": {}, + "source": [ + "## 预测-目标检测" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c85b7e77-eb3b-4275-8630-6439d3dca3cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/03 13:57:46 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The current default scope \"mmpose\" is not \"mmdet\", `init_default_scope` will force set the currentdefault scope to \"mmdet\".\n" + ] + } + ], + "source": [ + "init_default_scope(detector.cfg.get('default_scope', 'mmdet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a995834e-3f59-449b-962e-afe22b134dc6", + "metadata": {}, + "outputs": [], + "source": [ + "# 获取目标检测预测结果\n", + "detect_result = inference_detector(detector, img_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4f015376-4a07-4902-af7b-679c152675e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ignored_instances', 'gt_instances', 'pred_instances']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "detect_result.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "132a3a28-a8ef-457c-be2c-82856b3fa5bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 58, 0, 0, 58, 0, 0, 2,\n", + " 0, 0, 2, 0, 0, 0, 0, 24, 2, 58, 26, 26, 0, 0, 58, 26, 0, 0,\n", + " 27, 24, 0, 26, 26, 26, 26, 26, 58, 0, 26, 26, 26, 2, 32, 2, 58, 39,\n", + " 2, 0, 24, 58, 58, 32, 24, 26, 0, 0, 2, 37, 0, 39, 9, 74, 0, 2,\n", + " 58, 24, 0, 0, 26, 29], device='cuda:0')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 预测类别\n", + "detect_result.pred_instances.labels" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d0916bb1-177f-4a43-b827-7f371d249737", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.9994, 0.9988, 0.9974, 0.9963, 0.9959, 0.9930, 0.9914, 0.9901, 0.9868,\n", + " 0.9824, 0.9631, 0.9272, 0.9079, 0.8950, 0.8793, 0.8581, 0.8248, 0.8189,\n", + " 0.7240, 0.7184, 0.7055, 0.6653, 0.6202, 0.6010, 0.6001, 0.5708, 0.5651,\n", + " 0.5058, 0.4550, 0.4086, 0.3941, 0.3906, 0.3535, 0.3200, 0.3102, 0.2593,\n", + " 0.2291, 0.2223, 0.2156, 0.1888, 0.1865, 0.1845, 0.1792, 0.1771, 0.1715,\n", + " 0.1672, 0.1396, 0.1391, 0.1390, 0.1371, 0.1350, 0.1249, 0.1204, 0.1155,\n", + " 0.1129, 0.1121, 0.1010, 0.1009, 0.0939, 0.0922, 0.0919, 0.0916, 0.0885,\n", + " 0.0822, 0.0807, 0.0724, 0.0697, 0.0686, 0.0675, 0.0630, 0.0627, 0.0624,\n", + " 0.0587, 0.0580, 0.0557, 0.0536, 0.0509, 0.0503], device='cuda:0')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 置信度\n", + "detect_result.pred_instances.scores" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a3bc5825-41e8-4b24-bd8e-837d38996609", + "metadata": {}, + "outputs": [], + "source": [ + "# 框坐标：左上角X坐标、左上角Y坐标、右下角X坐标、右下角Y坐标\n", + "# detect_result.pred_instances.bboxes" + ] + }, + { + "cell_type": "markdown", + "id": "6b75053f-00c2-4490-873f-6b0e61f98085", + "metadata": {}, + "source": [ + "## 置信度阈值过滤，获得最终目标检测预测结果" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b9b1c936-02d8-4705-a9cd-d7d1ac47ad0f", + "metadata": {}, + "outputs": [], + "source": [ + "# 置信度阈值\n", + "CONF_THRES = 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d9741fe4-42df-4aea-b79c-810fc06c0b83", + "metadata": {}, + "outputs": [], + "source": [ + "pred_instance = detect_result.pred_instances.cpu().numpy()\n", + "bboxes = np.concatenate((pred_instance.bboxes, pred_instance.scores[:, None]), axis=1)\n", + "bboxes = bboxes[np.logical_and(pred_instance.labels == 0, pred_instance.scores > CONF_THRES)]\n", + "bboxes = bboxes[nms(bboxes, 0.3)][:, :4]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "32c59cb1-5f52-4c84-b85e-836ee407d079", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1705.6345 , 96.277565 , 2114.5242 , 1301.1721 ],\n", + " [1267.5804 , 94.68924 , 1701.053 , 1316.3138 ],\n", + " [ 720.3783 , 172.2562 , 1152.6825 , 1267.8347 ],\n", + " [ 7.2657757, 238.25836 , 171.0014 , 1140.8154 ],\n", + " [1063.183 , 219.23286 , 1348.9962 , 1244.7703 ],\n", + " [ 436.2397 , 428.5876 , 586.0957 , 911.89386 ],\n", + " [ 110.00883 , 212.14699 , 324.64886 , 1122.9819 ],\n", + " [2079.3267 , 478.2376 , 2224.4104 , 925.2163 ],\n", + " [ 543.71204 , 406.47882 , 650.8417 , 919.4732 ],\n", + " [2363.2961 , 501.86667 , 2455.2178 , 857.2747 ],\n", + " [2308.2004 , 561.4538 , 2372.7483 , 715.7691 ],\n", + " [1990.6042 , 485.10385 , 2105.6318 , 923.45557 ],\n", + " [2255.5461 , 568.4462 , 2326.8745 , 702.4359 ],\n", + " [ 830.3824 , 297.2083 , 897.0394 , 349.59433 ],\n", + " [ 704.9901 , 419.5507 , 808.7075 , 989.6197 ],\n", + " [1646.6725 , 484.38348 , 1787.3292 , 936.4939 ],\n", + " [2437.2651 , 596.7573 , 2516.4998 , 722.8525 ],\n", + " [2176.0454 , 506.09146 , 2239.0215 , 640.1361 ]],\n", + " dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bboxes" + ] + }, + { + "cell_type": "markdown", + "id": "3e63aa2b-483e-40e5-a9d5-72f0fa32f980", + "metadata": {}, + "source": [ + "## 预测-关键点" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "187310c7-d081-4400-9882-d71ea351a250", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/03 13:57:56 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The current default scope \"mmdet\" is not \"mmpose\", `init_default_scope` will force set the currentdefault scope to \"mmpose\".\n" + ] + } + ], + "source": [ + "# 获取每个 bbox 的关键点预测结果\n", + "pose_results = inference_topdown(pose_estimator, img_path, bboxes)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a8c22cb5-76f0-4f4d-98df-8e28d6c4dc3a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pose_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d824170d-58c9-444a-ab7f-6e930fe31c06", + "metadata": {}, + "outputs": [], + "source": [ + "# 把多个bbox的pose结果打包到一起\n", + "data_samples = merge_data_samples(pose_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1e12e814-b4ea-4147-90d7-0a3e1740d6d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['pred_fields', '_pred_heatmaps', 'gt_instances', 'pred_instances']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_samples.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "f2012096-1280-48c4-9bc7-82c82dbbc62f", + "metadata": {}, + "source": [ + "## 预测结果-关键点坐标" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6c18638f-986d-4071-ab10-6831eec8a99c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 17, 2)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 每个人 17个关键点 坐标\n", + "data_samples.pred_instances.keypoints.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0e208ea5-d921-4712-893d-559a41d26d62", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1915.96261978, 186.8800087 ],\n", + " [1927.72916794, 175.11346054],\n", + " [1892.42952347, 175.11346054],\n", + " [1963.02881241, 198.64655685],\n", + " [1857.129879 , 186.8800087 ],\n", + " [2010.09500504, 316.31203842],\n", + " [1810.06368637, 328.07858658],\n", + " [2092.46084213, 492.81026077],\n", + " [1786.53059006, 445.74406815],\n", + " [1986.56190872, 410.44442368],\n", + " [1762.99749374, 504.57680893],\n", + " [1963.02881241, 657.54193497],\n", + " [1821.83023453, 645.77538681],\n", + " [1939.49571609, 939.93909073],\n", + " [1821.83023453, 904.63944626],\n", + " [1927.72916794, 1116.43731308],\n", + " [1857.129879 , 1187.03660202]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 索引为 0 的人，每个关键点的坐标\n", + "data_samples.pred_instances.keypoints[0,:,:]" + ] + }, + { + "cell_type": "markdown", + "id": "3a58fabb-e6e7-4b73-a2c5-bac3b0b5f90f", + "metadata": {}, + "source": [ + "## 预测结果-关键点热力图" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9315414f-4163-44e1-b1f1-10b0a72f13ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(17, 1418, 2520)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 每一类关键点的预测热力图\n", + "data_samples.pred_fields.heatmaps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2c808379-d5c3-4d0a-9ea1-a4ba8ad6abaf", + "metadata": {}, + "outputs": [], + "source": [ + "idx_point = 13\n", + "heatmap = data_samples.pred_fields.heatmaps[idx_point,:,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5f610db5-bdb6-48a3-be74-e9b409a2e878", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1418, 2520)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heatmap.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0965731c-485b-4d26-9155-2f96a7dc27ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LzHPUe8SHeNbOywBi3B3SMwYpGUGGAyjyOHFoWnQ6RWfzaCTut/tw/+SwKOKxa9Qf0sNiYOJwgwGyNobREB3k8bXagCsrdDaH2Zwwm6N1dYLHu7y/66cuMP2mIdW6w1fK8EbL8Ot5msjtnEn6ap8HxzjcJgV1sRTsBmnv9xgGXRuh4wHtOKcdZTQjTzsU2lwWS2RXK75W/FzJdj1+4nFuHl0jpLS07g1VF/rup7Ks7pbwkoLnoyHhwhrzJwbsPuuYvlDxpmev8chwwpXpBpdffQRXF7g2Y/aE0Dwz53e96VV+54XLlCHjU8WbeGXnKZq1nHbgo0vtbnEOcQ7NM9pRRrXuqC5B9UTD8NKcYVFT1hmzW0PU57hKyGeObJYhs2yRn62cTfqdcQok6Qa3uYFeWKfZHNKMcxDws4bs5gi5sQVb22mGfg+D2P7J3nHuo+SeEe+Q4QBZX0dHyZCFgJuV6GSGTiaE2WzPykLyDBmPCI9epH58TLWZEXLBNUqx1ZDfmOFu7MTx4ARXEOIEGQ4IF9aYPTVk682e8pLiS8f4NcHVawy3p+h0irjmzPJg4JjGQUT+W+C/JI6bv0ps/fk08BHgUeAXgT+pqpWIDIAPA78HuA78CVX98l28Sfq/35VJ9hqIRVGWQ1UXMx0pCnQ0IKwNaTcL6vWMat1Tj4VmJISCxajvKvClks+UIhMKH9/XhRB30YC0wpnNiTSAplVNUdCsRVfS/LHAc89c5z975pd4rrjOF+ZP80/1d3D96hMU20K9qWxcmPHbN17n20evcr1d50vDx/jiIBCy9JIiKwZ2BellczmHeiHkQjuAZqTkGxWPb+6yXpRM64LXWkc9yWgH0b0UvAMve+I6xoNHN3C6tTHtU5eYfdMakyc91Wa8NgY3CzYu5wwBaRocU0J5Bx/9foOw51q8h9HvoBWvd8hoBI9epHrqAtXFgmbsFoN8cXWCuxKVTENZLiZhMhqilzaZP7vBznMZ80eFdgiuhNE1z/rXPSMRXHKThvkJzeK9R4ZD6gsjJk86dt/csPbUhPk8ZzcfUezkDL4xQm74sxt7Ekc2DiLyDPDfAG9R1ZmI/BTwbmKf6B9V1Y+IyP8OvBf48fT/TVX9NhF5N/A3gT9x5zfqpXH2q3OdW+TYSydwpSlmkGdx6TsaousjmgsDqosZ5Ua8qOt1aMYQBl2RGLhK8HOh2Y0rCs2gcEIGOJE4Y0gaKZ3C4krc4URXD70bRjVW4bvo498czHkyv8VTfosr2SbDrCHkiiaDFlQoQ8YkFOy2Q3brAVo7pLueRRYiYSru4JmeBha6jF1ar5P0o3gf8C7gUrqeagxMi8LKVdy5qFpWz9MbPfvlQaarlxkM0AsbzJ5e49a3ZEyeC7SPVtAK5TdyRHP8bJ1idxbvv6ZZjWX1X3Px+CA5lttItKywd9W+cCVtrlM9fZFb3zZk8k1xwuQqYfz6gM2xZxxA6joeZ9A4pqyvUT2xzs5zGVsvgD47ZXN9xnRecOura7R5jjRjRpM5Mp1BWR7/uhVZ1AqF3NEOBFlreHxjl8mg4NrGME5ihxnO+ztP6E6Y47qVMmAkIjUwBl4D/gDwn6fnPwT8VaJxeDE9Bvgo8HdERFRvf4ZXJBv6OumdHzRto23BV/ECHMRVQ7tWUG9klJue8qJQXoR6Q2k3Whh0VbwCpcNPHWHg0iDrQWMsIgZWG6g8Ig2InFHcIV4MWte4eUM2V7Kp8PXtTT574U18vbjE53af4bWbm+TbjmIn0Nxw7FxZ45Oj57k8v8itcsSXrjxKfi1jsKXkuw0yi/UOhwYRk5CYagrSqy7uQ1HFNVBNC64XY3azAbMqp94ZUEwFX4KvFdf2Zo+9QUB7Bmr5Oc1AnFuCQpHTXhgxe9Qze0opnt/lWx67TtlmfDF7gtn2gNG1nHw0gOldDCkHFXJ22w7weIrnwPtrMTGDZaxhOCBcXGf32QHb3wry7+3w/KO32JoPufqVS6AZ+c6YwdYYmUyhaeIkqcipNnJmjwvyzbt81/Nf4j/YeJXL5SX+efYWZtNNRtczhqNi6So9zADeLb1r3rUBV4FOM7ZmQ5qQJnPKfbtXjmwcVPWyiPzPwFeBGfAviG6kW6qLthavAs+kx88AX0t/24jIFtH1dO3QNxEWS8VF4VrSSO8CzjrIYyGWd9C0yDyDpkEzTygy2qGnHjuqdaHahOpSIFysGW/O2RiVANStYzofUE5zyqxAWhdXEpXDlxluFusLpKzQ5jb++tP44tJKxc0rBrdaRldybq1f5P9uv51RUXNrZ0R4dczmVWV4M468zTDn6/XjXB49ArWjuO4ZXxbGV2qyrRn0g4eHvW1QROJKSZsGKSuyWUu+m5FvC+0wZ6ddY1K0hNrjtzz5jpBNlGwWax6kbGJxXqrOXrQyvIukgnPJ7Y77jWrcnCB5TrNeUF0Q6sdq3v7MV/mPLv4GW+2Yf9pkvPaNp6g2HONhvmxAcxD7+x/EB4visD1FqZ07srunwgGTrza6XruVhXgXXbDrA+aPOpo3zfhPvvXz/MELv8bl+hE+7N/O1RtPsv5aTvHaMLqdmyYZlugtCDkMBg1PDrZ5Lr9BrZ5RUbOVx5V79Fac3PUbqhqZTMluzli7UlBezripF0BgdN0x2GmReXN2Rbc9juNWukRcDbwZuAX8X8A7j3tAIvIS8BLAkPFSsiHvdT8axOpcBgWhyCCLs31pAjIokLKKfvLc0RaOZiQ061BvKuFizcVHJjxzYYunRzGbpwyeK7MNrgzWuQXU5SC6mOZCOxPCIMMV6cJ38bX3BKVPCw2L6mfZnTG4NmR9zSGNZ37jImUeVxLD68r6ay2D6yV+liNtzuCWJxQ+ppnuKmtXaoavT3FbE3Seqpfv5oILAaoa2Z2S3xoyWvO0RQY46llOKDKyRii2hcFNZbATyCYtblpFY1rXy5L/O1Vk39O5uZ8ZMfv0q+7DjXsm9OJOEjTNYhUvipdAi9BqLIKUQFxdHlbBe0Ds8EA3Mex1FUO6Dzr3pfb+vI3XZxviPt5DlhEKRzOC8XrJC6PXeUt+jYtuyhPjHb4xeDK6jfMsjiniFu8tLfg5THaG/LudJ6nV83q5wc3tcVwV1wHak578BXQWg91rrxYEP2ZwIw7LoxuB0TdK3O6UUNVnngl2HLfSfwz8lqpeBRCRfwz8XuCiiGRp9fAscDntfxl4DnhVRDLgAjEwvQdVfRl4GWDTPaq4WMRGXqTq4AIdFoRBThjH7BvNhOAF1yp+2uDnGdIENPeEgdAMoRlBs94y2ih5cmOH37bxOs8NbxDUMQ0FA9fSqmM6L6gGBSGPBV7BS3QzOZapnadNpwOfXDtUNexOyK56NhQGWwOq1zzBxwu22GrIb83xN3bxeUa2O2Z8pSBk8Vh9GchvzXG3dtHdCTqbxTTWgy60/sw+yRDTNFBVuO0pxThnnAm+9tRj0MwhrZLvKsNbLcV2TbZVItMy9rbdrwWzX+MqPoj/HzSDZO+AACy1rzqdqNMqUjogcLri5kwruxUj8aCvJLq2lM5B0+DnDflE8TdzPnf9KVoVblVjvnH1AsNtIZ+2C7nqO/nG94s4LmKH3ay829bVGvS+48U3EuJ1qm2bEkYkGqa6xpUt2Qy2tkZ8duebueinfL2+yNe2L5FPBF+FeKy9Tmqyvcvw6pi119aoNwZ8Vp/jC+tPUM4L3FeHjF5XilsNbjonzMvbxEHuEdXYrGdrmyzzbDaB0dUBAPlOhbu5i26dfRorHM84fBV4u4iMiW6ldwCfBn4O+GPEjKX3AD+d9v9Y+v3/S8//qzvGG6SrVyiQ8RAdDgjrA9pRTrOW0Yx9DNhkggq4Rsnmnmyak81aEGhziRk0I4VRy+Z4ztPjbZ4Z3FosG7fDiK1mxCjbwPsArjc7CSCtIm1Xyn7QgHoKM8eUMiek2Wm68XxVM7w5YDgo4sDcBCgrZHeKzueQZWSzEn9zqaskTYvO5oTpLK4a7mYG0jUwCXFaqFWNTGdkN3OGCtkkIwwcIRdQyHdbsp0KP6liQ5LpLHaqqurlTbhfJ6o/yPb1rvalJsu+G3Fx/GnWGOMYJ2AgDhI3PCje1TtG0ZCkV1JRYV/y4DQN11kRAto0uN2K4c0R49c81waP8G+ub6Clp3g9Y/y6UtyqkXm1uiK93TldyNp0buNUZNfFEDsZie6+62mfqSq0DmkEdS5qe4WAlhXZrRlr3xgy/60Bn5DfxmceeYayzpl9dYOLX1eG1ypkMiN0hW1O0NkMf22HjVEGOmB+fUgzHDKuYHRFWXu9ZvCNCTqZxmv6JOt1QhsnbDcEPy/xw2gcqJt4D81mZ6qp1HGcmMMnReSjwGeABvgl4oz//wE+IiJ/I237QPqTDwD/QEReAW4QM5vuTJEjgwIdD2k3hjQbBfWGp1pz1OtCvSaoj/5AVwvZVMknjmLX4eeBkAkhh5ApvgisFRUX8hmPZLs86neZaw7AwDVk0u6N/YRuOZ0uyrBc3h6k3Hri9AW8NMRc56pCdnxSRU0DaNPE2UxK49WyWuqzBCX0qjzvarBKq4eo9BoHX3FRv0lEyOqGbLtAc4/mcXYnZY1MyzhAlBWkIrs98t0Hzbz3Jxjs6xexFD5crhakjcqW2gaEOg3EjiMbiEMGsJVG8fsHsO5ctSEOFkGRTpcrqdSemOE6a7QXIyor3NYuoytDQjbAV556zeNqGNxU1r9ek9+YxglBVe+VloGDJwS9iuToLk5VyUnWRr1bxAIWhkE1FoSFEP8XQcXtKVLVqsLd3Gb9KwNCtsbk1pDZ+hAJcOmasvHVmvxaWkFX8drpZLDl5hYFcHGyTnO5IBRx8lVsVbitKdJVKzcnX7OjTUO7O0Fm8z0raO1WpvehuPBY2Uqq+sPAD+/b/CXgbQfsOwf++D29gcRgmI4GhPUh9cUB5QVPuemoN4VqA5o1RTONK4dKyCdCO4KQOwbbcVx1LUgjtLVjUhXcqMZcHWwwlJpJGLDVjrlRr7FVjSjnOVILrgFp009IA0BnvQ8KjsUPeU8f765ILiZtiRdJ3cQbbdZPdV1qPWl9SAbFvc50+u/bqUuGEA1QXcc6kq6BukhSfa3QOmVBdQapEynkYHeCeB+FELvXc27pvlsIH+6dOaqP34XQoMGn/dqjGYg79axIM9huEKMzGBLTemWxagjJhRYWqZzi4jnXtvvnAUPD4nrS3QnZ6znr7QbFzoCQCxJi9lt+bYrc2CJMpssJyCGGQXrnr5sQSJFHOZs8WxgIFYl1Mn0pjFYX+mfStPE1kmFI5UqxBmF3gr+SsyEwvDGgHThQKHZq8qsT5OZ2rHROldLatkmpuEGqGr8zIUvClsCyOrqqogvotAbp0KK91YH2syDvw+TifFdICzAcoGtD6o2CatMzv+QoL0XdoGYzwHqNywMOaCtPu5PRDl2MFYgjnyrSRA2ipnTszgdcm69zubhEUMc85Gw3Q74+vcCt2ZCm9OSV4OpkVIJG100aGM9kxbCf/oWht1FTPbX3TUZC0yBY17GJSc8NpN32dI72+0hXVgy91UKXeUbm984Y+8fRnzU2qeOWajLcbZxBuiOuIPpyC92xFcVyRpuEGheaXC4V+EFUymziSkEaHwctnwxcvS928wCuHhDi+Z3NkJsSXZY3RovvR6oa3Z0SptHdspCmOKjQrYthLAQdC2RQRL2zIo8S8LlH87hyUN+bJGg8HlcHpG6hbiHzSO2RuomGmmQg2hbd2iYLit8axtWIKjKvYDqLs/9+zC1l5CGC7tbIbLYni+pMdaP23ev3k/NtHIhZBWEY4wvlhqO6IJSXlObRhuHFOY9uTBhkDU6UrfmQW4MxVT4A9bhG8HWsfM6mQjPxTIcDXvObAFwbrFMFz3Y15MruOlvbY2Q7J9uJ7qlsFshmbdRbauLS7tAwyYN2498LnfywCtCirQPqWLfQ7dIPyPYHh/7MEfYahi7JIA0O5BmauTj47iualbaNM8emReomunVElkaCOrm/wt3fU/t1ePqGYb9QY54RimwxcC2W/nWL1CH1s0g9LepoNLtj6xov3XfdoaMQ4mQkzq5rxO/GAtOEptXsHXt27Hcl+SjoSJEvEkx04AkDT8gcIXcxoSKN+JJqbVwd8HVA6oArG9y8ScfVy3YSicd0/Qbc8gsX5cK9ertkDDh+/cIbhPNtHITFbLIdRsmLeh2aiy3jR6Y8d+kW37ZxjZGvCCq8Xm7yW/4RrqhQl0P8TMim4EvIplBsO0pfcCsIVeO5OlinajxVnTHfLZDtnMFNR7ET0z+LnUC2G33p1E2cNfd4Q6cxHkQ36HedrNre9ruhmzl2s8d+9tkoJwzi4Bsyt6j2hmVCgDRRGNFVbYxxlBlS+uVKo4urSMr2utvj6lI2u5TpIo858MMCHRToMCMMMtqBJxQuxbji8bm6E2xMg1XVQlkjzvUyazrRQT2Z2eBBGXOnbXS6CUJo97gxD41h7blWUtZR50ryLhrezjCMi3h+h5524GhTokObx/MsqikxBFwTRelcFciSoXZphSHd+1bJpjRNTMC4m+M1VjjnxkHQNItoC6EZQ7MW8JsVT13Y4bdfeJ3ftfZVxq4kqOMrxWMEFaom49oso5nktDtCPgnkO+mGVkddFUxLz7QI0AhSO7JdR74r5NswuKUMtqNhcJMy5ut3WTede+lh5SQkA7rgc54tBod2LaceZ4RCUhpx55KJrj3XJHHEKuBnLT5zuDQASxcLgZTNcverhz2rmpQnL3kctHQUj60Z57QjRzOMEgdddhzEDDlfxQErm/mYSp25heSKdHIrnbvtqIPTnVKoz7By9q5m1v0g/6I3yPL89g1Dl33YDoVm6GgGUfesLWKyCRprEFyrqThV8ZWgmZAlaRYHC9fjwhCkTCQ6l+jDfN8egfNvHIqMdhAvmHYAYaSsj0ueHO3wzYMbPJdfZyg1tWZMwoCLxYxB1iBZiDM8H2MHg+2Aax0+aSi1uxmhUFwtSA3ZDLKpUuwqg60Q8/V3q1hjUNXLfP1eL2bjEA4K8sLeAaJIGWjrsWNdvREr2dvue857xqFVXB2VUbLSkeex9iQTWTQV6vubu0D2bQexvvhgl1vf6xSog4IwzKnXc+qN2NioSatXzYjVsrAcrOaQF0KepUCqRgkRSYHqRULDvZ7Dxe93o6tzTuos9n3/nez8otnWoECLfDkpWM+o1zpBTJKRgFCAekWCRJdSI7gKspng51FLrFvBERQXQJo26aCFeK2leNTpV6y+8Tj3xiEUcSkfiiiUp8OWjWHJY4Ndnsy3eMrv4lBK9Wz4GSNXRePgYzCtm+EVOyHO7uZxhdAMQX2U5HVdXGKuZNNAvhMNwyI1s+lSE1PMwQzDnTks+2cwWKYmbw6pN2OiQbXu4gA8gnYIoejcALIYFDr3YMh8HBQkXsAuhJVZehRU8wfPGPeJv0mXNdVJsgwLdBQlI+rNjPmFmDbdrMViypBrklJgMVhlM5LLKb12coPFQHWSEUkG6I6xh7soFLwt++ss4HSMxUEiiovHy1jOwvB2K4YiR0dxxVCvZVQbniqlpTdjaMZKGEA7CmiWjEPKOMxmsW4pn0qKSzkkJB20NqBVFrPHGh+zUJyLhvkwgUnjUM63cdC9jyUINNFtNGsLdtoht0IMjk3CgKvNJrfqMZOqINQO35JSUhU/b8lmcekf/ZrLqmrXKK7sXBYNfppy9pP8A/1c/S5l9LRvvAeVLovlgFqBLs4Q3QkD6vV8T2pyvR5Tk5uRooN0bkMcFPzc4dPAoGlQQLOYrdQUe2bp0hUN3q7+oTerXa4afAqOZ7TDjGZtmQRRbUKzrjTrLeQKWYhhjrmnmTqySbyeACT4eE1VLb7Ooayj4ekGqdvFRJzfs9raUzXe1xta7L9vRdGlWXcGqKve5pSu1c5AHGQY9hteHzO+tMjTOfY0Y0c9lliztA71utKuBXQY8OMG52OKsqoQ6igLv4xJSUpWiCJ1rsqQMo+rhyyLruCuYNHswj1zzo1DSl1rFF9F3ROZe3amA16bbfLF/AnypEM9DQO+OH+cy9MLbE+HMPP4ueAqTXUOgWy3wnuJWSeFI3i3SFV1TUyRk7JF5mXsAldFt1Ks+tTFqsEMwyEc5hfvtneGImWgtaPoSuoGhmozquW6tZrhqI4DQhDa1tFMM9ppbO0qKrgUnHS1x5UeqiwmDfjejHG/O+EwV00n3JjFzKQwiNlx9TiuZuoNqC4GwmZDsVExGNTkvsU7ZXc2YD4tqIucOIuVtBJ1+FmGK5tFgVcXjD00JtJJVuwrBNwjT9+vIIdVSZcu3bpt46QGljUgpyEt33/f28VF+skIXTZS7pKCQVwtNuNkGNYbinHN2qhkVNS0wdEGR9l4pvmQxueo80iQOHFIMR9feFzu0cwjtVtcb4vMMVP/vSfOvXGQqiGbt2QzTzYR8h2hHA/5WnERgFlbADBpC16bbnJ56wKzrSHZtiebQjYHP0+D/6xCQsBlqbLXuVRtGVMk6aS56ybOOjpd+hSIvi81Dg842rl7OrxDvUeLmGTQDqAdQb0WDUO2WbG5MeWR8YygQlBh3mRsD4bMigG1yxequdHV5AgTh8tSFoyLr4/coTd2h1tmKsUAeU4YxVqZZhhdSfW6Ei40jC7MefLCDheKOeOswolybbTG1cEaW36Nuh3gK6GeC9nMkRcOLaLBWcxkDzJa3fHtjyvs71mSKrQXaZt+mQWEk73Fgsk4SBJsi5XbYe/k5qwGStebHKT6BfUuaqLlsTlUW0A7VHStZbhecWFtxmPjCet5SVChCY5pU3DVt2zLmIaCpvVRFWEObRmznHzuYl1DEu4TSfIafbl4464438YBRaYlWe4php6Qg2YOJGO73eA3ZgWvb2wAUDWe6XRAs12Q3/IUN4XBrZSOOl2qhFJWy1mcSJI+CEvNlkUAcSmDcKBeDtgs5DZo0FVtfunNHLPOOMSOW23KQru4OeWZjS2+abwFQKvCpBnwWr7JNR/YAeqqwM8FP4d2GuNSmrllY/jOBeME0bsYFNIsXTOfEiCS67ETbNxoGWyUPHlhh2/bvMbjxQ4XshlOApcHl1jPL/IVgWulp57l0f21K4vByiVJCPbk4t/eB941gVkYhq5YsKvS9qlg0PVWELAwENK00TB4H2Mevap1ET1zWY/+59lrGGLguR0qYRzIx9EwPLW2w7PjW1zMpwQVavVM2gGZi/felgpNJfiZpxkK2UwJuYt1MlmcJIh3y4D18kDsvr1LzrdxUEVmJc45ityDK2JZfRBclVFPHNfWBtGHGwQ/dQx3HPkOFFsp62injcHlWQXzMorT9aofuyX4UsMn3TD9iugur99WDkdDJPmeXRyA82Xcpx3FtqM6bhmPSx5f2+Wb127wLaOrANTq2W2HOInfQd14ZuOMZiRkXUvSXOJKcFFd7RaD0WHfmBw0aEhMUVUnBE/U5SoUBoHxsOLR4YRvGt7iiXybi36KJxDUUYWMm+WYm8MxociWOfpdNk3fIHTyIHczi+0bhhTM7fz2pPOokgKz3aAXYvaWNgHJPFJ5KKvohqmr+LpJBkYP6sx3Gsiy6r1bNQQfOy6GnGggBgqDlrVRxSOjKU+OtnlueIPHsh1aHLV6tpoxZZsxa3LmVc50kNQQimX3xs5gqu+ugeU10WmUGXfHOTcOLJbGflaTTT1FEStoXRuDlO3QgaQCmQry3VTAtpsMw04dVw1VHXsLVEk0q6/ZAnv0km5rFGzVcFfI/kBqN6vPlzPzZkhyKwWyUcPF0ZynR9t8y+gqLwy+AUCtGbfaMa06muCZVAXzUREraQcpFz7r3BSph3B/xti5cA7KqjlIgt11BiKmQYcMXNGyPqi4kM+5lE14PNtm083JpWESBtzI1ljLK/K8pcx1kUKtHvCCdrEEONho9Su1uyB+nscGU3m+aGrVBcs1j706NI+fUyVmbi06F3YFg3MfVy2ZjzG0vsREl+7bBauPej3f6e86Y+jSd7LoR+6iJH4ui0zEfNiwMYxp6s8Nb/Km4hqPZ9u06gg4rvt1pqFgqx5GN+OwJeQ+TRCiMdcksb+47pzcm0E2Fpxz45C6kFU1Utb4iSf3gqjH1ylDJIqqRoG9CvKZkk9asmkbi9imSUK6rGIsoatTOPQ9e0aiX1XZOybj3lgEUbPo/tHcpUFBaAcxM2kwrLk4nC06cD2f3QSgVsctN2UecnbbAdfna9wYjGkGxWLmGfJoIBYBz26meNQ0xpQIow5wIF7JfcvI14xdxVBqxq7EExi6mqGrKVyDcwH1Mc01/qwansVbOImX1UGGIaX9SlFEbbFhgRYxUB6KKDHRDuKMOfb0jinbEmKhmDRRZiIrHDpzuHkKzELqOpkE5UjB6pMcNLu4SX91Tvc5ZSmo50hGIqYtaxEoBg2bgzmPFbs8kW/zTflNHndTAkKLMJSaG/k6rxebXCsqbuWBttDYwS31dFms1Ho/XUA6nnNLab1bzrdxgBgU9h6ZlXjnEAVfZmRTv6ykTVe9q5Rs3uLmLX5WxwB0uXQndcHlu3IP7V9+mlG4M905EtkTc1hWLyd/eOh+iCOUyuJPW1zsMJbEJ7rHLY7QFa2k/0VTVmgvU/OeWMixLwO50iaphiTXIE0UdJxUBbfqEVvtiKFUBByOwNVmg5v1mJ1qSF3HLBnXLFOoaVNAPnQV0r1U6P3pn51h6FI/Uz1IJy2ykJdIVcRdBXFXcxELxWIP75UK4oWAXawBkRSQjsehRxswD7knVlx2ad9OwVYaXagdS5B0DUAIMfBchowy5MxDTil+cQ3MNWeuGWXw1MERgiwK5BbXVBeQ7x3fQ61ocAzOvXHQNkSXECBtwM9K3CAnK5JIW+onoCIxI6mskTKuNqibg3sL7E+76w1qZgROga7rVgixlWs3ODSpQr2N6aplk1GGjGkYsBMKPEqFZ645Zchp1FMFT0hyCp1RiI91309PWn1/85l+VlAXW+r9RNdMclXWgs49k7LgVjXiWr2BR5lrzJK7Vm9wo1pjty5oqgxXs/zbRpf9QHrsGaz6woTOLSTMZVBEqfpxQbOW04z8MoMqVWqHQXR7dSsV1xLVhOtYQZyneEzmo5Hw3eeDKOPRyY2fkmrsIgjdpda2bRIqbHGVx9V+ca4IQtM45k3OTjNkqx1xK4wppKVFFu7FrWbMtCmY1xnaOFyd0ppblv1XAisGYnnyLeZwt5xr47BodAGx41ZVL7T1JckoL3yZErX1qZsoHZ0kCxZGYX+jm9uoMhrHRDWt5uKgs1g1tG1SVW3xVcDVLg6mldDUMdC4Uw+50axz3a8DEHBst3Gw2K6HzJssFjg2sefGQlY9xFn6oh5lcSwHdCVLxXHiAgv5124QbzUarUaTLIbgZo7p7oArxTrr+SNMigEDV+NEuTy7yOuzDW5NRug0i1lUJfgqvk6sku4y4trDe4F07pBFpXaGDnKaJC/RFYs1I2g6mYmREjyxn4kHaboai1RJXCQdqLR6QMGnt+9m2NolYJxmG8quMVObejHUIYoVNskF1sZ6hdB6pnXOVjXkZj7mRraORxfG4Uazzq20SivrHK1cqmFiob1Eq8ng6eHn2rgrzrVxAHqtMkOcWbUSB/5ezveiWCjFKLqmK9Q12oZlD2Mb/O8P3YDdxl4Mrmzw8xDlSmZCNhWq3YzrwzGD7BJOlGkocCgBYacdcnl2kcuTC9zaHadBmMUg7Ko42Cya7uxz4fSP43aFWp1bwrUhyqnMNDaP2hGqvOCGW0dVeH2wsUypnA/ZmQ6Zbw/wO7EWJ5spWaq4lzosZs173FgH4ZaKpVrkUZBu5KnXHdWa0KwJ9VosFmvWYuoneUCygMuU0Ajt3NNUjnaSJK+dEB38aaXWpPPUtNBkSN729IdO9h5Z1Lh0yR1tlL6XusXVLb7U+DOPmYZNkbGVjfDp3LY4Xs8uxJiDOm7VY748eYQru+vMJgUy8/iZ9K6D9PmaZIgW7rx4TVi24b1xzo2DLmMEbhlYikHHrgp2ebPvaanXNZy5X0ZhfzbUw8T+ATikytyQ+h1UDX7eks8ymonSjIR24JkWQ14TaIJjpxksjMO8ybk6W+PWZMR8Z4Df9WTTOAj7UpO+f7soZIwz1fbgAaEzEElKWlUXrpVutil1iMeXx4B59O07agqut8JWPkJSn/G6zNCZx+96im0h3436T36uuLJzo6TJSrs8vsWp6vWSkL6U9aigHWU0a9Ew1BvJMGwozXqA9YbhWkWRNwzyhty3VE3GZF5QznPqPAc8C0XTVEnsKo/UGdQZUmeLiZa45mRarcLeGpdOMbeNHfwkxN4X3eoxnzmaSbfCyagEridJ/Uk9YKOYxyI49Uzqgmu7a+zuDNHtgnw3SvJ314GretdBr+jvobwHT4A7GgcR+SDwR4ArqvrtadsjwD8Enge+DHyfqt6UOIX/28C7gCnwp1X1M+lv3gP89+ll/4aqfuiOR9d9pxogOBRNKWltd2x75ZcOKla7XxfGneQEHhaCLlRKF1W7ZY2b12TTnGwUW7uGgSPkORONvufdchCLtVRoWsdkOqCe5rjtjHxHyCZxUMjmAUmDgiwEEsMy7nAb9gxiqtFwzWu8SzNtBfVZrLBVwdWeuhSaPLnNAFdKdONMJNbXbMc06nzS4GYp9tW0C30u7YLA2uksuT2ihF1/i3ac04x7vdLXod5Qms0Wt1GzuTHjsfUJa1nFOKsY+ZqdZsDV2TpbxTBWETeCqxxNGWfX2dzhi5gG66vkls0yxKekj5PMXEr3LF6WBjFoOhfJOJRR+SDf7SusZtRBuFl75lXOIF9DVWhVqOuMclLATka268g64zCPopm+TMZhcQ2E5cpBwx2vB2Mvd7Ny+PvA3wE+3Nv2fuATqvojIvL+9PtfAr4HeCH9fCfw48B3JmPyw8BbiUP+L4rIx1T15p3ePDa5l+UXG5bBRO1MQ78e4TzVIZyHY7gfaJc+tK9EOkQft9QNUqbBYe7IpnF2HrzQNhll5SgnxXLx1Qoy82RJ4K7YjrUs+VTxsxC7gXWyJ70Z+qEThIMSEuoGdVV0UYZAVre4OuVJaxabzJSxtib0PpavwZXRUOVTJZ8oxXaDn9S4ebW3F0j3kwzDHuG/JGetg9j4qB1l1Gsu9TCJEh7NhRa/WXEpVZE/v36dC9mMsasYu4obzRqv5pd4Ld9EVbhVOZp5jp/HIHZbSFSOzWMBXWeUNM9i3+vjZC4tzmXYE/Dfk6kW2oVribpFqpDcizGlVVNFe9N6mtIxLT2zPMSvTwVqwU2T624isSnXJLr//Cy6K6VsogROF3Pc373xYb0nj8AdjYOq/msReX7f5heB358efwj4eaJxeBH4sMZv4xdE5KKIPJ32/biq3gAQkY8D7wR+8o5HuFDW7EirBidWnPYgknr1SlXj5i3Z3JNPU1MXhLYU2pkQcr+YnRNIAzPkE8h3lMFOIJu0+FmDK+s0Q2/2zNDviqAgcRCRukI1LOQmXF2QE2MRvsqSmF53rKm2po5NiLIytZWdtvhJjZ/062u6TLmw0lt7UQXtUoV36nwYhelI8iJKO1Jk1LCWqsifX7/OC6PXedTvMnYlQ6m5mm3iUyX5pC6YzAqaYRb7YxTEupAsFQ1mbilQt5C2SHIjR5lg326lnGJO0k0OmuhedFUT09LLriWoLsT0XLoONFe6dGfXgp9GefRsEg1DPg1k0+gGdOVypbbQREveBIs33DtHjTk8qaqvpcffAJ5Mj58Bvtbb79W07bDtK4jIS8BLAEPGvVnoXlYmN2YUzh1x1bfMWIq+5zZWDFc1flpFcTqJFWfSpsFwkKQQUjGahDQ7nyvZDPJpIN8NZJMGP0vNmOomyqvvb8h00HWxZ9WQjq9to/uIpGSaZrxu4sjoKo8zfJWE4yQZh0ZTq1Alm0VXkpvWC8OgdR37GXcrhj3H4ZaCjiG5QpoWabsAu1+6VkURp2S+ZZiK8S76KRf9lLErWUu1Fzf8OpvZjFFWk2WBOtOUzSSLeohVvaGeRtOxvvC98Zw9dOnMrYuuRe8Wq0c/d+SOZBhiA6UwJ8mzd8vHeB1ks+RO7H6mgXwarwOZ10vRzG7VsIg7WuzhXjl2QFpVVURO7Kyr6svAywCb8oimjSf18sb9IGhKSCRV5TYwL5HMx4G3UVyT4+skxpcqn7tmTRLiDD1L2S3ZNFbA+1md6lrqZZZa00S//r2kZrZt9Ll3adM0i9oHAVySdZc64Mts2cKUVMvQpj7SZYPMG2ReRqn3KrqUVlKp982wtW2j67SJLhGps5h1k3L340mIY3juA4VvGfuKNVey4WZsuIo1aZjrLDa88jVDX5NlLfi9ch7qSXIbpxQPS5+vmxgQXPoCXVKH9ai0iIuzfFdmZLMY44lB806lNbmaFjGhmLbsF5OEgC9Ty9h5g5s3C4mcvWrKtmo4Kkc1Dq+LyNOq+lpyG11J2y8Dz/X2ezZtu8zSDdVt//kjvrfxIJBWfIt6guCWBiIEkCqqVNRNyugZ4MucduCjFEK+nOmSZuhdRoqfpxl6mWbodVw9LNw295Cltohp7TdgKR0WkUXzIN8EXNXumXl39RXStkt/d1mhZRlnsN1qZt/xdIVvXbpnpwbcZfJIo1EKI3QGYnnMmQRyaRlKzVAahtIyENLjJOfh25hu6zVqRS1+Dqpe7hUMnhS9WhJNRYrifZTD6c5x5nGzDO8EaZRQOnyRpFXyJKbXqXForDj3VZf+2uKqLs7QLuRxOkXlla6NNsG8Z45qHD4GvAf4kfT/T/e2/6CIfIQYkN5KBuRngf9BRC6l/b4b+KGjH7bxwNANEr3URpxb9ndOs3NPrIB3RRZdIFlX3Bh3c2nm7qoWqZplFXznummaxUB8x5niQf7xzhWS0m5jhlKIM9GU7ip1g5bZ0kfffZ6UqknTxtqaJPCoVbU8nsOkJnr6P331UkgT7kaRJhWJNTHFc9rkbDUjrrexUHCuM4bScLXd4Hq7zs16zG49oKwzqGVZsb2/YLAnVb88DScwiPbcSyuTgxAWRa2UUVrbQzS6uY9d3hbNgGShWtAZh1jTsqy0ljqlR5d1VEOom6USwuLcW5bSUbibVNafJM76HxORV4lZRz8C/JSIvBf4CvB9afefIaaxvkJMZf0BAFW9ISJ/HfhU2u+vdcFp4w1KT6b6IK2dReFiiMVxVHVUnq7bhXLnnkG469TXpsGgbuJqoVPa7Qoe7yWFOWXW7Fk9uFQ93A1iIWUyeb/ooyA9w9KlT3dyFF3PhD1xhj1yGfvkO6CnHtr7vJ2MR9JpcpXQVJ6qydiph9xqxlxtNqjVs+2GMSDdbPJadZEb9Rrb5ZC6yvZpPS3dYLHeICxjHrcrzjsK+wwEtDHY7T1S12jw4OtY3wExW8sn+ZCs1xDIu0UhnQTdaxC6zLQuAF1XyzhD3zDYquFI3E220vcf8tQ7DthXgfcd8jofBD54T0dnPJjctl2kW32+bZEyBhFj5y5ZzKgXdFkuXYFTVzuQVgwLl1K4y1hDP3i6MBC9GW6asS6GlbZdtpzsuq4tXkqX+4Rwb1X5i77QvUr/buXQJiG9Kv5PLVRlxm5VcK1cYyO7yNQPGLqaXFpuNGu8Xm5ydb7OblnQlBlSSeyaV3eGIa7QpAkp3berBziFOoCDzrH0DHDdxOQEiN+tk9TJr2vc5PYYy874xlTVnuuoa4laN3vFNc0wHItzXiFtPJDsETI8wLXQSSgvdg9p8I8DpUBPervnvumqXkOvAna/RMpRjlPb6EpqiVlBKlFOYt/Asr/oMr5E2rK//8dhA1OnO9URQqxz0LCs1q6b6E+vo4/dlVFipB146rzghlsDoGozxllF5lpyCWzVQ67P17g1HbG9M4rFYpNlFXEM6EdffRxcl+d0z6rrNOjqH3R5HSzORxtiwUi/d7b3yxXVQjpfkxGolxlpsEcZYRH47/Y3jowZB+P0WNycPQPRshx423YxCCxm5H2XS+oDvKiK7wazTlRtMbCdgExK/1h7RgJ6fviee+zAGps9r3O799qX6tnJizRN1DtyS4mRrHSxBeYgBmlxnioMuNo6JmVB7lu8U7wLTMuC2Tynmeewk5FvOYodWRQMZtOAm6eYTa9QjKMY1rulH9/Z72IK0Rhq27Jo7QosBAgPkv7uxxT6dKuFu105GnfEjINx+misulXtBv80OEAcnGDpSurT9TaAPQZh+fsp6Gf1feX7xpk9xZjHbADVSXcss5ViOqtWdXSzzDNc2eLnnmwghAkpS8ohjdDWjp1ZhnhNqTygtYPS4WcOP5VoGLaVYlf31IXIvF6uvO5UTX4S7FtJ9t14XSZTfDoZY+cWbrwV9q8OeufTAs8nixkH4+zoFTQuB9pU8a6rLpvF6sK5Zd+Fu3XfHPs42evvXhR37dvnWO/Tc7V5H2WtJRXh5VlsjTvwKWvHpYydGENoy9j0R1OqKgK+TrGFMlYR57tKnjSoFhXEnQZVE+Mz2inmnoV/vrc6AxYrNNqY2qsQJw9t2Ns1rzMa+ycEh76+cRKYcTDuD3v6LLiDUyhDc2dXzmkOCKelyXO7GhBSSKKskFlFlvu4qmpBWpf6NUA7F5ohi3RfdSmrqU4FgzMlm8Zq8q5gbJFu263C+ufzFCS7b//5u8fdSrD7nttkIHq70KsLsUDzmWHGwThbDq0x6D1/yFNvKA4zEF0GkRNkXuK8Iwdcm+GaKN+RJcOQFaQ2oakeJMQWodLEavJsHoUJ/bxd1AZ0tQ17spTOA/tiEytPdytNMwxnhhkH4+w4sDr3Ib7RDzIQjkUtB2WFeB/7P3d9JkpPM/Rkc6Et4v6aYg5dJblrYz91P2/jT9krHEz1Ict+Bydc33AcDpw47HPpPczXyxljxsE4G8ww3JY9dRaOZe+LFKB2IaTeyxmujD0ZQibLYkEgVmpHyQ1X9eQl6haqek/h4NJAnDNhusPSf40zx4yDcfqYYbg9/R4IXZOcLqe/rKJCbJuC1pnHZbEfg2b7CgpVIaQCsyb1zeg3G+oXDvYriTtpa8PoYcbBOH32uwtsIFqyokPUpdG2URcJYpFg5Zb90rMMSX0f9iMLKYywlJVYGINe6mrqlGaKpcZhmHEwzgYzCIezokMEIqnqF6KsiHdo01UMx2ryrgbkwNfrjMH+VUIvHXhRSGbfjXEAZhwM4zywL/Aai8PCMue/qyDu+k54H7vHLf4+rL5eVzC23yhYOqhxF5hxMIzzwp7BuqvQbpcSIhAfd+qwvZWDqq6qxR6kULryPoZxMGYcDOM8coB4IbBXcmJ/TUj/l66i3AyDcUTMOBjGeWa/5MQ+4brb/625kIyjY8bBMB4EDtIl6jQm9jcPAlspGMfGjINhPEjsH+z3CwLebl/DuAfMOBjGg4wZAOOUOGA9uhcR+aCIXBGRX+tt+59E5N+JyK+IyD8RkYu9535IRF4RkS+IyB/qbX9n2vaKiLz/xD+JYRiGcWLc0TgAfx94575tHwe+XVV/B/AbwA8BiMhbgHcD/376m/9NRLyIeODvAt8DvAX4/rSvYRiGcQ65o3FQ1X8N3Ni37V+oapN+/QXg2fT4ReAjqlqq6m8BrwBvSz+vqOqXVLUCPpL2NQzDMM4hd7NyuBN/Bvjn6fEzwNd6z72ath22fQUReUlEPi0in64pT+DwDMMwjHvlWMZBRP4K0AA/cTKHA6r6sqq+VVXfmjM4qZc1DMMw7oEjZyuJyJ8G/gjwDtVFysRl4Lnebs+mbdxmu2EYhnHOONLKQUTeCfxF4I+q6rT31MeAd4vIQETeDLwA/FvgU8ALIvJmESmIQeuPHe/QDcMwjNPijisHEflJ4PcDj4nIq8APE7OTBsDHk9jXL6jqf6WqnxORnwJ+nehuep9qrOUUkR8EfhbwwAdV9XOn8HkMwzCME0D0HBfRbMoj+p3yjvt9GIZhGA8U/1I/+ouq+tbjvMZJZCsZhmEYbzDMOBiGYRgrmHEwDMMwVjDjYBiGYaxgxsEwDMNYwYyDYRiGsYIZB8MwDGMFMw6GYRjGCmYcDMMwjBXMOBiGYRgrmHEwDMMwVjDjYBiGYaxgxsEwDMNYwYyDYRiGsYIZB8MwDGMFMw6GYRjGCmYcDMMwjBXuaBxE5IMickVEfu2A5/6CiKiIPJZ+FxH5MRF5RUR+RUS+o7fve0TkN9PPe072YxiGYRgnyd2sHP4+8M79G0XkOeC7ga/2Nn8P8EL6eQn48bTvI8Te098JvA34YRG5dJwDNwzDME6POxoHVf3XwI0DnvpR4C8C/SbULwIf1sgvABdF5GngDwEfV9UbqnoT+DgHGBzDMAzjfHCkmIOIvAhcVtVf3vfUM8DXer+/mrYdtv2g135JRD4tIp+uKY9yeIZhGMYxye71D0RkDPxlokvpxFHVl4GXATblEb3D7oZhGMYpcJSVw7cCbwZ+WUS+DDwLfEZEngIuA8/19n02bTtsu2EYhnEOEdU7T85F5Hngn6nqtx/w3JeBt6rqNRH5w8APAu8iBp9/TFXflgLSvwh02UufAX6Pqh4Uy+i/9lVgAly760/0xuUx7DyAnYc+di4idh6WdOfiTar6+HFe6I5uJRH5SeD3A4+JyKvAD6vqBw7Z/WeIhuEVYAr8AICq3hCRvw58Ku331+5kGNLfPS4in1bVt97xk7zBsfMQsfOwxM5FxM7DkpM8F3c0Dqr6/Xd4/vneYwXed8h+HwQ+eI/HZxiGYdwHrELaMAzDWOFBMA4v3+8DOCfYeYjYeVhi5yJi52HJiZ2LuwpIG4ZhGA8XD8LKwTAMwzhjzDgYhmEYK5xb4yAi7xSRLySF1/ff7+M5bUTkyyLyqyLyWRH5dNr2iIh8PCnZfrwTK7yd+u2DyEHKv0f57A+68u8h5+GvisjldF18VkTe1Xvuh9J5+IKI/KHe9gf63hGR50Tk50Tk10XkcyLyZ9P2h/GaOOxcnP51oarn7gfwwBeBbwEK4JeBt9zv4zrlz/xl4LF92/5H4P3p8fuBv5kevwv454AAbwc+eb+P/5if/buIBZK/dtTPDjwCfCn9fyk9vnS/P9sJnIe/Cvx3B+z7lnRfDIiKBV9M980Df+8ATwPfkR5vAL+RPu/DeE0cdi5O/bo4ryuHtwGvqOqXVLUCPkJUfH3YeBH4UHr8IeB7e9sPUr99INGDlX/v9bM/8Mq/h5yHw3gR+Iiqlqr6W8TC07fxBrh3VPU1Vf1MerwDfJ4o1PkwXhOHnYvDOLHr4rwah7tWcX0DocC/EJFfFJGX0rYnVfW19PgbwJPp8cNwfu71s7+Rz8kPJnfJB3t9UB6K85Cke3438Eke8mti37mAU74uzqtxeBj5far6HcSGSe8Tke/qP6lxzfhQ5h0/zJ+d2DDrW4HfBbwG/C/39WjOEBFZB/4R8OdUdbv/3MN2TRxwLk79ujivxuGhU3FV1cvp/yvAPyEuA1/v3EXp/ytp94fh/NzrZ39DnhNVfV1VW1UNwP9BvC7gDX4eRCQnDoY/oar/OG1+KK+Jg87FWVwX59U4fAp4QUTeLCIF8G7gY/f5mE4NEVkTkY3uMbFXxq8RP3OXYfEe4KfT448Bfyplabwd2Oott98o3Otn/1ngu0XkUlpif3fa9kCzL5b0nxKvC4jn4d0iMhCRNxNb8/5b3gD3jogI8AHg86r6t3pPPXTXxGHn4kyui/sdjb9NlP5dxMj8F4G/cr+P55Q/67cQswd+Gfhc93mBR4FPAL8J/EvgkbRdgL+bzs2vEiXT7/vnOMbn/0ni0rgm+kLfe5TPDvwZYgDuFeAH7vfnOqHz8A/S5/yVdDM/3dv/r6Tz8AXge3rbH+h7B/h9RJfRrwCfTT/vekivicPOxalfFyafYRiGYaxwXt1KhmEYxn3EjINhGIaxghkHwzAMYwUzDoZhGMYKZhwMwzCMFcw4GIZhGCuYcTAMwzBW+P8B/Kzl4288f60AAAAASUVORK5CYII=\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 索引为 idx 的关键点，在全图上的预测热力图\n", + "plt.imshow(heatmap)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ddda86b-d99a-4c26-b73e-889372438a01", + "metadata": {}, + "source": [ + "## 可视化配置" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8e165403-cce7-45a8-9b62-de22ce964348", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/03 13:58:22 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n" + ] + } + ], + "source": [ + "# 半径\n", + "pose_estimator.cfg.visualizer.radius = 3\n", + "# 线宽\n", + "pose_estimator.cfg.visualizer.line_width = 1\n", + "visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer)\n", + "# 元数据\n", + "visualizer.set_dataset_meta(pose_estimator.dataset_meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6a3b42d2-8baf-4735-b9ca-8ea8b9423349", + "metadata": {}, + "outputs": [], + "source": [ + "# 元数据\n", + "# pose_estimator.dataset_meta" + ] + }, + { + "cell_type": "markdown", + "id": "459c4ec1-9144-435b-ad12-87f6972f3161", + "metadata": {}, + "source": [ + "## 展示可视化效果" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f7b868d0-4807-4f43-abf1-c2451a1448d8", + "metadata": {}, + "outputs": [], + "source": [ + "img = mmcv.imread(img_path)\n", + "img = mmcv.imconvert(img, 'bgr', 'rgb')\n", + "\n", + "img_output = visualizer.add_datasample(\n", + " 'result',\n", + " img,\n", + " data_sample=data_samples,\n", + " draw_gt=False,\n", + " draw_heatmap=True,\n", + " draw_bbox=True,\n", + " show=False,\n", + " wait_time=0,\n", + " out_file='outputs/B2.jpg',\n", + " kpt_score_thr=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "78fa761a-36d1-457c-9eda-0fb4928b78e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2836, 2520, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_output.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "09b19767-6873-42a9-9b64-0b71ca2fafe8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(img_output)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c936cfdf-be2c-4bde-9c19-285d2c67bb34", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "093e3ae4-a24f-437a-b47c-4338884bd5fe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220C\343\200\221\344\270\213\350\275\275\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\225\260\346\215\256\351\233\206.ipynb" "b/2023/0404/\343\200\220C\343\200\221\344\270\213\350\275\275\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\225\260\346\215\256\351\233\206.ipynb" new file mode 100644 index 0000000..6f0370b --- /dev/null +++ "b/2023/0404/\343\200\220C\343\200\221\344\270\213\350\275\275\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\225\260\346\215\256\351\233\206.ipynb" @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "65827da0-e3f9-4da1-bc9c-dec8ef42c5f5", + "metadata": {}, + "source": [ + "# 下载三角板关键点检测数据集\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "bb91a05b-5aa9-4027-bf0f-912b934d59cd", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录 或 mmdetection 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "185fb8f7-771b-4ac0-bd86-de663021412b", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')\n", + "# os.chdir('mmdetection')" + ] + }, + { + "cell_type": "markdown", + "id": "d16cb5c3-1b43-4d2a-ba5d-af8f74c00c2e", + "metadata": {}, + "source": [ + "## 下载数据集至`data`目录" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "abe084b5-e1b2-4074-a490-ae82f6f6ef46", + "metadata": {}, + "outputs": [], + "source": [ + "# 删除原有的数据集文件（如有）\n", + "!rm -rf data/Triangle_140_Keypoint_Dataset data/Triangle_140_Keypoint_Dataset.zip" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b663508f-6700-4a9a-a0a0-6e01373f4411", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 13:41:32-- http://wget/\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 502 Bad Gateway\n", + "2023-04-04 13:41:32 错误 502：Bad Gateway。\n", + "\n", + "--2023-04-04 13:41:32-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/triangle_dataset/Triangle_140_Keypoint_Dataset.zip\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 551582420 (526M) [application/zip]\n", + "正在保存至: “data/Triangle_140_Keypoint_Dataset.zip”\n", + "\n", + "Triangle_140_Keypoi 100%[===================>] 526.03M 51.7MB/s 用时 10s \n", + "\n", + "2023-04-04 13:41:43 (50.3 MB/s) - 已保存 “data/Triangle_140_Keypoint_Dataset.zip” [551582420/551582420])\n", + "\n", + "下载完毕 --2023-04-04 13:41:43--\n", + "总用时：11s\n", + "下载了：1 个文件，10s (50.3 MB/s) 中的 526M\n" + ] + } + ], + "source": [ + "# 下载数据集压缩包\n", + "!wget wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/triangle_dataset/Triangle_140_Keypoint_Dataset.zip -P data\n" + ] + }, + { + "cell_type": "markdown", + "id": "60a0683c-a93b-43e3-bea7-c2c57d5f1c92", + "metadata": {}, + "source": [ + "## 下载用于测试的图像和视频" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bbf22f49-892f-4cc8-b3ca-e5e71f4315e8", + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir data/test_triangle" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "30e91855-3a6c-4eac-9914-5b27660e7e9d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 13:48:57-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_1.jpg\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 1361630 (1.3M) [image/jpeg]\n", + "正在保存至: “data/test_triangle/triangle_1.jpg”\n", + "\n", + "triangle_1.jpg 100%[===================>] 1.30M 4.57MB/s 用时 0.3s \n", + "\n", + "2023-04-04 13:48:57 (4.57 MB/s) - 已保存 “data/test_triangle/triangle_1.jpg” [1361630/1361630])\n", + "\n", + "--2023-04-04 13:48:58-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_2.jpg\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 4653317 (4.4M) [image/jpeg]\n", + "正在保存至: “data/test_triangle/triangle_2.jpg”\n", + "\n", + "triangle_2.jpg 100%[===================>] 4.44M 12.1MB/s 用时 0.4s \n", + "\n", + "2023-04-04 13:48:58 (12.1 MB/s) - 已保存 “data/test_triangle/triangle_2.jpg” [4653317/4653317])\n", + "\n", + "--2023-04-04 13:48:58-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_3.jpg\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 865194 (845K) [image/jpeg]\n", + "正在保存至: “data/test_triangle/triangle_3.jpg”\n", + "\n", + "triangle_3.jpg 100%[===================>] 844.92K 3.33MB/s 用时 0.2s \n", + "\n", + "2023-04-04 13:48:59 (3.33 MB/s) - 已保存 “data/test_triangle/triangle_3.jpg” [865194/865194])\n", + "\n", + "--2023-04-04 13:48:59-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/videos/triangle_2.mov\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 29949823 (29M) [video/quicktime]\n", + "正在保存至: “data/test_triangle/triangle_2.mov”\n", + "\n", + "triangle_2.mov 100%[===================>] 28.56M 38.0MB/s 用时 0.8s \n", + "\n", + "2023-04-04 13:49:00 (38.0 MB/s) - 已保存 “data/test_triangle/triangle_2.mov” [29949823/29949823])\n", + "\n", + "--2023-04-04 13:49:00-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/videos/triangle_5.mov\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 20428086 (19M) [video/quicktime]\n", + "正在保存至: “data/test_triangle/triangle_5.mov”\n", + "\n", + "triangle_5.mov 100%[===================>] 19.48M 32.7MB/s 用时 0.6s \n", + "\n", + "2023-04-04 13:49:01 (32.7 MB/s) - 已保存 “data/test_triangle/triangle_5.mov” [20428086/20428086])\n", + "\n" + ] + } + ], + "source": [ + "# 图像\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_1.jpg -P data/test_triangle\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_2.jpg -P data/test_triangle\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/test_img/triangle_3.jpg -P data/test_triangle\n", + "\n", + "# 视频\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/videos/triangle_6.mp4 -P data/test_triangle\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/videos/triangle_7.mp4 -P data/test_triangle\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/videos/triangle_9.mp4 -P data/test_triangle" + ] + }, + { + "cell_type": "markdown", + "id": "d44532ac-0937-405c-899a-e68adcdd32cd", + "metadata": {}, + "source": [ + "## 解压数据集至`data`目录" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7f81f573-6171-4c97-84ec-e5e092dc35cd", + "metadata": {}, + "outputs": [], + "source": [ + "!unzip data/Triangle_140_Keypoint_Dataset.zip -d data >> /dev/null\n", + "!rm -rf data/Triangle_140_Keypoint_Dataset.zip # 删除压缩包" + ] + }, + { + "cell_type": "markdown", + "id": "e647bdbe-f3bb-458d-8574-d2f40e85ed16", + "metadata": {}, + "source": [ + "## 查看数据集中的图片" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0c06c724-64df-43c7-b1fd-1987e1db82f6", + "metadata": {}, + "outputs": [], + "source": [ + "# from PIL import Image\n", + "# Image.open('data/Triangle_140_Keypoint_Dataset/images/DSC_0373.jpg')" + ] + }, + { + "cell_type": "markdown", + "id": "a8df8967-67a8-45c2-8e81-880ecaae1ab2", + "metadata": {}, + "source": [ + "## 删除系统自动生成的多余文件\n", + "\n", + "建议在 Linux 系统中运行爬虫、划分训练集测试集代码" + ] + }, + { + "cell_type": "markdown", + "id": "48c0e95b-f383-4765-92e0-1c0359704b26", + "metadata": {}, + "source": [ + "### 查看待删除的多余文件" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a8356247-4b89-42e9-aa87-4c630f05e1f8", + "metadata": {}, + "outputs": [], + "source": [ + "!find . -iname '__MACOSX'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a1b64d9d-bdf8-43fe-8321-bb6d241137dc", + "metadata": {}, + "outputs": [], + "source": [ + "!find . -iname '.DS_Store'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a5f038c2-72ba-4724-a190-b2ec97d8bcf0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/.ipynb_checkpoints\n" + ] + } + ], + "source": [ + "!find . -iname '.ipynb_checkpoints'" + ] + }, + { + "cell_type": "markdown", + "id": "44bb281b-c851-4818-b0c8-49cabe62e925", + "metadata": {}, + "source": [ + "### 删除多余文件" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4bf04f95-0750-4248-8696-fa2aa4ff03e5", + "metadata": {}, + "outputs": [], + "source": [ + "!for i in `find . -iname '__MACOSX'`; do rm -rf $i;done" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "217b2325-835b-451c-89d7-3d39c5dfc5c2", + "metadata": {}, + "outputs": [], + "source": [ + "!for i in `find . -iname '.DS_Store'`; do rm -rf $i;done" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cfb7afa4-106a-4e6a-ab35-86f0eb902432", + "metadata": {}, + "outputs": [], + "source": [ + "!for i in `find . -iname '.ipynb_checkpoints'`; do rm -rf $i;done" + ] + }, + { + "cell_type": "markdown", + "id": "c5591044-de11-4084-a830-a89d8bdc5b3a", + "metadata": {}, + "source": [ + "### 验证多余文件已删除" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "33239471-a70e-426d-beba-3517c14311e6", + "metadata": {}, + "outputs": [], + "source": [ + "!find . -iname '__MACOSX'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f82a7703-8b94-4103-9672-ec29849616b9", + "metadata": {}, + "outputs": [], + "source": [ + "!find . -iname '.DS_Store'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0101460a-d0bc-4388-aa0b-d30b0f0ef538", + "metadata": {}, + "outputs": [], + "source": [ + "!find . -iname '.ipynb_checkpoints'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8243b592-b03d-48f2-958b-19cc380e8a1f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220D1\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\350\256\255\347\273\203.ipynb" "b/2023/0404/\343\200\220D1\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\350\256\255\347\273\203.ipynb" new file mode 100644 index 0000000..21e1926 --- /dev/null +++ "b/2023/0404/\343\200\220D1\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\350\256\255\347\273\203.ipynb" @@ -0,0 +1,2582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "283db6c5-7f78-4a9b-8212-f61269bd9a2d", + "metadata": {}, + "source": [ + "# 三角板目标检测-训练\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "ff24ce0c-d715-4864-992f-c44e3d69ba25", + "metadata": {}, + "source": [ + "## 进入mmdetection主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ec62b16-80ce-41eb-8c78-9c6d374bb961", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmdetection')" + ] + }, + { + "cell_type": "markdown", + "id": "6c338d79-bff4-426d-8b05-19fcaddbafb5", + "metadata": {}, + "source": [ + "## Faster R CNN" + ] + }, + { + "cell_type": "markdown", + "id": "0a087b0e-80ce-4d46-a04f-32d84615a466", + "metadata": {}, + "source": [ + "### 下载config配置文件" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c12435b7-7a18-44e7-a434-7baed8a0c668", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 17:48:17-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/faster_r_cnn_triangle.py\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 7587 (7.4K) [binary/octet-stream]\n", + "正在保存至: “data/faster_r_cnn_triangle.py”\n", + "\n", + "faster_r_cnn_triang 100%[===================>] 7.41K --.-KB/s 用时 0.006s \n", + "\n", + "2023-04-04 17:48:17 (1.21 MB/s) - 已保存 “data/faster_r_cnn_triangle.py” [7587/7587])\n", + "\n" + ] + } + ], + "source": [ + "!rm -rf data/faster_r_cnn_triangle.py\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/faster_r_cnn_triangle.py -P data" + ] + }, + { + "cell_type": "markdown", + "id": "b454fed1-6f8a-47e9-b2e6-f953085bd4cd", + "metadata": {}, + "source": [ + "### 训练" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ef42087d-8ff3-433e-a06d-3bdeff6614d7", + "metadata": {}, + "outputs": [], + "source": [ + "!python tools/train.py data/faster_r_cnn_triangle.py" + ] + }, + { + "cell_type": "markdown", + "id": "518c6242-40ae-46a5-9e97-20334a5b9df3", + "metadata": {}, + "source": [ + "## RTMDet" + ] + }, + { + "cell_type": "markdown", + "id": "74984c5e-6d72-4bf2-9217-74b6288e7956", + "metadata": {}, + "source": [ + "### 下载config配置文件" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "52d7bb83-aa4e-4a61-abdb-5cbb026f8559", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 17:48:21-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/rtmdet_tiny_triangle.py\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 8694 (8.5K) [binary/octet-stream]\n", + "正在保存至: “data/rtmdet_tiny_triangle.py”\n", + "\n", + "rtmdet_tiny_triangl 100%[===================>] 8.49K --.-KB/s 用时 0.01s \n", + "\n", + "2023-04-04 17:48:22 (885 KB/s) - 已保存 “data/rtmdet_tiny_triangle.py” [8694/8694])\n", + "\n" + ] + } + ], + "source": [ + "!rm -rf data/rtmdet_tiny_triangle.py\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/rtmdet_tiny_triangle.py -P data" + ] + }, + { + "cell_type": "markdown", + "id": "5810f490-749f-46d9-863e-7f0c7468fac0", + "metadata": {}, + "source": [ + "### 训练" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5079c6-75c5-4c1b-a258-a47d210d45dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/04 17:48:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n", + "------------------------------------------------------------\n", + "System environment:\n", + " sys.platform: linux\n", + " Python: 3.7.10 (default, Jun 4 2021, 14:48:32) [GCC 7.5.0]\n", + " CUDA available: True\n", + " numpy_random_seed: 1459478410\n", + " GPU 0: NVIDIA GeForce RTX 3080\n", + " CUDA_HOME: /usr/local/cuda\n", + " NVCC: Cuda compilation tools, release 11.2, V11.2.152\n", + " GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\n", + " PyTorch: 1.10.1+cu113\n", + " PyTorch compiling details: PyTorch built with:\n", + " - GCC 7.3\n", + " - C++ Version: 201402\n", + " - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n", + " - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n", + " - OpenMP 201511 (a.k.a. OpenMP 4.5)\n", + " - LAPACK is enabled (usually provided by MKL)\n", + " - NNPACK is enabled\n", + " - CPU capability usage: AVX512\n", + " - CUDA Runtime 11.3\n", + " - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n", + " - CuDNN 8.2\n", + " - Magma 2.5.2\n", + " - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n", + "\n", + " TorchVision: 0.11.2+cu113\n", + " OpenCV: 4.5.4\n", + " MMEngine: 0.7.0\n", + "\n", + "Runtime environment:\n", + " cudnn_benchmark: False\n", + " mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n", + " dist_cfg: {'backend': 'nccl'}\n", + " seed: None\n", + " Distributed launcher: none\n", + " Distributed training: False\n", + " GPU number: 1\n", + "------------------------------------------------------------\n", + "\n", + "04/04 17:48:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n", + "dataset_type = 'CocoDataset'\n", + "data_root = 'data/Triangle_140_Keypoint_Dataset/'\n", + "metainfo = dict(classes=('sjb_rect', ))\n", + "NUM_CLASSES = 1\n", + "MAX_EPOCHS = 200\n", + "TRAIN_BATCH_SIZE = 4\n", + "VAL_BATCH_SIZE = 2\n", + "stage2_num_epochs = 20\n", + "base_lr = 0.004\n", + "VAL_INTERVAL = 5\n", + "default_scope = 'mmdet'\n", + "default_hooks = dict(\n", + " timer=dict(type='IterTimerHook'),\n", + " logger=dict(type='LoggerHook', interval=1),\n", + " param_scheduler=dict(type='ParamSchedulerHook'),\n", + " checkpoint=dict(\n", + " type='CheckpointHook',\n", + " interval=10,\n", + " max_keep_ckpts=2,\n", + " save_best='coco/bbox_mAP'),\n", + " sampler_seed=dict(type='DistSamplerSeedHook'),\n", + " visualization=dict(type='DetVisualizationHook'))\n", + "env_cfg = dict(\n", + " cudnn_benchmark=False,\n", + " mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n", + " dist_cfg=dict(backend='nccl'))\n", + "vis_backends = [dict(type='LocalVisBackend')]\n", + "visualizer = dict(\n", + " type='DetLocalVisualizer',\n", + " vis_backends=[dict(type='LocalVisBackend')],\n", + " name='visualizer')\n", + "log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)\n", + "log_level = 'INFO'\n", + "load_from = None\n", + "resume = False\n", + "train_cfg = dict(\n", + " type='EpochBasedTrainLoop',\n", + " max_epochs=200,\n", + " val_interval=5,\n", + " dynamic_intervals=[(180, 1)])\n", + "val_cfg = dict(type='ValLoop')\n", + "test_cfg = dict(type='TestLoop')\n", + "param_scheduler = [\n", + " dict(\n", + " type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,\n", + " end=1000),\n", + " dict(\n", + " type='CosineAnnealingLR',\n", + " eta_min=0.0002,\n", + " begin=150,\n", + " end=300,\n", + " T_max=150,\n", + " by_epoch=True,\n", + " convert_to_iter_based=True)\n", + "]\n", + "optim_wrapper = dict(\n", + " type='OptimWrapper',\n", + " optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),\n", + " paramwise_cfg=dict(\n", + " norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))\n", + "auto_scale_lr = dict(enable=False, base_batch_size=16)\n", + "backend_args = None\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='CachedMosaic',\n", + " img_scale=(640, 640),\n", + " pad_val=114.0,\n", + " max_cached_images=20,\n", + " random_pop=False),\n", + " dict(\n", + " type='RandomResize',\n", + " scale=(1280, 1280),\n", + " ratio_range=(0.5, 2.0),\n", + " keep_ratio=True),\n", + " dict(type='RandomCrop', crop_size=(640, 640)),\n", + " dict(type='YOLOXHSVRandomAug'),\n", + " dict(type='RandomFlip', prob=0.5),\n", + " dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),\n", + " dict(\n", + " type='CachedMixUp',\n", + " img_scale=(640, 640),\n", + " ratio_range=(1.0, 1.0),\n", + " max_cached_images=10,\n", + " random_pop=False,\n", + " pad_val=(114, 114, 114),\n", + " prob=0.5),\n", + " dict(type='PackDetInputs')\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='Resize', scale=(640, 640), keep_ratio=True),\n", + " dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),\n", + " dict(\n", + " type='PackDetInputs',\n", + " meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n", + " 'scale_factor'))\n", + "]\n", + "train_dataloader = dict(\n", + " batch_size=4,\n", + " num_workers=4,\n", + " persistent_workers=True,\n", + " sampler=dict(type='DefaultSampler', shuffle=True),\n", + " batch_sampler=None,\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(classes=('sjb_rect', )),\n", + " ann_file='train_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " filter_cfg=dict(filter_empty_gt=True, min_size=32),\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='CachedMosaic',\n", + " img_scale=(640, 640),\n", + " pad_val=114.0,\n", + " max_cached_images=20,\n", + " random_pop=False),\n", + " dict(\n", + " type='RandomResize',\n", + " scale=(1280, 1280),\n", + " ratio_range=(0.5, 2.0),\n", + " keep_ratio=True),\n", + " dict(type='RandomCrop', crop_size=(640, 640)),\n", + " dict(type='YOLOXHSVRandomAug'),\n", + " dict(type='RandomFlip', prob=0.5),\n", + " dict(\n", + " type='Pad', size=(640, 640),\n", + " pad_val=dict(img=(114, 114, 114))),\n", + " dict(\n", + " type='CachedMixUp',\n", + " img_scale=(640, 640),\n", + " ratio_range=(1.0, 1.0),\n", + " max_cached_images=10,\n", + " random_pop=False,\n", + " pad_val=(114, 114, 114),\n", + " prob=0.5),\n", + " dict(type='PackDetInputs')\n", + " ],\n", + " backend_args=None),\n", + " pin_memory=True)\n", + "val_dataloader = dict(\n", + " batch_size=2,\n", + " num_workers=2,\n", + " persistent_workers=True,\n", + " drop_last=False,\n", + " sampler=dict(type='DefaultSampler', shuffle=False),\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(classes=('sjb_rect', )),\n", + " ann_file='val_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " test_mode=True,\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='Resize', scale=(640, 640), keep_ratio=True),\n", + " dict(\n", + " type='Pad', size=(640, 640),\n", + " pad_val=dict(img=(114, 114, 114))),\n", + " dict(\n", + " type='PackDetInputs',\n", + " meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n", + " 'scale_factor'))\n", + " ],\n", + " backend_args=None))\n", + "test_dataloader = dict(\n", + " batch_size=2,\n", + " num_workers=2,\n", + " persistent_workers=True,\n", + " drop_last=False,\n", + " sampler=dict(type='DefaultSampler', shuffle=False),\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(classes=('sjb_rect', )),\n", + " ann_file='val_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " test_mode=True,\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='Resize', scale=(640, 640), keep_ratio=True),\n", + " dict(\n", + " type='Pad', size=(640, 640),\n", + " pad_val=dict(img=(114, 114, 114))),\n", + " dict(\n", + " type='PackDetInputs',\n", + " meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n", + " 'scale_factor'))\n", + " ],\n", + " backend_args=None))\n", + "val_evaluator = dict(\n", + " type='CocoMetric',\n", + " ann_file='data/Triangle_140_Keypoint_Dataset/val_coco.json',\n", + " metric=['bbox'],\n", + " format_only=False,\n", + " backend_args=None,\n", + " proposal_nums=(100, 1, 10))\n", + "test_evaluator = dict(\n", + " type='CocoMetric',\n", + " ann_file='data/Triangle_140_Keypoint_Dataset/val_coco.json',\n", + " metric=['bbox'],\n", + " format_only=False,\n", + " backend_args=None,\n", + " proposal_nums=(100, 1, 10))\n", + "tta_model = dict(\n", + " type='DetTTAModel',\n", + " tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))\n", + "tta_pipeline = [\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(\n", + " type='TestTimeAug',\n", + " transforms=[[{\n", + " 'type': 'Resize',\n", + " 'scale': (640, 640),\n", + " 'keep_ratio': True\n", + " }, {\n", + " 'type': 'Resize',\n", + " 'scale': (320, 320),\n", + " 'keep_ratio': True\n", + " }, {\n", + " 'type': 'Resize',\n", + " 'scale': (960, 960),\n", + " 'keep_ratio': True\n", + " }],\n", + " [{\n", + " 'type': 'RandomFlip',\n", + " 'prob': 1.0\n", + " }, {\n", + " 'type': 'RandomFlip',\n", + " 'prob': 0.0\n", + " }],\n", + " [{\n", + " 'type': 'Pad',\n", + " 'size': (960, 960),\n", + " 'pad_val': {\n", + " 'img': (114, 114, 114)\n", + " }\n", + " }],\n", + " [{\n", + " 'type':\n", + " 'PackDetInputs',\n", + " 'meta_keys':\n", + " ('img_id', 'img_path', 'ori_shape', 'img_shape',\n", + " 'scale_factor', 'flip', 'flip_direction')\n", + " }]])\n", + "]\n", + "model = dict(\n", + " type='RTMDet',\n", + " data_preprocessor=dict(\n", + " type='DetDataPreprocessor',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[57.375, 57.12, 58.395],\n", + " bgr_to_rgb=False,\n", + " batch_augments=None),\n", + " backbone=dict(\n", + " type='CSPNeXt',\n", + " arch='P5',\n", + " expand_ratio=0.5,\n", + " deepen_factor=0.167,\n", + " widen_factor=0.375,\n", + " channel_attention=True,\n", + " norm_cfg=dict(type='SyncBN'),\n", + " act_cfg=dict(type='SiLU', inplace=True),\n", + " init_cfg=dict(\n", + " type='Pretrained',\n", + " prefix='backbone.',\n", + " checkpoint=\n", + " 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'\n", + " )),\n", + " neck=dict(\n", + " type='CSPNeXtPAFPN',\n", + " in_channels=[96, 192, 384],\n", + " out_channels=96,\n", + " num_csp_blocks=1,\n", + " expand_ratio=0.5,\n", + " norm_cfg=dict(type='SyncBN'),\n", + " act_cfg=dict(type='SiLU', inplace=True)),\n", + " bbox_head=dict(\n", + " type='RTMDetSepBNHead',\n", + " num_classes=1,\n", + " in_channels=96,\n", + " stacked_convs=2,\n", + " feat_channels=96,\n", + " anchor_generator=dict(\n", + " type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),\n", + " bbox_coder=dict(type='DistancePointBBoxCoder'),\n", + " loss_cls=dict(\n", + " type='QualityFocalLoss',\n", + " use_sigmoid=True,\n", + " beta=2.0,\n", + " loss_weight=1.0),\n", + " loss_bbox=dict(type='GIoULoss', loss_weight=2.0),\n", + " with_objectness=False,\n", + " exp_on_reg=False,\n", + " share_conv=True,\n", + " pred_kernel_size=1,\n", + " norm_cfg=dict(type='SyncBN'),\n", + " act_cfg=dict(type='SiLU', inplace=True)),\n", + " train_cfg=dict(\n", + " assigner=dict(type='DynamicSoftLabelAssigner', topk=13),\n", + " allowed_border=-1,\n", + " pos_weight=-1,\n", + " debug=False),\n", + " test_cfg=dict(\n", + " nms_pre=30000,\n", + " min_bbox_size=0,\n", + " score_thr=0.001,\n", + " nms=dict(type='nms', iou_threshold=0.65),\n", + " max_per_img=300))\n", + "train_pipeline_stage2 = [\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='RandomResize',\n", + " scale=(640, 640),\n", + " ratio_range=(0.5, 2.0),\n", + " keep_ratio=True),\n", + " dict(type='RandomCrop', crop_size=(640, 640)),\n", + " dict(type='YOLOXHSVRandomAug'),\n", + " dict(type='RandomFlip', prob=0.5),\n", + " dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),\n", + " dict(type='PackDetInputs')\n", + "]\n", + "custom_hooks = [\n", + " dict(\n", + " type='EMAHook',\n", + " ema_type='ExpMomentumEMA',\n", + " momentum=0.0002,\n", + " update_buffers=True,\n", + " priority=49),\n", + " dict(\n", + " type='PipelineSwitchHook',\n", + " switch_epoch=180,\n", + " switch_pipeline=[\n", + " dict(type='LoadImageFromFile', backend_args=None),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='RandomResize',\n", + " scale=(640, 640),\n", + " ratio_range=(0.5, 2.0),\n", + " keep_ratio=True),\n", + " dict(type='RandomCrop', crop_size=(640, 640)),\n", + " dict(type='YOLOXHSVRandomAug'),\n", + " dict(type='RandomFlip', prob=0.5),\n", + " dict(\n", + " type='Pad', size=(640, 640),\n", + " pad_val=dict(img=(114, 114, 114))),\n", + " dict(type='PackDetInputs')\n", + " ])\n", + "]\n", + "launcher = 'none'\n", + "work_dir = './work_dirs/rtmdet_tiny_triangle'\n", + "\n", + "04/04 17:48:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n", + "04/04 17:48:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_load_checkpoint:\n", + "(49 ) EMAHook \n", + " -------------------- \n", + "before_train:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) DistSamplerSeedHook \n", + "(NORMAL ) PipelineSwitchHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) IterTimerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) DetVisualizationHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_save_checkpoint:\n", + "(49 ) EMAHook \n", + " -------------------- \n", + "before_test_epoch:\n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "before_test_iter:\n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_test_iter:\n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) DetVisualizationHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_test_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_run:\n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.2.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.2.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.main_conv.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.main_conv.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.short_conv.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.short_conv.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.final_conv.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.final_conv.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.blocks.0.conv1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - 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paramwise_options -- neck.out_convs.2.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.0.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.0.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.0.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.0.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.cls_convs.1.0.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.1.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.1.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.cls_convs.1.1.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.1.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.1.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.cls_convs.2.0.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.2.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.2.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.cls_convs.2.1.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.2.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.cls_convs.2.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.0.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.0.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.0.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.0.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.reg_convs.1.0.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.1.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.1.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.reg_convs.1.1.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.1.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.1.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.reg_convs.2.0.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.2.0.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.2.0.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - bbox_head.reg_convs.2.1.conv is duplicate. It is skipped since bypass_duplicate=True\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.2.1.bn.weight:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.reg_convs.2.1.bn.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_cls.0.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_cls.1.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_cls.2.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_reg.0.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_reg.1.bias:weight_decay=0.0\n", + "04/04 17:48:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- bbox_head.rtm_reg.2.bias:weight_decay=0.0\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "04/04 17:48:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load backbone. in model from: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth\n", + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth\n", + "04/04 17:48:41 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n", + "04/04 17:48:41 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"HardDiskBackend\" is the alias of \"LocalBackend\" and the former will be deprecated in future.\n", + "04/04 17:48:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle.\n", + "/environment/miniconda3/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", + "04/04 17:48:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 1/28] lr: 4.0000e-08 eta: 2:58:14 time: 1.9100 data_time: 1.5784 memory: 1280 loss: 0.0453 loss_cls: 0.0337 loss_bbox: 0.0116\n", + "04/04 17:48:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 2/28] lr: 4.0440e-06 eta: 1:36:46 time: 1.0372 data_time: 0.7930 memory: 1337 loss: 0.0423 loss_cls: 0.0337 loss_bbox: 0.0086\n", + "04/04 17:48:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 3/28] lr: 8.0479e-06 eta: 1:09:09 time: 0.7414 data_time: 0.5307 memory: 1337 loss: 0.0402 loss_cls: 0.0337 loss_bbox: 0.0065\n", + "04/04 17:48:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 4/28] lr: 1.2052e-05 eta: 0:54:52 time: 0.5884 data_time: 0.3990 memory: 1338 loss: 0.0403 loss_cls: 0.0337 loss_bbox: 0.0066\n", + "04/04 17:48:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 5/28] lr: 1.6056e-05 eta: 1:19:22 time: 0.8512 data_time: 0.6692 memory: 1340 loss: 0.0455 loss_cls: 0.0336 loss_bbox: 0.0118\n", + "04/04 17:48:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 6/28] lr: 2.0060e-05 eta: 1:08:47 time: 0.7378 data_time: 0.5586 memory: 1338 loss: 0.0473 loss_cls: 0.0336 loss_bbox: 0.0137\n", + "04/04 17:48:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 7/28] lr: 2.4064e-05 eta: 1:16:42 time: 0.8229 data_time: 0.6466 memory: 1337 loss: 0.0495 loss_cls: 0.0336 loss_bbox: 0.0159\n", + "04/04 17:48:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 8/28] lr: 2.8068e-05 eta: 1:09:04 time: 0.7412 data_time: 0.5665 memory: 1337 loss: 0.0523 loss_cls: 0.0335 loss_bbox: 0.0188\n", + "04/04 17:48:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][ 9/28] lr: 3.2072e-05 eta: 1:17:03 time: 0.8269 data_time: 0.6536 memory: 1338 loss: 0.0534 loss_cls: 0.0335 loss_bbox: 0.0199\n", + "04/04 17:48:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][10/28] lr: 3.6076e-05 eta: 1:10:52 time: 0.7607 data_time: 0.5888 memory: 1337 loss: 0.0543 loss_cls: 0.0335 loss_bbox: 0.0208\n", + "04/04 17:48:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][11/28] lr: 4.0080e-05 eta: 1:10:08 time: 0.7530 data_time: 0.5828 memory: 1339 loss: 0.0542 loss_cls: 0.0334 loss_bbox: 0.0208\n", + "04/04 17:48:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][12/28] lr: 4.4084e-05 eta: 1:05:33 time: 0.7039 data_time: 0.5346 memory: 1337 loss: 0.0577 loss_cls: 0.0334 loss_bbox: 0.0243\n", + "04/04 17:48:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][13/28] lr: 4.8088e-05 eta: 1:09:54 time: 0.7507 data_time: 0.5810 memory: 1338 loss: 0.0615 loss_cls: 0.0333 loss_bbox: 0.0282\n", + "04/04 17:48:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][14/28] lr: 5.2092e-05 eta: 1:05:58 time: 0.7087 data_time: 0.5399 memory: 1337 loss: 0.0668 loss_cls: 0.0333 loss_bbox: 0.0335\n", + "04/04 17:48:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][15/28] lr: 5.6095e-05 eta: 1:07:08 time: 0.7213 data_time: 0.5536 memory: 1338 loss: 0.0677 loss_cls: 0.0332 loss_bbox: 0.0344\n", + "04/04 17:48:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][16/28] lr: 6.0099e-05 eta: 1:03:57 time: 0.6873 data_time: 0.5193 memory: 1342 loss: 0.0843 loss_cls: 0.0331 loss_bbox: 0.0512\n", + "04/04 17:48:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][17/28] lr: 6.4103e-05 eta: 1:06:11 time: 0.7113 data_time: 0.5441 memory: 1338 loss: 0.0882 loss_cls: 0.0331 loss_bbox: 0.0551\n", + "04/04 17:48:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][18/28] lr: 6.8107e-05 eta: 1:03:18 time: 0.6806 data_time: 0.5141 memory: 1337 loss: 0.0933 loss_cls: 0.0330 loss_bbox: 0.0602\n", + "04/04 17:48:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][19/28] lr: 7.2111e-05 eta: 1:05:24 time: 0.7032 data_time: 0.5374 memory: 1338 loss: 0.1142 loss_cls: 0.0332 loss_bbox: 0.0810\n", + "04/04 17:48:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][20/28] lr: 7.6115e-05 eta: 1:02:51 time: 0.6759 data_time: 0.5108 memory: 1337 loss: 0.1185 loss_cls: 0.0331 loss_bbox: 0.0854\n", + "04/04 17:48:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][21/28] lr: 8.0119e-05 eta: 1:04:18 time: 0.6917 data_time: 0.5286 memory: 1337 loss: 0.1199 loss_cls: 0.0330 loss_bbox: 0.0869\n", + "04/04 17:48:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][22/28] lr: 8.4123e-05 eta: 1:02:00 time: 0.6670 data_time: 0.5047 memory: 1342 loss: 0.1459 loss_cls: 0.0330 loss_bbox: 0.1129\n", + "04/04 17:48:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][23/28] lr: 8.8127e-05 eta: 1:04:15 time: 0.6912 data_time: 0.5293 memory: 1338 loss: 0.1602 loss_cls: 0.0329 loss_bbox: 0.1273\n", + "04/04 17:48:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][24/28] lr: 9.2131e-05 eta: 1:02:10 time: 0.6691 data_time: 0.5074 memory: 1338 loss: 0.1883 loss_cls: 0.0338 loss_bbox: 0.1545\n", + "04/04 17:48:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][25/28] lr: 9.6135e-05 eta: 1:02:55 time: 0.6772 data_time: 0.5159 memory: 1338 loss: 0.2375 loss_cls: 0.0415 loss_bbox: 0.1960\n", + "04/04 17:48:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][26/28] lr: 1.0014e-04 eta: 1:01:01 time: 0.6568 data_time: 0.4962 memory: 1338 loss: 0.2791 loss_cls: 0.0433 loss_bbox: 0.2358\n", + "04/04 17:49:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][27/28] lr: 1.0414e-04 eta: 1:04:13 time: 0.6915 data_time: 0.5313 memory: 1337 loss: 0.2823 loss_cls: 0.0429 loss_bbox: 0.2394\n", + "04/04 17:49:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:49:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][28/28] lr: 1.0815e-04 eta: 1:02:25 time: 0.6723 data_time: 0.5125 memory: 1338 loss: 0.2843 loss_cls: 0.0424 loss_bbox: 0.2418\n", + "04/04 17:49:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][ 6/28] lr: 1.3217e-04 eta: 1:06:02 time: 0.7119 data_time: 0.5539 memory: 1337 loss: 0.4437 loss_cls: 0.0542 loss_bbox: 0.3896\n", + "04/04 17:49:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][ 7/28] lr: 1.3617e-04 eta: 1:04:31 time: 0.6957 data_time: 0.5382 memory: 1338 loss: 0.4829 loss_cls: 0.0658 loss_bbox: 0.4171\n", + "04/04 17:49:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][ 8/28] lr: 1.4018e-04 eta: 1:03:05 time: 0.6803 data_time: 0.5233 memory: 1337 loss: 0.5146 loss_cls: 0.0701 loss_bbox: 0.4446\n", + "04/04 17:49:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][ 9/28] lr: 1.4418e-04 eta: 1:07:54 time: 0.7323 data_time: 0.5759 memory: 1337 loss: 0.5357 loss_cls: 0.0696 loss_bbox: 0.4661\n", + "04/04 17:49:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][10/28] lr: 1.4819e-04 eta: 1:06:26 time: 0.7168 data_time: 0.5608 memory: 1338 loss: 0.5637 loss_cls: 0.0753 loss_bbox: 0.4884\n", + "04/04 17:49:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][11/28] lr: 1.5219e-04 eta: 1:05:04 time: 0.7022 data_time: 0.5466 memory: 1338 loss: 0.5967 loss_cls: 0.0820 loss_bbox: 0.5147\n", + "04/04 17:49:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][12/28] lr: 1.5619e-04 eta: 1:03:47 time: 0.6884 data_time: 0.5330 memory: 1340 loss: 0.6260 loss_cls: 0.0815 loss_bbox: 0.5445\n", + "04/04 17:49:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][13/28] lr: 1.6020e-04 eta: 1:07:03 time: 0.7238 data_time: 0.5686 memory: 1338 loss: 0.6518 loss_cls: 0.0840 loss_bbox: 0.5678\n", + "04/04 17:49:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][14/28] lr: 1.6420e-04 eta: 1:05:46 time: 0.7101 data_time: 0.5552 memory: 1337 loss: 0.6756 loss_cls: 0.0846 loss_bbox: 0.5910\n", + "04/04 17:49:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][15/28] lr: 1.6821e-04 eta: 1:04:33 time: 0.6971 data_time: 0.5424 memory: 1338 loss: 0.6939 loss_cls: 0.0849 loss_bbox: 0.6090\n", + "04/04 17:49:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][16/28] lr: 1.7221e-04 eta: 1:03:23 time: 0.6845 data_time: 0.5302 memory: 1338 loss: 0.7058 loss_cls: 0.0864 loss_bbox: 0.6194\n", + "04/04 17:49:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][17/28] lr: 1.7621e-04 eta: 1:06:01 time: 0.7132 data_time: 0.5585 memory: 1338 loss: 0.7233 loss_cls: 0.0877 loss_bbox: 0.6356\n", + "04/04 17:49:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][18/28] lr: 1.8022e-04 eta: 1:04:54 time: 0.7012 data_time: 0.5465 memory: 1340 loss: 0.7469 loss_cls: 0.0928 loss_bbox: 0.6541\n", + "04/04 17:49:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][19/28] lr: 1.8422e-04 eta: 1:03:50 time: 0.6898 data_time: 0.5350 memory: 1341 loss: 0.7627 loss_cls: 0.0970 loss_bbox: 0.6657\n", + "04/04 17:49:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][20/28] lr: 1.8823e-04 eta: 1:02:49 time: 0.6789 data_time: 0.5240 memory: 1339 loss: 0.7773 loss_cls: 0.1016 loss_bbox: 0.6757\n", + "04/04 17:49:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][21/28] lr: 1.9223e-04 eta: 1:05:18 time: 0.7059 data_time: 0.5505 memory: 1337 loss: 0.7915 loss_cls: 0.1016 loss_bbox: 0.6898\n", + "04/04 17:49:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][22/28] lr: 1.9623e-04 eta: 1:04:16 time: 0.6949 data_time: 0.5396 memory: 1337 loss: 0.8188 loss_cls: 0.1124 loss_bbox: 0.7064\n", + "04/04 17:49:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][23/28] lr: 2.0024e-04 eta: 1:03:18 time: 0.6600 data_time: 0.5081 memory: 1341 loss: 0.8516 loss_cls: 0.1208 loss_bbox: 0.7307\n", + "04/04 17:49:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][24/28] lr: 2.0424e-04 eta: 1:02:21 time: 0.6600 data_time: 0.5081 memory: 1338 loss: 0.8850 loss_cls: 0.1276 loss_bbox: 0.7573\n", + "04/04 17:49:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][25/28] lr: 2.0825e-04 eta: 1:04:28 time: 0.6948 data_time: 0.5427 memory: 1337 loss: 0.9153 loss_cls: 0.1323 loss_bbox: 0.7830\n", + "04/04 17:49:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][26/28] lr: 2.1225e-04 eta: 1:03:33 time: 0.6956 data_time: 0.5427 memory: 1340 loss: 0.9496 loss_cls: 0.1438 loss_bbox: 0.8058\n", + "04/04 17:49:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][27/28] lr: 2.1625e-04 eta: 1:02:39 time: 0.6606 data_time: 0.5078 memory: 1338 loss: 0.9821 loss_cls: 0.1564 loss_bbox: 0.8258\n", + "04/04 17:49:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:49:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][28/28] lr: 2.2026e-04 eta: 1:01:46 time: 0.6602 data_time: 0.5078 memory: 1343 loss: 1.0152 loss_cls: 0.1633 loss_bbox: 0.8518\n", + "04/04 17:49:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][ 1/28] lr: 2.2426e-04 eta: 1:05:04 time: 0.6878 data_time: 0.5358 memory: 1338 loss: 1.0476 loss_cls: 0.1726 loss_bbox: 0.8749\n", + "04/04 17:49:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][ 7/28] lr: 2.4829e-04 eta: 1:03:17 time: 0.6689 data_time: 0.5168 memory: 1339 loss: 1.2436 loss_cls: 0.2250 loss_bbox: 1.0186\n", + "04/04 17:49:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][ 8/28] lr: 2.5229e-04 eta: 1:02:29 time: 0.6685 data_time: 0.5168 memory: 1341 loss: 1.2756 loss_cls: 0.2361 loss_bbox: 1.0394\n", + "04/04 17:49:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][ 9/28] lr: 2.5629e-04 eta: 1:03:38 time: 0.6805 data_time: 0.5289 memory: 1340 loss: 1.3055 loss_cls: 0.2445 loss_bbox: 1.0610\n", + "04/04 17:49:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][10/28] lr: 2.6030e-04 eta: 1:04:05 time: 0.6973 data_time: 0.5464 memory: 1338 loss: 1.3293 loss_cls: 0.2532 loss_bbox: 1.0762\n", + "04/04 17:49:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][11/28] lr: 2.6430e-04 eta: 1:03:19 time: 0.6783 data_time: 0.5277 memory: 1338 loss: 1.3591 loss_cls: 0.2652 loss_bbox: 1.0939\n", + "04/04 17:49:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][12/28] lr: 2.6831e-04 eta: 1:03:34 time: 0.6927 data_time: 0.5424 memory: 1341 loss: 1.3868 loss_cls: 0.2746 loss_bbox: 1.1122\n", + "04/04 17:49:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][13/28] lr: 2.7231e-04 eta: 1:02:49 time: 0.6733 data_time: 0.5234 memory: 1338 loss: 1.4073 loss_cls: 0.2784 loss_bbox: 1.1289\n", + "04/04 17:49:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][14/28] lr: 2.7631e-04 eta: 1:03:01 time: 0.6870 data_time: 0.5372 memory: 1338 loss: 1.4357 loss_cls: 0.2856 loss_bbox: 1.1501\n", + "04/04 17:49:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][15/28] lr: 2.8032e-04 eta: 1:02:25 time: 0.6715 data_time: 0.5216 memory: 1338 loss: 1.4639 loss_cls: 0.2908 loss_bbox: 1.1732\n", + "04/04 17:49:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][16/28] lr: 2.8432e-04 eta: 1:03:09 time: 0.6937 data_time: 0.5437 memory: 1337 loss: 1.4753 loss_cls: 0.2990 loss_bbox: 1.1763\n", + "04/04 17:49:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][17/28] lr: 2.8833e-04 eta: 1:02:28 time: 0.6723 data_time: 0.5224 memory: 1340 loss: 1.4959 loss_cls: 0.3047 loss_bbox: 1.1912\n", + "04/04 17:49:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][18/28] lr: 2.9233e-04 eta: 1:02:46 time: 0.6877 data_time: 0.5379 memory: 1338 loss: 1.5108 loss_cls: 0.3094 loss_bbox: 1.2014\n", + "04/04 17:49:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][19/28] lr: 2.9633e-04 eta: 1:02:10 time: 0.6742 data_time: 0.5245 memory: 1342 loss: 1.5184 loss_cls: 0.3154 loss_bbox: 1.2029\n", + "04/04 17:49:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][20/28] lr: 3.0034e-04 eta: 1:03:33 time: 0.7078 data_time: 0.5581 memory: 1339 loss: 1.5188 loss_cls: 0.3229 loss_bbox: 1.1959\n", + "04/04 17:49:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][21/28] lr: 3.0434e-04 eta: 1:02:54 time: 0.6790 data_time: 0.5293 memory: 1338 loss: 1.5351 loss_cls: 0.3242 loss_bbox: 1.2109\n", + "04/04 17:49:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][22/28] lr: 3.0835e-04 eta: 1:02:30 time: 0.6830 data_time: 0.5334 memory: 1338 loss: 1.5637 loss_cls: 0.3281 loss_bbox: 1.2356\n", + "04/04 17:49:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][23/28] lr: 3.1235e-04 eta: 1:01:54 time: 0.6382 data_time: 0.4886 memory: 1339 loss: 1.5780 loss_cls: 0.3326 loss_bbox: 1.2454\n", + "04/04 17:49:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][24/28] lr: 3.1635e-04 eta: 1:02:33 time: 0.6596 data_time: 0.5107 memory: 1339 loss: 1.5716 loss_cls: 0.3397 loss_bbox: 1.2319\n", + "04/04 17:49:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][25/28] lr: 3.2036e-04 eta: 1:01:54 time: 0.6593 data_time: 0.5107 memory: 1339 loss: 1.5849 loss_cls: 0.3476 loss_bbox: 1.2373\n", + "04/04 17:49:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][26/28] lr: 3.2436e-04 eta: 1:02:24 time: 0.6783 data_time: 0.5305 memory: 1337 loss: 1.5694 loss_cls: 0.3446 loss_bbox: 1.2248\n", + "04/04 17:49:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][27/28] lr: 3.2837e-04 eta: 1:01:47 time: 0.6342 data_time: 0.4868 memory: 1337 loss: 1.5887 loss_cls: 0.3525 loss_bbox: 1.2362\n", + "04/04 17:49:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:49:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [3][28/28] lr: 3.3237e-04 eta: 1:02:12 time: 0.6527 data_time: 0.5053 memory: 1338 loss: 1.5915 loss_cls: 0.3581 loss_bbox: 1.2334\n", + "04/04 17:49:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 1/28] lr: 3.3637e-04 eta: 1:03:56 time: 0.6955 data_time: 0.5480 memory: 1337 loss: 1.5895 loss_cls: 0.3585 loss_bbox: 1.2309\n", + "04/04 17:49:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 2/28] lr: 3.4038e-04 eta: 1:03:44 time: 0.7033 data_time: 0.5559 memory: 1339 loss: 1.5878 loss_cls: 0.3613 loss_bbox: 1.2265\n", + "04/04 17:49:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 3/28] lr: 3.4438e-04 eta: 1:03:08 time: 0.6539 data_time: 0.5067 memory: 1337 loss: 1.5970 loss_cls: 0.3679 loss_bbox: 1.2291\n", + "04/04 17:49:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 4/28] lr: 3.4838e-04 eta: 1:02:34 time: 0.6540 data_time: 0.5067 memory: 1337 loss: 1.5922 loss_cls: 0.3651 loss_bbox: 1.2271\n", + "04/04 17:49:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 5/28] lr: 3.5239e-04 eta: 1:02:53 time: 0.6712 data_time: 0.5239 memory: 1342 loss: 1.5922 loss_cls: 0.3732 loss_bbox: 1.2190\n", + "04/04 17:49:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 6/28] lr: 3.5639e-04 eta: 1:03:29 time: 0.6938 data_time: 0.5469 memory: 1337 loss: 1.5823 loss_cls: 0.3759 loss_bbox: 1.2064\n", + "04/04 17:49:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 7/28] lr: 3.6040e-04 eta: 1:02:55 time: 0.6537 data_time: 0.5072 memory: 1338 loss: 1.5766 loss_cls: 0.3775 loss_bbox: 1.1991\n", + "04/04 17:49:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 8/28] lr: 3.6440e-04 eta: 1:02:21 time: 0.6535 data_time: 0.5071 memory: 1338 loss: 1.5729 loss_cls: 0.3806 loss_bbox: 1.1922\n", + "04/04 17:49:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][ 9/28] lr: 3.6840e-04 eta: 1:02:08 time: 0.6600 data_time: 0.5137 memory: 1337 loss: 1.5743 loss_cls: 0.3861 loss_bbox: 1.1883\n", + "04/04 17:49:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][10/28] lr: 3.7241e-04 eta: 1:03:45 time: 0.7039 data_time: 0.5577 memory: 1339 loss: 1.5811 loss_cls: 0.3908 loss_bbox: 1.1903\n", + "04/04 17:49:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][11/28] lr: 3.7641e-04 eta: 1:03:13 time: 0.6673 data_time: 0.5217 memory: 1337 loss: 1.5833 loss_cls: 0.3971 loss_bbox: 1.1862\n", + "04/04 17:49:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][12/28] lr: 3.8042e-04 eta: 1:02:41 time: 0.6670 data_time: 0.5216 memory: 1338 loss: 1.5778 loss_cls: 0.3997 loss_bbox: 1.1780\n", + "04/04 17:49:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][13/28] lr: 3.8442e-04 eta: 1:02:10 time: 0.6667 data_time: 0.5216 memory: 1338 loss: 1.5769 loss_cls: 0.4004 loss_bbox: 1.1765\n", + "04/04 17:49:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][14/28] lr: 3.8842e-04 eta: 1:02:45 time: 0.6898 data_time: 0.5453 memory: 1338 loss: 1.5778 loss_cls: 0.4024 loss_bbox: 1.1754\n", + "04/04 17:49:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][15/28] lr: 3.9243e-04 eta: 1:02:15 time: 0.6527 data_time: 0.5088 memory: 1337 loss: 1.5778 loss_cls: 0.4082 loss_bbox: 1.1695\n", + "04/04 17:49:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][16/28] lr: 3.9643e-04 eta: 1:01:45 time: 0.6524 data_time: 0.5088 memory: 1337 loss: 1.5644 loss_cls: 0.4034 loss_bbox: 1.1610\n", + "04/04 17:49:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][17/28] lr: 4.0044e-04 eta: 1:02:20 time: 0.6758 data_time: 0.5327 memory: 1340 loss: 1.5619 loss_cls: 0.4021 loss_bbox: 1.1598\n", + "04/04 17:49:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][18/28] lr: 4.0444e-04 eta: 1:02:25 time: 0.6883 data_time: 0.5455 memory: 1337 loss: 1.5540 loss_cls: 0.4015 loss_bbox: 1.1525\n", + "04/04 17:49:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][19/28] lr: 4.0844e-04 eta: 1:01:56 time: 0.6533 data_time: 0.5109 memory: 1339 loss: 1.5514 loss_cls: 0.4027 loss_bbox: 1.1487\n", + "04/04 17:49:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][20/28] lr: 4.1245e-04 eta: 1:01:27 time: 0.6528 data_time: 0.5109 memory: 1337 loss: 1.5436 loss_cls: 0.3951 loss_bbox: 1.1486\n", + "04/04 17:49:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][21/28] lr: 4.1645e-04 eta: 1:02:38 time: 0.6908 data_time: 0.5487 memory: 1340 loss: 1.5371 loss_cls: 0.3912 loss_bbox: 1.1459\n", + "04/04 17:49:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][22/28] lr: 4.2046e-04 eta: 1:02:11 time: 0.6910 data_time: 0.5487 memory: 1339 loss: 1.5288 loss_cls: 0.3894 loss_bbox: 1.1394\n", + "04/04 17:49:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][23/28] lr: 4.2446e-04 eta: 1:01:43 time: 0.6399 data_time: 0.4973 memory: 1338 loss: 1.5221 loss_cls: 0.3846 loss_bbox: 1.1374\n", + "04/04 17:49:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][24/28] lr: 4.2846e-04 eta: 1:01:16 time: 0.6391 data_time: 0.4973 memory: 1338 loss: 1.5183 loss_cls: 0.3865 loss_bbox: 1.1318\n", + "04/04 17:49:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][25/28] lr: 4.3247e-04 eta: 1:02:29 time: 0.6780 data_time: 0.5371 memory: 1338 loss: 1.5130 loss_cls: 0.3802 loss_bbox: 1.1327\n", + "04/04 17:49:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][26/28] lr: 4.3647e-04 eta: 1:02:01 time: 0.6775 data_time: 0.5371 memory: 1337 loss: 1.5018 loss_cls: 0.3797 loss_bbox: 1.1220\n", + "04/04 17:49:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][27/28] lr: 4.4048e-04 eta: 1:01:33 time: 0.6419 data_time: 0.5019 memory: 1338 loss: 1.4933 loss_cls: 0.3732 loss_bbox: 1.1202\n", + "04/04 17:49:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:49:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][28/28] lr: 4.4448e-04 eta: 1:01:06 time: 0.6415 data_time: 0.5019 memory: 1339 loss: 1.4864 loss_cls: 0.3652 loss_bbox: 1.1212\n", + "04/04 17:49:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][ 1/28] lr: 4.4848e-04 eta: 1:02:40 time: 0.6847 data_time: 0.5455 memory: 1339 loss: 1.4806 loss_cls: 0.3615 loss_bbox: 1.1192\n", + "04/04 17:49:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][ 2/28] lr: 4.5249e-04 eta: 1:02:12 time: 0.6843 data_time: 0.5454 memory: 1337 loss: 1.4750 loss_cls: 0.3557 loss_bbox: 1.1193\n", + "04/04 17:49:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][ 3/28] lr: 4.5649e-04 eta: 1:01:46 time: 0.6573 data_time: 0.5185 memory: 1338 loss: 1.4675 loss_cls: 0.3531 loss_bbox: 1.1144\n", + "04/04 17:49:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][14/28] lr: 5.0054e-04 eta: 1:02:00 time: 0.6635 data_time: 0.5246 memory: 1340 loss: 1.4038 loss_cls: 0.3243 loss_bbox: 1.0794\n", + "04/04 17:50:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][15/28] lr: 5.0454e-04 eta: 1:01:36 time: 0.6633 data_time: 0.5246 memory: 1339 loss: 1.4073 loss_cls: 0.3299 loss_bbox: 1.0774\n", + "04/04 17:50:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][16/28] lr: 5.0854e-04 eta: 1:01:13 time: 0.6590 data_time: 0.5204 memory: 1342 loss: 1.3967 loss_cls: 0.3304 loss_bbox: 1.0663\n", + "04/04 17:50:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][17/28] lr: 5.1255e-04 eta: 1:02:20 time: 0.7007 data_time: 0.5622 memory: 1337 loss: 1.3927 loss_cls: 0.3287 loss_bbox: 1.0640\n", + "04/04 17:50:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][18/28] lr: 5.1655e-04 eta: 1:01:56 time: 0.6788 data_time: 0.5400 memory: 1338 loss: 1.3881 loss_cls: 0.3252 loss_bbox: 1.0629\n", + "04/04 17:50:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][19/28] lr: 5.2056e-04 eta: 1:01:34 time: 0.6794 data_time: 0.5400 memory: 1341 loss: 1.3875 loss_cls: 0.3243 loss_bbox: 1.0632\n", + "04/04 17:50:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][20/28] lr: 5.2456e-04 eta: 1:01:12 time: 0.6601 data_time: 0.5202 memory: 1340 loss: 1.3897 loss_cls: 0.3273 loss_bbox: 1.0624\n", + "04/04 17:50:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][21/28] lr: 5.2856e-04 eta: 1:02:06 time: 0.6978 data_time: 0.5576 memory: 1338 loss: 1.3836 loss_cls: 0.3241 loss_bbox: 1.0594\n", + "04/04 17:50:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][22/28] lr: 5.3257e-04 eta: 1:01:43 time: 0.6792 data_time: 0.5391 memory: 1339 loss: 1.3810 loss_cls: 0.3206 loss_bbox: 1.0604\n", + "04/04 17:50:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][23/28] lr: 5.3657e-04 eta: 1:01:22 time: 0.6367 data_time: 0.4964 memory: 1340 loss: 1.3697 loss_cls: 0.3159 loss_bbox: 1.0538\n", + "04/04 17:50:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][24/28] lr: 5.4058e-04 eta: 1:01:00 time: 0.6289 data_time: 0.4884 memory: 1340 loss: 1.3669 loss_cls: 0.3186 loss_bbox: 1.0483\n", + "04/04 17:50:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][25/28] lr: 5.4458e-04 eta: 1:01:49 time: 0.6650 data_time: 0.5242 memory: 1341 loss: 1.3554 loss_cls: 0.3159 loss_bbox: 1.0395\n", + "04/04 17:50:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][26/28] lr: 5.4858e-04 eta: 1:01:28 time: 0.6650 data_time: 0.5242 memory: 1338 loss: 1.3528 loss_cls: 0.3158 loss_bbox: 1.0371\n", + "04/04 17:50:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][27/28] lr: 5.5259e-04 eta: 1:01:06 time: 0.6477 data_time: 0.5069 memory: 1339 loss: 1.3407 loss_cls: 0.3065 loss_bbox: 1.0342\n", + "04/04 17:50:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:50:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][28/28] lr: 5.5659e-04 eta: 1:00:45 time: 0.6251 data_time: 0.4839 memory: 1337 loss: 1.3398 loss_cls: 0.3093 loss_bbox: 1.0305\n", + "04/04 17:50:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 1/14] eta: 0:00:08 time: 0.6653 data_time: 0.6139 memory: 169 \n", + "04/04 17:50:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 2/14] eta: 0:00:04 time: 0.3476 data_time: 0.3090 memory: 169 \n", + "04/04 17:50:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 3/14] eta: 0:00:03 time: 0.3558 data_time: 0.3193 memory: 169 \n", + "04/04 17:50:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 4/14] eta: 0:00:02 time: 0.2818 data_time: 0.2465 memory: 169 \n", + "04/04 17:50:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 5/14] eta: 0:00:02 time: 0.3065 data_time: 0.2714 memory: 169 \n", + "04/04 17:50:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 6/14] eta: 0:00:02 time: 0.2601 data_time: 0.2266 memory: 169 \n", + "04/04 17:50:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 7/14] eta: 0:00:01 time: 0.2838 data_time: 0.2503 memory: 169 \n", + "04/04 17:50:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 8/14] eta: 0:00:01 time: 0.2536 data_time: 0.2207 memory: 169 \n", + "04/04 17:50:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][ 9/14] eta: 0:00:01 time: 0.2732 data_time: 0.2406 memory: 169 \n", + "04/04 17:50:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][10/14] eta: 0:00:00 time: 0.2487 data_time: 0.2167 memory: 169 \n", + "04/04 17:50:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][11/14] eta: 0:00:00 time: 0.2674 data_time: 0.2353 memory: 169 \n", + "04/04 17:50:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][12/14] eta: 0:00:00 time: 0.2474 data_time: 0.2158 memory: 169 \n", + "04/04 17:50:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][13/14] eta: 0:00:00 time: 0.2591 data_time: 0.2275 memory: 169 \n", + "04/04 17:50:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][14/14] eta: 0:00:00 time: 0.2424 data_time: 0.2113 memory: 169 \n", + "04/04 17:50:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.16s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.047\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.169\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.047\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.030\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.224\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.266\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.266\n", + "04/04 17:50:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.047 0.169 0.006 -1.000 -1.000 0.047\n", + "04/04 17:50:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [5][14/14] coco/bbox_mAP: 0.0470 coco/bbox_mAP_50: 0.1690 coco/bbox_mAP_75: 0.0060 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.0470data_time: 0.2113 time: 0.2424 \n", + "04/04 17:50:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.0470 coco/bbox_mAP at 5 epoch is saved to best_coco/bbox_mAP_epoch_5.pth.\n", + "04/04 17:50:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 1/28] lr: 5.6059e-04 eta: 1:02:04 time: 0.6766 data_time: 0.5353 memory: 1339 loss: 1.3358 loss_cls: 0.3098 loss_bbox: 1.0260\n", + "04/04 17:50:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 2/28] lr: 5.6460e-04 eta: 1:01:42 time: 0.6764 data_time: 0.5353 memory: 1339 loss: 1.3325 loss_cls: 0.3094 loss_bbox: 1.0231\n", + "04/04 17:50:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 3/28] lr: 5.6860e-04 eta: 1:01:21 time: 0.6699 data_time: 0.5287 memory: 1340 loss: 1.3272 loss_cls: 0.3081 loss_bbox: 1.0191\n", + "04/04 17:50:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 4/28] lr: 5.7261e-04 eta: 1:01:00 time: 0.6260 data_time: 0.4848 memory: 1338 loss: 1.3179 loss_cls: 0.3058 loss_bbox: 1.0121\n", + "04/04 17:50:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 5/28] lr: 5.7661e-04 eta: 1:01:57 time: 0.6672 data_time: 0.5261 memory: 1339 loss: 1.3111 loss_cls: 0.3035 loss_bbox: 1.0076\n", + "04/04 17:50:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 6/28] lr: 5.8061e-04 eta: 1:01:37 time: 0.6672 data_time: 0.5261 memory: 1339 loss: 1.3071 loss_cls: 0.3021 loss_bbox: 1.0050\n", + "04/04 17:50:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 7/28] lr: 5.8462e-04 eta: 1:01:16 time: 0.6672 data_time: 0.5261 memory: 1339 loss: 1.3058 loss_cls: 0.3052 loss_bbox: 1.0006\n", + "04/04 17:50:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 8/28] lr: 5.8862e-04 eta: 1:00:56 time: 0.6438 data_time: 0.5025 memory: 1341 loss: 1.2991 loss_cls: 0.3026 loss_bbox: 0.9965\n", + "04/04 17:50:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][ 9/28] lr: 5.9263e-04 eta: 1:02:08 time: 0.6937 data_time: 0.5526 memory: 1341 loss: 1.2948 loss_cls: 0.3013 loss_bbox: 0.9935\n", + "04/04 17:50:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][10/28] lr: 5.9663e-04 eta: 1:01:47 time: 0.6937 data_time: 0.5526 memory: 1338 loss: 1.2928 loss_cls: 0.3039 loss_bbox: 0.9889\n", + "04/04 17:50:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][11/28] lr: 6.0063e-04 eta: 1:01:27 time: 0.6700 data_time: 0.5287 memory: 1342 loss: 1.2880 loss_cls: 0.3041 loss_bbox: 0.9839\n", + "04/04 17:50:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][12/28] lr: 6.0464e-04 eta: 1:01:08 time: 0.6572 data_time: 0.5159 memory: 1340 loss: 1.2850 loss_cls: 0.3038 loss_bbox: 0.9812\n", + "04/04 17:50:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][13/28] lr: 6.0864e-04 eta: 1:01:57 time: 0.6960 data_time: 0.5546 memory: 1339 loss: 1.2808 loss_cls: 0.3061 loss_bbox: 0.9747\n", + "04/04 17:50:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][14/28] lr: 6.1265e-04 eta: 1:01:37 time: 0.6956 data_time: 0.5545 memory: 1337 loss: 1.2781 loss_cls: 0.3080 loss_bbox: 0.9701\n", + "04/04 17:50:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][15/28] lr: 6.1665e-04 eta: 1:01:18 time: 0.6576 data_time: 0.5168 memory: 1342 loss: 1.2744 loss_cls: 0.3050 loss_bbox: 0.9694\n", + "04/04 17:50:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][16/28] lr: 6.2065e-04 eta: 1:00:59 time: 0.6577 data_time: 0.5168 memory: 1337 loss: 1.2717 loss_cls: 0.3028 loss_bbox: 0.9690\n", + "04/04 17:50:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][17/28] lr: 6.2466e-04 eta: 1:01:40 time: 0.6922 data_time: 0.5516 memory: 1339 loss: 1.2707 loss_cls: 0.3054 loss_bbox: 0.9652\n", + "04/04 17:50:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][18/28] lr: 6.2866e-04 eta: 1:01:33 time: 0.6987 data_time: 0.5587 memory: 1338 loss: 1.2613 loss_cls: 0.3025 loss_bbox: 0.9588\n", + "04/04 17:50:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][19/28] lr: 6.3267e-04 eta: 1:01:14 time: 0.6590 data_time: 0.5189 memory: 1338 loss: 1.2584 loss_cls: 0.3059 loss_bbox: 0.9525\n", + "04/04 17:50:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][20/28] lr: 6.3667e-04 eta: 1:00:55 time: 0.6591 data_time: 0.5189 memory: 1343 loss: 1.2604 loss_cls: 0.3104 loss_bbox: 0.9500\n", + "04/04 17:50:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][21/28] lr: 6.4067e-04 eta: 1:01:58 time: 0.7077 data_time: 0.5669 memory: 1340 loss: 1.2595 loss_cls: 0.3156 loss_bbox: 0.9438\n", + "04/04 17:50:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][22/28] lr: 6.4468e-04 eta: 1:01:40 time: 0.7080 data_time: 0.5669 memory: 1338 loss: 1.2562 loss_cls: 0.3168 loss_bbox: 0.9394\n", + "04/04 17:50:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][23/28] lr: 6.4868e-04 eta: 1:01:22 time: 0.6590 data_time: 0.5175 memory: 1338 loss: 1.2486 loss_cls: 0.3155 loss_bbox: 0.9331\n", + "04/04 17:50:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][24/28] lr: 6.5269e-04 eta: 1:01:04 time: 0.6599 data_time: 0.5176 memory: 1338 loss: 1.2432 loss_cls: 0.3167 loss_bbox: 0.9265\n", + "04/04 17:50:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][25/28] lr: 6.5669e-04 eta: 1:01:46 time: 0.6960 data_time: 0.5537 memory: 1339 loss: 1.2409 loss_cls: 0.3159 loss_bbox: 0.9250\n", + "04/04 17:50:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][26/28] lr: 6.6069e-04 eta: 1:01:27 time: 0.6955 data_time: 0.5537 memory: 1338 loss: 1.2390 loss_cls: 0.3169 loss_bbox: 0.9221\n", + "04/04 17:50:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][27/28] lr: 6.6470e-04 eta: 1:01:08 time: 0.6570 data_time: 0.5158 memory: 1339 loss: 1.2374 loss_cls: 0.3194 loss_bbox: 0.9180\n", + "04/04 17:50:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:50:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][28/28] lr: 6.6870e-04 eta: 1:00:49 time: 0.6565 data_time: 0.5158 memory: 1337 loss: 1.2364 loss_cls: 0.3203 loss_bbox: 0.9162\n", + "04/04 17:50:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 1/28] lr: 6.7271e-04 eta: 1:01:54 time: 0.7076 data_time: 0.5672 memory: 1337 loss: 1.2430 loss_cls: 0.3239 loss_bbox: 0.9191\n", + "04/04 17:50:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 2/28] lr: 6.7671e-04 eta: 1:01:36 time: 0.7076 data_time: 0.5672 memory: 1337 loss: 1.2397 loss_cls: 0.3256 loss_bbox: 0.9141\n", + "04/04 17:50:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 3/28] lr: 6.8071e-04 eta: 1:01:26 time: 0.6619 data_time: 0.5216 memory: 1337 loss: 1.2455 loss_cls: 0.3279 loss_bbox: 0.9176\n", + "04/04 17:50:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 4/28] lr: 6.8472e-04 eta: 1:01:08 time: 0.6619 data_time: 0.5216 memory: 1340 loss: 1.2422 loss_cls: 0.3259 loss_bbox: 0.9163\n", + "04/04 17:50:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 5/28] lr: 6.8872e-04 eta: 1:01:44 time: 0.6958 data_time: 0.5552 memory: 1337 loss: 1.2420 loss_cls: 0.3280 loss_bbox: 0.9140\n", + "04/04 17:50:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 6/28] lr: 6.9273e-04 eta: 1:01:27 time: 0.6961 data_time: 0.5552 memory: 1339 loss: 1.2394 loss_cls: 0.3296 loss_bbox: 0.9098\n", + "04/04 17:50:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 7/28] lr: 6.9673e-04 eta: 1:01:18 time: 0.6637 data_time: 0.5228 memory: 1341 loss: 1.2401 loss_cls: 0.3312 loss_bbox: 0.9088\n", + "04/04 17:50:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][ 8/28] lr: 7.0073e-04 eta: 1:01:01 time: 0.6636 data_time: 0.5228 memory: 1339 loss: 1.2376 loss_cls: 0.3308 loss_bbox: 0.9068\n", + "04/04 17:50:48 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][16/28] lr: 9.5699e-04 eta: 0:59:51 time: 0.6443 data_time: 0.4912 memory: 1338 loss: 1.2230 loss_cls: 0.3769 loss_bbox: 0.8460\n", + "04/04 17:51:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][17/28] lr: 9.6099e-04 eta: 1:00:09 time: 0.6546 data_time: 0.5018 memory: 1337 loss: 1.2278 loss_cls: 0.3791 loss_bbox: 0.8486\n", + "04/04 17:51:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][18/28] lr: 9.6500e-04 eta: 0:59:56 time: 0.6545 data_time: 0.5017 memory: 1337 loss: 1.2307 loss_cls: 0.3824 loss_bbox: 0.8483\n", + "04/04 17:51:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][19/28] lr: 9.6900e-04 eta: 0:59:58 time: 0.6456 data_time: 0.4928 memory: 1340 loss: 1.2321 loss_cls: 0.3814 loss_bbox: 0.8507\n", + "04/04 17:51:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][20/28] lr: 9.7300e-04 eta: 0:59:48 time: 0.6482 data_time: 0.4954 memory: 1339 loss: 1.2326 loss_cls: 0.3807 loss_bbox: 0.8519\n", + "04/04 17:51:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][21/28] lr: 9.7701e-04 eta: 0:59:59 time: 0.6552 data_time: 0.5017 memory: 1337 loss: 1.2375 loss_cls: 0.3830 loss_bbox: 0.8545\n", + "04/04 17:51:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][22/28] lr: 9.8101e-04 eta: 0:59:47 time: 0.6554 data_time: 0.5018 memory: 1342 loss: 1.2330 loss_cls: 0.3833 loss_bbox: 0.8497\n", + "04/04 17:51:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][23/28] lr: 9.8502e-04 eta: 0:59:49 time: 0.6159 data_time: 0.4616 memory: 1340 loss: 1.2327 loss_cls: 0.3851 loss_bbox: 0.8476\n", + "04/04 17:51:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][24/28] lr: 9.8902e-04 eta: 0:59:45 time: 0.6236 data_time: 0.4690 memory: 1339 loss: 1.2353 loss_cls: 0.3876 loss_bbox: 0.8478\n", + "04/04 17:51:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][25/28] lr: 9.9302e-04 eta: 1:00:03 time: 0.6514 data_time: 0.4963 memory: 1340 loss: 1.2381 loss_cls: 0.3887 loss_bbox: 0.8494\n", + "04/04 17:51:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][26/28] lr: 9.9703e-04 eta: 0:59:51 time: 0.6518 data_time: 0.4963 memory: 1337 loss: 1.2360 loss_cls: 0.3878 loss_bbox: 0.8483\n", + "04/04 17:51:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][27/28] lr: 1.0010e-03 eta: 0:59:47 time: 0.6246 data_time: 0.4690 memory: 1340 loss: 1.2367 loss_cls: 0.3886 loss_bbox: 0.8481\n", + "04/04 17:51:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:51:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][28/28] lr: 1.0050e-03 eta: 0:59:43 time: 0.6321 data_time: 0.4764 memory: 1337 loss: 1.2360 loss_cls: 0.3909 loss_bbox: 0.8451\n", + "04/04 17:51:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 1/28] lr: 1.0090e-03 eta: 1:00:20 time: 0.6775 data_time: 0.5216 memory: 1340 loss: 1.2330 loss_cls: 0.3889 loss_bbox: 0.8442\n", + "04/04 17:51:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 2/28] lr: 1.0130e-03 eta: 1:00:13 time: 0.6825 data_time: 0.5262 memory: 1342 loss: 1.2338 loss_cls: 0.3884 loss_bbox: 0.8454\n", + "04/04 17:51:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 3/28] lr: 1.0170e-03 eta: 1:00:12 time: 0.6607 data_time: 0.5044 memory: 1339 loss: 1.2338 loss_cls: 0.3871 loss_bbox: 0.8467\n", + "04/04 17:51:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 4/28] lr: 1.0211e-03 eta: 1:00:01 time: 0.6603 data_time: 0.5040 memory: 1339 loss: 1.2387 loss_cls: 0.3893 loss_bbox: 0.8494\n", + "04/04 17:51:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 5/28] lr: 1.0251e-03 eta: 1:00:09 time: 0.6787 data_time: 0.5225 memory: 1339 loss: 1.2365 loss_cls: 0.3875 loss_bbox: 0.8490\n", + "04/04 17:51:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 6/28] lr: 1.0291e-03 eta: 1:00:07 time: 0.6857 data_time: 0.5293 memory: 1340 loss: 1.2293 loss_cls: 0.3828 loss_bbox: 0.8465\n", + "04/04 17:51:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 7/28] lr: 1.0331e-03 eta: 1:00:07 time: 0.6647 data_time: 0.5083 memory: 1342 loss: 1.2223 loss_cls: 0.3828 loss_bbox: 0.8395\n", + "04/04 17:51:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 8/28] lr: 1.0371e-03 eta: 0:59:56 time: 0.6617 data_time: 0.5055 memory: 1337 loss: 1.2199 loss_cls: 0.3827 loss_bbox: 0.8372\n", + "04/04 17:51:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][ 9/28] lr: 1.0411e-03 eta: 0:59:55 time: 0.6715 data_time: 0.5153 memory: 1337 loss: 1.2166 loss_cls: 0.3838 loss_bbox: 0.8327\n", + "04/04 17:51:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][10/28] lr: 1.0451e-03 eta: 0:59:59 time: 0.6834 data_time: 0.5275 memory: 1338 loss: 1.2139 loss_cls: 0.3833 loss_bbox: 0.8306\n", + "04/04 17:51:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][11/28] lr: 1.0491e-03 eta: 1:00:02 time: 0.6702 data_time: 0.5141 memory: 1341 loss: 1.2100 loss_cls: 0.3858 loss_bbox: 0.8242\n", + "04/04 17:51:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][12/28] lr: 1.0531e-03 eta: 0:59:50 time: 0.6633 data_time: 0.5075 memory: 1338 loss: 1.2086 loss_cls: 0.3862 loss_bbox: 0.8223\n", + "04/04 17:51:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][13/28] lr: 1.0571e-03 eta: 0:59:47 time: 0.6618 data_time: 0.5058 memory: 1339 loss: 1.2029 loss_cls: 0.3851 loss_bbox: 0.8178\n", + "04/04 17:51:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][14/28] lr: 1.0611e-03 eta: 0:59:54 time: 0.6795 data_time: 0.5235 memory: 1337 loss: 1.2006 loss_cls: 0.3839 loss_bbox: 0.8167\n", + "04/04 17:51:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][15/28] lr: 1.0651e-03 eta: 0:59:50 time: 0.6730 data_time: 0.5171 memory: 1337 loss: 1.2018 loss_cls: 0.3844 loss_bbox: 0.8173\n", + "04/04 17:51:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][16/28] lr: 1.0691e-03 eta: 0:59:39 time: 0.6575 data_time: 0.5019 memory: 1338 loss: 1.1992 loss_cls: 0.3801 loss_bbox: 0.8191\n", + "04/04 17:51:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][17/28] lr: 1.0731e-03 eta: 0:59:41 time: 0.6592 data_time: 0.5039 memory: 1338 loss: 1.1944 loss_cls: 0.3773 loss_bbox: 0.8171\n", + "04/04 17:51:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][18/28] lr: 1.0771e-03 eta: 0:59:48 time: 0.6772 data_time: 0.5219 memory: 1339 loss: 1.1955 loss_cls: 0.3785 loss_bbox: 0.8170\n", + "04/04 17:51:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][19/28] lr: 1.0811e-03 eta: 0:59:38 time: 0.6713 data_time: 0.5161 memory: 1342 loss: 1.1958 loss_cls: 0.3797 loss_bbox: 0.8161\n", + "04/04 17:51:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][20/28] lr: 1.0851e-03 eta: 0:59:33 time: 0.6601 data_time: 0.5052 memory: 1341 loss: 1.1915 loss_cls: 0.3767 loss_bbox: 0.8148\n", + "04/04 17:51:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][21/28] lr: 1.0891e-03 eta: 0:59:47 time: 0.6736 data_time: 0.5187 memory: 1337 loss: 1.1871 loss_cls: 0.3767 loss_bbox: 0.8104\n", + "04/04 17:51:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][22/28] lr: 1.0931e-03 eta: 0:59:40 time: 0.6774 data_time: 0.5220 memory: 1342 loss: 1.1841 loss_cls: 0.3752 loss_bbox: 0.8089\n", + "04/04 17:51:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][23/28] lr: 1.0971e-03 eta: 0:59:29 time: 0.6287 data_time: 0.4731 memory: 1338 loss: 1.1855 loss_cls: 0.3755 loss_bbox: 0.8100\n", + "04/04 17:51:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][24/28] lr: 1.1011e-03 eta: 0:59:26 time: 0.6368 data_time: 0.4810 memory: 1338 loss: 1.1845 loss_cls: 0.3748 loss_bbox: 0.8097\n", + "04/04 17:51:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][25/28] lr: 1.1051e-03 eta: 0:59:38 time: 0.6597 data_time: 0.5037 memory: 1339 loss: 1.1796 loss_cls: 0.3741 loss_bbox: 0.8055\n", + "04/04 17:51:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][26/28] lr: 1.1091e-03 eta: 0:59:34 time: 0.6671 data_time: 0.5108 memory: 1341 loss: 1.1791 loss_cls: 0.3743 loss_bbox: 0.8048\n", + "04/04 17:51:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][27/28] lr: 1.1131e-03 eta: 0:59:29 time: 0.6359 data_time: 0.4794 memory: 1339 loss: 1.1781 loss_cls: 0.3746 loss_bbox: 0.8035\n", + "04/04 17:51:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:51:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][28/28] lr: 1.1171e-03 eta: 0:59:21 time: 0.6379 data_time: 0.4813 memory: 1337 loss: 1.1794 loss_cls: 0.3733 loss_bbox: 0.8061\n", + "04/04 17:51:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 10 epochs\n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 1/14] eta: 0:00:08 time: 0.2673 data_time: 0.2361 memory: 169 \n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 2/14] eta: 0:00:03 time: 0.2527 data_time: 0.2214 memory: 169 \n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 3/14] eta: 0:00:03 time: 0.2604 data_time: 0.2291 memory: 169 \n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 4/14] eta: 0:00:02 time: 0.2478 data_time: 0.2165 memory: 169 \n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 5/14] eta: 0:00:02 time: 0.2574 data_time: 0.2260 memory: 169 \n", + "04/04 17:51:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 6/14] eta: 0:00:02 time: 0.2460 data_time: 0.2148 memory: 169 \n", + "04/04 17:51:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 7/14] eta: 0:00:01 time: 0.2565 data_time: 0.2252 memory: 169 \n", + "04/04 17:51:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 8/14] eta: 0:00:01 time: 0.2463 data_time: 0.2151 memory: 169 \n", + "04/04 17:51:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][ 9/14] eta: 0:00:01 time: 0.2555 data_time: 0.2242 memory: 169 \n", + "04/04 17:51:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][10/14] eta: 0:00:01 time: 0.2462 data_time: 0.2149 memory: 169 \n", + "04/04 17:52:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][11/14] eta: 0:00:00 time: 0.2549 data_time: 0.2234 memory: 169 \n", + "04/04 17:52:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][12/14] eta: 0:00:00 time: 0.2463 data_time: 0.2149 memory: 169 \n", + "04/04 17:52:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][13/14] eta: 0:00:00 time: 0.2520 data_time: 0.2204 memory: 169 \n", + "04/04 17:52:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][14/14] eta: 0:00:00 time: 0.2440 data_time: 0.2126 memory: 169 \n", + "04/04 17:52:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.116\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.187\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.114\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.372\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.406\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406\n", + "04/04 17:52:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.187 0.487 0.116 -1.000 -1.000 0.187\n", + "04/04 17:52:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [10][14/14] coco/bbox_mAP: 0.1870 coco/bbox_mAP_50: 0.4870 coco/bbox_mAP_75: 0.1160 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.1870data_time: 0.2126 time: 0.2440 \n", + "04/04 17:52:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_5.pth is removed\n", + "04/04 17:52:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.1870 coco/bbox_mAP at 10 epoch is saved to best_coco/bbox_mAP_epoch_10.pth.\n", + "04/04 17:52:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 1/28] lr: 1.1211e-03 eta: 1:00:00 time: 0.6905 data_time: 0.5341 memory: 1340 loss: 1.1774 loss_cls: 0.3727 loss_bbox: 0.8047\n", + "04/04 17:52:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 2/28] lr: 1.1252e-03 eta: 0:59:49 time: 0.6906 data_time: 0.5341 memory: 1342 loss: 1.1765 loss_cls: 0.3736 loss_bbox: 0.8029\n", + "04/04 17:52:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 3/28] lr: 1.1292e-03 eta: 0:59:39 time: 0.6556 data_time: 0.4989 memory: 1339 loss: 1.1613 loss_cls: 0.3653 loss_bbox: 0.7960\n", + "04/04 17:52:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 4/28] lr: 1.1332e-03 eta: 0:59:29 time: 0.6554 data_time: 0.4989 memory: 1337 loss: 1.1563 loss_cls: 0.3652 loss_bbox: 0.7911\n", + "04/04 17:52:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 5/28] lr: 1.1372e-03 eta: 0:59:43 time: 0.6819 data_time: 0.5254 memory: 1338 loss: 1.1525 loss_cls: 0.3642 loss_bbox: 0.7884\n", + "04/04 17:52:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 6/28] lr: 1.1412e-03 eta: 0:59:56 time: 0.7063 data_time: 0.5496 memory: 1343 loss: 1.1493 loss_cls: 0.3640 loss_bbox: 0.7853\n", + "04/04 17:52:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 7/28] lr: 1.1452e-03 eta: 0:59:47 time: 0.6745 data_time: 0.5176 memory: 1339 loss: 1.1493 loss_cls: 0.3635 loss_bbox: 0.7858\n", + "04/04 17:52:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 8/28] lr: 1.1492e-03 eta: 0:59:37 time: 0.6746 data_time: 0.5176 memory: 1338 loss: 1.1447 loss_cls: 0.3605 loss_bbox: 0.7842\n", + "04/04 17:52:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][ 9/28] lr: 1.1532e-03 eta: 0:59:28 time: 0.6701 data_time: 0.5130 memory: 1339 loss: 1.1362 loss_cls: 0.3557 loss_bbox: 0.7805\n", + "04/04 17:52:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][10/28] lr: 1.1572e-03 eta: 0:59:45 time: 0.7007 data_time: 0.5435 memory: 1337 loss: 1.1350 loss_cls: 0.3585 loss_bbox: 0.7764\n", + "04/04 17:52:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][11/28] lr: 1.1612e-03 eta: 0:59:41 time: 0.6797 data_time: 0.5224 memory: 1338 loss: 1.1252 loss_cls: 0.3567 loss_bbox: 0.7685\n", + "04/04 17:52:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][12/28] lr: 1.1652e-03 eta: 0:59:31 time: 0.6798 data_time: 0.5224 memory: 1338 loss: 1.1226 loss_cls: 0.3544 loss_bbox: 0.7682\n", + "04/04 17:52:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][13/28] lr: 1.1692e-03 eta: 0:59:20 time: 0.6674 data_time: 0.5107 memory: 1337 loss: 1.1173 loss_cls: 0.3540 loss_bbox: 0.7633\n", + "04/04 17:52:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][14/28] lr: 1.1732e-03 eta: 0:59:40 time: 0.6981 data_time: 0.5421 memory: 1339 loss: 1.1165 loss_cls: 0.3540 loss_bbox: 0.7624\n", + "04/04 17:52:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][15/28] lr: 1.1772e-03 eta: 0:59:32 time: 0.6805 data_time: 0.5253 memory: 1341 loss: 1.1135 loss_cls: 0.3520 loss_bbox: 0.7614\n", + "04/04 17:52:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][16/28] lr: 1.1812e-03 eta: 0:59:27 time: 0.6850 data_time: 0.5305 memory: 1340 loss: 1.1093 loss_cls: 0.3486 loss_bbox: 0.7607\n", + "04/04 17:52:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][17/28] lr: 1.1852e-03 eta: 0:59:16 time: 0.6720 data_time: 0.5183 memory: 1340 loss: 1.1046 loss_cls: 0.3468 loss_bbox: 0.7578\n", + "04/04 17:52:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][18/28] lr: 1.1892e-03 eta: 0:59:33 time: 0.6947 data_time: 0.5415 memory: 1337 loss: 1.0988 loss_cls: 0.3453 loss_bbox: 0.7536\n", + "04/04 17:52:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][19/28] lr: 1.1932e-03 eta: 0:59:23 time: 0.6664 data_time: 0.5142 memory: 1338 loss: 1.0936 loss_cls: 0.3445 loss_bbox: 0.7490\n", + "04/04 17:52:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][20/28] lr: 1.1972e-03 eta: 0:59:30 time: 0.6857 data_time: 0.5334 memory: 1337 loss: 1.0895 loss_cls: 0.3428 loss_bbox: 0.7467\n", + "04/04 17:52:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][21/28] lr: 1.2012e-03 eta: 0:59:20 time: 0.6785 data_time: 0.5267 memory: 1337 loss: 1.0916 loss_cls: 0.3471 loss_bbox: 0.7445\n", + "04/04 17:52:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][22/28] lr: 1.2052e-03 eta: 0:59:24 time: 0.6862 data_time: 0.5345 memory: 1339 loss: 1.0911 loss_cls: 0.3467 loss_bbox: 0.7444\n", + "04/04 17:52:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][23/28] lr: 1.2092e-03 eta: 0:59:25 time: 0.6528 data_time: 0.5014 memory: 1337 loss: 1.1040 loss_cls: 0.3550 loss_bbox: 0.7491\n", + "04/04 17:52:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][24/28] lr: 1.2132e-03 eta: 0:59:30 time: 0.6653 data_time: 0.5141 memory: 1337 loss: 1.1015 loss_cls: 0.3556 loss_bbox: 0.7459\n", + "04/04 17:52:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][25/28] lr: 1.2172e-03 eta: 0:59:20 time: 0.6553 data_time: 0.5042 memory: 1338 loss: 1.0997 loss_cls: 0.3574 loss_bbox: 0.7423\n", + "04/04 17:52:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][26/28] lr: 1.2212e-03 eta: 0:59:18 time: 0.6639 data_time: 0.5127 memory: 1338 loss: 1.0949 loss_cls: 0.3573 loss_bbox: 0.7376\n", + "04/04 17:52:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][27/28] lr: 1.2253e-03 eta: 0:59:25 time: 0.6645 data_time: 0.5133 memory: 1337 loss: 1.0950 loss_cls: 0.3593 loss_bbox: 0.7357\n", + "04/04 17:52:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:52:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][28/28] lr: 1.2293e-03 eta: 0:59:22 time: 0.6622 data_time: 0.5111 memory: 1337 loss: 1.0983 loss_cls: 0.3617 loss_bbox: 0.7366\n", + "04/04 17:52:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 1/28] lr: 1.2333e-03 eta: 0:59:59 time: 0.7052 data_time: 0.5544 memory: 1340 loss: 1.1001 loss_cls: 0.3646 loss_bbox: 0.7355\n", + "04/04 17:52:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 2/28] lr: 1.2373e-03 eta: 0:59:49 time: 0.7050 data_time: 0.5543 memory: 1341 loss: 1.1009 loss_cls: 0.3665 loss_bbox: 0.7344\n", + "04/04 17:52:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 3/28] lr: 1.2413e-03 eta: 0:59:40 time: 0.6948 data_time: 0.5445 memory: 1338 loss: 1.1055 loss_cls: 0.3667 loss_bbox: 0.7388\n", + "04/04 17:52:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 4/28] lr: 1.2453e-03 eta: 0:59:30 time: 0.6797 data_time: 0.5296 memory: 1341 loss: 1.1076 loss_cls: 0.3696 loss_bbox: 0.7380\n", + "04/04 17:52:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 5/28] lr: 1.2493e-03 eta: 0:59:50 time: 0.7015 data_time: 0.5516 memory: 1341 loss: 1.1067 loss_cls: 0.3692 loss_bbox: 0.7375\n", + "04/04 17:52:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 6/28] lr: 1.2533e-03 eta: 0:59:41 time: 0.7017 data_time: 0.5516 memory: 1339 loss: 1.1056 loss_cls: 0.3700 loss_bbox: 0.7356\n", + "04/04 17:52:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 7/28] lr: 1.2573e-03 eta: 0:59:38 time: 0.7020 data_time: 0.5519 memory: 1338 loss: 1.1049 loss_cls: 0.3721 loss_bbox: 0.7328\n", + "04/04 17:52:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 8/28] lr: 1.2613e-03 eta: 0:59:29 time: 0.6843 data_time: 0.5342 memory: 1338 loss: 1.1274 loss_cls: 0.3847 loss_bbox: 0.7428\n", + "04/04 17:52:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][ 9/28] lr: 1.2653e-03 eta: 0:59:46 time: 0.7094 data_time: 0.5593 memory: 1338 loss: 1.1217 loss_cls: 0.3834 loss_bbox: 0.7383\n", + "04/04 17:52:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][10/28] lr: 1.2693e-03 eta: 0:59:37 time: 0.7093 data_time: 0.5593 memory: 1338 loss: 1.1208 loss_cls: 0.3852 loss_bbox: 0.7356\n", + "04/04 17:52:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][11/28] lr: 1.2733e-03 eta: 0:59:34 time: 0.7033 data_time: 0.5530 memory: 1343 loss: 1.1263 loss_cls: 0.3892 loss_bbox: 0.7372\n", + "04/04 17:52:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][12/28] lr: 1.2773e-03 eta: 0:59:24 time: 0.6850 data_time: 0.5350 memory: 1337 loss: 1.1309 loss_cls: 0.3902 loss_bbox: 0.7407\n", + "04/04 17:52:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][13/28] lr: 1.2813e-03 eta: 0:59:42 time: 0.7176 data_time: 0.5675 memory: 1340 loss: 1.1327 loss_cls: 0.3914 loss_bbox: 0.7413\n", + "04/04 17:52:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][14/28] lr: 1.2853e-03 eta: 0:59:33 time: 0.7110 data_time: 0.5610 memory: 1337 loss: 1.1358 loss_cls: 0.3942 loss_bbox: 0.7416\n", + "04/04 17:52:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][15/28] lr: 1.2893e-03 eta: 0:59:24 time: 0.6868 data_time: 0.5372 memory: 1338 loss: 1.1377 loss_cls: 0.3945 loss_bbox: 0.7432\n", + "04/04 17:52:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][16/28] lr: 1.2933e-03 eta: 0:59:15 time: 0.6830 data_time: 0.5339 memory: 1337 loss: 1.1405 loss_cls: 0.3970 loss_bbox: 0.7436\n", + "04/04 17:52:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][17/28] lr: 1.2973e-03 eta: 0:59:32 time: 0.7156 data_time: 0.5668 memory: 1337 loss: 1.1402 loss_cls: 0.3986 loss_bbox: 0.7416\n", + "04/04 17:52:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][18/28] lr: 1.3013e-03 eta: 0:59:23 time: 0.7076 data_time: 0.5590 memory: 1338 loss: 1.1429 loss_cls: 0.4027 loss_bbox: 0.7401\n", + "04/04 17:52:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][19/28] lr: 1.3053e-03 eta: 0:59:39 time: 0.7161 data_time: 0.5674 memory: 1343 loss: 1.1483 loss_cls: 0.4060 loss_bbox: 0.7423\n", + "04/04 17:52:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][20/28] lr: 1.3093e-03 eta: 0:59:30 time: 0.7092 data_time: 0.5602 memory: 1342 loss: 1.1543 loss_cls: 0.4088 loss_bbox: 0.7454\n", + "04/04 17:52:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][21/28] lr: 1.3133e-03 eta: 0:59:27 time: 0.7097 data_time: 0.5611 memory: 1342 loss: 1.1595 loss_cls: 0.4141 loss_bbox: 0.7454\n", + "04/04 17:52:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][22/28] lr: 1.3173e-03 eta: 0:59:18 time: 0.7075 data_time: 0.5592 memory: 1337 loss: 1.1608 loss_cls: 0.4167 loss_bbox: 0.7441\n", + "04/04 17:52:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][23/28] lr: 1.3213e-03 eta: 0:59:44 time: 0.6999 data_time: 0.5509 memory: 1340 loss: 1.1652 loss_cls: 0.4179 loss_bbox: 0.7473\n", + "04/04 17:52:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][24/28] lr: 1.3254e-03 eta: 0:59:35 time: 0.6994 data_time: 0.5509 memory: 1341 loss: 1.1662 loss_cls: 0.4183 loss_bbox: 0.7480\n", + "04/04 17:52:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][25/28] lr: 1.3294e-03 eta: 0:59:26 time: 0.6986 data_time: 0.5509 memory: 1337 loss: 1.1684 loss_cls: 0.4193 loss_bbox: 0.7491\n", + "04/04 17:52:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][26/28] lr: 1.3334e-03 eta: 0:59:16 time: 0.6981 data_time: 0.5509 memory: 1338 loss: 1.1688 loss_cls: 0.4191 loss_bbox: 0.7497\n", + "04/04 17:52:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][27/28] lr: 1.3374e-03 eta: 0:59:35 time: 0.7069 data_time: 0.5597 memory: 1341 loss: 1.1693 loss_cls: 0.4194 loss_bbox: 0.7499\n", + "04/04 17:52:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:52:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][28/28] lr: 1.3414e-03 eta: 0:59:26 time: 0.6822 data_time: 0.5355 memory: 1337 loss: 1.1678 loss_cls: 0.4189 loss_bbox: 0.7490\n", + "04/04 17:52:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][ 1/28] lr: 1.3454e-03 eta: 0:59:57 time: 0.7315 data_time: 0.5853 memory: 1337 loss: 1.1653 loss_cls: 0.4187 loss_bbox: 0.7466\n", + "04/04 17:52:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][ 2/28] lr: 1.3494e-03 eta: 0:59:48 time: 0.7310 data_time: 0.5853 memory: 1338 loss: 1.1650 loss_cls: 0.4189 loss_bbox: 0.7461\n", + "04/04 17:52:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][ 3/28] lr: 1.3534e-03 eta: 0:59:42 time: 0.7333 data_time: 0.5881 memory: 1341 loss: 1.1683 loss_cls: 0.4214 loss_bbox: 0.7469\n", + "04/04 17:52:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][ 9/28] lr: 1.3774e-03 eta: 0:59:46 time: 0.7359 data_time: 0.5900 memory: 1339 loss: 1.1809 loss_cls: 0.4280 loss_bbox: 0.7529\n", + "04/04 17:52:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][10/28] lr: 1.3814e-03 eta: 0:59:38 time: 0.7314 data_time: 0.5849 memory: 1337 loss: 1.1890 loss_cls: 0.4314 loss_bbox: 0.7576\n", + "04/04 17:52:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][11/28] lr: 1.3854e-03 eta: 0:59:29 time: 0.7319 data_time: 0.5849 memory: 1337 loss: 1.1963 loss_cls: 0.4356 loss_bbox: 0.7607\n", + "04/04 17:52:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][12/28] lr: 1.3894e-03 eta: 0:59:29 time: 0.7131 data_time: 0.5652 memory: 1338 loss: 1.1993 loss_cls: 0.4396 loss_bbox: 0.7597\n", + "04/04 17:52:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][13/28] lr: 1.3934e-03 eta: 0:59:39 time: 0.7381 data_time: 0.5894 memory: 1339 loss: 1.2037 loss_cls: 0.4424 loss_bbox: 0.7613\n", + "04/04 17:52:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][14/28] lr: 1.3974e-03 eta: 0:59:30 time: 0.7190 data_time: 0.5702 memory: 1340 loss: 1.2061 loss_cls: 0.4455 loss_bbox: 0.7606\n", + "04/04 17:52:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][15/28] lr: 1.4014e-03 eta: 0:59:22 time: 0.7195 data_time: 0.5702 memory: 1337 loss: 1.2052 loss_cls: 0.4449 loss_bbox: 0.7603\n", + "04/04 17:52:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][16/28] lr: 1.4054e-03 eta: 0:59:29 time: 0.7253 data_time: 0.5762 memory: 1340 loss: 1.2084 loss_cls: 0.4453 loss_bbox: 0.7631\n", + "04/04 17:52:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][17/28] lr: 1.4094e-03 eta: 0:59:32 time: 0.7278 data_time: 0.5789 memory: 1337 loss: 1.1966 loss_cls: 0.4389 loss_bbox: 0.7576\n", + "04/04 17:52:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][18/28] lr: 1.4134e-03 eta: 0:59:23 time: 0.7101 data_time: 0.5616 memory: 1341 loss: 1.1993 loss_cls: 0.4408 loss_bbox: 0.7585\n", + "04/04 17:52:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][19/28] lr: 1.4174e-03 eta: 0:59:14 time: 0.7097 data_time: 0.5616 memory: 1340 loss: 1.1988 loss_cls: 0.4394 loss_bbox: 0.7594\n", + "04/04 17:52:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][20/28] lr: 1.4214e-03 eta: 0:59:26 time: 0.7281 data_time: 0.5799 memory: 1341 loss: 1.1966 loss_cls: 0.4370 loss_bbox: 0.7595\n", + "04/04 17:52:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][21/28] lr: 1.4255e-03 eta: 0:59:24 time: 0.7180 data_time: 0.5698 memory: 1337 loss: 1.1988 loss_cls: 0.4379 loss_bbox: 0.7609\n", + "04/04 17:52:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][22/28] lr: 1.4295e-03 eta: 0:59:16 time: 0.7106 data_time: 0.5622 memory: 1342 loss: 1.1947 loss_cls: 0.4365 loss_bbox: 0.7582\n", + "04/04 17:52:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][23/28] lr: 1.4335e-03 eta: 0:59:08 time: 0.6567 data_time: 0.5081 memory: 1337 loss: 1.1928 loss_cls: 0.4345 loss_bbox: 0.7583\n", + "04/04 17:53:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][24/28] lr: 1.4375e-03 eta: 0:59:20 time: 0.6854 data_time: 0.5366 memory: 1340 loss: 1.1909 loss_cls: 0.4321 loss_bbox: 0.7588\n", + "04/04 17:53:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][25/28] lr: 1.4415e-03 eta: 0:59:14 time: 0.6882 data_time: 0.5391 memory: 1337 loss: 1.1903 loss_cls: 0.4366 loss_bbox: 0.7537\n", + "04/04 17:53:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][26/28] lr: 1.4455e-03 eta: 0:59:06 time: 0.6887 data_time: 0.5391 memory: 1341 loss: 1.1894 loss_cls: 0.4350 loss_bbox: 0.7544\n", + "04/04 17:53:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][27/28] lr: 1.4495e-03 eta: 0:58:58 time: 0.6532 data_time: 0.5037 memory: 1339 loss: 1.1908 loss_cls: 0.4359 loss_bbox: 0.7549\n", + "04/04 17:53:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:53:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][ 5/28] lr: 1.4735e-03 eta: 0:59:33 time: 0.7245 data_time: 0.5769 memory: 1339 loss: 1.1689 loss_cls: 0.4254 loss_bbox: 0.7435\n", + "04/04 17:53:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][ 6/28] lr: 1.4775e-03 eta: 0:59:25 time: 0.7246 data_time: 0.5770 memory: 1337 loss: 1.1663 loss_cls: 0.4258 loss_bbox: 0.7405\n", + "04/04 17:53:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][ 7/28] lr: 1.4815e-03 eta: 0:59:18 time: 0.6922 data_time: 0.5445 memory: 1340 loss: 1.1644 loss_cls: 0.4248 loss_bbox: 0.7396\n", + "04/04 17:53:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][ 8/28] lr: 1.4855e-03 eta: 0:59:10 time: 0.6926 data_time: 0.5445 memory: 1339 loss: 1.1636 loss_cls: 0.4244 loss_bbox: 0.7392\n", + "04/04 17:53:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][ 9/28] lr: 1.4895e-03 eta: 0:59:29 time: 0.7309 data_time: 0.5824 memory: 1341 loss: 1.1642 loss_cls: 0.4238 loss_bbox: 0.7404\n", + "04/04 17:53:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][10/28] lr: 1.4935e-03 eta: 0:59:21 time: 0.7313 data_time: 0.5824 memory: 1338 loss: 1.1602 loss_cls: 0.4234 loss_bbox: 0.7367\n", + "04/04 17:53:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][11/28] lr: 1.4975e-03 eta: 0:59:23 time: 0.7130 data_time: 0.5636 memory: 1337 loss: 1.1576 loss_cls: 0.4215 loss_bbox: 0.7361\n", + "04/04 17:53:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][12/28] lr: 1.5015e-03 eta: 0:59:16 time: 0.7134 data_time: 0.5636 memory: 1339 loss: 1.1558 loss_cls: 0.4183 loss_bbox: 0.7375\n", + "04/04 17:53:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][13/28] lr: 1.5055e-03 eta: 0:59:32 time: 0.7175 data_time: 0.5681 memory: 1337 loss: 1.1514 loss_cls: 0.4160 loss_bbox: 0.7354\n", + "04/04 17:53:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][14/28] lr: 1.5095e-03 eta: 0:59:24 time: 0.7176 data_time: 0.5680 memory: 1341 loss: 1.1476 loss_cls: 0.4111 loss_bbox: 0.7365\n", + "04/04 17:53:14 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][ 6/28] lr: 1.5896e-03 eta: 0:59:06 time: 0.6960 data_time: 0.5480 memory: 1337 loss: 1.1520 loss_cls: 0.4354 loss_bbox: 0.7166\n", + "04/04 17:53:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][ 7/28] lr: 1.5936e-03 eta: 0:58:59 time: 0.6721 data_time: 0.5247 memory: 1337 loss: 1.1518 loss_cls: 0.4351 loss_bbox: 0.7167\n", + "04/04 17:53:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][ 8/28] lr: 1.5976e-03 eta: 0:58:51 time: 0.6724 data_time: 0.5253 memory: 1340 loss: 1.1480 loss_cls: 0.4325 loss_bbox: 0.7155\n", + "04/04 17:53:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][ 9/28] lr: 1.6016e-03 eta: 0:59:06 time: 0.7065 data_time: 0.5596 memory: 1338 loss: 1.1429 loss_cls: 0.4295 loss_bbox: 0.7134\n", + "04/04 17:53:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][10/28] lr: 1.6056e-03 eta: 0:58:59 time: 0.6854 data_time: 0.5384 memory: 1337 loss: 1.1421 loss_cls: 0.4295 loss_bbox: 0.7126\n", + "04/04 17:53:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][11/28] lr: 1.6096e-03 eta: 0:58:55 time: 0.6768 data_time: 0.5298 memory: 1339 loss: 1.1383 loss_cls: 0.4280 loss_bbox: 0.7103\n", + "04/04 17:53:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][12/28] lr: 1.6136e-03 eta: 0:58:54 time: 0.6870 data_time: 0.5400 memory: 1338 loss: 1.1324 loss_cls: 0.4248 loss_bbox: 0.7076\n", + "04/04 17:53:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][13/28] lr: 1.6176e-03 eta: 0:59:02 time: 0.7111 data_time: 0.5641 memory: 1338 loss: 1.1293 loss_cls: 0.4236 loss_bbox: 0.7057\n", + "04/04 17:53:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][14/28] lr: 1.6216e-03 eta: 0:58:54 time: 0.6837 data_time: 0.5373 memory: 1341 loss: 1.1257 loss_cls: 0.4219 loss_bbox: 0.7037\n", + "04/04 17:53:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][15/28] lr: 1.6256e-03 eta: 0:58:49 time: 0.6788 data_time: 0.5322 memory: 1339 loss: 1.1214 loss_cls: 0.4198 loss_bbox: 0.7017\n", + "04/04 17:53:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][16/28] lr: 1.6297e-03 eta: 0:58:47 time: 0.6866 data_time: 0.5406 memory: 1338 loss: 1.1181 loss_cls: 0.4188 loss_bbox: 0.6994\n", + "04/04 17:53:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][17/28] lr: 1.6337e-03 eta: 0:58:54 time: 0.7085 data_time: 0.5625 memory: 1337 loss: 1.1157 loss_cls: 0.4191 loss_bbox: 0.6965\n", + "04/04 17:53:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][18/28] lr: 1.6377e-03 eta: 0:58:49 time: 0.6837 data_time: 0.5377 memory: 1338 loss: 1.1134 loss_cls: 0.4197 loss_bbox: 0.6937\n", + "04/04 17:53:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][19/28] lr: 1.6417e-03 eta: 0:58:46 time: 0.6881 data_time: 0.5420 memory: 1338 loss: 1.1164 loss_cls: 0.4211 loss_bbox: 0.6953\n", + "04/04 17:53:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][20/28] lr: 1.6457e-03 eta: 0:58:39 time: 0.6880 data_time: 0.5420 memory: 1340 loss: 1.1107 loss_cls: 0.4189 loss_bbox: 0.6918\n", + "04/04 17:53:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][21/28] lr: 1.6497e-03 eta: 0:58:56 time: 0.7275 data_time: 0.5812 memory: 1338 loss: 1.1053 loss_cls: 0.4163 loss_bbox: 0.6890\n", + "04/04 17:53:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][22/28] lr: 1.6537e-03 eta: 0:58:49 time: 0.6955 data_time: 0.5493 memory: 1337 loss: 1.1033 loss_cls: 0.4143 loss_bbox: 0.6890\n", + "04/04 17:53:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][23/28] lr: 1.6577e-03 eta: 0:58:41 time: 0.6437 data_time: 0.4979 memory: 1337 loss: 1.1059 loss_cls: 0.4165 loss_bbox: 0.6894\n", + "04/04 17:53:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][24/28] lr: 1.6617e-03 eta: 0:58:34 time: 0.6438 data_time: 0.4979 memory: 1338 loss: 1.1063 loss_cls: 0.4174 loss_bbox: 0.6889\n", + "04/04 17:53:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][25/28] lr: 1.6657e-03 eta: 0:58:50 time: 0.6814 data_time: 0.5354 memory: 1338 loss: 1.1114 loss_cls: 0.4192 loss_bbox: 0.6922\n", + "04/04 17:53:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][26/28] lr: 1.6697e-03 eta: 0:58:43 time: 0.6813 data_time: 0.5354 memory: 1340 loss: 1.1122 loss_cls: 0.4187 loss_bbox: 0.6936\n", + "04/04 17:53:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][27/28] lr: 1.6737e-03 eta: 0:58:36 time: 0.6461 data_time: 0.4995 memory: 1339 loss: 1.1150 loss_cls: 0.4198 loss_bbox: 0.6953\n", + "04/04 17:53:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:53:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][28/28] lr: 1.6777e-03 eta: 0:58:30 time: 0.6463 data_time: 0.4995 memory: 1337 loss: 1.1154 loss_cls: 0.4208 loss_bbox: 0.6945\n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 1/14] eta: 0:00:06 time: 0.2538 data_time: 0.2223 memory: 169 \n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 2/14] eta: 0:00:03 time: 0.2462 data_time: 0.2150 memory: 169 \n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 3/14] eta: 0:00:03 time: 0.2513 data_time: 0.2198 memory: 169 \n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 4/14] eta: 0:00:02 time: 0.2444 data_time: 0.2130 memory: 169 \n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 5/14] eta: 0:00:02 time: 0.2500 data_time: 0.2186 memory: 169 \n", + "04/04 17:53:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 6/14] eta: 0:00:01 time: 0.2436 data_time: 0.2122 memory: 169 \n", + "04/04 17:53:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 7/14] eta: 0:00:01 time: 0.2493 data_time: 0.2179 memory: 169 \n", + "04/04 17:53:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 8/14] eta: 0:00:01 time: 0.2435 data_time: 0.2119 memory: 169 \n", + "04/04 17:53:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][ 9/14] eta: 0:00:01 time: 0.2490 data_time: 0.2174 memory: 169 \n", + "04/04 17:53:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][10/14] eta: 0:00:00 time: 0.2434 data_time: 0.2117 memory: 169 \n", + "04/04 17:53:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][11/14] eta: 0:00:00 time: 0.2489 data_time: 0.2171 memory: 169 \n", + "04/04 17:53:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][12/14] eta: 0:00:00 time: 0.2435 data_time: 0.2117 memory: 169 \n", + "04/04 17:53:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][13/14] eta: 0:00:00 time: 0.2471 data_time: 0.2154 memory: 169 \n", + "04/04 17:53:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][14/14] eta: 0:00:00 time: 0.2419 data_time: 0.2103 memory: 169 \n", + "04/04 17:53:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.515\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.099\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.191\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.112\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.446\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.478\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478\n", + "04/04 17:53:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.191 0.515 0.099 -1.000 -1.000 0.191\n", + "04/04 17:53:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [15][14/14] coco/bbox_mAP: 0.1910 coco/bbox_mAP_50: 0.5150 coco/bbox_mAP_75: 0.0990 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.1910data_time: 0.2103 time: 0.2419 \n", + "04/04 17:53:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_10.pth is removed\n", + "04/04 17:53:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.1910 coco/bbox_mAP at 15 epoch is saved to best_coco/bbox_mAP_epoch_15.pth.\n", + "04/04 17:53:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 1/28] lr: 1.6817e-03 eta: 0:58:55 time: 0.6993 data_time: 0.5534 memory: 1338 loss: 1.1171 loss_cls: 0.4217 loss_bbox: 0.6953\n", + "04/04 17:53:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 2/28] lr: 1.6857e-03 eta: 0:58:48 time: 0.6988 data_time: 0.5534 memory: 1339 loss: 1.1177 loss_cls: 0.4215 loss_bbox: 0.6962\n", + "04/04 17:53:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 3/28] lr: 1.6897e-03 eta: 0:58:41 time: 0.6595 data_time: 0.5142 memory: 1339 loss: 1.1176 loss_cls: 0.4221 loss_bbox: 0.6955\n", + "04/04 17:53:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 4/28] lr: 1.6937e-03 eta: 0:58:34 time: 0.6593 data_time: 0.5142 memory: 1338 loss: 1.1188 loss_cls: 0.4228 loss_bbox: 0.6960\n", + "04/04 17:53:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 5/28] lr: 1.6977e-03 eta: 0:58:50 time: 0.6830 data_time: 0.5386 memory: 1338 loss: 1.1181 loss_cls: 0.4229 loss_bbox: 0.6953\n", + "04/04 17:53:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 6/28] lr: 1.7017e-03 eta: 0:58:43 time: 0.6826 data_time: 0.5386 memory: 1340 loss: 1.1201 loss_cls: 0.4244 loss_bbox: 0.6957\n", + "04/04 17:53:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 7/28] lr: 1.7057e-03 eta: 0:58:35 time: 0.6470 data_time: 0.5032 memory: 1337 loss: 1.1227 loss_cls: 0.4234 loss_bbox: 0.6994\n", + "04/04 17:53:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 8/28] lr: 1.7097e-03 eta: 0:58:28 time: 0.6465 data_time: 0.5032 memory: 1341 loss: 1.1196 loss_cls: 0.4256 loss_bbox: 0.6940\n", + "04/04 17:53:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][ 9/28] lr: 1.7137e-03 eta: 0:58:47 time: 0.6884 data_time: 0.5456 memory: 1338 loss: 1.1170 loss_cls: 0.4256 loss_bbox: 0.6914\n", + "04/04 17:53:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][10/28] lr: 1.7177e-03 eta: 0:58:40 time: 0.6881 data_time: 0.5456 memory: 1339 loss: 1.1170 loss_cls: 0.4253 loss_bbox: 0.6917\n", + "04/04 17:53:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][11/28] lr: 1.7217e-03 eta: 0:58:32 time: 0.6563 data_time: 0.5137 memory: 1337 loss: 1.1169 loss_cls: 0.4264 loss_bbox: 0.6904\n", + "04/04 17:53:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][12/28] lr: 1.7257e-03 eta: 0:58:25 time: 0.6563 data_time: 0.5137 memory: 1341 loss: 1.1109 loss_cls: 0.4231 loss_bbox: 0.6878\n", + "04/04 17:53:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][13/28] lr: 1.7298e-03 eta: 0:58:42 time: 0.6883 data_time: 0.5461 memory: 1341 loss: 1.1106 loss_cls: 0.4234 loss_bbox: 0.6872\n", + "04/04 17:53:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][14/28] lr: 1.7338e-03 eta: 0:58:35 time: 0.6883 data_time: 0.5461 memory: 1341 loss: 1.1111 loss_cls: 0.4239 loss_bbox: 0.6873\n", + "04/04 17:53:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][15/28] lr: 1.7378e-03 eta: 0:58:28 time: 0.6425 data_time: 0.4999 memory: 1337 loss: 1.1133 loss_cls: 0.4265 loss_bbox: 0.6868\n", + "04/04 17:53:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][16/28] lr: 1.7418e-03 eta: 0:58:21 time: 0.6426 data_time: 0.5000 memory: 1342 loss: 1.1119 loss_cls: 0.4245 loss_bbox: 0.6874\n", + "04/04 17:53:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][17/28] lr: 1.7458e-03 eta: 0:58:36 time: 0.6791 data_time: 0.5365 memory: 1343 loss: 1.1136 loss_cls: 0.4247 loss_bbox: 0.6889\n", + "04/04 17:53:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][18/28] lr: 1.7498e-03 eta: 0:58:29 time: 0.6790 data_time: 0.5365 memory: 1337 loss: 1.0787 loss_cls: 0.3979 loss_bbox: 0.6808\n", + "04/04 17:53:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][19/28] lr: 1.7538e-03 eta: 0:58:22 time: 0.6415 data_time: 0.4994 memory: 1337 loss: 1.0881 loss_cls: 0.4037 loss_bbox: 0.6843\n", + "04/04 17:53:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][20/28] lr: 1.7578e-03 eta: 0:58:15 time: 0.6412 data_time: 0.4994 memory: 1337 loss: 1.0877 loss_cls: 0.4047 loss_bbox: 0.6829\n", + "04/04 17:54:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][21/28] lr: 1.7618e-03 eta: 0:58:25 time: 0.6715 data_time: 0.5296 memory: 1342 loss: 1.0854 loss_cls: 0.4020 loss_bbox: 0.6834\n", + "04/04 17:54:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][22/28] lr: 1.7658e-03 eta: 0:58:18 time: 0.6713 data_time: 0.5296 memory: 1337 loss: 1.0906 loss_cls: 0.4058 loss_bbox: 0.6848\n", + "04/04 17:54:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][23/28] lr: 1.7698e-03 eta: 0:58:11 time: 0.6230 data_time: 0.4818 memory: 1340 loss: 1.0884 loss_cls: 0.4042 loss_bbox: 0.6842\n", + "04/04 17:54:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][24/28] lr: 1.7738e-03 eta: 0:58:04 time: 0.6227 data_time: 0.4818 memory: 1342 loss: 1.0894 loss_cls: 0.4025 loss_bbox: 0.6869\n", + "04/04 17:54:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][25/28] lr: 1.7778e-03 eta: 0:58:19 time: 0.6592 data_time: 0.5177 memory: 1341 loss: 1.0819 loss_cls: 0.3999 loss_bbox: 0.6820\n", + "04/04 17:54:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][26/28] lr: 1.7818e-03 eta: 0:58:12 time: 0.6607 data_time: 0.5188 memory: 1338 loss: 1.0798 loss_cls: 0.3963 loss_bbox: 0.6836\n", + "04/04 17:54:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][27/28] lr: 1.7858e-03 eta: 0:58:06 time: 0.6268 data_time: 0.4845 memory: 1342 loss: 1.0767 loss_cls: 0.3948 loss_bbox: 0.6819\n", + "04/04 17:54:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:54:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][28/28] lr: 1.7898e-03 eta: 0:57:59 time: 0.6245 data_time: 0.4823 memory: 1337 loss: 1.0767 loss_cls: 0.3935 loss_bbox: 0.6832\n", + "04/04 17:54:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][ 1/28] lr: 1.7938e-03 eta: 0:58:18 time: 0.6690 data_time: 0.5265 memory: 1337 loss: 1.0762 loss_cls: 0.3931 loss_bbox: 0.6831\n", + "04/04 17:54:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][ 2/28] lr: 1.7978e-03 eta: 0:58:12 time: 0.6691 data_time: 0.5264 memory: 1338 loss: 1.0779 loss_cls: 0.3933 loss_bbox: 0.6846\n", + "04/04 17:54:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][ 8/28] lr: 1.8218e-03 eta: 0:57:53 time: 0.6322 data_time: 0.4892 memory: 1338 loss: 1.0878 loss_cls: 0.4006 loss_bbox: 0.6872\n", + "04/04 17:54:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][ 9/28] lr: 1.8258e-03 eta: 0:58:04 time: 0.6597 data_time: 0.5166 memory: 1338 loss: 1.0858 loss_cls: 0.4002 loss_bbox: 0.6856\n", + "04/04 17:54:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][10/28] lr: 1.8299e-03 eta: 0:58:00 time: 0.6560 data_time: 0.5129 memory: 1339 loss: 1.0845 loss_cls: 0.3990 loss_bbox: 0.6854\n", + "04/04 17:54:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][11/28] lr: 1.8339e-03 eta: 0:57:54 time: 0.6337 data_time: 0.4910 memory: 1339 loss: 1.0855 loss_cls: 0.3998 loss_bbox: 0.6857\n", + "04/04 17:54:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][12/28] lr: 1.8379e-03 eta: 0:57:47 time: 0.6297 data_time: 0.4873 memory: 1338 loss: 1.0883 loss_cls: 0.4010 loss_bbox: 0.6873\n", + "04/04 17:54:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][13/28] lr: 1.8419e-03 eta: 0:57:56 time: 0.6517 data_time: 0.5091 memory: 1337 loss: 1.0757 loss_cls: 0.3935 loss_bbox: 0.6822\n", + "04/04 17:54:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][14/28] lr: 1.8459e-03 eta: 0:57:54 time: 0.6592 data_time: 0.5171 memory: 1340 loss: 1.0779 loss_cls: 0.3952 loss_bbox: 0.6827\n", + "04/04 17:54:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][15/28] lr: 1.8499e-03 eta: 0:57:52 time: 0.6271 data_time: 0.4853 memory: 1342 loss: 1.0786 loss_cls: 0.3956 loss_bbox: 0.6830\n", + "04/04 17:54:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][16/28] lr: 1.8539e-03 eta: 0:57:45 time: 0.6268 data_time: 0.4853 memory: 1339 loss: 1.0679 loss_cls: 0.3901 loss_bbox: 0.6779\n", + "04/04 17:54:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][17/28] lr: 1.8579e-03 eta: 0:57:52 time: 0.6506 data_time: 0.5082 memory: 1341 loss: 1.0625 loss_cls: 0.3868 loss_bbox: 0.6757\n", + "04/04 17:54:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][18/28] lr: 1.8619e-03 eta: 0:57:50 time: 0.6593 data_time: 0.5164 memory: 1341 loss: 1.0594 loss_cls: 0.3863 loss_bbox: 0.6731\n", + "04/04 17:54:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][19/28] lr: 1.8659e-03 eta: 0:57:49 time: 0.6317 data_time: 0.4886 memory: 1342 loss: 1.0525 loss_cls: 0.3830 loss_bbox: 0.6695\n", + "04/04 17:54:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][20/28] lr: 1.8699e-03 eta: 0:57:42 time: 0.6319 data_time: 0.4886 memory: 1340 loss: 1.0545 loss_cls: 0.3848 loss_bbox: 0.6697\n", + "04/04 17:54:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][21/28] lr: 1.8739e-03 eta: 0:57:48 time: 0.6529 data_time: 0.5102 memory: 1339 loss: 1.0513 loss_cls: 0.3842 loss_bbox: 0.6671\n", + "04/04 17:54:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][22/28] lr: 1.8779e-03 eta: 0:57:55 time: 0.6763 data_time: 0.5340 memory: 1339 loss: 1.0517 loss_cls: 0.3836 loss_bbox: 0.6682\n", + "04/04 17:54:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][23/28] lr: 1.8819e-03 eta: 0:57:49 time: 0.6231 data_time: 0.4801 memory: 1337 loss: 1.0477 loss_cls: 0.3838 loss_bbox: 0.6639\n", + "04/04 17:54:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][24/28] lr: 1.8859e-03 eta: 0:57:42 time: 0.6232 data_time: 0.4801 memory: 1343 loss: 1.0441 loss_cls: 0.3819 loss_bbox: 0.6621\n", + "04/04 17:54:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][25/28] lr: 1.8899e-03 eta: 0:57:44 time: 0.6390 data_time: 0.4957 memory: 1337 loss: 1.0394 loss_cls: 0.3811 loss_bbox: 0.6582\n", + "04/04 17:54:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][26/28] lr: 1.8939e-03 eta: 0:57:49 time: 0.6590 data_time: 0.5155 memory: 1338 loss: 1.0393 loss_cls: 0.3807 loss_bbox: 0.6586\n", + "04/04 17:54:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][27/28] lr: 1.8979e-03 eta: 0:57:45 time: 0.6248 data_time: 0.4810 memory: 1339 loss: 1.0377 loss_cls: 0.3802 loss_bbox: 0.6575\n", + "04/04 17:54:22 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][24/28] lr: 1.9980e-03 eta: 0:57:16 time: 0.6357 data_time: 0.4909 memory: 1337 loss: 1.0024 loss_cls: 0.3758 loss_bbox: 0.6265\n", + "04/04 17:54:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][25/28] lr: 2.0020e-03 eta: 0:57:30 time: 0.6745 data_time: 0.5299 memory: 1339 loss: 1.0018 loss_cls: 0.3751 loss_bbox: 0.6267\n", + "04/04 17:54:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][26/28] lr: 2.0060e-03 eta: 0:57:24 time: 0.6739 data_time: 0.5299 memory: 1341 loss: 1.0015 loss_cls: 0.3742 loss_bbox: 0.6273\n", + "04/04 17:54:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][27/28] lr: 2.0100e-03 eta: 0:57:21 time: 0.6503 data_time: 0.5066 memory: 1338 loss: 0.9968 loss_cls: 0.3723 loss_bbox: 0.6245\n", + "04/04 17:54:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:54:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][ 5/28] lr: 2.0341e-03 eta: 0:57:32 time: 0.7006 data_time: 0.5583 memory: 1341 loss: 1.0096 loss_cls: 0.3762 loss_bbox: 0.6334\n", + "04/04 17:54:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][ 6/28] lr: 2.0381e-03 eta: 0:57:39 time: 0.7264 data_time: 0.5840 memory: 1338 loss: 1.0103 loss_cls: 0.3767 loss_bbox: 0.6336\n", + "04/04 17:54:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][ 7/28] lr: 2.0421e-03 eta: 0:57:33 time: 0.6975 data_time: 0.5554 memory: 1337 loss: 1.0162 loss_cls: 0.3789 loss_bbox: 0.6372\n", + "04/04 17:54:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][ 8/28] lr: 2.0461e-03 eta: 0:57:27 time: 0.6899 data_time: 0.5474 memory: 1340 loss: 1.0157 loss_cls: 0.3787 loss_bbox: 0.6370\n", + "04/04 17:54:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][ 9/28] lr: 2.0501e-03 eta: 0:57:27 time: 0.6945 data_time: 0.5521 memory: 1341 loss: 1.0155 loss_cls: 0.3794 loss_bbox: 0.6361\n", + "04/04 17:54:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][10/28] lr: 2.0541e-03 eta: 0:57:34 time: 0.7203 data_time: 0.5777 memory: 1337 loss: 1.0243 loss_cls: 0.3825 loss_bbox: 0.6418\n", + "04/04 17:54:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][11/28] lr: 2.0581e-03 eta: 0:57:28 time: 0.6966 data_time: 0.5548 memory: 1338 loss: 1.0233 loss_cls: 0.3826 loss_bbox: 0.6407\n", + "04/04 17:54:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][12/28] lr: 2.0621e-03 eta: 0:57:22 time: 0.6881 data_time: 0.5466 memory: 1338 loss: 1.0225 loss_cls: 0.3820 loss_bbox: 0.6405\n", + "04/04 17:54:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][13/28] lr: 2.0661e-03 eta: 0:57:21 time: 0.6882 data_time: 0.5468 memory: 1339 loss: 1.0232 loss_cls: 0.3822 loss_bbox: 0.6410\n", + "04/04 17:54:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][14/28] lr: 2.0701e-03 eta: 0:57:29 time: 0.7161 data_time: 0.5754 memory: 1337 loss: 1.0256 loss_cls: 0.3829 loss_bbox: 0.6427\n", + "04/04 17:54:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][15/28] lr: 2.0741e-03 eta: 0:57:23 time: 0.6933 data_time: 0.5532 memory: 1338 loss: 1.0261 loss_cls: 0.3834 loss_bbox: 0.6427\n", + "04/04 17:54:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][16/28] lr: 2.0781e-03 eta: 0:57:17 time: 0.6691 data_time: 0.5293 memory: 1340 loss: 1.0214 loss_cls: 0.3821 loss_bbox: 0.6393\n", + "04/04 17:54:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][17/28] lr: 2.0821e-03 eta: 0:57:14 time: 0.6753 data_time: 0.5359 memory: 1341 loss: 1.0229 loss_cls: 0.3806 loss_bbox: 0.6423\n", + "04/04 17:54:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][18/28] lr: 2.0861e-03 eta: 0:57:29 time: 0.7179 data_time: 0.5786 memory: 1337 loss: 1.0238 loss_cls: 0.3830 loss_bbox: 0.6408\n", + "04/04 17:54:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][19/28] lr: 2.0901e-03 eta: 0:57:23 time: 0.7019 data_time: 0.5630 memory: 1338 loss: 1.0265 loss_cls: 0.3834 loss_bbox: 0.6431\n", + "04/04 17:54:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][20/28] lr: 2.0941e-03 eta: 0:57:17 time: 0.6817 data_time: 0.5432 memory: 1338 loss: 1.0232 loss_cls: 0.3813 loss_bbox: 0.6418\n", + "04/04 17:54:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][21/28] lr: 2.0981e-03 eta: 0:57:11 time: 0.6771 data_time: 0.5390 memory: 1341 loss: 1.0264 loss_cls: 0.3832 loss_bbox: 0.6432\n", + "04/04 17:54:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][22/28] lr: 2.1021e-03 eta: 0:57:17 time: 0.7001 data_time: 0.5624 memory: 1338 loss: 1.0247 loss_cls: 0.3836 loss_bbox: 0.6411\n", + "04/04 17:54:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][23/28] lr: 2.1061e-03 eta: 0:57:11 time: 0.6497 data_time: 0.5125 memory: 1337 loss: 1.0307 loss_cls: 0.3863 loss_bbox: 0.6444\n", + "04/04 17:54:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][24/28] lr: 2.1101e-03 eta: 0:57:05 time: 0.6498 data_time: 0.5125 memory: 1337 loss: 1.0374 loss_cls: 0.3871 loss_bbox: 0.6503\n", + "04/04 17:54:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][25/28] lr: 2.1141e-03 eta: 0:57:04 time: 0.6608 data_time: 0.5234 memory: 1340 loss: 1.0400 loss_cls: 0.3887 loss_bbox: 0.6513\n", + "04/04 17:55:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][26/28] lr: 2.1181e-03 eta: 0:57:12 time: 0.6878 data_time: 0.5503 memory: 1337 loss: 1.0403 loss_cls: 0.3889 loss_bbox: 0.6514\n", + "04/04 17:55:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][27/28] lr: 2.1221e-03 eta: 0:57:06 time: 0.6473 data_time: 0.5100 memory: 1337 loss: 1.0431 loss_cls: 0.3890 loss_bbox: 0.6542\n", + "04/04 17:55:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:55:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][28/28] lr: 2.1261e-03 eta: 0:57:00 time: 0.6477 data_time: 0.5100 memory: 1340 loss: 1.0457 loss_cls: 0.3895 loss_bbox: 0.6562\n", + "04/04 17:55:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 1/28] lr: 2.1301e-03 eta: 0:57:19 time: 0.7001 data_time: 0.5624 memory: 1337 loss: 1.0459 loss_cls: 0.3899 loss_bbox: 0.6560\n", + "04/04 17:55:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 2/28] lr: 2.1342e-03 eta: 0:57:14 time: 0.7004 data_time: 0.5624 memory: 1339 loss: 1.0437 loss_cls: 0.3893 loss_bbox: 0.6544\n", + "04/04 17:55:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 3/28] lr: 2.1382e-03 eta: 0:57:08 time: 0.6622 data_time: 0.5240 memory: 1339 loss: 1.0446 loss_cls: 0.3899 loss_bbox: 0.6546\n", + "04/04 17:55:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 4/28] lr: 2.1422e-03 eta: 0:57:02 time: 0.6628 data_time: 0.5240 memory: 1340 loss: 1.0439 loss_cls: 0.3902 loss_bbox: 0.6537\n", + "04/04 17:55:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 5/28] lr: 2.1462e-03 eta: 0:57:14 time: 0.6996 data_time: 0.5606 memory: 1338 loss: 1.0482 loss_cls: 0.3920 loss_bbox: 0.6562\n", + "04/04 17:55:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 6/28] lr: 2.1502e-03 eta: 0:57:09 time: 0.6950 data_time: 0.5553 memory: 1342 loss: 1.0507 loss_cls: 0.3924 loss_bbox: 0.6583\n", + "04/04 17:55:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 7/28] lr: 2.1542e-03 eta: 0:57:03 time: 0.6617 data_time: 0.5218 memory: 1341 loss: 1.0503 loss_cls: 0.3936 loss_bbox: 0.6567\n", + "04/04 17:55:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 8/28] lr: 2.1582e-03 eta: 0:56:58 time: 0.6614 data_time: 0.5217 memory: 1338 loss: 1.0361 loss_cls: 0.3817 loss_bbox: 0.6545\n", + "04/04 17:55:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][ 9/28] lr: 2.1622e-03 eta: 0:57:09 time: 0.6964 data_time: 0.5573 memory: 1339 loss: 1.0343 loss_cls: 0.3811 loss_bbox: 0.6531\n", + "04/04 17:55:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][10/28] lr: 2.1662e-03 eta: 0:57:08 time: 0.7068 data_time: 0.5672 memory: 1339 loss: 1.0337 loss_cls: 0.3814 loss_bbox: 0.6523\n", + "04/04 17:55:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][11/28] lr: 2.1702e-03 eta: 0:57:03 time: 0.6722 data_time: 0.5314 memory: 1338 loss: 1.0328 loss_cls: 0.3814 loss_bbox: 0.6513\n", + "04/04 17:55:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][12/28] lr: 2.1742e-03 eta: 0:56:57 time: 0.6730 data_time: 0.5314 memory: 1343 loss: 1.0334 loss_cls: 0.3818 loss_bbox: 0.6517\n", + "04/04 17:55:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][13/28] lr: 2.1782e-03 eta: 0:57:05 time: 0.7007 data_time: 0.5584 memory: 1337 loss: 1.0300 loss_cls: 0.3808 loss_bbox: 0.6492\n", + "04/04 17:55:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][14/28] lr: 2.1822e-03 eta: 0:57:04 time: 0.7083 data_time: 0.5655 memory: 1338 loss: 1.0253 loss_cls: 0.3787 loss_bbox: 0.6466\n", + "04/04 17:55:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][15/28] lr: 2.1862e-03 eta: 0:56:59 time: 0.6730 data_time: 0.5295 memory: 1339 loss: 1.0212 loss_cls: 0.3784 loss_bbox: 0.6428\n", + "04/04 17:55:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][16/28] lr: 2.1902e-03 eta: 0:56:53 time: 0.6728 data_time: 0.5295 memory: 1341 loss: 1.0154 loss_cls: 0.3758 loss_bbox: 0.6397\n", + "04/04 17:55:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][17/28] lr: 2.1942e-03 eta: 0:56:59 time: 0.6976 data_time: 0.5543 memory: 1340 loss: 1.0122 loss_cls: 0.3742 loss_bbox: 0.6380\n", + "04/04 17:55:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][18/28] lr: 2.1982e-03 eta: 0:57:03 time: 0.7189 data_time: 0.5753 memory: 1337 loss: 1.0140 loss_cls: 0.3757 loss_bbox: 0.6383\n", + "04/04 17:55:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][19/28] lr: 2.2022e-03 eta: 0:56:58 time: 0.6800 data_time: 0.5363 memory: 1337 loss: 1.0087 loss_cls: 0.3738 loss_bbox: 0.6349\n", + "04/04 17:55:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][20/28] lr: 2.2062e-03 eta: 0:56:52 time: 0.6805 data_time: 0.5363 memory: 1337 loss: 1.0079 loss_cls: 0.3740 loss_bbox: 0.6339\n", + "04/04 17:55:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][21/28] lr: 2.2102e-03 eta: 0:56:54 time: 0.6893 data_time: 0.5448 memory: 1342 loss: 1.0105 loss_cls: 0.3745 loss_bbox: 0.6361\n", + "04/04 17:55:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][22/28] lr: 2.2142e-03 eta: 0:56:58 time: 0.7124 data_time: 0.5670 memory: 1342 loss: 1.0098 loss_cls: 0.3747 loss_bbox: 0.6351\n", + "04/04 17:55:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][23/28] lr: 2.2182e-03 eta: 0:56:53 time: 0.6591 data_time: 0.5130 memory: 1339 loss: 1.0059 loss_cls: 0.3734 loss_bbox: 0.6324\n", + "04/04 17:55:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][24/28] lr: 2.2222e-03 eta: 0:56:48 time: 0.6469 data_time: 0.5001 memory: 1339 loss: 1.0039 loss_cls: 0.3739 loss_bbox: 0.6300\n", + "04/04 17:55:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][25/28] lr: 2.2262e-03 eta: 0:56:55 time: 0.6744 data_time: 0.5270 memory: 1339 loss: 0.9957 loss_cls: 0.3706 loss_bbox: 0.6251\n", + "04/04 17:55:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][26/28] lr: 2.2302e-03 eta: 0:56:49 time: 0.6747 data_time: 0.5270 memory: 1340 loss: 0.9969 loss_cls: 0.3714 loss_bbox: 0.6256\n", + "04/04 17:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][27/28] lr: 2.2343e-03 eta: 0:56:44 time: 0.6459 data_time: 0.4984 memory: 1337 loss: 0.9956 loss_cls: 0.3712 loss_bbox: 0.6244\n", + "04/04 17:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][28/28] lr: 2.2383e-03 eta: 0:56:38 time: 0.6203 data_time: 0.4727 memory: 1341 loss: 0.9938 loss_cls: 0.3698 loss_bbox: 0.6240\n", + "04/04 17:55:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 20 epochs\n", + "04/04 17:55:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 1/14] eta: 0:00:06 time: 0.2485 data_time: 0.2168 memory: 169 \n", + "04/04 17:55:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 2/14] eta: 0:00:03 time: 0.2436 data_time: 0.2119 memory: 169 \n", + "04/04 17:55:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 3/14] eta: 0:00:03 time: 0.2461 data_time: 0.2144 memory: 169 \n", + "04/04 17:55:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 4/14] eta: 0:00:02 time: 0.2436 data_time: 0.2118 memory: 169 \n", + "04/04 17:55:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 5/14] eta: 0:00:02 time: 0.2447 data_time: 0.2128 memory: 169 \n", + "04/04 17:55:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 6/14] eta: 0:00:01 time: 0.2427 data_time: 0.2108 memory: 169 \n", + "04/04 17:55:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 7/14] eta: 0:00:01 time: 0.2434 data_time: 0.2114 memory: 169 \n", + "04/04 17:55:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 8/14] eta: 0:00:01 time: 0.2428 data_time: 0.2108 memory: 169 \n", + "04/04 17:55:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][ 9/14] eta: 0:00:01 time: 0.2341 data_time: 0.2024 memory: 169 \n", + "04/04 17:55:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][10/14] eta: 0:00:00 time: 0.2376 data_time: 0.2058 memory: 169 \n", + "04/04 17:55:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][11/14] eta: 0:00:00 time: 0.2349 data_time: 0.2031 memory: 169 \n", + "04/04 17:55:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][12/14] eta: 0:00:00 time: 0.2376 data_time: 0.2058 memory: 169 \n", + "04/04 17:55:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][13/14] eta: 0:00:00 time: 0.2336 data_time: 0.2018 memory: 169 \n", + "04/04 17:55:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][14/14] eta: 0:00:00 time: 0.2381 data_time: 0.2061 memory: 169 \n", + "04/04 17:55:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.872\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.153\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.353\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.540\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.540\n", + "04/04 17:55:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.353 0.872 0.153 -1.000 -1.000 0.353\n", + "04/04 17:55:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][14/14] coco/bbox_mAP: 0.3530 coco/bbox_mAP_50: 0.8720 coco/bbox_mAP_75: 0.1530 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.3530data_time: 0.2061 time: 0.2381 \n", + "04/04 17:55:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_15.pth is removed\n", + "04/04 17:55:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.3530 coco/bbox_mAP at 20 epoch is saved to best_coco/bbox_mAP_epoch_20.pth.\n", + "04/04 17:55:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 1/28] lr: 2.2423e-03 eta: 0:56:54 time: 0.6679 data_time: 0.5206 memory: 1339 loss: 0.9929 loss_cls: 0.3698 loss_bbox: 0.6231\n", + "04/04 17:55:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 2/28] lr: 2.2463e-03 eta: 0:56:49 time: 0.6675 data_time: 0.5206 memory: 1339 loss: 0.9904 loss_cls: 0.3705 loss_bbox: 0.6199\n", + "04/04 17:55:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 3/28] lr: 2.2503e-03 eta: 0:56:43 time: 0.6551 data_time: 0.5087 memory: 1337 loss: 0.9931 loss_cls: 0.3724 loss_bbox: 0.6207\n", + "04/04 17:55:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 4/28] lr: 2.2543e-03 eta: 0:56:38 time: 0.6296 data_time: 0.4830 memory: 1342 loss: 0.9875 loss_cls: 0.3705 loss_bbox: 0.6170\n", + "04/04 17:55:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 5/28] lr: 2.2583e-03 eta: 0:56:50 time: 0.6690 data_time: 0.5222 memory: 1338 loss: 0.9897 loss_cls: 0.3716 loss_bbox: 0.6182\n", + "04/04 17:55:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 6/28] lr: 2.2623e-03 eta: 0:56:45 time: 0.6692 data_time: 0.5222 memory: 1337 loss: 0.9988 loss_cls: 0.3779 loss_bbox: 0.6209\n", + "04/04 17:55:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 7/28] lr: 2.2663e-03 eta: 0:56:39 time: 0.6597 data_time: 0.5124 memory: 1339 loss: 0.9982 loss_cls: 0.3793 loss_bbox: 0.6189\n", + "04/04 17:55:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 8/28] lr: 2.2703e-03 eta: 0:56:34 time: 0.6318 data_time: 0.4838 memory: 1338 loss: 0.9945 loss_cls: 0.3787 loss_bbox: 0.6158\n", + "04/04 17:55:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][ 9/28] lr: 2.2743e-03 eta: 0:56:47 time: 0.6722 data_time: 0.5239 memory: 1341 loss: 0.9969 loss_cls: 0.3793 loss_bbox: 0.6176\n", + "04/04 17:55:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][10/28] lr: 2.2783e-03 eta: 0:56:41 time: 0.6726 data_time: 0.5239 memory: 1337 loss: 0.9966 loss_cls: 0.3793 loss_bbox: 0.6173\n", + "04/04 17:55:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][11/28] lr: 2.2823e-03 eta: 0:56:36 time: 0.6663 data_time: 0.5174 memory: 1337 loss: 0.9934 loss_cls: 0.3805 loss_bbox: 0.6129\n", + "04/04 17:55:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][12/28] lr: 2.2863e-03 eta: 0:56:31 time: 0.6237 data_time: 0.4747 memory: 1338 loss: 0.9907 loss_cls: 0.3786 loss_bbox: 0.6121\n", + "04/04 17:55:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][13/28] lr: 2.2903e-03 eta: 0:56:42 time: 0.6608 data_time: 0.5113 memory: 1340 loss: 0.9885 loss_cls: 0.3785 loss_bbox: 0.6100\n", + "04/04 17:55:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][14/28] lr: 2.2943e-03 eta: 0:56:36 time: 0.6612 data_time: 0.5113 memory: 1338 loss: 0.9911 loss_cls: 0.3801 loss_bbox: 0.6110\n", + "04/04 17:55:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][15/28] lr: 2.2983e-03 eta: 0:56:31 time: 0.6618 data_time: 0.5114 memory: 1338 loss: 0.9853 loss_cls: 0.3779 loss_bbox: 0.6075\n", + "04/04 17:55:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][16/28] lr: 2.3023e-03 eta: 0:56:26 time: 0.6391 data_time: 0.4881 memory: 1337 loss: 0.9871 loss_cls: 0.3795 loss_bbox: 0.6077\n", + "04/04 17:55:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][17/28] lr: 2.3063e-03 eta: 0:56:36 time: 0.6747 data_time: 0.5228 memory: 1337 loss: 0.9913 loss_cls: 0.3840 loss_bbox: 0.6073\n", + "04/04 17:55:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][18/28] lr: 2.3103e-03 eta: 0:56:31 time: 0.6752 data_time: 0.5228 memory: 1337 loss: 0.9830 loss_cls: 0.3813 loss_bbox: 0.6017\n", + "04/04 17:55:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][19/28] lr: 2.3143e-03 eta: 0:56:26 time: 0.6644 data_time: 0.5119 memory: 1340 loss: 0.9797 loss_cls: 0.3796 loss_bbox: 0.6001\n", + "04/04 17:55:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][20/28] lr: 2.3183e-03 eta: 0:56:21 time: 0.6383 data_time: 0.4850 memory: 1339 loss: 0.9761 loss_cls: 0.3791 loss_bbox: 0.5970\n", + "04/04 17:55:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][21/28] lr: 2.3223e-03 eta: 0:56:34 time: 0.6798 data_time: 0.5265 memory: 1337 loss: 0.9726 loss_cls: 0.3771 loss_bbox: 0.5954\n", + "04/04 17:55:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][22/28] lr: 2.3263e-03 eta: 0:56:29 time: 0.6800 data_time: 0.5265 memory: 1338 loss: 0.9720 loss_cls: 0.3769 loss_bbox: 0.5951\n", + "04/04 17:55:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][23/28] lr: 2.3303e-03 eta: 0:56:23 time: 0.6281 data_time: 0.4740 memory: 1338 loss: 0.9706 loss_cls: 0.3778 loss_bbox: 0.5928\n", + "04/04 17:55:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][24/28] lr: 2.3344e-03 eta: 0:56:18 time: 0.6281 data_time: 0.4740 memory: 1339 loss: 0.9704 loss_cls: 0.3778 loss_bbox: 0.5926\n", + "04/04 17:55:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][25/28] lr: 2.3384e-03 eta: 0:56:28 time: 0.6634 data_time: 0.5095 memory: 1337 loss: 0.9680 loss_cls: 0.3761 loss_bbox: 0.5919\n", + "04/04 17:55:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][26/28] lr: 2.3424e-03 eta: 0:56:23 time: 0.6633 data_time: 0.5094 memory: 1339 loss: 0.9679 loss_cls: 0.3748 loss_bbox: 0.5931\n", + "04/04 17:55:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][27/28] lr: 2.3464e-03 eta: 0:56:18 time: 0.6265 data_time: 0.4729 memory: 1337 loss: 0.9665 loss_cls: 0.3752 loss_bbox: 0.5913\n", + "04/04 17:55:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:55:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][28/28] lr: 2.3504e-03 eta: 0:56:13 time: 0.6260 data_time: 0.4729 memory: 1338 loss: 0.9681 loss_cls: 0.3771 loss_bbox: 0.5910\n", + "04/04 17:55:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 1/28] lr: 2.3544e-03 eta: 0:56:29 time: 0.6749 data_time: 0.5224 memory: 1337 loss: 0.9694 loss_cls: 0.3771 loss_bbox: 0.5923\n", + "04/04 17:55:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 2/28] lr: 2.3584e-03 eta: 0:56:23 time: 0.6746 data_time: 0.5223 memory: 1338 loss: 0.9754 loss_cls: 0.3798 loss_bbox: 0.5956\n", + "04/04 17:55:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 3/28] lr: 2.3624e-03 eta: 0:56:18 time: 0.6387 data_time: 0.4867 memory: 1339 loss: 0.9796 loss_cls: 0.3825 loss_bbox: 0.5970\n", + "04/04 17:55:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 4/28] lr: 2.3664e-03 eta: 0:56:13 time: 0.6279 data_time: 0.4769 memory: 1338 loss: 0.9777 loss_cls: 0.3823 loss_bbox: 0.5953\n", + "04/04 17:55:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 5/28] lr: 2.3704e-03 eta: 0:56:18 time: 0.6514 data_time: 0.5011 memory: 1337 loss: 0.9823 loss_cls: 0.3827 loss_bbox: 0.5996\n", + "04/04 17:55:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 6/28] lr: 2.3744e-03 eta: 0:56:14 time: 0.6539 data_time: 0.5043 memory: 1337 loss: 0.9805 loss_cls: 0.3827 loss_bbox: 0.5978\n", + "04/04 17:55:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 7/28] lr: 2.3784e-03 eta: 0:56:09 time: 0.6269 data_time: 0.4774 memory: 1340 loss: 0.9828 loss_cls: 0.3839 loss_bbox: 0.5989\n", + "04/04 17:55:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 8/28] lr: 2.3824e-03 eta: 0:56:05 time: 0.6184 data_time: 0.4690 memory: 1338 loss: 0.9879 loss_cls: 0.3853 loss_bbox: 0.6026\n", + "04/04 17:55:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][ 9/28] lr: 2.3864e-03 eta: 0:56:12 time: 0.6473 data_time: 0.4981 memory: 1338 loss: 0.9888 loss_cls: 0.3858 loss_bbox: 0.6029\n", + "04/04 17:55:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][10/28] lr: 2.3904e-03 eta: 0:56:08 time: 0.6476 data_time: 0.4981 memory: 1341 loss: 0.9891 loss_cls: 0.3870 loss_bbox: 0.6021\n", + "04/04 17:55:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][11/28] lr: 2.3944e-03 eta: 0:56:03 time: 0.6231 data_time: 0.4734 memory: 1341 loss: 0.9920 loss_cls: 0.3879 loss_bbox: 0.6041\n", + "04/04 17:55:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][12/28] lr: 2.3984e-03 eta: 0:55:58 time: 0.6023 data_time: 0.4523 memory: 1338 loss: 0.9925 loss_cls: 0.3870 loss_bbox: 0.6055\n", + "04/04 17:55:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][13/28] lr: 2.4024e-03 eta: 0:56:07 time: 0.6367 data_time: 0.4866 memory: 1341 loss: 0.9967 loss_cls: 0.3886 loss_bbox: 0.6081\n", + "04/04 17:55:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][14/28] lr: 2.4064e-03 eta: 0:56:02 time: 0.6363 data_time: 0.4866 memory: 1338 loss: 0.9955 loss_cls: 0.3883 loss_bbox: 0.6072\n", + "04/04 17:55:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][15/28] lr: 2.4104e-03 eta: 0:55:57 time: 0.6210 data_time: 0.4714 memory: 1342 loss: 0.9972 loss_cls: 0.3902 loss_bbox: 0.6070\n", + "04/04 17:55:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][16/28] lr: 2.4144e-03 eta: 0:55:52 time: 0.5983 data_time: 0.4492 memory: 1337 loss: 1.0004 loss_cls: 0.3930 loss_bbox: 0.6074\n", + "04/04 17:55:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][17/28] lr: 2.4184e-03 eta: 0:55:57 time: 0.6235 data_time: 0.4742 memory: 1342 loss: 1.0010 loss_cls: 0.3937 loss_bbox: 0.6072\n", + "04/04 17:55:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][18/28] lr: 2.4224e-03 eta: 0:55:52 time: 0.6233 data_time: 0.4743 memory: 1339 loss: 1.0012 loss_cls: 0.3933 loss_bbox: 0.6079\n", + "04/04 17:55:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][19/28] lr: 2.4264e-03 eta: 0:55:47 time: 0.5958 data_time: 0.4474 memory: 1339 loss: 1.0017 loss_cls: 0.3949 loss_bbox: 0.6068\n", + "04/04 17:55:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][20/28] lr: 2.4304e-03 eta: 0:55:42 time: 0.5956 data_time: 0.4474 memory: 1338 loss: 0.9995 loss_cls: 0.3945 loss_bbox: 0.6050\n", + "04/04 17:55:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][21/28] lr: 2.4345e-03 eta: 0:55:53 time: 0.6341 data_time: 0.4858 memory: 1338 loss: 1.0004 loss_cls: 0.3953 loss_bbox: 0.6051\n", + "04/04 17:55:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][22/28] lr: 2.4385e-03 eta: 0:55:48 time: 0.6342 data_time: 0.4858 memory: 1338 loss: 1.0014 loss_cls: 0.3956 loss_bbox: 0.6058\n", + "04/04 17:55:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][23/28] lr: 2.4425e-03 eta: 0:55:47 time: 0.5949 data_time: 0.4461 memory: 1339 loss: 0.9991 loss_cls: 0.3964 loss_bbox: 0.6026\n", + "04/04 17:55:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][24/28] lr: 2.4465e-03 eta: 0:55:42 time: 0.5956 data_time: 0.4464 memory: 1342 loss: 1.0035 loss_cls: 0.3976 loss_bbox: 0.6059\n", + "04/04 17:56:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][25/28] lr: 2.4505e-03 eta: 0:55:53 time: 0.6348 data_time: 0.4850 memory: 1339 loss: 1.0026 loss_cls: 0.3965 loss_bbox: 0.6062\n", + "04/04 17:56:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][26/28] lr: 2.4545e-03 eta: 0:55:48 time: 0.6347 data_time: 0.4850 memory: 1337 loss: 1.0007 loss_cls: 0.3967 loss_bbox: 0.6039\n", + "04/04 17:56:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][27/28] lr: 2.4585e-03 eta: 0:55:43 time: 0.5953 data_time: 0.4458 memory: 1341 loss: 1.0009 loss_cls: 0.3985 loss_bbox: 0.6024\n", + "04/04 17:56:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:56:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][28/28] lr: 2.4625e-03 eta: 0:55:38 time: 0.5947 data_time: 0.4458 memory: 1339 loss: 0.9964 loss_cls: 0.3952 loss_bbox: 0.6012\n", + "04/04 17:56:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][ 1/28] lr: 2.4665e-03 eta: 0:55:54 time: 0.6476 data_time: 0.4992 memory: 1338 loss: 0.9965 loss_cls: 0.3954 loss_bbox: 0.6011\n", + "04/04 17:56:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][ 2/28] lr: 2.4705e-03 eta: 0:55:49 time: 0.6470 data_time: 0.4992 memory: 1339 loss: 0.9943 loss_cls: 0.3944 loss_bbox: 0.5999\n", + "04/04 17:56:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][ 3/28] lr: 2.4745e-03 eta: 0:55:44 time: 0.6068 data_time: 0.4590 memory: 1339 loss: 0.9903 loss_cls: 0.3933 loss_bbox: 0.5971\n", + "04/04 17:56:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][ 9/28] lr: 2.4985e-03 eta: 0:55:47 time: 0.6474 data_time: 0.4998 memory: 1341 loss: 0.9874 loss_cls: 0.3919 loss_bbox: 0.5955\n", + "04/04 17:56:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][10/28] lr: 2.5025e-03 eta: 0:55:42 time: 0.6472 data_time: 0.4998 memory: 1343 loss: 0.9843 loss_cls: 0.3905 loss_bbox: 0.5938\n", + "04/04 17:56:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][11/28] lr: 2.5065e-03 eta: 0:55:37 time: 0.6119 data_time: 0.4650 memory: 1339 loss: 0.9734 loss_cls: 0.3823 loss_bbox: 0.5911\n", + "04/04 17:56:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][12/28] lr: 2.5105e-03 eta: 0:55:32 time: 0.6117 data_time: 0.4651 memory: 1339 loss: 0.9725 loss_cls: 0.3822 loss_bbox: 0.5904\n", + "04/04 17:56:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][13/28] lr: 2.5145e-03 eta: 0:55:43 time: 0.6504 data_time: 0.5039 memory: 1341 loss: 0.9722 loss_cls: 0.3821 loss_bbox: 0.5901\n", + "04/04 17:56:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][14/28] lr: 2.5185e-03 eta: 0:55:38 time: 0.6495 data_time: 0.5039 memory: 1339 loss: 0.9746 loss_cls: 0.3835 loss_bbox: 0.5911\n", + "04/04 17:56:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][15/28] lr: 2.5225e-03 eta: 0:55:33 time: 0.6081 data_time: 0.4624 memory: 1338 loss: 0.9751 loss_cls: 0.3841 loss_bbox: 0.5910\n", + "04/04 17:56:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][16/28] lr: 2.5265e-03 eta: 0:55:28 time: 0.6079 data_time: 0.4625 memory: 1339 loss: 0.9749 loss_cls: 0.3846 loss_bbox: 0.5903\n", + "04/04 17:56:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][17/28] lr: 2.5305e-03 eta: 0:55:37 time: 0.6424 data_time: 0.4972 memory: 1340 loss: 0.9779 loss_cls: 0.3866 loss_bbox: 0.5913\n", + "04/04 17:56:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][18/28] lr: 2.5345e-03 eta: 0:55:32 time: 0.6424 data_time: 0.4973 memory: 1340 loss: 0.9747 loss_cls: 0.3852 loss_bbox: 0.5894\n", + "04/04 17:56:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][19/28] lr: 2.5386e-03 eta: 0:55:28 time: 0.6067 data_time: 0.4619 memory: 1341 loss: 0.9771 loss_cls: 0.3861 loss_bbox: 0.5910\n", + "04/04 17:56:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][20/28] lr: 2.5426e-03 eta: 0:55:23 time: 0.6066 data_time: 0.4619 memory: 1344 loss: 0.9737 loss_cls: 0.3856 loss_bbox: 0.5880\n", + "04/04 17:56:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][21/28] lr: 2.5466e-03 eta: 0:55:30 time: 0.6369 data_time: 0.4916 memory: 1337 loss: 0.9710 loss_cls: 0.3852 loss_bbox: 0.5858\n", + "04/04 17:56:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][22/28] lr: 2.5506e-03 eta: 0:55:25 time: 0.6371 data_time: 0.4916 memory: 1339 loss: 0.9689 loss_cls: 0.3844 loss_bbox: 0.5845\n", + "04/04 17:56:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][23/28] lr: 2.5546e-03 eta: 0:55:21 time: 0.5882 data_time: 0.4421 memory: 1341 loss: 0.9691 loss_cls: 0.3845 loss_bbox: 0.5846\n", + "04/04 17:56:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][24/28] lr: 2.5586e-03 eta: 0:55:17 time: 0.5903 data_time: 0.4438 memory: 1338 loss: 0.9646 loss_cls: 0.3825 loss_bbox: 0.5821\n", + "04/04 17:56:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][25/28] lr: 2.5626e-03 eta: 0:55:22 time: 0.6153 data_time: 0.4684 memory: 1338 loss: 0.9619 loss_cls: 0.3810 loss_bbox: 0.5809\n", + "04/04 17:56:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][26/28] lr: 2.5666e-03 eta: 0:55:17 time: 0.6158 data_time: 0.4684 memory: 1341 loss: 0.9621 loss_cls: 0.3816 loss_bbox: 0.5805\n", + "04/04 17:56:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][27/28] lr: 2.5706e-03 eta: 0:55:14 time: 0.5962 data_time: 0.4486 memory: 1338 loss: 0.9511 loss_cls: 0.3791 loss_bbox: 0.5720\n", + "04/04 17:56:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:56:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][28/28] lr: 2.5746e-03 eta: 0:55:12 time: 0.6004 data_time: 0.4528 memory: 1340 loss: 0.9480 loss_cls: 0.3776 loss_bbox: 0.5705\n", + "04/04 17:56:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 1/28] lr: 2.5786e-03 eta: 0:55:23 time: 0.6392 data_time: 0.4922 memory: 1340 loss: 0.9454 loss_cls: 0.3770 loss_bbox: 0.5683\n", + "04/04 17:56:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 2/28] lr: 2.5826e-03 eta: 0:55:21 time: 0.6451 data_time: 0.4985 memory: 1337 loss: 0.9437 loss_cls: 0.3781 loss_bbox: 0.5656\n", + "04/04 17:56:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 3/28] lr: 2.5866e-03 eta: 0:55:16 time: 0.6156 data_time: 0.4693 memory: 1341 loss: 0.9486 loss_cls: 0.3807 loss_bbox: 0.5679\n", + "04/04 17:56:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 4/28] lr: 2.5906e-03 eta: 0:55:12 time: 0.6151 data_time: 0.4694 memory: 1337 loss: 0.9480 loss_cls: 0.3799 loss_bbox: 0.5681\n", + "04/04 17:56:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 5/28] lr: 2.5946e-03 eta: 0:55:21 time: 0.6521 data_time: 0.5068 memory: 1337 loss: 0.9473 loss_cls: 0.3824 loss_bbox: 0.5649\n", + "04/04 17:56:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 6/28] lr: 2.5986e-03 eta: 0:55:20 time: 0.6610 data_time: 0.5162 memory: 1338 loss: 0.9460 loss_cls: 0.3829 loss_bbox: 0.5631\n", + "04/04 17:56:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 7/28] lr: 2.6026e-03 eta: 0:55:15 time: 0.6264 data_time: 0.4819 memory: 1338 loss: 0.9418 loss_cls: 0.3819 loss_bbox: 0.5600\n", + "04/04 17:56:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 8/28] lr: 2.6066e-03 eta: 0:55:11 time: 0.6266 data_time: 0.4819 memory: 1339 loss: 0.9446 loss_cls: 0.3825 loss_bbox: 0.5621\n", + "04/04 17:56:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][ 9/28] lr: 2.6106e-03 eta: 0:55:18 time: 0.6583 data_time: 0.5139 memory: 1338 loss: 0.9404 loss_cls: 0.3816 loss_bbox: 0.5588\n", + "04/04 17:56:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][10/28] lr: 2.6146e-03 eta: 0:55:17 time: 0.6669 data_time: 0.5227 memory: 1338 loss: 0.9413 loss_cls: 0.3813 loss_bbox: 0.5600\n", + "04/04 17:56:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][11/28] lr: 2.6186e-03 eta: 0:55:12 time: 0.6413 data_time: 0.4977 memory: 1341 loss: 0.9405 loss_cls: 0.3820 loss_bbox: 0.5585\n", + "04/04 17:56:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][12/28] lr: 2.6226e-03 eta: 0:55:07 time: 0.6411 data_time: 0.4976 memory: 1339 loss: 0.9402 loss_cls: 0.3824 loss_bbox: 0.5579\n", + "04/04 17:56:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][13/28] lr: 2.6266e-03 eta: 0:55:14 time: 0.6716 data_time: 0.5278 memory: 1338 loss: 0.9406 loss_cls: 0.3803 loss_bbox: 0.5603\n", + "04/04 17:56:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][14/28] lr: 2.6306e-03 eta: 0:55:10 time: 0.6721 data_time: 0.5278 memory: 1342 loss: 0.9439 loss_cls: 0.3829 loss_bbox: 0.5610\n", + "04/04 17:56:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][15/28] lr: 2.6346e-03 eta: 0:55:05 time: 0.6338 data_time: 0.4894 memory: 1337 loss: 0.9472 loss_cls: 0.3845 loss_bbox: 0.5626\n", + "04/04 17:56:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][16/28] lr: 2.6387e-03 eta: 0:55:02 time: 0.6384 data_time: 0.4941 memory: 1340 loss: 0.9430 loss_cls: 0.3838 loss_bbox: 0.5592\n", + "04/04 17:56:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][17/28] lr: 2.6427e-03 eta: 0:55:10 time: 0.6635 data_time: 0.5195 memory: 1339 loss: 0.9411 loss_cls: 0.3812 loss_bbox: 0.5598\n", + "04/04 17:56:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][18/28] lr: 2.6467e-03 eta: 0:55:06 time: 0.6631 data_time: 0.5191 memory: 1338 loss: 0.9408 loss_cls: 0.3821 loss_bbox: 0.5587\n", + "04/04 17:56:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][19/28] lr: 2.6507e-03 eta: 0:55:01 time: 0.6245 data_time: 0.4805 memory: 1340 loss: 0.9417 loss_cls: 0.3829 loss_bbox: 0.5589\n", + "04/04 17:56:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][20/28] lr: 2.6547e-03 eta: 0:54:57 time: 0.6261 data_time: 0.4819 memory: 1339 loss: 0.9428 loss_cls: 0.3824 loss_bbox: 0.5604\n", + "04/04 17:56:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][21/28] lr: 2.6587e-03 eta: 0:55:04 time: 0.6551 data_time: 0.5111 memory: 1339 loss: 0.9422 loss_cls: 0.3807 loss_bbox: 0.5615\n", + "04/04 17:56:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][22/28] lr: 2.6627e-03 eta: 0:54:59 time: 0.6553 data_time: 0.5112 memory: 1340 loss: 0.9374 loss_cls: 0.3780 loss_bbox: 0.5594\n", + "04/04 17:56:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][23/28] lr: 2.6667e-03 eta: 0:54:54 time: 0.6021 data_time: 0.4578 memory: 1337 loss: 0.9374 loss_cls: 0.3772 loss_bbox: 0.5602\n", + "04/04 17:56:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][24/28] lr: 2.6707e-03 eta: 0:55:03 time: 0.6385 data_time: 0.4941 memory: 1341 loss: 0.9372 loss_cls: 0.3777 loss_bbox: 0.5595\n", + "04/04 17:56:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][25/28] lr: 2.6747e-03 eta: 0:54:59 time: 0.6387 data_time: 0.4941 memory: 1338 loss: 0.9356 loss_cls: 0.3775 loss_bbox: 0.5580\n", + "04/04 17:56:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][26/28] lr: 2.6787e-03 eta: 0:54:54 time: 0.6385 data_time: 0.4940 memory: 1339 loss: 0.9326 loss_cls: 0.3760 loss_bbox: 0.5565\n", + "04/04 17:56:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][27/28] lr: 2.6827e-03 eta: 0:54:50 time: 0.5977 data_time: 0.4536 memory: 1341 loss: 0.9308 loss_cls: 0.3753 loss_bbox: 0.5555\n", + "04/04 17:56:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:56:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [24][28/28] lr: 2.6867e-03 eta: 0:54:59 time: 0.6366 data_time: 0.4925 memory: 1338 loss: 0.9342 loss_cls: 0.3756 loss_bbox: 0.5585\n", + "04/04 17:56:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 1/28] lr: 2.6907e-03 eta: 0:55:08 time: 0.6727 data_time: 0.5289 memory: 1338 loss: 0.9366 loss_cls: 0.3768 loss_bbox: 0.5598\n", + "04/04 17:56:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 2/28] lr: 2.6947e-03 eta: 0:55:09 time: 0.6879 data_time: 0.5442 memory: 1339 loss: 0.9374 loss_cls: 0.3770 loss_bbox: 0.5605\n", + "04/04 17:56:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 3/28] lr: 2.6987e-03 eta: 0:55:05 time: 0.6511 data_time: 0.5072 memory: 1340 loss: 0.9392 loss_cls: 0.3786 loss_bbox: 0.5605\n", + "04/04 17:56:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 4/28] lr: 2.7027e-03 eta: 0:55:01 time: 0.6514 data_time: 0.5073 memory: 1338 loss: 0.9426 loss_cls: 0.3810 loss_bbox: 0.5616\n", + "04/04 17:56:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 5/28] lr: 2.7067e-03 eta: 0:55:04 time: 0.6737 data_time: 0.5293 memory: 1338 loss: 0.9476 loss_cls: 0.3844 loss_bbox: 0.5631\n", + "04/04 17:56:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 6/28] lr: 2.7107e-03 eta: 0:55:10 time: 0.7018 data_time: 0.5577 memory: 1339 loss: 0.9476 loss_cls: 0.3852 loss_bbox: 0.5624\n", + "04/04 17:56:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 7/28] lr: 2.7147e-03 eta: 0:55:06 time: 0.6632 data_time: 0.5188 memory: 1340 loss: 0.9476 loss_cls: 0.3858 loss_bbox: 0.5618\n", + "04/04 17:56:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 8/28] lr: 2.7187e-03 eta: 0:55:01 time: 0.6635 data_time: 0.5188 memory: 1337 loss: 0.9509 loss_cls: 0.3881 loss_bbox: 0.5628\n", + "04/04 17:56:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][ 9/28] lr: 2.7227e-03 eta: 0:54:59 time: 0.6705 data_time: 0.5264 memory: 1337 loss: 0.9580 loss_cls: 0.3937 loss_bbox: 0.5643\n", + "04/04 17:56:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][10/28] lr: 2.7267e-03 eta: 0:55:06 time: 0.7003 data_time: 0.5563 memory: 1341 loss: 0.9565 loss_cls: 0.3940 loss_bbox: 0.5625\n", + "04/04 17:56:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][11/28] lr: 2.7307e-03 eta: 0:55:01 time: 0.6654 data_time: 0.5215 memory: 1340 loss: 0.9528 loss_cls: 0.3924 loss_bbox: 0.5605\n", + "04/04 17:56:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][12/28] lr: 2.7347e-03 eta: 0:54:57 time: 0.6652 data_time: 0.5215 memory: 1339 loss: 0.9560 loss_cls: 0.3955 loss_bbox: 0.5605\n", + "04/04 17:56:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][13/28] lr: 2.7388e-03 eta: 0:54:55 time: 0.6727 data_time: 0.5288 memory: 1337 loss: 0.9629 loss_cls: 0.4012 loss_bbox: 0.5617\n", + "04/04 17:56:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][14/28] lr: 2.7428e-03 eta: 0:55:03 time: 0.7067 data_time: 0.5626 memory: 1342 loss: 0.9658 loss_cls: 0.4028 loss_bbox: 0.5630\n", + "04/04 17:56:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][15/28] lr: 2.7468e-03 eta: 0:54:58 time: 0.6769 data_time: 0.5329 memory: 1338 loss: 0.9657 loss_cls: 0.4025 loss_bbox: 0.5632\n", + "04/04 17:56:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][16/28] lr: 2.7508e-03 eta: 0:54:54 time: 0.6770 data_time: 0.5330 memory: 1339 loss: 0.9654 loss_cls: 0.4028 loss_bbox: 0.5626\n", + "04/04 17:56:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][17/28] lr: 2.7548e-03 eta: 0:54:51 time: 0.6809 data_time: 0.5373 memory: 1337 loss: 0.9807 loss_cls: 0.4087 loss_bbox: 0.5720\n", + "04/04 17:56:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][18/28] lr: 2.7588e-03 eta: 0:54:59 time: 0.7144 data_time: 0.5710 memory: 1338 loss: 0.9802 loss_cls: 0.4088 loss_bbox: 0.5713\n", + "04/04 17:56:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][19/28] lr: 2.7628e-03 eta: 0:54:55 time: 0.6898 data_time: 0.5465 memory: 1338 loss: 0.9798 loss_cls: 0.4093 loss_bbox: 0.5705\n", + "04/04 17:56:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][20/28] lr: 2.7668e-03 eta: 0:54:51 time: 0.6897 data_time: 0.5465 memory: 1338 loss: 0.9800 loss_cls: 0.4093 loss_bbox: 0.5708\n", + "04/04 17:56:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][21/28] lr: 2.7708e-03 eta: 0:54:49 time: 0.6909 data_time: 0.5475 memory: 1342 loss: 0.9881 loss_cls: 0.4133 loss_bbox: 0.5748\n", + "04/04 17:56:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][22/28] lr: 2.7748e-03 eta: 0:54:55 time: 0.7144 data_time: 0.5713 memory: 1337 loss: 0.9895 loss_cls: 0.4140 loss_bbox: 0.5755\n", + "04/04 17:56:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][23/28] lr: 2.7788e-03 eta: 0:54:51 time: 0.6748 data_time: 0.5320 memory: 1337 loss: 0.9898 loss_cls: 0.4132 loss_bbox: 0.5765\n", + "04/04 17:56:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][24/28] lr: 2.7828e-03 eta: 0:54:46 time: 0.6667 data_time: 0.5239 memory: 1338 loss: 0.9896 loss_cls: 0.4124 loss_bbox: 0.5772\n", + "04/04 17:56:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][25/28] lr: 2.7868e-03 eta: 0:54:44 time: 0.6716 data_time: 0.5292 memory: 1338 loss: 0.9876 loss_cls: 0.4107 loss_bbox: 0.5769\n", + "04/04 17:56:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][26/28] lr: 2.7908e-03 eta: 0:54:50 time: 0.7023 data_time: 0.5596 memory: 1339 loss: 0.9845 loss_cls: 0.4098 loss_bbox: 0.5748\n", + "04/04 17:56:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][27/28] lr: 2.7948e-03 eta: 0:54:46 time: 0.6648 data_time: 0.5222 memory: 1337 loss: 0.9813 loss_cls: 0.4073 loss_bbox: 0.5739\n", + "04/04 17:56:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:56:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [25][28/28] lr: 2.7988e-03 eta: 0:54:41 time: 0.6555 data_time: 0.5128 memory: 1338 loss: 0.9840 loss_cls: 0.4094 loss_bbox: 0.5746\n", + "04/04 17:56:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 1/14] eta: 0:00:07 time: 0.2406 data_time: 0.2086 memory: 169 \n", + "04/04 17:56:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 2/14] eta: 0:00:03 time: 0.2404 data_time: 0.2084 memory: 169 \n", + "04/04 17:56:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 3/14] eta: 0:00:03 time: 0.2394 data_time: 0.2074 memory: 169 \n", + "04/04 17:56:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 4/14] eta: 0:00:02 time: 0.2395 data_time: 0.2075 memory: 169 \n", + "04/04 17:56:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 5/14] eta: 0:00:02 time: 0.2392 data_time: 0.2071 memory: 169 \n", + "04/04 17:56:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 6/14] eta: 0:00:01 time: 0.2392 data_time: 0.2071 memory: 169 \n", + "04/04 17:57:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 7/14] eta: 0:00:01 time: 0.2400 data_time: 0.2077 memory: 169 \n", + "04/04 17:57:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 8/14] eta: 0:00:01 time: 0.2406 data_time: 0.2082 memory: 169 \n", + "04/04 17:57:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][ 9/14] eta: 0:00:01 time: 0.2370 data_time: 0.2046 memory: 169 \n", + "04/04 17:57:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][10/14] eta: 0:00:00 time: 0.2369 data_time: 0.2046 memory: 169 \n", + "04/04 17:57:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][11/14] eta: 0:00:00 time: 0.2385 data_time: 0.2061 memory: 169 \n", + "04/04 17:57:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][12/14] eta: 0:00:00 time: 0.2384 data_time: 0.2061 memory: 169 \n", + "04/04 17:57:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][13/14] eta: 0:00:00 time: 0.2378 data_time: 0.2055 memory: 169 \n", + "04/04 17:57:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][14/14] eta: 0:00:00 time: 0.2378 data_time: 0.2055 memory: 169 \n", + "04/04 17:57:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.16s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.210\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.390\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.198\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.496\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.502\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.502\n", + "04/04 17:57:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.390 0.833 0.210 -1.000 -1.000 0.390\n", + "04/04 17:57:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [25][14/14] coco/bbox_mAP: 0.3900 coco/bbox_mAP_50: 0.8330 coco/bbox_mAP_75: 0.2100 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.3900data_time: 0.2055 time: 0.2378 \n", + "04/04 17:57:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_20.pth is removed\n", + "04/04 17:57:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.3900 coco/bbox_mAP at 25 epoch is saved to best_coco/bbox_mAP_epoch_25.pth.\n", + "04/04 17:57:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 1/28] lr: 2.8028e-03 eta: 0:54:56 time: 0.7099 data_time: 0.5668 memory: 1341 loss: 0.9838 loss_cls: 0.4085 loss_bbox: 0.5752\n", + "04/04 17:57:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 2/28] lr: 2.8068e-03 eta: 0:54:53 time: 0.7150 data_time: 0.5719 memory: 1338 loss: 0.9815 loss_cls: 0.4083 loss_bbox: 0.5733\n", + "04/04 17:57:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 3/28] lr: 2.8108e-03 eta: 0:54:49 time: 0.6832 data_time: 0.5400 memory: 1338 loss: 0.9798 loss_cls: 0.4061 loss_bbox: 0.5736\n", + "04/04 17:57:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 4/28] lr: 2.8148e-03 eta: 0:54:45 time: 0.6746 data_time: 0.5311 memory: 1340 loss: 0.9760 loss_cls: 0.4048 loss_bbox: 0.5713\n", + "04/04 17:57:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 5/28] lr: 2.8188e-03 eta: 0:54:52 time: 0.7089 data_time: 0.5653 memory: 1338 loss: 0.9739 loss_cls: 0.4034 loss_bbox: 0.5704\n", + "04/04 17:57:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 6/28] lr: 2.8228e-03 eta: 0:54:50 time: 0.7145 data_time: 0.5707 memory: 1338 loss: 0.9730 loss_cls: 0.4033 loss_bbox: 0.5696\n", + "04/04 17:57:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 7/28] lr: 2.8268e-03 eta: 0:54:45 time: 0.6845 data_time: 0.5405 memory: 1337 loss: 0.9681 loss_cls: 0.4031 loss_bbox: 0.5650\n", + "04/04 17:57:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 8/28] lr: 2.8308e-03 eta: 0:54:41 time: 0.6842 data_time: 0.5405 memory: 1337 loss: 0.9601 loss_cls: 0.3985 loss_bbox: 0.5616\n", + "04/04 17:57:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][ 9/28] lr: 2.8348e-03 eta: 0:54:48 time: 0.7157 data_time: 0.5719 memory: 1337 loss: 0.9530 loss_cls: 0.3955 loss_bbox: 0.5575\n", + "04/04 17:57:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][10/28] lr: 2.8389e-03 eta: 0:54:45 time: 0.7140 data_time: 0.5699 memory: 1342 loss: 0.9540 loss_cls: 0.3957 loss_bbox: 0.5583\n", + "04/04 17:57:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][11/28] lr: 2.8429e-03 eta: 0:54:40 time: 0.6807 data_time: 0.5363 memory: 1337 loss: 0.9561 loss_cls: 0.3967 loss_bbox: 0.5594\n", + "04/04 17:57:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][12/28] lr: 2.8469e-03 eta: 0:54:36 time: 0.6809 data_time: 0.5364 memory: 1337 loss: 0.9525 loss_cls: 0.3949 loss_bbox: 0.5577\n", + "04/04 17:57:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][13/28] lr: 2.8509e-03 eta: 0:54:45 time: 0.7178 data_time: 0.5732 memory: 1337 loss: 0.9496 loss_cls: 0.3937 loss_bbox: 0.5559\n", + "04/04 17:57:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][14/28] lr: 2.8549e-03 eta: 0:54:41 time: 0.7182 data_time: 0.5734 memory: 1339 loss: 0.9461 loss_cls: 0.3921 loss_bbox: 0.5540\n", + "04/04 17:57:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][15/28] lr: 2.8589e-03 eta: 0:54:37 time: 0.6897 data_time: 0.5443 memory: 1338 loss: 0.9453 loss_cls: 0.3916 loss_bbox: 0.5537\n", + "04/04 17:57:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][16/28] lr: 2.8629e-03 eta: 0:54:33 time: 0.6903 data_time: 0.5442 memory: 1337 loss: 0.9440 loss_cls: 0.3911 loss_bbox: 0.5529\n", + "04/04 17:57:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][17/28] lr: 2.8669e-03 eta: 0:54:45 time: 0.7381 data_time: 0.5920 memory: 1337 loss: 0.9430 loss_cls: 0.3905 loss_bbox: 0.5525\n", + "04/04 17:57:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][18/28] lr: 2.8709e-03 eta: 0:54:42 time: 0.7050 data_time: 0.5583 memory: 1337 loss: 0.9392 loss_cls: 0.3900 loss_bbox: 0.5492\n", + "04/04 17:57:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][19/28] lr: 2.8749e-03 eta: 0:54:37 time: 0.7050 data_time: 0.5583 memory: 1338 loss: 0.9412 loss_cls: 0.3902 loss_bbox: 0.5511\n", + "04/04 17:57:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][20/28] lr: 2.8789e-03 eta: 0:54:33 time: 0.7052 data_time: 0.5583 memory: 1337 loss: 0.9437 loss_cls: 0.3920 loss_bbox: 0.5517\n", + "04/04 17:57:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][21/28] lr: 2.8829e-03 eta: 0:54:41 time: 0.7411 data_time: 0.5943 memory: 1340 loss: 0.9416 loss_cls: 0.3912 loss_bbox: 0.5504\n", + "04/04 17:57:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][22/28] lr: 2.8869e-03 eta: 0:54:39 time: 0.7071 data_time: 0.5602 memory: 1343 loss: 0.9400 loss_cls: 0.3917 loss_bbox: 0.5483\n", + "04/04 17:57:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][23/28] lr: 2.8909e-03 eta: 0:54:34 time: 0.6709 data_time: 0.5238 memory: 1337 loss: 0.9356 loss_cls: 0.3893 loss_bbox: 0.5463\n", + "04/04 17:57:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][24/28] lr: 2.8949e-03 eta: 0:54:30 time: 0.6556 data_time: 0.5085 memory: 1337 loss: 0.9324 loss_cls: 0.3896 loss_bbox: 0.5428\n", + "04/04 17:57:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][25/28] lr: 2.8989e-03 eta: 0:54:38 time: 0.6903 data_time: 0.5426 memory: 1338 loss: 0.9316 loss_cls: 0.3882 loss_bbox: 0.5434\n", + "04/04 17:57:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][26/28] lr: 2.9029e-03 eta: 0:54:35 time: 0.6941 data_time: 0.5460 memory: 1339 loss: 0.9291 loss_cls: 0.3862 loss_bbox: 0.5429\n", + "04/04 17:57:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][27/28] lr: 2.9069e-03 eta: 0:54:31 time: 0.6724 data_time: 0.5239 memory: 1338 loss: 0.9244 loss_cls: 0.3831 loss_bbox: 0.5413\n", + "04/04 17:57:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:57:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [26][28/28] lr: 2.9109e-03 eta: 0:54:27 time: 0.6446 data_time: 0.4956 memory: 1337 loss: 0.9231 loss_cls: 0.3816 loss_bbox: 0.5415\n", + "04/04 17:57:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 1/28] lr: 2.9149e-03 eta: 0:54:41 time: 0.6981 data_time: 0.5493 memory: 1338 loss: 0.9212 loss_cls: 0.3797 loss_bbox: 0.5415\n", + "04/04 17:57:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 2/28] lr: 2.9189e-03 eta: 0:54:37 time: 0.6980 data_time: 0.5492 memory: 1340 loss: 0.9187 loss_cls: 0.3766 loss_bbox: 0.5421\n", + "04/04 17:57:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 3/28] lr: 2.9229e-03 eta: 0:54:33 time: 0.6913 data_time: 0.5416 memory: 1340 loss: 0.9143 loss_cls: 0.3736 loss_bbox: 0.5407\n", + "04/04 17:57:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 4/28] lr: 2.9269e-03 eta: 0:54:29 time: 0.6617 data_time: 0.5117 memory: 1343 loss: 0.9159 loss_cls: 0.3738 loss_bbox: 0.5421\n", + "04/04 17:57:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 5/28] lr: 2.9309e-03 eta: 0:54:35 time: 0.6945 data_time: 0.5445 memory: 1340 loss: 0.9147 loss_cls: 0.3715 loss_bbox: 0.5432\n", + "04/04 17:57:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 6/28] lr: 2.9349e-03 eta: 0:54:31 time: 0.6950 data_time: 0.5445 memory: 1341 loss: 0.9128 loss_cls: 0.3699 loss_bbox: 0.5428\n", + "04/04 17:57:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 7/28] lr: 2.9389e-03 eta: 0:54:27 time: 0.6880 data_time: 0.5372 memory: 1338 loss: 0.9013 loss_cls: 0.3625 loss_bbox: 0.5388\n", + "04/04 17:57:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 8/28] lr: 2.9430e-03 eta: 0:54:25 time: 0.6608 data_time: 0.5102 memory: 1337 loss: 0.9012 loss_cls: 0.3631 loss_bbox: 0.5380\n", + "04/04 17:57:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][ 9/28] lr: 2.9470e-03 eta: 0:54:32 time: 0.6933 data_time: 0.5429 memory: 1342 loss: 0.9022 loss_cls: 0.3637 loss_bbox: 0.5385\n", + "04/04 17:57:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][10/28] lr: 2.9510e-03 eta: 0:54:28 time: 0.6928 data_time: 0.5429 memory: 1337 loss: 0.9021 loss_cls: 0.3632 loss_bbox: 0.5389\n", + "04/04 17:57:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][11/28] lr: 2.9550e-03 eta: 0:54:24 time: 0.6886 data_time: 0.5386 memory: 1338 loss: 0.8860 loss_cls: 0.3570 loss_bbox: 0.5290\n", + "04/04 17:57:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][12/28] lr: 2.9590e-03 eta: 0:54:22 time: 0.6605 data_time: 0.5106 memory: 1339 loss: 0.8855 loss_cls: 0.3562 loss_bbox: 0.5292\n", + "04/04 17:57:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][13/28] lr: 2.9630e-03 eta: 0:54:28 time: 0.6923 data_time: 0.5429 memory: 1342 loss: 0.8862 loss_cls: 0.3553 loss_bbox: 0.5309\n", + "04/04 17:57:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][14/28] lr: 2.9670e-03 eta: 0:54:24 time: 0.6920 data_time: 0.5429 memory: 1339 loss: 0.8881 loss_cls: 0.3555 loss_bbox: 0.5326\n", + "04/04 17:57:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][15/28] lr: 2.9710e-03 eta: 0:54:20 time: 0.6860 data_time: 0.5373 memory: 1338 loss: 0.8834 loss_cls: 0.3523 loss_bbox: 0.5310\n", + "04/04 17:57:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][16/28] lr: 2.9750e-03 eta: 0:54:19 time: 0.6648 data_time: 0.5160 memory: 1337 loss: 0.8851 loss_cls: 0.3530 loss_bbox: 0.5321\n", + "04/04 17:57:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][17/28] lr: 2.9790e-03 eta: 0:54:25 time: 0.6956 data_time: 0.5460 memory: 1340 loss: 0.8844 loss_cls: 0.3540 loss_bbox: 0.5304\n", + "04/04 17:57:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][18/28] lr: 2.9830e-03 eta: 0:54:21 time: 0.6957 data_time: 0.5460 memory: 1337 loss: 0.8853 loss_cls: 0.3555 loss_bbox: 0.5298\n", + "04/04 17:57:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][19/28] lr: 2.9870e-03 eta: 0:54:17 time: 0.6905 data_time: 0.5407 memory: 1338 loss: 0.8851 loss_cls: 0.3550 loss_bbox: 0.5301\n", + "04/04 17:57:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][20/28] lr: 2.9910e-03 eta: 0:54:15 time: 0.6682 data_time: 0.5187 memory: 1339 loss: 0.8863 loss_cls: 0.3551 loss_bbox: 0.5312\n", + "04/04 17:57:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][21/28] lr: 2.9950e-03 eta: 0:54:21 time: 0.6995 data_time: 0.5499 memory: 1343 loss: 0.8884 loss_cls: 0.3548 loss_bbox: 0.5336\n", + "04/04 17:57:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][22/28] lr: 2.9990e-03 eta: 0:54:17 time: 0.6997 data_time: 0.5499 memory: 1337 loss: 0.8856 loss_cls: 0.3524 loss_bbox: 0.5332\n", + "04/04 17:57:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][23/28] lr: 3.0030e-03 eta: 0:54:13 time: 0.6456 data_time: 0.4959 memory: 1338 loss: 0.8884 loss_cls: 0.3539 loss_bbox: 0.5345\n", + "04/04 17:57:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][24/28] lr: 3.0070e-03 eta: 0:54:12 time: 0.6500 data_time: 0.5001 memory: 1341 loss: 0.8878 loss_cls: 0.3540 loss_bbox: 0.5338\n", + "04/04 17:57:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][25/28] lr: 3.0110e-03 eta: 0:54:18 time: 0.6810 data_time: 0.5308 memory: 1341 loss: 0.8904 loss_cls: 0.3557 loss_bbox: 0.5347\n", + "04/04 17:57:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][26/28] lr: 3.0150e-03 eta: 0:54:14 time: 0.6813 data_time: 0.5308 memory: 1337 loss: 0.8948 loss_cls: 0.3579 loss_bbox: 0.5369\n", + "04/04 17:57:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][27/28] lr: 3.0190e-03 eta: 0:54:10 time: 0.6472 data_time: 0.4966 memory: 1338 loss: 0.8954 loss_cls: 0.3581 loss_bbox: 0.5372\n", + "04/04 17:57:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:57:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [27][28/28] lr: 3.0230e-03 eta: 0:54:09 time: 0.6503 data_time: 0.4998 memory: 1342 loss: 0.8968 loss_cls: 0.3592 loss_bbox: 0.5376\n", + "04/04 17:57:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 1/28] lr: 3.0270e-03 eta: 0:54:21 time: 0.7003 data_time: 0.5500 memory: 1341 loss: 0.8982 loss_cls: 0.3582 loss_bbox: 0.5400\n", + "04/04 17:57:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 2/28] lr: 3.0310e-03 eta: 0:54:20 time: 0.7095 data_time: 0.5594 memory: 1337 loss: 0.9015 loss_cls: 0.3603 loss_bbox: 0.5412\n", + "04/04 17:57:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 3/28] lr: 3.0350e-03 eta: 0:54:16 time: 0.6776 data_time: 0.5280 memory: 1340 loss: 0.9046 loss_cls: 0.3609 loss_bbox: 0.5437\n", + "04/04 17:57:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 4/28] lr: 3.0390e-03 eta: 0:54:12 time: 0.6741 data_time: 0.5253 memory: 1338 loss: 0.9031 loss_cls: 0.3612 loss_bbox: 0.5419\n", + "04/04 17:57:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 5/28] lr: 3.0431e-03 eta: 0:54:18 time: 0.7049 data_time: 0.5559 memory: 1344 loss: 0.9024 loss_cls: 0.3609 loss_bbox: 0.5414\n", + "04/04 17:57:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 6/28] lr: 3.0471e-03 eta: 0:54:16 time: 0.7116 data_time: 0.5632 memory: 1340 loss: 0.9071 loss_cls: 0.3619 loss_bbox: 0.5452\n", + "04/04 17:57:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 7/28] lr: 3.0511e-03 eta: 0:54:12 time: 0.6741 data_time: 0.5264 memory: 1339 loss: 0.9082 loss_cls: 0.3624 loss_bbox: 0.5458\n", + "04/04 17:57:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 8/28] lr: 3.0551e-03 eta: 0:54:08 time: 0.6719 data_time: 0.5247 memory: 1338 loss: 0.9073 loss_cls: 0.3624 loss_bbox: 0.5448\n", + "04/04 17:57:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][ 9/28] lr: 3.0591e-03 eta: 0:54:13 time: 0.7027 data_time: 0.5559 memory: 1337 loss: 0.9102 loss_cls: 0.3643 loss_bbox: 0.5459\n", + "04/04 17:57:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][10/28] lr: 3.0631e-03 eta: 0:54:11 time: 0.7084 data_time: 0.5619 memory: 1338 loss: 0.9135 loss_cls: 0.3648 loss_bbox: 0.5487\n", + "04/04 17:57:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][11/28] lr: 3.0671e-03 eta: 0:54:07 time: 0.6604 data_time: 0.5141 memory: 1338 loss: 0.9111 loss_cls: 0.3636 loss_bbox: 0.5475\n", + "04/04 17:57:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][12/28] lr: 3.0711e-03 eta: 0:54:03 time: 0.6573 data_time: 0.5115 memory: 1342 loss: 0.9124 loss_cls: 0.3629 loss_bbox: 0.5495\n", + "04/04 17:57:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][13/28] lr: 3.0751e-03 eta: 0:54:10 time: 0.6905 data_time: 0.5443 memory: 1341 loss: 0.9149 loss_cls: 0.3638 loss_bbox: 0.5511\n", + "04/04 17:57:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][14/28] lr: 3.0791e-03 eta: 0:54:07 time: 0.6957 data_time: 0.5496 memory: 1344 loss: 0.9134 loss_cls: 0.3627 loss_bbox: 0.5507\n", + "04/04 17:57:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][15/28] lr: 3.0831e-03 eta: 0:54:03 time: 0.6599 data_time: 0.5136 memory: 1338 loss: 0.9158 loss_cls: 0.3630 loss_bbox: 0.5528\n", + "04/04 17:57:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][16/28] lr: 3.0871e-03 eta: 0:53:59 time: 0.6544 data_time: 0.5089 memory: 1338 loss: 0.9136 loss_cls: 0.3609 loss_bbox: 0.5527\n", + "04/04 17:57:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][17/28] lr: 3.0911e-03 eta: 0:54:06 time: 0.6886 data_time: 0.5434 memory: 1340 loss: 0.9174 loss_cls: 0.3621 loss_bbox: 0.5553\n", + "04/04 17:57:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][18/28] lr: 3.0951e-03 eta: 0:54:08 time: 0.7064 data_time: 0.5617 memory: 1337 loss: 0.9190 loss_cls: 0.3614 loss_bbox: 0.5576\n", + "04/04 17:57:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][19/28] lr: 3.0991e-03 eta: 0:54:04 time: 0.6714 data_time: 0.5275 memory: 1342 loss: 0.9183 loss_cls: 0.3618 loss_bbox: 0.5564\n", + "04/04 17:57:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][20/28] lr: 3.1031e-03 eta: 0:54:00 time: 0.6672 data_time: 0.5242 memory: 1339 loss: 0.9157 loss_cls: 0.3597 loss_bbox: 0.5559\n", + "04/04 17:57:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][21/28] lr: 3.1071e-03 eta: 0:54:02 time: 0.6852 data_time: 0.5426 memory: 1337 loss: 0.9164 loss_cls: 0.3628 loss_bbox: 0.5537\n", + "04/04 17:57:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][22/28] lr: 3.1111e-03 eta: 0:54:05 time: 0.7070 data_time: 0.5644 memory: 1342 loss: 0.9202 loss_cls: 0.3653 loss_bbox: 0.5549\n", + "04/04 17:57:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][23/28] lr: 3.1151e-03 eta: 0:54:01 time: 0.6535 data_time: 0.5107 memory: 1339 loss: 0.9218 loss_cls: 0.3671 loss_bbox: 0.5548\n", + "04/04 17:57:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][24/28] lr: 3.1191e-03 eta: 0:53:57 time: 0.6534 data_time: 0.5107 memory: 1340 loss: 0.9212 loss_cls: 0.3673 loss_bbox: 0.5540\n", + "04/04 17:58:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][25/28] lr: 3.1231e-03 eta: 0:53:58 time: 0.6704 data_time: 0.5277 memory: 1338 loss: 0.9207 loss_cls: 0.3667 loss_bbox: 0.5540\n", + "04/04 17:58:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][26/28] lr: 3.1271e-03 eta: 0:54:02 time: 0.6932 data_time: 0.5509 memory: 1337 loss: 0.9234 loss_cls: 0.3689 loss_bbox: 0.5544\n", + "04/04 17:58:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][27/28] lr: 3.1311e-03 eta: 0:53:58 time: 0.6602 data_time: 0.5181 memory: 1338 loss: 0.9228 loss_cls: 0.3689 loss_bbox: 0.5539\n", + "04/04 17:58:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:58:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [28][28/28] lr: 3.1351e-03 eta: 0:53:54 time: 0.6599 data_time: 0.5181 memory: 1340 loss: 0.9242 loss_cls: 0.3690 loss_bbox: 0.5552\n", + "04/04 17:58:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 1/28] lr: 3.1391e-03 eta: 0:54:05 time: 0.7087 data_time: 0.5671 memory: 1337 loss: 0.9324 loss_cls: 0.3756 loss_bbox: 0.5568\n", + "04/04 17:58:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 2/28] lr: 3.1432e-03 eta: 0:54:05 time: 0.7134 data_time: 0.5715 memory: 1338 loss: 0.9323 loss_cls: 0.3749 loss_bbox: 0.5574\n", + "04/04 17:58:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 3/28] lr: 3.1472e-03 eta: 0:54:04 time: 0.6898 data_time: 0.5476 memory: 1338 loss: 0.9313 loss_cls: 0.3745 loss_bbox: 0.5568\n", + "04/04 17:58:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 4/28] lr: 3.1512e-03 eta: 0:54:00 time: 0.6903 data_time: 0.5476 memory: 1343 loss: 0.9374 loss_cls: 0.3783 loss_bbox: 0.5590\n", + "04/04 17:58:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 5/28] lr: 3.1552e-03 eta: 0:54:02 time: 0.7099 data_time: 0.5669 memory: 1337 loss: 0.9386 loss_cls: 0.3799 loss_bbox: 0.5587\n", + "04/04 17:58:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 6/28] lr: 3.1592e-03 eta: 0:54:01 time: 0.7124 data_time: 0.5692 memory: 1337 loss: 0.9429 loss_cls: 0.3839 loss_bbox: 0.5589\n", + "04/04 17:58:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 7/28] lr: 3.1632e-03 eta: 0:53:59 time: 0.6853 data_time: 0.5417 memory: 1341 loss: 0.9440 loss_cls: 0.3856 loss_bbox: 0.5584\n", + "04/04 17:58:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 8/28] lr: 3.1672e-03 eta: 0:53:55 time: 0.6856 data_time: 0.5417 memory: 1341 loss: 0.9435 loss_cls: 0.3864 loss_bbox: 0.5571\n", + "04/04 17:58:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][ 9/28] lr: 3.1712e-03 eta: 0:53:58 time: 0.7090 data_time: 0.5649 memory: 1340 loss: 0.9414 loss_cls: 0.3862 loss_bbox: 0.5551\n", + "04/04 17:58:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][10/28] lr: 3.1752e-03 eta: 0:53:57 time: 0.7083 data_time: 0.5640 memory: 1337 loss: 0.9394 loss_cls: 0.3865 loss_bbox: 0.5529\n", + "04/04 17:58:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][11/28] lr: 3.1792e-03 eta: 0:53:55 time: 0.6842 data_time: 0.5401 memory: 1339 loss: 0.9428 loss_cls: 0.3878 loss_bbox: 0.5550\n", + "04/04 17:58:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][12/28] lr: 3.1832e-03 eta: 0:53:51 time: 0.6846 data_time: 0.5401 memory: 1341 loss: 0.9429 loss_cls: 0.3873 loss_bbox: 0.5556\n", + "04/04 17:58:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][13/28] lr: 3.1872e-03 eta: 0:53:55 time: 0.7101 data_time: 0.5650 memory: 1340 loss: 0.9412 loss_cls: 0.3870 loss_bbox: 0.5542\n", + "04/04 17:58:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][14/28] lr: 3.1912e-03 eta: 0:53:52 time: 0.7042 data_time: 0.5587 memory: 1342 loss: 0.9401 loss_cls: 0.3870 loss_bbox: 0.5531\n", + "04/04 17:58:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][15/28] lr: 3.1952e-03 eta: 0:53:51 time: 0.6823 data_time: 0.5367 memory: 1340 loss: 0.9483 loss_cls: 0.3935 loss_bbox: 0.5548\n", + "04/04 17:58:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][16/28] lr: 3.1992e-03 eta: 0:53:47 time: 0.6823 data_time: 0.5367 memory: 1339 loss: 0.9468 loss_cls: 0.3938 loss_bbox: 0.5530\n", + "04/04 17:58:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][17/28] lr: 3.2032e-03 eta: 0:53:51 time: 0.7088 data_time: 0.5632 memory: 1339 loss: 0.9434 loss_cls: 0.3922 loss_bbox: 0.5512\n", + "04/04 17:58:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][18/28] lr: 3.2072e-03 eta: 0:53:47 time: 0.6996 data_time: 0.5538 memory: 1340 loss: 0.9423 loss_cls: 0.3921 loss_bbox: 0.5502\n", + "04/04 17:58:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][19/28] lr: 3.2112e-03 eta: 0:53:47 time: 0.6795 data_time: 0.5337 memory: 1338 loss: 0.9427 loss_cls: 0.3930 loss_bbox: 0.5497\n", + "04/04 17:58:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][20/28] lr: 3.2152e-03 eta: 0:53:43 time: 0.6792 data_time: 0.5337 memory: 1339 loss: 0.9374 loss_cls: 0.3903 loss_bbox: 0.5471\n", + "04/04 17:58:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][21/28] lr: 3.2192e-03 eta: 0:53:44 time: 0.6963 data_time: 0.5505 memory: 1341 loss: 0.9369 loss_cls: 0.3903 loss_bbox: 0.5466\n", + "04/04 17:58:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][22/28] lr: 3.2232e-03 eta: 0:53:41 time: 0.6888 data_time: 0.5427 memory: 1340 loss: 0.9359 loss_cls: 0.3887 loss_bbox: 0.5472\n", + "04/04 17:58:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][23/28] lr: 3.2272e-03 eta: 0:53:42 time: 0.6559 data_time: 0.5091 memory: 1342 loss: 0.9371 loss_cls: 0.3900 loss_bbox: 0.5470\n", + "04/04 17:58:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][24/28] lr: 3.2312e-03 eta: 0:53:38 time: 0.6468 data_time: 0.4998 memory: 1339 loss: 0.9407 loss_cls: 0.3911 loss_bbox: 0.5496\n", + "04/04 17:58:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][25/28] lr: 3.2352e-03 eta: 0:53:40 time: 0.6663 data_time: 0.5190 memory: 1340 loss: 0.9425 loss_cls: 0.3914 loss_bbox: 0.5511\n", + "04/04 17:58:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][26/28] lr: 3.2392e-03 eta: 0:53:37 time: 0.6697 data_time: 0.5221 memory: 1339 loss: 0.9433 loss_cls: 0.3907 loss_bbox: 0.5526\n", + "04/04 17:58:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][27/28] lr: 3.2433e-03 eta: 0:53:38 time: 0.6532 data_time: 0.5057 memory: 1341 loss: 0.9435 loss_cls: 0.3910 loss_bbox: 0.5525\n", + "04/04 17:58:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:58:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [29][28/28] lr: 3.2473e-03 eta: 0:53:34 time: 0.6461 data_time: 0.4984 memory: 1338 loss: 0.9365 loss_cls: 0.3884 loss_bbox: 0.5481\n", + "04/04 17:58:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 1/28] lr: 3.2513e-03 eta: 0:53:47 time: 0.7042 data_time: 0.5560 memory: 1342 loss: 0.9355 loss_cls: 0.3881 loss_bbox: 0.5474\n", + "04/04 17:58:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 2/28] lr: 3.2553e-03 eta: 0:53:45 time: 0.7088 data_time: 0.5604 memory: 1338 loss: 0.9369 loss_cls: 0.3889 loss_bbox: 0.5480\n", + "04/04 17:58:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 3/28] lr: 3.2593e-03 eta: 0:53:41 time: 0.6780 data_time: 0.5293 memory: 1341 loss: 0.9309 loss_cls: 0.3862 loss_bbox: 0.5447\n", + "04/04 17:58:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 4/28] lr: 3.2633e-03 eta: 0:53:38 time: 0.6722 data_time: 0.5233 memory: 1337 loss: 0.9284 loss_cls: 0.3865 loss_bbox: 0.5419\n", + "04/04 17:58:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 5/28] lr: 3.2673e-03 eta: 0:53:44 time: 0.7055 data_time: 0.5559 memory: 1341 loss: 0.9312 loss_cls: 0.3876 loss_bbox: 0.5436\n", + "04/04 17:58:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 6/28] lr: 3.2713e-03 eta: 0:53:41 time: 0.7109 data_time: 0.5608 memory: 1337 loss: 0.9283 loss_cls: 0.3859 loss_bbox: 0.5424\n", + "04/04 17:58:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 7/28] lr: 3.2753e-03 eta: 0:53:38 time: 0.6781 data_time: 0.5280 memory: 1337 loss: 0.9282 loss_cls: 0.3865 loss_bbox: 0.5417\n", + "04/04 17:58:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 8/28] lr: 3.2793e-03 eta: 0:53:34 time: 0.6732 data_time: 0.5227 memory: 1339 loss: 0.9265 loss_cls: 0.3860 loss_bbox: 0.5405\n", + "04/04 17:58:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][ 9/28] lr: 3.2833e-03 eta: 0:53:37 time: 0.6977 data_time: 0.5468 memory: 1337 loss: 0.9285 loss_cls: 0.3884 loss_bbox: 0.5401\n", + "04/04 17:58:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][10/28] lr: 3.2873e-03 eta: 0:53:37 time: 0.7089 data_time: 0.5572 memory: 1342 loss: 0.9292 loss_cls: 0.3898 loss_bbox: 0.5394\n", + "04/04 17:58:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][11/28] lr: 3.2913e-03 eta: 0:53:33 time: 0.6746 data_time: 0.5227 memory: 1338 loss: 0.9261 loss_cls: 0.3901 loss_bbox: 0.5360\n", + "04/04 17:58:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][12/28] lr: 3.2953e-03 eta: 0:53:30 time: 0.6570 data_time: 0.5044 memory: 1339 loss: 0.9259 loss_cls: 0.3911 loss_bbox: 0.5348\n", + "04/04 17:58:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][13/28] lr: 3.2993e-03 eta: 0:53:31 time: 0.6742 data_time: 0.5215 memory: 1339 loss: 0.9243 loss_cls: 0.3902 loss_bbox: 0.5340\n", + "04/04 17:58:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][14/28] lr: 3.3033e-03 eta: 0:53:32 time: 0.6910 data_time: 0.5382 memory: 1339 loss: 0.9257 loss_cls: 0.3922 loss_bbox: 0.5335\n", + "04/04 17:58:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][15/28] lr: 3.3073e-03 eta: 0:53:28 time: 0.6723 data_time: 0.5197 memory: 1339 loss: 0.9258 loss_cls: 0.3906 loss_bbox: 0.5352\n", + "04/04 17:58:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][16/28] lr: 3.3113e-03 eta: 0:53:24 time: 0.6498 data_time: 0.4979 memory: 1339 loss: 0.9215 loss_cls: 0.3886 loss_bbox: 0.5328\n", + "04/04 17:58:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][17/28] lr: 3.3153e-03 eta: 0:53:27 time: 0.6709 data_time: 0.5188 memory: 1337 loss: 0.9237 loss_cls: 0.3901 loss_bbox: 0.5336\n", + "04/04 17:58:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][18/28] lr: 3.3193e-03 eta: 0:53:28 time: 0.6860 data_time: 0.5339 memory: 1339 loss: 0.9220 loss_cls: 0.3903 loss_bbox: 0.5317\n", + "04/04 17:58:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][19/28] lr: 3.3233e-03 eta: 0:53:24 time: 0.6689 data_time: 0.5169 memory: 1337 loss: 0.9201 loss_cls: 0.3906 loss_bbox: 0.5296\n", + "04/04 17:58:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][20/28] lr: 3.3273e-03 eta: 0:53:20 time: 0.6461 data_time: 0.4937 memory: 1339 loss: 0.9153 loss_cls: 0.3873 loss_bbox: 0.5280\n", + "04/04 17:58:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][21/28] lr: 3.3313e-03 eta: 0:53:23 time: 0.6698 data_time: 0.5170 memory: 1337 loss: 0.9146 loss_cls: 0.3876 loss_bbox: 0.5270\n", + "04/04 17:58:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][22/28] lr: 3.3353e-03 eta: 0:53:26 time: 0.6911 data_time: 0.5381 memory: 1338 loss: 0.9139 loss_cls: 0.3888 loss_bbox: 0.5251\n", + "04/04 17:58:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][23/28] lr: 3.3393e-03 eta: 0:53:22 time: 0.6418 data_time: 0.4891 memory: 1343 loss: 0.9083 loss_cls: 0.3848 loss_bbox: 0.5236\n", + "04/04 17:58:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][24/28] lr: 3.3433e-03 eta: 0:53:18 time: 0.6302 data_time: 0.4779 memory: 1339 loss: 0.9060 loss_cls: 0.3839 loss_bbox: 0.5221\n", + "04/04 17:58:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][25/28] lr: 3.3474e-03 eta: 0:53:19 time: 0.6356 data_time: 0.4835 memory: 1338 loss: 0.9056 loss_cls: 0.3837 loss_bbox: 0.5219\n", + "04/04 17:58:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][26/28] lr: 3.3514e-03 eta: 0:53:19 time: 0.6475 data_time: 0.4953 memory: 1338 loss: 0.8991 loss_cls: 0.3800 loss_bbox: 0.5191\n", + "04/04 17:58:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][27/28] lr: 3.3554e-03 eta: 0:53:15 time: 0.6280 data_time: 0.4759 memory: 1338 loss: 0.9010 loss_cls: 0.3793 loss_bbox: 0.5217\n", + "04/04 17:58:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:58:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [30][28/28] lr: 3.3594e-03 eta: 0:53:12 time: 0.6182 data_time: 0.4662 memory: 1337 loss: 0.8993 loss_cls: 0.3763 loss_bbox: 0.5230\n", + "04/04 17:58:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 30 epochs\n", + "04/04 17:58:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 1/14] eta: 0:00:07 time: 0.2397 data_time: 0.2074 memory: 169 \n", + "04/04 17:58:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 2/14] eta: 0:00:03 time: 0.2398 data_time: 0.2076 memory: 169 \n", + "04/04 17:58:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 3/14] eta: 0:00:03 time: 0.2382 data_time: 0.2061 memory: 169 \n", + "04/04 17:58:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 4/14] eta: 0:00:02 time: 0.2412 data_time: 0.2091 memory: 169 \n", + "04/04 17:58:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 5/14] eta: 0:00:02 time: 0.2384 data_time: 0.2063 memory: 169 \n", + "04/04 17:58:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 6/14] eta: 0:00:02 time: 0.2401 data_time: 0.2081 memory: 169 \n", + "04/04 17:58:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 7/14] eta: 0:00:01 time: 0.2389 data_time: 0.2070 memory: 169 \n", + "04/04 17:58:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 8/14] eta: 0:00:01 time: 0.2411 data_time: 0.2091 memory: 169 \n", + "04/04 17:58:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][ 9/14] eta: 0:00:01 time: 0.2376 data_time: 0.2056 memory: 169 \n", + "04/04 17:58:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][10/14] eta: 0:00:01 time: 0.2387 data_time: 0.2065 memory: 169 \n", + "04/04 17:58:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][11/14] eta: 0:00:00 time: 0.2387 data_time: 0.2066 memory: 169 \n", + "04/04 17:58:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][12/14] eta: 0:00:00 time: 0.2386 data_time: 0.2066 memory: 169 \n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][13/14] eta: 0:00:00 time: 0.2379 data_time: 0.2059 memory: 169 \n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][14/14] eta: 0:00:00 time: 0.2380 data_time: 0.2059 memory: 169 \n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.14s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.518\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.936\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.473\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.306\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.576\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.584\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584\n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.518 0.936 0.473 -1.000 -1.000 0.518\n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [30][14/14] coco/bbox_mAP: 0.5180 coco/bbox_mAP_50: 0.9360 coco/bbox_mAP_75: 0.4730 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.5180data_time: 0.2059 time: 0.2380 \n", + "04/04 17:58:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_25.pth is removed\n", + "04/04 17:58:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.5180 coco/bbox_mAP at 30 epoch is saved to best_coco/bbox_mAP_epoch_30.pth.\n", + "04/04 17:58:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 1/28] lr: 3.3634e-03 eta: 0:53:22 time: 0.6642 data_time: 0.5120 memory: 1339 loss: 0.8938 loss_cls: 0.3728 loss_bbox: 0.5210\n", + "04/04 17:58:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 2/28] lr: 3.3674e-03 eta: 0:53:19 time: 0.6639 data_time: 0.5120 memory: 1338 loss: 0.8900 loss_cls: 0.3712 loss_bbox: 0.5189\n", + "04/04 17:58:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 3/28] lr: 3.3714e-03 eta: 0:53:15 time: 0.6407 data_time: 0.4889 memory: 1339 loss: 0.8904 loss_cls: 0.3712 loss_bbox: 0.5192\n", + "04/04 17:58:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 4/28] lr: 3.3754e-03 eta: 0:53:11 time: 0.6317 data_time: 0.4800 memory: 1338 loss: 0.8913 loss_cls: 0.3698 loss_bbox: 0.5216\n", + "04/04 17:58:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 5/28] lr: 3.3794e-03 eta: 0:53:22 time: 0.6742 data_time: 0.5226 memory: 1337 loss: 0.8874 loss_cls: 0.3679 loss_bbox: 0.5195\n", + "04/04 17:58:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 6/28] lr: 3.3834e-03 eta: 0:53:18 time: 0.6742 data_time: 0.5226 memory: 1337 loss: 0.8822 loss_cls: 0.3651 loss_bbox: 0.5171\n", + "04/04 17:58:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 7/28] lr: 3.3874e-03 eta: 0:53:14 time: 0.6492 data_time: 0.4976 memory: 1338 loss: 0.8794 loss_cls: 0.3643 loss_bbox: 0.5151\n", + "04/04 17:58:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 8/28] lr: 3.3914e-03 eta: 0:53:11 time: 0.6464 data_time: 0.4956 memory: 1337 loss: 0.8829 loss_cls: 0.3667 loss_bbox: 0.5163\n", + "04/04 17:58:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][ 9/28] lr: 3.3954e-03 eta: 0:53:17 time: 0.6730 data_time: 0.5216 memory: 1339 loss: 0.8753 loss_cls: 0.3614 loss_bbox: 0.5139\n", + "04/04 17:58:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][10/28] lr: 3.3994e-03 eta: 0:53:13 time: 0.6729 data_time: 0.5216 memory: 1337 loss: 0.8736 loss_cls: 0.3600 loss_bbox: 0.5136\n", + "04/04 17:58:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][11/28] lr: 3.4034e-03 eta: 0:53:10 time: 0.6469 data_time: 0.4951 memory: 1338 loss: 0.8699 loss_cls: 0.3586 loss_bbox: 0.5113\n", + "04/04 17:58:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][12/28] lr: 3.4074e-03 eta: 0:53:07 time: 0.6471 data_time: 0.4951 memory: 1337 loss: 0.8661 loss_cls: 0.3562 loss_bbox: 0.5099\n", + "04/04 17:58:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][13/28] lr: 3.4114e-03 eta: 0:53:12 time: 0.6692 data_time: 0.5172 memory: 1338 loss: 0.8613 loss_cls: 0.3534 loss_bbox: 0.5079\n", + "04/04 17:58:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][14/28] lr: 3.4154e-03 eta: 0:53:09 time: 0.6697 data_time: 0.5172 memory: 1340 loss: 0.8619 loss_cls: 0.3528 loss_bbox: 0.5091\n", + "04/04 17:58:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][15/28] lr: 3.4194e-03 eta: 0:53:05 time: 0.6530 data_time: 0.5004 memory: 1338 loss: 0.8612 loss_cls: 0.3527 loss_bbox: 0.5086\n", + "04/04 17:58:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][16/28] lr: 3.4234e-03 eta: 0:53:02 time: 0.6521 data_time: 0.4998 memory: 1341 loss: 0.8605 loss_cls: 0.3518 loss_bbox: 0.5087\n", + "04/04 17:58:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][17/28] lr: 3.4274e-03 eta: 0:53:09 time: 0.6727 data_time: 0.5210 memory: 1337 loss: 0.8574 loss_cls: 0.3507 loss_bbox: 0.5067\n", + "04/04 17:58:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][18/28] lr: 3.4314e-03 eta: 0:53:05 time: 0.6723 data_time: 0.5210 memory: 1337 loss: 0.8522 loss_cls: 0.3479 loss_bbox: 0.5043\n", + "04/04 17:58:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][19/28] lr: 3.4354e-03 eta: 0:53:01 time: 0.6528 data_time: 0.5017 memory: 1339 loss: 0.8496 loss_cls: 0.3475 loss_bbox: 0.5021\n", + "04/04 17:58:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][20/28] lr: 3.4394e-03 eta: 0:52:58 time: 0.6497 data_time: 0.4986 memory: 1339 loss: 0.8505 loss_cls: 0.3485 loss_bbox: 0.5019\n", + "04/04 17:59:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][21/28] lr: 3.4434e-03 eta: 0:53:06 time: 0.6783 data_time: 0.5279 memory: 1337 loss: 0.8475 loss_cls: 0.3467 loss_bbox: 0.5008\n", + "04/04 17:59:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][22/28] lr: 3.4475e-03 eta: 0:53:03 time: 0.6783 data_time: 0.5279 memory: 1339 loss: 0.8467 loss_cls: 0.3460 loss_bbox: 0.5007\n", + "04/04 17:59:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][23/28] lr: 3.4515e-03 eta: 0:52:59 time: 0.6206 data_time: 0.4702 memory: 1337 loss: 0.8503 loss_cls: 0.3470 loss_bbox: 0.5033\n", + "04/04 17:59:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][24/28] lr: 3.4555e-03 eta: 0:52:55 time: 0.6159 data_time: 0.4658 memory: 1337 loss: 0.8485 loss_cls: 0.3464 loss_bbox: 0.5021\n", + "04/04 17:59:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][25/28] lr: 3.4595e-03 eta: 0:52:59 time: 0.6428 data_time: 0.4929 memory: 1339 loss: 0.8494 loss_cls: 0.3474 loss_bbox: 0.5020\n", + "04/04 17:59:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][26/28] lr: 3.4635e-03 eta: 0:52:56 time: 0.6425 data_time: 0.4929 memory: 1337 loss: 0.8473 loss_cls: 0.3462 loss_bbox: 0.5011\n", + "04/04 17:59:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][27/28] lr: 3.4675e-03 eta: 0:52:52 time: 0.6096 data_time: 0.4603 memory: 1339 loss: 0.8463 loss_cls: 0.3467 loss_bbox: 0.4996\n", + "04/04 17:59:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 17:59:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [31][28/28] lr: 3.4715e-03 eta: 0:52:52 time: 0.6159 data_time: 0.4668 memory: 1339 loss: 0.8501 loss_cls: 0.3492 loss_bbox: 0.5008\n", + "04/04 17:59:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [32][ 1/28] lr: 3.4755e-03 eta: 0:52:59 time: 0.6542 data_time: 0.5057 memory: 1337 loss: 0.8455 loss_cls: 0.3475 loss_bbox: 0.4980\n", + "04/04 17:59:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [32][ 2/28] lr: 3.4795e-03 eta: 0:53:01 time: 0.6742 data_time: 0.5262 memory: 1337 loss: 0.8492 loss_cls: 0.3509 loss_bbox: 0.4983\n", + "04/04 17:59:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][26/28] lr: 3.9119e-03 eta: 0:51:51 time: 0.7188 data_time: 0.5741 memory: 1338 loss: 0.8990 loss_cls: 0.3877 loss_bbox: 0.5113\n", + "04/04 18:00:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][27/28] lr: 3.9159e-03 eta: 0:51:48 time: 0.6841 data_time: 0.5398 memory: 1337 loss: 0.9011 loss_cls: 0.3903 loss_bbox: 0.5108\n", + "04/04 18:00:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:00:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][28/28] lr: 3.9199e-03 eta: 0:51:45 time: 0.6825 data_time: 0.5383 memory: 1339 loss: 0.9023 loss_cls: 0.3912 loss_bbox: 0.5111\n", + "04/04 18:00:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 1/14] eta: 0:00:07 time: 0.2399 data_time: 0.2080 memory: 169 \n", + "04/04 18:00:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 2/14] eta: 0:00:03 time: 0.2397 data_time: 0.2079 memory: 169 \n", + "04/04 18:00:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 3/14] eta: 0:00:03 time: 0.2380 data_time: 0.2064 memory: 169 \n", + "04/04 18:00:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 4/14] eta: 0:00:02 time: 0.2390 data_time: 0.2074 memory: 169 \n", + "04/04 18:00:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 5/14] eta: 0:00:02 time: 0.2368 data_time: 0.2053 memory: 169 \n", + "04/04 18:00:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 6/14] eta: 0:00:01 time: 0.2379 data_time: 0.2065 memory: 169 \n", + "04/04 18:00:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 7/14] eta: 0:00:01 time: 0.2370 data_time: 0.2056 memory: 169 \n", + "04/04 18:00:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 8/14] eta: 0:00:01 time: 0.2392 data_time: 0.2077 memory: 169 \n", + "04/04 18:00:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][ 9/14] eta: 0:00:01 time: 0.2350 data_time: 0.2035 memory: 169 \n", + "04/04 18:00:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][10/14] eta: 0:00:00 time: 0.2367 data_time: 0.2052 memory: 169 \n", + "04/04 18:00:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][11/14] eta: 0:00:00 time: 0.2362 data_time: 0.2047 memory: 169 \n", + "04/04 18:00:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][12/14] eta: 0:00:00 time: 0.2355 data_time: 0.2041 memory: 169 \n", + "04/04 18:00:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][13/14] eta: 0:00:00 time: 0.2357 data_time: 0.2043 memory: 169 \n", + "04/04 18:00:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][14/14] eta: 0:00:00 time: 0.2356 data_time: 0.2043 memory: 169 \n", + "04/04 18:00:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.927\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.227\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.340\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.622\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622\n", + "04/04 18:00:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.340 0.927 0.227 -1.000 -1.000 0.340\n", + "04/04 18:00:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [35][14/14] coco/bbox_mAP: 0.3400 coco/bbox_mAP_50: 0.9270 coco/bbox_mAP_75: 0.2270 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.3400data_time: 0.2043 time: 0.2356 \n", + "04/04 18:00:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 1/28] lr: 3.9239e-03 eta: 0:51:57 time: 0.7469 data_time: 0.6026 memory: 1341 loss: 0.9028 loss_cls: 0.3900 loss_bbox: 0.5128\n", + "04/04 18:00:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 2/28] lr: 3.9279e-03 eta: 0:51:54 time: 0.7471 data_time: 0.6026 memory: 1338 loss: 0.8992 loss_cls: 0.3862 loss_bbox: 0.5130\n", + "04/04 18:00:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 3/28] lr: 3.9319e-03 eta: 0:51:51 time: 0.6984 data_time: 0.5537 memory: 1341 loss: 0.8988 loss_cls: 0.3852 loss_bbox: 0.5136\n", + "04/04 18:00:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 4/28] lr: 3.9359e-03 eta: 0:51:48 time: 0.6987 data_time: 0.5537 memory: 1338 loss: 0.8984 loss_cls: 0.3848 loss_bbox: 0.5136\n", + "04/04 18:00:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 5/28] lr: 3.9399e-03 eta: 0:51:54 time: 0.7383 data_time: 0.5932 memory: 1339 loss: 0.9007 loss_cls: 0.3866 loss_bbox: 0.5140\n", + "04/04 18:00:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 6/28] lr: 3.9439e-03 eta: 0:51:51 time: 0.7384 data_time: 0.5933 memory: 1337 loss: 0.9017 loss_cls: 0.3878 loss_bbox: 0.5139\n", + "04/04 18:00:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 7/28] lr: 3.9479e-03 eta: 0:51:48 time: 0.7008 data_time: 0.5551 memory: 1342 loss: 0.9061 loss_cls: 0.3897 loss_bbox: 0.5164\n", + "04/04 18:00:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 8/28] lr: 3.9520e-03 eta: 0:51:45 time: 0.7014 data_time: 0.5552 memory: 1337 loss: 0.9090 loss_cls: 0.3925 loss_bbox: 0.5166\n", + "04/04 18:00:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][ 9/28] lr: 3.9560e-03 eta: 0:51:50 time: 0.7385 data_time: 0.5921 memory: 1341 loss: 0.9071 loss_cls: 0.3916 loss_bbox: 0.5155\n", + "04/04 18:00:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][10/28] lr: 3.9600e-03 eta: 0:51:47 time: 0.7384 data_time: 0.5921 memory: 1340 loss: 0.9060 loss_cls: 0.3919 loss_bbox: 0.5141\n", + "04/04 18:00:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][11/28] lr: 3.9640e-03 eta: 0:51:44 time: 0.6743 data_time: 0.5284 memory: 1337 loss: 0.9080 loss_cls: 0.3951 loss_bbox: 0.5130\n", + "04/04 18:00:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][12/28] lr: 3.9680e-03 eta: 0:51:41 time: 0.6741 data_time: 0.5284 memory: 1339 loss: 0.9106 loss_cls: 0.3961 loss_bbox: 0.5145\n", + "04/04 18:00:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][13/28] lr: 3.9720e-03 eta: 0:51:47 time: 0.7137 data_time: 0.5683 memory: 1340 loss: 0.9149 loss_cls: 0.3968 loss_bbox: 0.5181\n", + "04/04 18:00:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][14/28] lr: 3.9760e-03 eta: 0:51:44 time: 0.7133 data_time: 0.5683 memory: 1339 loss: 0.9126 loss_cls: 0.3965 loss_bbox: 0.5161\n", + "04/04 18:00:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][15/28] lr: 3.9800e-03 eta: 0:51:41 time: 0.6649 data_time: 0.5202 memory: 1339 loss: 0.9106 loss_cls: 0.3936 loss_bbox: 0.5169\n", + "04/04 18:00:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][16/28] lr: 3.9840e-03 eta: 0:51:38 time: 0.6653 data_time: 0.5202 memory: 1342 loss: 0.9089 loss_cls: 0.3935 loss_bbox: 0.5154\n", + "04/04 18:00:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][17/28] lr: 3.9880e-03 eta: 0:51:44 time: 0.7073 data_time: 0.5618 memory: 1341 loss: 0.9055 loss_cls: 0.3906 loss_bbox: 0.5149\n", + "04/04 18:00:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][18/28] lr: 3.9920e-03 eta: 0:51:41 time: 0.7076 data_time: 0.5618 memory: 1339 loss: 0.9023 loss_cls: 0.3895 loss_bbox: 0.5129\n", + "04/04 18:00:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][19/28] lr: 3.9960e-03 eta: 0:51:38 time: 0.6698 data_time: 0.5243 memory: 1338 loss: 0.9008 loss_cls: 0.3898 loss_bbox: 0.5110\n", + "04/04 18:00:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:00:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][20/28] lr: 4.0000e-03 eta: 0:51:35 time: 0.6697 data_time: 0.5243 memory: 1339 loss: 0.8972 loss_cls: 0.3876 loss_bbox: 0.5096\n", + "04/04 18:00:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][21/28] lr: 4.0000e-03 eta: 0:51:40 time: 0.7071 data_time: 0.5616 memory: 1337 loss: 0.8987 loss_cls: 0.3884 loss_bbox: 0.5103\n", + "04/04 18:00:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][22/28] lr: 4.0000e-03 eta: 0:51:37 time: 0.7068 data_time: 0.5616 memory: 1340 loss: 0.8975 loss_cls: 0.3878 loss_bbox: 0.5097\n", + "04/04 18:00:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][23/28] lr: 4.0000e-03 eta: 0:51:34 time: 0.6449 data_time: 0.4996 memory: 1342 loss: 0.8965 loss_cls: 0.3867 loss_bbox: 0.5098\n", + "04/04 18:00:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][24/28] lr: 4.0000e-03 eta: 0:51:31 time: 0.6452 data_time: 0.4996 memory: 1337 loss: 0.8922 loss_cls: 0.3840 loss_bbox: 0.5082\n", + "04/04 18:00:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][25/28] lr: 4.0000e-03 eta: 0:51:36 time: 0.6821 data_time: 0.5364 memory: 1338 loss: 0.8938 loss_cls: 0.3843 loss_bbox: 0.5095\n", + "04/04 18:00:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][26/28] lr: 4.0000e-03 eta: 0:51:33 time: 0.6825 data_time: 0.5364 memory: 1338 loss: 0.8908 loss_cls: 0.3832 loss_bbox: 0.5076\n", + "04/04 18:00:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][27/28] lr: 4.0000e-03 eta: 0:51:30 time: 0.6418 data_time: 0.4958 memory: 1339 loss: 0.8855 loss_cls: 0.3784 loss_bbox: 0.5071\n", + "04/04 18:00:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:00:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][28/28] lr: 4.0000e-03 eta: 0:51:27 time: 0.6417 data_time: 0.4957 memory: 1341 loss: 0.8843 loss_cls: 0.3790 loss_bbox: 0.5054\n", + "04/04 18:00:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 1/28] lr: 4.0000e-03 eta: 0:51:36 time: 0.6934 data_time: 0.5477 memory: 1339 loss: 0.8826 loss_cls: 0.3779 loss_bbox: 0.5047\n", + "04/04 18:00:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 2/28] lr: 4.0000e-03 eta: 0:51:33 time: 0.6932 data_time: 0.5476 memory: 1339 loss: 0.8776 loss_cls: 0.3751 loss_bbox: 0.5025\n", + "04/04 18:00:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 3/28] lr: 4.0000e-03 eta: 0:51:30 time: 0.6512 data_time: 0.5063 memory: 1338 loss: 0.8743 loss_cls: 0.3748 loss_bbox: 0.4995\n", + "04/04 18:00:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 4/28] lr: 4.0000e-03 eta: 0:51:27 time: 0.6510 data_time: 0.5063 memory: 1339 loss: 0.8746 loss_cls: 0.3747 loss_bbox: 0.4999\n", + "04/04 18:00:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 5/28] lr: 4.0000e-03 eta: 0:51:32 time: 0.6890 data_time: 0.5441 memory: 1341 loss: 0.8772 loss_cls: 0.3760 loss_bbox: 0.5012\n", + "04/04 18:00:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 6/28] lr: 4.0000e-03 eta: 0:51:29 time: 0.6893 data_time: 0.5441 memory: 1340 loss: 0.8717 loss_cls: 0.3703 loss_bbox: 0.5014\n", + "04/04 18:00:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 7/28] lr: 4.0000e-03 eta: 0:51:26 time: 0.6481 data_time: 0.5029 memory: 1338 loss: 0.8679 loss_cls: 0.3687 loss_bbox: 0.4992\n", + "04/04 18:00:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 8/28] lr: 4.0000e-03 eta: 0:51:23 time: 0.6483 data_time: 0.5029 memory: 1338 loss: 0.8710 loss_cls: 0.3701 loss_bbox: 0.5009\n", + "04/04 18:00:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][ 9/28] lr: 4.0000e-03 eta: 0:51:29 time: 0.6883 data_time: 0.5428 memory: 1337 loss: 0.8771 loss_cls: 0.3769 loss_bbox: 0.5002\n", + "04/04 18:00:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][10/28] lr: 4.0000e-03 eta: 0:51:26 time: 0.6883 data_time: 0.5428 memory: 1344 loss: 0.8757 loss_cls: 0.3756 loss_bbox: 0.5001\n", + "04/04 18:00:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][11/28] lr: 4.0000e-03 eta: 0:51:23 time: 0.6496 data_time: 0.5046 memory: 1337 loss: 0.8730 loss_cls: 0.3747 loss_bbox: 0.4983\n", + "04/04 18:00:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][12/28] lr: 4.0000e-03 eta: 0:51:20 time: 0.6493 data_time: 0.5046 memory: 1339 loss: 0.8693 loss_cls: 0.3713 loss_bbox: 0.4980\n", + "04/04 18:00:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][13/28] lr: 4.0000e-03 eta: 0:51:24 time: 0.6820 data_time: 0.5375 memory: 1344 loss: 0.8646 loss_cls: 0.3666 loss_bbox: 0.4981\n", + "04/04 18:00:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][14/28] lr: 4.0000e-03 eta: 0:51:21 time: 0.6819 data_time: 0.5375 memory: 1340 loss: 0.8672 loss_cls: 0.3687 loss_bbox: 0.4985\n", + "04/04 18:00:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][15/28] lr: 4.0000e-03 eta: 0:51:18 time: 0.6429 data_time: 0.4985 memory: 1341 loss: 0.8691 loss_cls: 0.3696 loss_bbox: 0.4995\n", + "04/04 18:00:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][16/28] lr: 4.0000e-03 eta: 0:51:15 time: 0.6427 data_time: 0.4985 memory: 1338 loss: 0.8672 loss_cls: 0.3693 loss_bbox: 0.4978\n", + "04/04 18:00:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][17/28] lr: 4.0000e-03 eta: 0:51:20 time: 0.6796 data_time: 0.5353 memory: 1342 loss: 0.8661 loss_cls: 0.3684 loss_bbox: 0.4978\n", + "04/04 18:00:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][18/28] lr: 4.0000e-03 eta: 0:51:17 time: 0.6793 data_time: 0.5353 memory: 1338 loss: 0.8703 loss_cls: 0.3720 loss_bbox: 0.4983\n", + "04/04 18:00:57 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][25/28] lr: 4.0000e-03 eta: 0:50:44 time: 0.6154 data_time: 0.4706 memory: 1338 loss: 0.8314 loss_cls: 0.3543 loss_bbox: 0.4771\n", + "04/04 18:01:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][26/28] lr: 4.0000e-03 eta: 0:50:41 time: 0.6158 data_time: 0.4706 memory: 1340 loss: 0.8318 loss_cls: 0.3555 loss_bbox: 0.4763\n", + "04/04 18:01:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][27/28] lr: 4.0000e-03 eta: 0:50:40 time: 0.5903 data_time: 0.4453 memory: 1340 loss: 0.8295 loss_cls: 0.3535 loss_bbox: 0.4760\n", + "04/04 18:01:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:01:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][28/28] lr: 4.0000e-03 eta: 0:50:38 time: 0.5900 data_time: 0.4453 memory: 1337 loss: 0.8290 loss_cls: 0.3532 loss_bbox: 0.4758\n", + "04/04 18:01:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][21/28] lr: 4.0000e-03 eta: 0:50:25 time: 0.6443 data_time: 0.4953 memory: 1337 loss: 0.7970 loss_cls: 0.3332 loss_bbox: 0.4637\n", + "04/04 18:01:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][22/28] lr: 4.0000e-03 eta: 0:50:22 time: 0.6444 data_time: 0.4953 memory: 1337 loss: 0.7991 loss_cls: 0.3339 loss_bbox: 0.4652\n", + "04/04 18:01:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][23/28] lr: 4.0000e-03 eta: 0:50:27 time: 0.6188 data_time: 0.4691 memory: 1341 loss: 0.8002 loss_cls: 0.3347 loss_bbox: 0.4655\n", + "04/04 18:01:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][24/28] lr: 4.0000e-03 eta: 0:50:24 time: 0.6192 data_time: 0.4691 memory: 1340 loss: 0.8011 loss_cls: 0.3351 loss_bbox: 0.4660\n", + "04/04 18:01:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][25/28] lr: 4.0000e-03 eta: 0:50:21 time: 0.6199 data_time: 0.4691 memory: 1340 loss: 0.7981 loss_cls: 0.3332 loss_bbox: 0.4649\n", + "04/04 18:01:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][26/28] lr: 4.0000e-03 eta: 0:50:18 time: 0.6201 data_time: 0.4691 memory: 1339 loss: 0.8005 loss_cls: 0.3331 loss_bbox: 0.4674\n", + "04/04 18:01:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][27/28] lr: 4.0000e-03 eta: 0:50:22 time: 0.6057 data_time: 0.4545 memory: 1340 loss: 0.7986 loss_cls: 0.3329 loss_bbox: 0.4657\n", + "04/04 18:01:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:01:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][28/28] lr: 4.0000e-03 eta: 0:50:19 time: 0.6051 data_time: 0.4545 memory: 1337 loss: 0.8056 loss_cls: 0.3359 loss_bbox: 0.4697\n", + "04/04 18:01:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 1/28] lr: 4.0000e-03 eta: 0:50:24 time: 0.6399 data_time: 0.4902 memory: 1341 loss: 0.8037 loss_cls: 0.3355 loss_bbox: 0.4682\n", + "04/04 18:01:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 2/28] lr: 4.0000e-03 eta: 0:50:22 time: 0.6480 data_time: 0.4987 memory: 1338 loss: 0.8039 loss_cls: 0.3365 loss_bbox: 0.4674\n", + "04/04 18:01:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 3/28] lr: 4.0000e-03 eta: 0:50:20 time: 0.6104 data_time: 0.4611 memory: 1337 loss: 0.7907 loss_cls: 0.3310 loss_bbox: 0.4597\n", + "04/04 18:01:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 4/28] lr: 4.0000e-03 eta: 0:50:17 time: 0.6103 data_time: 0.4611 memory: 1339 loss: 0.7943 loss_cls: 0.3327 loss_bbox: 0.4616\n", + "04/04 18:01:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 5/28] lr: 4.0000e-03 eta: 0:50:20 time: 0.6377 data_time: 0.4885 memory: 1337 loss: 0.7889 loss_cls: 0.3303 loss_bbox: 0.4586\n", + "04/04 18:01:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 6/28] lr: 4.0000e-03 eta: 0:50:17 time: 0.6381 data_time: 0.4885 memory: 1339 loss: 0.7886 loss_cls: 0.3300 loss_bbox: 0.4585\n", + "04/04 18:01:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 7/28] lr: 4.0000e-03 eta: 0:50:17 time: 0.6221 data_time: 0.4730 memory: 1338 loss: 0.7850 loss_cls: 0.3293 loss_bbox: 0.4556\n", + "04/04 18:01:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 8/28] lr: 4.0000e-03 eta: 0:50:14 time: 0.6229 data_time: 0.4739 memory: 1337 loss: 0.7902 loss_cls: 0.3330 loss_bbox: 0.4573\n", + "04/04 18:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][ 9/28] lr: 4.0000e-03 eta: 0:50:19 time: 0.6616 data_time: 0.5125 memory: 1337 loss: 0.7944 loss_cls: 0.3347 loss_bbox: 0.4597\n", + "04/04 18:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][10/28] lr: 4.0000e-03 eta: 0:50:16 time: 0.6616 data_time: 0.5125 memory: 1338 loss: 0.8032 loss_cls: 0.3389 loss_bbox: 0.4643\n", + "04/04 18:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][11/28] lr: 4.0000e-03 eta: 0:50:14 time: 0.6389 data_time: 0.4898 memory: 1338 loss: 0.8050 loss_cls: 0.3385 loss_bbox: 0.4665\n", + "04/04 18:01:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][12/28] lr: 4.0000e-03 eta: 0:50:11 time: 0.6388 data_time: 0.4899 memory: 1342 loss: 0.8066 loss_cls: 0.3397 loss_bbox: 0.4669\n", + "04/04 18:01:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][13/28] lr: 4.0000e-03 eta: 0:50:16 time: 0.6779 data_time: 0.5291 memory: 1340 loss: 0.8073 loss_cls: 0.3393 loss_bbox: 0.4680\n", + "04/04 18:01:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][14/28] lr: 4.0000e-03 eta: 0:50:13 time: 0.6777 data_time: 0.5291 memory: 1337 loss: 0.8027 loss_cls: 0.3360 loss_bbox: 0.4667\n", + "04/04 18:01:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][15/28] lr: 4.0000e-03 eta: 0:50:11 time: 0.6433 data_time: 0.4945 memory: 1341 loss: 0.8039 loss_cls: 0.3363 loss_bbox: 0.4676\n", + "04/04 18:01:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][16/28] lr: 4.0000e-03 eta: 0:50:08 time: 0.6433 data_time: 0.4945 memory: 1337 loss: 0.8032 loss_cls: 0.3372 loss_bbox: 0.4660\n", + "04/04 18:01:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][17/28] lr: 4.0000e-03 eta: 0:50:13 time: 0.6826 data_time: 0.5335 memory: 1338 loss: 0.7992 loss_cls: 0.3378 loss_bbox: 0.4614\n", + "04/04 18:01:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][18/28] lr: 4.0000e-03 eta: 0:50:10 time: 0.6826 data_time: 0.5335 memory: 1339 loss: 0.8037 loss_cls: 0.3408 loss_bbox: 0.4629\n", + "04/04 18:01:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][19/28] lr: 4.0000e-03 eta: 0:50:08 time: 0.6582 data_time: 0.5090 memory: 1337 loss: 0.8130 loss_cls: 0.3481 loss_bbox: 0.4649\n", + "04/04 18:01:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][20/28] lr: 4.0000e-03 eta: 0:50:05 time: 0.6586 data_time: 0.5090 memory: 1342 loss: 0.8130 loss_cls: 0.3472 loss_bbox: 0.4657\n", + "04/04 18:01:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][21/28] lr: 4.0000e-03 eta: 0:50:09 time: 0.6810 data_time: 0.5317 memory: 1338 loss: 0.8137 loss_cls: 0.3486 loss_bbox: 0.4651\n", + "04/04 18:01:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][22/28] lr: 4.0000e-03 eta: 0:50:06 time: 0.6812 data_time: 0.5318 memory: 1338 loss: 0.8206 loss_cls: 0.3532 loss_bbox: 0.4675\n", + "04/04 18:01:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][23/28] lr: 4.0000e-03 eta: 0:50:04 time: 0.6431 data_time: 0.4936 memory: 1339 loss: 0.8233 loss_cls: 0.3542 loss_bbox: 0.4691\n", + "04/04 18:01:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][24/28] lr: 4.0000e-03 eta: 0:50:01 time: 0.6337 data_time: 0.4844 memory: 1341 loss: 0.8287 loss_cls: 0.3564 loss_bbox: 0.4722\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][25/28] lr: 4.0000e-03 eta: 0:50:06 time: 0.6720 data_time: 0.5230 memory: 1339 loss: 0.8282 loss_cls: 0.3567 loss_bbox: 0.4716\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][26/28] lr: 4.0000e-03 eta: 0:50:03 time: 0.6714 data_time: 0.5230 memory: 1338 loss: 0.8260 loss_cls: 0.3552 loss_bbox: 0.4708\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][27/28] lr: 4.0000e-03 eta: 0:50:00 time: 0.6542 data_time: 0.5063 memory: 1337 loss: 0.8238 loss_cls: 0.3552 loss_bbox: 0.4686\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][28/28] lr: 4.0000e-03 eta: 0:49:57 time: 0.6462 data_time: 0.4988 memory: 1340 loss: 0.8266 loss_cls: 0.3572 loss_bbox: 0.4695\n", + "04/04 18:01:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 40 epochs\n", + "04/04 18:01:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 1/14] eta: 0:00:06 time: 0.2404 data_time: 0.2092 memory: 169 \n", + "04/04 18:01:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 2/14] eta: 0:00:03 time: 0.2367 data_time: 0.2056 memory: 169 \n", + "04/04 18:01:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 3/14] eta: 0:00:03 time: 0.2397 data_time: 0.2087 memory: 169 \n", + "04/04 18:01:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 4/14] eta: 0:00:02 time: 0.2375 data_time: 0.2065 memory: 169 \n", + "04/04 18:02:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 5/14] eta: 0:00:02 time: 0.2410 data_time: 0.2100 memory: 169 \n", + "04/04 18:02:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 6/14] eta: 0:00:01 time: 0.2377 data_time: 0.2069 memory: 169 \n", + "04/04 18:02:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 7/14] eta: 0:00:01 time: 0.2423 data_time: 0.2115 memory: 169 \n", + "04/04 18:02:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 8/14] eta: 0:00:01 time: 0.2382 data_time: 0.2076 memory: 169 \n", + "04/04 18:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][ 9/14] eta: 0:00:01 time: 0.2360 data_time: 0.2055 memory: 169 \n", + "04/04 18:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][10/14] eta: 0:00:00 time: 0.2360 data_time: 0.2055 memory: 169 \n", + "04/04 18:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][11/14] eta: 0:00:00 time: 0.2377 data_time: 0.2072 memory: 169 \n", + "04/04 18:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][12/14] eta: 0:00:00 time: 0.2375 data_time: 0.2072 memory: 169 \n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][13/14] eta: 0:00:00 time: 0.2369 data_time: 0.2067 memory: 169 \n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][14/14] eta: 0:00:00 time: 0.2368 data_time: 0.2067 memory: 169 \n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.649\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.969\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.727\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.649\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.376\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.712\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.714\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.714\n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.649 0.969 0.727 -1.000 -1.000 0.649\n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][14/14] coco/bbox_mAP: 0.6490 coco/bbox_mAP_50: 0.9690 coco/bbox_mAP_75: 0.7270 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.6490data_time: 0.2067 time: 0.2368 \n", + "04/04 18:02:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmdetection/work_dirs/rtmdet_tiny_triangle/best_coco/bbox_mAP_epoch_30.pth is removed\n", + "04/04 18:02:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.6490 coco/bbox_mAP at 40 epoch is saved to best_coco/bbox_mAP_epoch_40.pth.\n", + "04/04 18:02:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 1/28] lr: 4.0000e-03 eta: 0:50:05 time: 0.6598 data_time: 0.5127 memory: 1338 loss: 0.8241 loss_cls: 0.3572 loss_bbox: 0.4670\n", + "04/04 18:02:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 2/28] lr: 4.0000e-03 eta: 0:50:03 time: 0.6607 data_time: 0.5136 memory: 1339 loss: 0.8214 loss_cls: 0.3563 loss_bbox: 0.4651\n", + "04/04 18:02:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 3/28] lr: 4.0000e-03 eta: 0:50:00 time: 0.6603 data_time: 0.5136 memory: 1337 loss: 0.8307 loss_cls: 0.3636 loss_bbox: 0.4671\n", + "04/04 18:02:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 4/28] lr: 4.0000e-03 eta: 0:49:57 time: 0.6601 data_time: 0.5136 memory: 1341 loss: 0.8344 loss_cls: 0.3653 loss_bbox: 0.4692\n", + "04/04 18:02:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 5/28] lr: 4.0000e-03 eta: 0:50:01 time: 0.6580 data_time: 0.5117 memory: 1337 loss: 0.8378 loss_cls: 0.3683 loss_bbox: 0.4694\n", + "04/04 18:02:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 6/28] lr: 4.0000e-03 eta: 0:49:59 time: 0.6579 data_time: 0.5117 memory: 1337 loss: 0.8379 loss_cls: 0.3681 loss_bbox: 0.4698\n", + "04/04 18:02:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 7/28] lr: 4.0000e-03 eta: 0:49:58 time: 0.6698 data_time: 0.5237 memory: 1337 loss: 0.8396 loss_cls: 0.3700 loss_bbox: 0.4695\n", + "04/04 18:02:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 8/28] lr: 4.0000e-03 eta: 0:49:56 time: 0.6697 data_time: 0.5237 memory: 1340 loss: 0.8421 loss_cls: 0.3716 loss_bbox: 0.4705\n", + "04/04 18:02:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][ 9/28] lr: 4.0000e-03 eta: 0:49:56 time: 0.6468 data_time: 0.5008 memory: 1338 loss: 0.8471 loss_cls: 0.3743 loss_bbox: 0.4728\n", + "04/04 18:02:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][10/28] lr: 4.0000e-03 eta: 0:49:54 time: 0.6510 data_time: 0.5049 memory: 1338 loss: 0.8496 loss_cls: 0.3751 loss_bbox: 0.4745\n", + "04/04 18:02:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][11/28] lr: 4.0000e-03 eta: 0:49:55 time: 0.6715 data_time: 0.5257 memory: 1337 loss: 0.8537 loss_cls: 0.3769 loss_bbox: 0.4768\n", + "04/04 18:02:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][12/28] lr: 4.0000e-03 eta: 0:49:52 time: 0.6711 data_time: 0.5257 memory: 1339 loss: 0.8604 loss_cls: 0.3779 loss_bbox: 0.4825\n", + "04/04 18:02:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][13/28] lr: 4.0000e-03 eta: 0:49:50 time: 0.6357 data_time: 0.4906 memory: 1338 loss: 0.8611 loss_cls: 0.3783 loss_bbox: 0.4829\n", + "04/04 18:02:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][14/28] lr: 4.0000e-03 eta: 0:49:50 time: 0.6512 data_time: 0.5060 memory: 1339 loss: 0.8611 loss_cls: 0.3786 loss_bbox: 0.4825\n", + "04/04 18:02:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][15/28] lr: 4.0000e-03 eta: 0:49:52 time: 0.6735 data_time: 0.5282 memory: 1341 loss: 0.8649 loss_cls: 0.3804 loss_bbox: 0.4845\n", + "04/04 18:02:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][16/28] lr: 4.0000e-03 eta: 0:49:49 time: 0.6735 data_time: 0.5282 memory: 1339 loss: 0.8625 loss_cls: 0.3778 loss_bbox: 0.4847\n", + "04/04 18:02:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][17/28] lr: 4.0000e-03 eta: 0:49:47 time: 0.6386 data_time: 0.4938 memory: 1337 loss: 0.8610 loss_cls: 0.3770 loss_bbox: 0.4840\n", + "04/04 18:02:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][18/28] lr: 4.0000e-03 eta: 0:49:47 time: 0.6536 data_time: 0.5090 memory: 1339 loss: 0.8589 loss_cls: 0.3751 loss_bbox: 0.4838\n", + "04/04 18:02:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][19/28] lr: 4.0000e-03 eta: 0:49:48 time: 0.6725 data_time: 0.5286 memory: 1339 loss: 0.8580 loss_cls: 0.3745 loss_bbox: 0.4835\n", + "04/04 18:02:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][20/28] lr: 4.0000e-03 eta: 0:49:45 time: 0.6723 data_time: 0.5286 memory: 1339 loss: 0.8533 loss_cls: 0.3723 loss_bbox: 0.4810\n", + "04/04 18:02:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][21/28] lr: 4.0000e-03 eta: 0:49:43 time: 0.6398 data_time: 0.4966 memory: 1338 loss: 0.8562 loss_cls: 0.3731 loss_bbox: 0.4831\n", + "04/04 18:02:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][22/28] lr: 4.0000e-03 eta: 0:49:43 time: 0.6574 data_time: 0.5140 memory: 1337 loss: 0.8509 loss_cls: 0.3700 loss_bbox: 0.4809\n", + "04/04 18:02:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][23/28] lr: 4.0000e-03 eta: 0:49:45 time: 0.6424 data_time: 0.4990 memory: 1337 loss: 0.8512 loss_cls: 0.3692 loss_bbox: 0.4820\n", + "04/04 18:02:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][24/28] lr: 4.0000e-03 eta: 0:49:42 time: 0.6343 data_time: 0.4905 memory: 1339 loss: 0.8516 loss_cls: 0.3684 loss_bbox: 0.4832\n", + "04/04 18:02:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][25/28] lr: 4.0000e-03 eta: 0:49:39 time: 0.6352 data_time: 0.4914 memory: 1338 loss: 0.8531 loss_cls: 0.3676 loss_bbox: 0.4855\n", + "04/04 18:02:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][26/28] lr: 4.0000e-03 eta: 0:49:40 time: 0.6506 data_time: 0.5065 memory: 1338 loss: 0.8505 loss_cls: 0.3667 loss_bbox: 0.4838\n", + "04/04 18:02:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][27/28] lr: 4.0000e-03 eta: 0:49:41 time: 0.6422 data_time: 0.4979 memory: 1338 loss: 0.8553 loss_cls: 0.3695 loss_bbox: 0.4858\n", + "04/04 18:02:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:02:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][28/28] lr: 4.0000e-03 eta: 0:49:38 time: 0.6421 data_time: 0.4979 memory: 1339 loss: 0.8594 loss_cls: 0.3714 loss_bbox: 0.4881\n", + "04/04 18:02:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][ 1/28] lr: 4.0000e-03 eta: 0:49:45 time: 0.6817 data_time: 0.5373 memory: 1339 loss: 0.8614 loss_cls: 0.3724 loss_bbox: 0.4890\n", + "04/04 18:02:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][ 2/28] lr: 4.0000e-03 eta: 0:49:43 time: 0.6809 data_time: 0.5364 memory: 1339 loss: 0.8572 loss_cls: 0.3699 loss_bbox: 0.4873\n", + "04/04 18:02:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][ 8/28] lr: 4.0000e-03 eta: 0:49:34 time: 0.6400 data_time: 0.4967 memory: 1338 loss: 0.8431 loss_cls: 0.3622 loss_bbox: 0.4809\n", + "04/04 18:02:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][ 9/28] lr: 4.0000e-03 eta: 0:49:39 time: 0.6793 data_time: 0.5360 memory: 1337 loss: 0.8381 loss_cls: 0.3597 loss_bbox: 0.4784\n", + "04/04 18:02:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][10/28] lr: 4.0000e-03 eta: 0:49:36 time: 0.6797 data_time: 0.5360 memory: 1342 loss: 0.8371 loss_cls: 0.3591 loss_bbox: 0.4780\n", + "04/04 18:02:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][11/28] lr: 4.0000e-03 eta: 0:49:34 time: 0.6406 data_time: 0.4970 memory: 1339 loss: 0.8353 loss_cls: 0.3577 loss_bbox: 0.4776\n", + "04/04 18:02:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][12/28] lr: 4.0000e-03 eta: 0:49:32 time: 0.6430 data_time: 0.4993 memory: 1342 loss: 0.8341 loss_cls: 0.3571 loss_bbox: 0.4770\n", + "04/04 18:02:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][13/28] lr: 4.0000e-03 eta: 0:49:36 time: 0.6779 data_time: 0.5343 memory: 1339 loss: 0.8260 loss_cls: 0.3511 loss_bbox: 0.4749\n", + "04/04 18:02:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][14/28] lr: 4.0000e-03 eta: 0:49:33 time: 0.6774 data_time: 0.5343 memory: 1338 loss: 0.8277 loss_cls: 0.3520 loss_bbox: 0.4757\n", + "04/04 18:02:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][15/28] lr: 4.0000e-03 eta: 0:49:30 time: 0.6422 data_time: 0.4991 memory: 1338 loss: 0.8341 loss_cls: 0.3557 loss_bbox: 0.4784\n", + "04/04 18:02:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][16/28] lr: 4.0000e-03 eta: 0:49:30 time: 0.6548 data_time: 0.5120 memory: 1341 loss: 0.8288 loss_cls: 0.3519 loss_bbox: 0.4769\n", + "04/04 18:02:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][17/28] lr: 4.0000e-03 eta: 0:49:32 time: 0.6801 data_time: 0.5365 memory: 1341 loss: 0.8294 loss_cls: 0.3521 loss_bbox: 0.4773\n", + "04/04 18:02:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][18/28] lr: 4.0000e-03 eta: 0:49:30 time: 0.6805 data_time: 0.5365 memory: 1340 loss: 0.8259 loss_cls: 0.3506 loss_bbox: 0.4754\n", + "04/04 18:02:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][19/28] lr: 4.0000e-03 eta: 0:49:27 time: 0.6444 data_time: 0.5000 memory: 1339 loss: 0.8306 loss_cls: 0.3532 loss_bbox: 0.4775\n", + "04/04 18:02:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][20/28] lr: 4.0000e-03 eta: 0:49:25 time: 0.6484 data_time: 0.5033 memory: 1337 loss: 0.8288 loss_cls: 0.3518 loss_bbox: 0.4770\n", + "04/04 18:02:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][21/28] lr: 4.0000e-03 eta: 0:49:28 time: 0.6785 data_time: 0.5323 memory: 1338 loss: 0.8267 loss_cls: 0.3500 loss_bbox: 0.4767\n", + "04/04 18:02:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][22/28] lr: 4.0000e-03 eta: 0:49:26 time: 0.6791 data_time: 0.5323 memory: 1337 loss: 0.8232 loss_cls: 0.3483 loss_bbox: 0.4749\n", + "04/04 18:02:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][23/28] lr: 4.0000e-03 eta: 0:49:26 time: 0.6394 data_time: 0.4923 memory: 1340 loss: 0.8234 loss_cls: 0.3484 loss_bbox: 0.4750\n", + "04/04 18:02:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][24/28] lr: 4.0000e-03 eta: 0:49:23 time: 0.6386 data_time: 0.4913 memory: 1340 loss: 0.8233 loss_cls: 0.3481 loss_bbox: 0.4752\n", + "04/04 18:02:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][25/28] lr: 4.0000e-03 eta: 0:49:26 time: 0.6647 data_time: 0.5163 memory: 1341 loss: 0.8143 loss_cls: 0.3402 loss_bbox: 0.4741\n", + "04/04 18:02:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][26/28] lr: 4.0000e-03 eta: 0:49:23 time: 0.6652 data_time: 0.5163 memory: 1339 loss: 0.8089 loss_cls: 0.3383 loss_bbox: 0.4706\n", + "04/04 18:02:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][27/28] lr: 4.0000e-03 eta: 0:49:22 time: 0.6385 data_time: 0.4890 memory: 1339 loss: 0.8016 loss_cls: 0.3339 loss_bbox: 0.4677\n", + "04/04 18:02:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:02:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][28/28] lr: 4.0000e-03 eta: 0:49:19 time: 0.6391 data_time: 0.4890 memory: 1341 loss: 0.7974 loss_cls: 0.3320 loss_bbox: 0.4654\n", + "04/04 18:02:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][ 1/28] lr: 4.0000e-03 eta: 0:49:27 time: 0.6794 data_time: 0.5295 memory: 1339 loss: 0.7953 loss_cls: 0.3308 loss_bbox: 0.4645\n", + "04/04 18:02:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][ 2/28] lr: 4.0000e-03 eta: 0:49:24 time: 0.6791 data_time: 0.5295 memory: 1337 loss: 0.7957 loss_cls: 0.3343 loss_bbox: 0.4614\n", + "04/04 18:02:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][ 3/28] lr: 4.0000e-03 eta: 0:49:21 time: 0.6650 data_time: 0.5160 memory: 1338 loss: 0.7969 loss_cls: 0.3364 loss_bbox: 0.4605\n", + "04/04 18:02:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][25/28] lr: 4.0000e-03 eta: 0:48:49 time: 0.6768 data_time: 0.5316 memory: 1343 loss: 0.7944 loss_cls: 0.3406 loss_bbox: 0.4539\n", + "04/04 18:03:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][26/28] lr: 4.0000e-03 eta: 0:48:46 time: 0.6771 data_time: 0.5316 memory: 1342 loss: 0.7941 loss_cls: 0.3399 loss_bbox: 0.4542\n", + "04/04 18:03:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][27/28] lr: 4.0000e-03 eta: 0:48:45 time: 0.6511 data_time: 0.5061 memory: 1337 loss: 0.7977 loss_cls: 0.3428 loss_bbox: 0.4549\n", + "04/04 18:03:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:03:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][28/28] lr: 4.0000e-03 eta: 0:48:42 time: 0.6507 data_time: 0.5061 memory: 1337 loss: 0.7974 loss_cls: 0.3432 loss_bbox: 0.4542\n", + "04/04 18:03:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][21/28] lr: 4.0000e-03 eta: 0:48:31 time: 0.6793 data_time: 0.5341 memory: 1339 loss: 0.7839 loss_cls: 0.3312 loss_bbox: 0.4528\n", + "04/04 18:03:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][22/28] lr: 4.0000e-03 eta: 0:48:35 time: 0.7171 data_time: 0.5722 memory: 1338 loss: 0.7901 loss_cls: 0.3325 loss_bbox: 0.4576\n", + "04/04 18:03:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][23/28] lr: 4.0000e-03 eta: 0:48:33 time: 0.6667 data_time: 0.5218 memory: 1337 loss: 0.7897 loss_cls: 0.3316 loss_bbox: 0.4581\n", + "04/04 18:03:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][24/28] lr: 4.0000e-03 eta: 0:48:31 time: 0.6665 data_time: 0.5218 memory: 1344 loss: 0.7883 loss_cls: 0.3309 loss_bbox: 0.4575\n", + "04/04 18:03:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][25/28] lr: 4.0000e-03 eta: 0:48:28 time: 0.6684 data_time: 0.5242 memory: 1339 loss: 0.7912 loss_cls: 0.3330 loss_bbox: 0.4582\n", + "04/04 18:03:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][26/28] lr: 4.0000e-03 eta: 0:48:30 time: 0.6942 data_time: 0.5505 memory: 1337 loss: 0.7940 loss_cls: 0.3341 loss_bbox: 0.4599\n", + "04/04 18:03:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][27/28] lr: 4.0000e-03 eta: 0:48:28 time: 0.6542 data_time: 0.5104 memory: 1339 loss: 0.7966 loss_cls: 0.3349 loss_bbox: 0.4618\n", + "04/04 18:03:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:03:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][28/28] lr: 4.0000e-03 eta: 0:48:26 time: 0.6544 data_time: 0.5104 memory: 1337 loss: 0.7961 loss_cls: 0.3345 loss_bbox: 0.4616\n", + "04/04 18:03:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 1/14] eta: 0:00:07 time: 0.2392 data_time: 0.2090 memory: 169 \n", + "04/04 18:03:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 2/14] eta: 0:00:03 time: 0.2386 data_time: 0.2086 memory: 169 \n", + "04/04 18:03:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 3/14] eta: 0:00:03 time: 0.2376 data_time: 0.2076 memory: 169 \n", + "04/04 18:03:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 4/14] eta: 0:00:02 time: 0.2376 data_time: 0.2077 memory: 169 \n", + "04/04 18:03:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 5/14] eta: 0:00:02 time: 0.2372 data_time: 0.2073 memory: 169 \n", + "04/04 18:03:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 6/14] eta: 0:00:01 time: 0.2372 data_time: 0.2073 memory: 169 \n", + "04/04 18:03:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 7/14] eta: 0:00:01 time: 0.2380 data_time: 0.2081 memory: 169 \n", + "04/04 18:03:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 8/14] eta: 0:00:01 time: 0.2380 data_time: 0.2081 memory: 169 \n", + "04/04 18:03:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][ 9/14] eta: 0:00:01 time: 0.2360 data_time: 0.2061 memory: 169 \n", + "04/04 18:03:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][10/14] eta: 0:00:00 time: 0.2358 data_time: 0.2059 memory: 169 \n", + "04/04 18:03:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][11/14] eta: 0:00:00 time: 0.2376 data_time: 0.2075 memory: 169 \n", + "04/04 18:03:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][12/14] eta: 0:00:00 time: 0.2346 data_time: 0.2045 memory: 169 \n", + "04/04 18:03:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][13/14] eta: 0:00:00 time: 0.2360 data_time: 0.2059 memory: 169 \n", + "04/04 18:03:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][14/14] eta: 0:00:00 time: 0.2341 data_time: 0.2042 memory: 169 \n", + "04/04 18:03:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating bbox...\n", + "Loading and preparing results...\n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.15s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.607\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.922\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.784\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.326\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.682\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.682\n", + "04/04 18:03:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - bbox_mAP_copypaste: 0.607 0.922 0.784 -1.000 -1.000 0.607\n", + "04/04 18:03:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [45][14/14] coco/bbox_mAP: 0.6070 coco/bbox_mAP_50: 0.9220 coco/bbox_mAP_75: 0.7840 coco/bbox_mAP_s: -1.0000 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: 0.6070data_time: 0.2042 time: 0.2341 \n", + "04/04 18:03:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 1/28] lr: 4.0000e-03 eta: 0:48:33 time: 0.7094 data_time: 0.5653 memory: 1340 loss: 0.7953 loss_cls: 0.3336 loss_bbox: 0.4617\n", + "04/04 18:03:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 2/28] lr: 4.0000e-03 eta: 0:48:30 time: 0.7092 data_time: 0.5653 memory: 1338 loss: 0.7960 loss_cls: 0.3342 loss_bbox: 0.4618\n", + "04/04 18:03:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 3/28] lr: 4.0000e-03 eta: 0:48:28 time: 0.6705 data_time: 0.5265 memory: 1345 loss: 0.7987 loss_cls: 0.3350 loss_bbox: 0.4637\n", + "04/04 18:03:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 4/28] lr: 4.0000e-03 eta: 0:48:25 time: 0.6704 data_time: 0.5265 memory: 1339 loss: 0.7959 loss_cls: 0.3345 loss_bbox: 0.4614\n", + "04/04 18:03:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 5/28] lr: 4.0000e-03 eta: 0:48:29 time: 0.7084 data_time: 0.5642 memory: 1338 loss: 0.7963 loss_cls: 0.3344 loss_bbox: 0.4620\n", + "04/04 18:03:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 6/28] lr: 4.0000e-03 eta: 0:48:27 time: 0.7083 data_time: 0.5642 memory: 1339 loss: 0.7969 loss_cls: 0.3356 loss_bbox: 0.4613\n", + "04/04 18:03:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 7/28] lr: 4.0000e-03 eta: 0:48:26 time: 0.6783 data_time: 0.5341 memory: 1337 loss: 0.8115 loss_cls: 0.3493 loss_bbox: 0.4622\n", + "04/04 18:03:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 8/28] lr: 4.0000e-03 eta: 0:48:23 time: 0.6783 data_time: 0.5341 memory: 1341 loss: 0.8113 loss_cls: 0.3491 loss_bbox: 0.4622\n", + "04/04 18:03:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][ 9/28] lr: 4.0000e-03 eta: 0:48:26 time: 0.7088 data_time: 0.5641 memory: 1340 loss: 0.8124 loss_cls: 0.3496 loss_bbox: 0.4628\n", + "04/04 18:03:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][10/28] lr: 4.0000e-03 eta: 0:48:23 time: 0.7089 data_time: 0.5641 memory: 1341 loss: 0.8195 loss_cls: 0.3542 loss_bbox: 0.4654\n", + "04/04 18:03:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][11/28] lr: 4.0000e-03 eta: 0:48:23 time: 0.6789 data_time: 0.5343 memory: 1339 loss: 0.8213 loss_cls: 0.3548 loss_bbox: 0.4665\n", + "04/04 18:03:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][12/28] lr: 4.0000e-03 eta: 0:48:22 time: 0.6919 data_time: 0.5474 memory: 1343 loss: 0.8205 loss_cls: 0.3546 loss_bbox: 0.4659\n", + "04/04 18:03:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][13/28] lr: 4.0000e-03 eta: 0:48:25 time: 0.7241 data_time: 0.5793 memory: 1337 loss: 0.8195 loss_cls: 0.3524 loss_bbox: 0.4671\n", + "04/04 18:03:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][14/28] lr: 4.0000e-03 eta: 0:48:23 time: 0.7240 data_time: 0.5793 memory: 1340 loss: 0.8214 loss_cls: 0.3536 loss_bbox: 0.4678\n", + "04/04 18:03:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][15/28] lr: 4.0000e-03 eta: 0:48:20 time: 0.6810 data_time: 0.5367 memory: 1337 loss: 0.8215 loss_cls: 0.3545 loss_bbox: 0.4670\n", + "04/04 18:03:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][16/28] lr: 4.0000e-03 eta: 0:48:19 time: 0.6889 data_time: 0.5449 memory: 1337 loss: 0.8215 loss_cls: 0.3550 loss_bbox: 0.4666\n", + "04/04 18:03:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][17/28] lr: 4.0000e-03 eta: 0:48:24 time: 0.7318 data_time: 0.5881 memory: 1339 loss: 0.8179 loss_cls: 0.3535 loss_bbox: 0.4644\n", + "04/04 18:03:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][18/28] lr: 4.0000e-03 eta: 0:48:22 time: 0.7318 data_time: 0.5880 memory: 1338 loss: 0.8154 loss_cls: 0.3531 loss_bbox: 0.4623\n", + "04/04 18:03:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][19/28] lr: 4.0000e-03 eta: 0:48:19 time: 0.6931 data_time: 0.5493 memory: 1340 loss: 0.8123 loss_cls: 0.3530 loss_bbox: 0.4594\n", + "04/04 18:03:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][20/28] lr: 4.0000e-03 eta: 0:48:17 time: 0.6929 data_time: 0.5493 memory: 1337 loss: 0.8121 loss_cls: 0.3540 loss_bbox: 0.4581\n", + "04/04 18:03:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][21/28] lr: 4.0000e-03 eta: 0:48:21 time: 0.7258 data_time: 0.5823 memory: 1341 loss: 0.8064 loss_cls: 0.3502 loss_bbox: 0.4562\n", + "04/04 18:03:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][22/28] lr: 4.0000e-03 eta: 0:48:18 time: 0.7256 data_time: 0.5823 memory: 1340 loss: 0.8036 loss_cls: 0.3475 loss_bbox: 0.4562\n", + "04/04 18:03:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][23/28] lr: 4.0000e-03 eta: 0:48:16 time: 0.6772 data_time: 0.5341 memory: 1342 loss: 0.8047 loss_cls: 0.3480 loss_bbox: 0.4567\n", + "04/04 18:03:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][24/28] lr: 4.0000e-03 eta: 0:48:13 time: 0.6556 data_time: 0.5127 memory: 1337 loss: 0.8043 loss_cls: 0.3493 loss_bbox: 0.4550\n", + "04/04 18:04:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][25/28] lr: 4.0000e-03 eta: 0:48:17 time: 0.6920 data_time: 0.5486 memory: 1338 loss: 0.8070 loss_cls: 0.3508 loss_bbox: 0.4561\n", + "04/04 18:04:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][26/28] lr: 4.0000e-03 eta: 0:48:14 time: 0.6920 data_time: 0.5486 memory: 1338 loss: 0.8028 loss_cls: 0.3484 loss_bbox: 0.4544\n", + "04/04 18:04:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][27/28] lr: 4.0000e-03 eta: 0:48:12 time: 0.6760 data_time: 0.5329 memory: 1342 loss: 0.8037 loss_cls: 0.3480 loss_bbox: 0.4558\n", + "04/04 18:04:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmdet_tiny_triangle_20230404_174824\n", + "04/04 18:04:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][28/28] lr: 4.0000e-03 eta: 0:48:09 time: 0.6418 data_time: 0.4990 memory: 1337 loss: 0.8017 loss_cls: 0.3465 loss_bbox: 0.4551\n", + "04/04 18:04:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 1/28] lr: 4.0000e-03 eta: 0:48:14 time: 0.6809 data_time: 0.5384 memory: 1342 loss: 0.7998 loss_cls: 0.3435 loss_bbox: 0.4563\n", + "04/04 18:04:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 2/28] lr: 4.0000e-03 eta: 0:48:12 time: 0.6839 data_time: 0.5417 memory: 1342 loss: 0.7973 loss_cls: 0.3429 loss_bbox: 0.4544\n", + "04/04 18:04:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 3/28] lr: 4.0000e-03 eta: 0:48:09 time: 0.6812 data_time: 0.5393 memory: 1338 loss: 0.8040 loss_cls: 0.3468 loss_bbox: 0.4572\n", + "04/04 18:04:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 4/28] lr: 4.0000e-03 eta: 0:48:07 time: 0.6466 data_time: 0.5049 memory: 1338 loss: 0.8034 loss_cls: 0.3472 loss_bbox: 0.4562\n", + "04/04 18:04:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 5/28] lr: 4.0000e-03 eta: 0:48:10 time: 0.6808 data_time: 0.5390 memory: 1339 loss: 0.8045 loss_cls: 0.3476 loss_bbox: 0.4569\n", + "04/04 18:04:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 6/28] lr: 4.0000e-03 eta: 0:48:08 time: 0.6814 data_time: 0.5396 memory: 1341 loss: 0.8051 loss_cls: 0.3474 loss_bbox: 0.4577\n", + "04/04 18:04:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 7/28] lr: 4.0000e-03 eta: 0:48:06 time: 0.6788 data_time: 0.5368 memory: 1344 loss: 0.8095 loss_cls: 0.3494 loss_bbox: 0.4601\n", + "04/04 18:04:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 8/28] lr: 4.0000e-03 eta: 0:48:03 time: 0.6502 data_time: 0.5085 memory: 1337 loss: 0.8135 loss_cls: 0.3533 loss_bbox: 0.4603\n", + "04/04 18:04:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][ 9/28] lr: 4.0000e-03 eta: 0:48:07 time: 0.6862 data_time: 0.5450 memory: 1340 loss: 0.8136 loss_cls: 0.3546 loss_bbox: 0.4590\n", + "04/04 18:04:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][10/28] lr: 4.0000e-03 eta: 0:48:04 time: 0.6859 data_time: 0.5450 memory: 1337 loss: 0.8137 loss_cls: 0.3551 loss_bbox: 0.4587\n", + "04/04 18:04:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][11/28] lr: 4.0000e-03 eta: 0:48:02 time: 0.6762 data_time: 0.5355 memory: 1339 loss: 0.8129 loss_cls: 0.3550 loss_bbox: 0.4579\n", + "04/04 18:04:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][12/28] lr: 4.0000e-03 eta: 0:48:00 time: 0.6502 data_time: 0.5093 memory: 1339 loss: 0.8134 loss_cls: 0.3562 loss_bbox: 0.4572\n", + "04/04 18:04:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][13/28] lr: 4.0000e-03 eta: 0:48:03 time: 0.6879 data_time: 0.5467 memory: 1342 loss: 0.8094 loss_cls: 0.3540 loss_bbox: 0.4553\n", + "04/04 18:04:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][14/28] lr: 4.0000e-03 eta: 0:48:01 time: 0.6879 data_time: 0.5467 memory: 1340 loss: 0.8095 loss_cls: 0.3536 loss_bbox: 0.4558\n", + "04/04 18:04:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][15/28] lr: 4.0000e-03 eta: 0:47:58 time: 0.6732 data_time: 0.5323 memory: 1341 loss: 0.8073 loss_cls: 0.3524 loss_bbox: 0.4549\n", + "04/04 18:04:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][16/28] lr: 4.0000e-03 eta: 0:47:56 time: 0.6354 data_time: 0.4942 memory: 1338 loss: 0.8048 loss_cls: 0.3522 loss_bbox: 0.4526\n", + "04/04 18:04:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][17/28] lr: 4.0000e-03 eta: 0:48:00 time: 0.6763 data_time: 0.5348 memory: 1342 loss: 0.8047 loss_cls: 0.3513 loss_bbox: 0.4534\n", + "04/04 18:04:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][18/28] lr: 4.0000e-03 eta: 0:47:58 time: 0.6766 data_time: 0.5348 memory: 1337 loss: 0.8050 loss_cls: 0.3510 loss_bbox: 0.4540\n", + "04/04 18:04:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][19/28] lr: 4.0000e-03 eta: 0:47:56 time: 0.6746 data_time: 0.5324 memory: 1337 loss: 0.8068 loss_cls: 0.3514 loss_bbox: 0.4554\n", + "04/04 18:04:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][20/28] lr: 4.0000e-03 eta: 0:47:53 time: 0.6490 data_time: 0.5061 memory: 1341 loss: 0.8066 loss_cls: 0.3503 loss_bbox: 0.4562\n", + "04/04 18:04:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][21/28] lr: 4.0000e-03 eta: 0:47:57 time: 0.6877 data_time: 0.5451 memory: 1342 loss: 0.8083 loss_cls: 0.3509 loss_bbox: 0.4573\n", + "04/04 18:04:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][22/28] lr: 4.0000e-03 eta: 0:47:55 time: 0.6875 data_time: 0.5451 memory: 1339 loss: 0.8071 loss_cls: 0.3496 loss_bbox: 0.4574\n", + "04/04 18:04:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][23/28] lr: 4.0000e-03 eta: 0:47:53 time: 0.6324 data_time: 0.4902 memory: 1337 loss: 0.8138 loss_cls: 0.3536 loss_bbox: 0.4602\n", + "04/04 18:04:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [47][24/28] lr: 4.0000e-03 eta: 0:47:50 time: 0.6325 data_time: 0.4901 memory: 1340 loss: 0.8172 loss_cls: 0.3556 loss_bbox: 0.4616\n" + ] + } + ], + "source": [ + "!python tools/train.py data/rtmdet_tiny_triangle.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92fbfcaa-be6b-471c-9acf-8d08d6f66210", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220D2\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\351\242\204\346\265\213.ipynb" "b/2023/0404/\343\200\220D2\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\351\242\204\346\265\213.ipynb" new file mode 100644 index 0000000..cea2741 --- /dev/null +++ "b/2023/0404/\343\200\220D2\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\351\242\204\346\265\213.ipynb" @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ab3fbe6f-6d2f-4a00-abd4-04a486d98fd1", + "metadata": {}, + "source": [ + "# 三角板目标检测-预测\n", + "\n", + "同济子豪兄 2023-4-4" + ] + }, + { + "cell_type": "markdown", + "id": "b80f81bd-021e-4fc8-be2c-637e74b5e739", + "metadata": {}, + "source": [ + "## 进入mmdetection主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c23edb0e-5239-4bd1-9b7c-90710eee37bd", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmdetection')" + ] + }, + { + "cell_type": "markdown", + "id": "165bbef3-c3d3-49ad-8308-9c6c2ee691f0", + "metadata": {}, + "source": [ + "## 单张-图像目标检测预测" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ffa84a03-216f-4b08-91db-73a1d22b4424", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by local backend from path: work_dirs/faster_r_cnn_triangle_epoch_50.pth\n", + "04/04 13:00:50 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - sjb_rect is not a meta file, simply parsed as meta information\n", + "04/04 13:00:58 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n", + "04/04 13:00:58 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[36m \u001b[0m\n", + "\u001b[?25hresults have been saved at outputs/D2\n" + ] + } + ], + "source": [ + "!python demo/image_demo.py \\\n", + " data/test_triangle/triangle_3.jpg \\\n", + " data/faster_r_cnn_triangle.py \\\n", + " --weights work_dirs/faster_r_cnn_triangle_epoch_50.pth \\\n", + " --out-dir outputs/D2 \\\n", + " --device cuda:0 \\\n", + " --pred-score-thr 0.3" + ] + }, + { + "cell_type": "markdown", + "id": "66e27a2b-e77a-4377-a28a-69eadce8b49c", + "metadata": {}, + "source": [ + "## 视频-目标检测预测" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0321b7be-6d8f-45af-afc4-5b980e9c4537", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by local backend from path: work_dirs/faster_r_cnn_triangle_epoch_50.pth\n", + "04/04 13:24:15 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - sjb_rect is not a meta file, simply parsed as meta information\n", + "04/04 13:24:22 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "[>>>>>>>>>>>>>>>>>>>> ] 316/446, 2.7 task/s, elapsed: 119s, ETA: 49s/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:743: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may not be in the image\n", + " ' the drawn bbox may not be in the image', UserWarning)\n", + "/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:814: UserWarning: Warning: The polygon is out of bounds, the drawn polygon may not be in the image\n", + " ' the drawn polygon may not be in the image', UserWarning)\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 446/446, 2.7 task/s, elapsed: 168s, ETA: 0s\n" + ] + } + ], + "source": [ + "!python demo/video_demo.py \\\n", + " data/test_triangle/triangle_5.mov \\\n", + " data/faster_r_cnn_triangle.py \\\n", + " work_dirs/faster_r_cnn_triangle_epoch_50.pth \\\n", + " --device cuda:0 \\\n", + " --score-thr 0.9 \\\n", + " --out outputs/D2/out_video.mp4" + ] + }, + { + "cell_type": "markdown", + "id": "1abd2de0-c316-49a3-931d-b0fe61723ee8", + "metadata": {}, + "source": [ + "## 摄像头实时画面-目标检测预测\n", + "\n", + "需在本地调用摄像头运行，不能在云GPU平台运行" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55905481-5943-4871-a456-fb6def0c5146", + "metadata": {}, + "outputs": [], + "source": [ + "!python demo/webcam_demo.py \\\n", + " data/faster_r_cnn_triangle.py \\\n", + " work_dirs/faster_r_cnn_triangle_epoch_50.pth \\\n", + " --device cuda:0 \\\n", + " --camera-id 0 \\\n", + " --score-thr 0.3" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220E1\343\200\221RTMPose\350\256\255\347\273\203\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" "b/2023/0404/\343\200\220E1\343\200\221RTMPose\350\256\255\347\273\203\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" new file mode 100644 index 0000000..d845b53 --- /dev/null +++ "b/2023/0404/\343\200\220E1\343\200\221RTMPose\350\256\255\347\273\203\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\346\250\241\345\236\213.ipynb" @@ -0,0 +1,1353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f60f9207-843d-4c0f-a359-29a7ac352e4e", + "metadata": {}, + "source": [ + "# RTMPose训练三角板关键点检测模型\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "1f169d12-1db1-4a26-a9b9-e6dfc0ac360c", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "30698c47-bfb2-4cd8-a4e8-906fa566d601", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "60118cda-8cc9-4ae1-9ad7-954d4eaeb7e2", + "metadata": {}, + "source": [ + "## 下载config配置文件至`data`目录" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c8c8bd5b-d987-4275-9245-a259ffd4d663", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-04-04 13:40:05-- https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/rtmpose-s_triangle_8xb256-420e_coco-256x192.py\n", + "正在连接 172.16.0.13:5848... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", + "长度： 12264 (12K) [binary/octet-stream]\n", + "正在保存至: “data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py”\n", + "\n", + "rtmpose-s_triangle_ 100%[===================>] 11.98K --.-KB/s 用时 0.01s \n", + "\n", + "2023-04-04 13:40:05 (1.11 MB/s) - 已保存 “data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py” [12264/12264])\n", + "\n" + ] + } + ], + "source": [ + "!rm -rf data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py\n", + "!wget https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/rtmpose-s_triangle_8xb256-420e_coco-256x192.py -P data\n" + ] + }, + { + "cell_type": "markdown", + "id": "71767f86-654b-4337-9d3d-d7cc99e85c4f", + "metadata": {}, + "source": [ + "## 训练" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12eb91fb-054c-4a40-857f-f3288d9f42e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/03 19:53:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n", + "------------------------------------------------------------\n", + "System environment:\n", + " sys.platform: linux\n", + " Python: 3.7.10 (default, Jun 4 2021, 14:48:32) [GCC 7.5.0]\n", + " CUDA available: True\n", + " numpy_random_seed: 21\n", + " GPU 0: NVIDIA GeForce RTX 3060\n", + " CUDA_HOME: /usr/local/cuda\n", + " NVCC: Cuda compilation tools, release 11.2, V11.2.152\n", + " GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\n", + " PyTorch: 1.10.1+cu113\n", + " PyTorch compiling details: PyTorch built with:\n", + " - GCC 7.3\n", + " - C++ Version: 201402\n", + " - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n", + " - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n", + " - OpenMP 201511 (a.k.a. OpenMP 4.5)\n", + " - LAPACK is enabled (usually provided by MKL)\n", + " - NNPACK is enabled\n", + " - CPU capability usage: AVX2\n", + " - CUDA Runtime 11.3\n", + " - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n", + " - CuDNN 8.2\n", + " - Magma 2.5.2\n", + " - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n", + "\n", + " TorchVision: 0.11.2+cu113\n", + " OpenCV: 4.5.4\n", + " MMEngine: 0.7.0\n", + "\n", + "Runtime environment:\n", + " cudnn_benchmark: False\n", + " mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n", + " dist_cfg: {'backend': 'nccl'}\n", + " seed: 21\n", + " Distributed launcher: none\n", + " Distributed training: False\n", + " GPU number: 1\n", + "------------------------------------------------------------\n", + "\n", + "04/03 19:53:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n", + "default_scope = 'mmpose'\n", + "default_hooks = dict(\n", + " timer=dict(type='IterTimerHook', _scope_='mmpose'),\n", + " logger=dict(type='LoggerHook', interval=1, _scope_='mmpose'),\n", + " param_scheduler=dict(type='ParamSchedulerHook', _scope_='mmpose'),\n", + " checkpoint=dict(\n", + " type='CheckpointHook',\n", + " interval=10,\n", + " _scope_='mmpose',\n", + " save_best='PCK',\n", + " rule='greater',\n", + " max_keep_ckpts=1),\n", + " sampler_seed=dict(type='DistSamplerSeedHook', _scope_='mmpose'),\n", + " visualization=dict(\n", + " type='PoseVisualizationHook', enable=False, _scope_='mmpose'))\n", + "custom_hooks = [\n", + " dict(\n", + " type='EMAHook',\n", + " ema_type='ExpMomentumEMA',\n", + " momentum=0.0002,\n", + " update_buffers=True,\n", + " priority=49),\n", + " dict(\n", + " type='mmdet.PipelineSwitchHook',\n", + " switch_epoch=220,\n", + " switch_pipeline=[\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='RandomFlip', direction='horizontal'),\n", + " dict(type='RandomHalfBody'),\n", + " dict(\n", + " type='RandomBBoxTransform',\n", + " shift_factor=0.0,\n", + " scale_factor=[0.75, 1.25],\n", + " rotate_factor=60),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='mmdet.YOLOXHSVRandomAug'),\n", + " dict(\n", + " type='Albumentation',\n", + " transforms=[\n", + " dict(type='Blur', p=0.1),\n", + " dict(type='MedianBlur', p=0.1),\n", + " dict(\n", + " type='CoarseDropout',\n", + " max_holes=1,\n", + " max_height=0.4,\n", + " max_width=0.4,\n", + " min_holes=1,\n", + " min_height=0.2,\n", + " min_width=0.2,\n", + " p=0.5)\n", + " ]),\n", + " dict(\n", + " type='GenerateTarget',\n", + " encoder=dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)),\n", + " dict(type='PackPoseInputs')\n", + " ])\n", + "]\n", + "env_cfg = dict(\n", + " cudnn_benchmark=False,\n", + " mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n", + " dist_cfg=dict(backend='nccl'))\n", + "vis_backends = [dict(type='LocalVisBackend', _scope_='mmpose')]\n", + "visualizer = dict(\n", + " type='PoseLocalVisualizer',\n", + " vis_backends=[dict(type='LocalVisBackend')],\n", + " name='visualizer',\n", + " _scope_='mmpose')\n", + "log_processor = dict(\n", + " type='LogProcessor',\n", + " window_size=50,\n", + " by_epoch=True,\n", + " num_digits=6,\n", + " _scope_='mmpose')\n", + "log_level = 'INFO'\n", + "load_from = None\n", + "resume = False\n", + "file_client_args = dict(backend='disk')\n", + "train_cfg = dict(by_epoch=True, max_epochs=220, val_interval=20)\n", + "val_cfg = dict()\n", + "test_cfg = dict()\n", + "dataset_type = 'CocoDataset'\n", + "data_mode = 'topdown'\n", + "data_root = 'data/Triangle_140_Keypoint_Dataset/'\n", + "dataset_info = dict(\n", + " dataset_name='Triangle_140_Keypoint',\n", + " classes='sjb_rect',\n", + " paper_info=dict(\n", + " author='Tongji Zihao',\n", + " title='Triangle Keypoints Detection',\n", + " container='OpenMMLab',\n", + " year='2023',\n", + " homepage='https://space.bilibili.com/1900783'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(name='angle_30', id=0, color=[255, 0, 0], type='', swap=''),\n", + " 1:\n", + " dict(name='angle_60', id=1, color=[0, 255, 0], type='', swap=''),\n", + " 2:\n", + " dict(name='angle_90', id=2, color=[0, 0, 255], type='', swap='')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(link=('angle_30', 'angle_60'), id=0, color=[100, 150, 200]),\n", + " 1:\n", + " dict(link=('angle_60', 'angle_90'), id=1, color=[200, 100, 150]),\n", + " 2:\n", + " dict(link=('angle_90', 'angle_30'), id=2, color=[150, 120, 100])\n", + " }),\n", + " joint_weights=[1.0, 1.0, 1.0],\n", + " sigmas=[0.026, 0.025, 0.025])\n", + "NUM_KEYPOINTS = 3\n", + "max_epochs = 220\n", + "val_interval = 20\n", + "train_batch_size = 64\n", + "val_batch_size = 8\n", + "stage2_num_epochs = 0\n", + "base_lr = 0.004\n", + "randomness = dict(seed=21)\n", + "optim_wrapper = dict(\n", + " type='OptimWrapper',\n", + " optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),\n", + " paramwise_cfg=dict(\n", + " norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))\n", + "param_scheduler = [\n", + " dict(type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0, end=20),\n", + " dict(\n", + " type='CosineAnnealingLR',\n", + " eta_min=0.0002,\n", + " begin=110,\n", + " end=220,\n", + " T_max=110,\n", + " by_epoch=True,\n", + " convert_to_iter_based=True)\n", + "]\n", + "auto_scale_lr = dict(base_batch_size=1024)\n", + "codec = dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)\n", + "model = dict(\n", + " type='TopdownPoseEstimator',\n", + " data_preprocessor=dict(\n", + " type='PoseDataPreprocessor',\n", + " mean=[123.675, 116.28, 103.53],\n", + " std=[58.395, 57.12, 57.375],\n", + " bgr_to_rgb=True),\n", + " backbone=dict(\n", + " _scope_='mmdet',\n", + " type='CSPNeXt',\n", + " arch='P5',\n", + " expand_ratio=0.5,\n", + " deepen_factor=0.67,\n", + " widen_factor=0.75,\n", + " out_indices=(4, ),\n", + " channel_attention=True,\n", + " norm_cfg=dict(type='SyncBN'),\n", + " act_cfg=dict(type='SiLU'),\n", + " init_cfg=dict(\n", + " type='Pretrained',\n", + " prefix='backbone.',\n", + " checkpoint=\n", + " 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth'\n", + " )),\n", + " head=dict(\n", + " type='RTMCCHead',\n", + " in_channels=768,\n", + " out_channels=3,\n", + " input_size=(256, 256),\n", + " in_featuremap_size=(8, 8),\n", + " simcc_split_ratio=2.0,\n", + " final_layer_kernel_size=7,\n", + " gau_cfg=dict(\n", + " hidden_dims=256,\n", + " s=128,\n", + " expansion_factor=2,\n", + " dropout_rate=0.0,\n", + " drop_path=0.0,\n", + " act_fn='SiLU',\n", + " use_rel_bias=False,\n", + " pos_enc=False),\n", + " loss=dict(\n", + " type='KLDiscretLoss',\n", + " use_target_weight=True,\n", + " beta=10.0,\n", + " label_softmax=True),\n", + " decoder=dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)),\n", + " test_cfg=dict(flip_test=True))\n", + "backend_args = dict(backend='local')\n", + "train_pipeline = [\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='RandomFlip', direction='horizontal'),\n", + " dict(\n", + " type='RandomBBoxTransform', scale_factor=[0.8, 1.2], rotate_factor=30),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='mmdet.YOLOXHSVRandomAug'),\n", + " dict(\n", + " type='Albumentation',\n", + " transforms=[\n", + " dict(type='ChannelShuffle', p=0.5),\n", + " dict(type='CLAHE', p=0.5),\n", + " dict(type='ColorJitter', p=0.5),\n", + " dict(\n", + " type='CoarseDropout',\n", + " max_holes=4,\n", + " max_height=0.3,\n", + " max_width=0.3,\n", + " min_holes=1,\n", + " min_height=0.2,\n", + " min_width=0.2,\n", + " p=0.5)\n", + " ]),\n", + " dict(\n", + " type='GenerateTarget',\n", + " encoder=dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)),\n", + " dict(type='PackPoseInputs')\n", + "]\n", + "val_pipeline = [\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='PackPoseInputs')\n", + "]\n", + "train_pipeline_stage2 = [\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='RandomFlip', direction='horizontal'),\n", + " dict(type='RandomHalfBody'),\n", + " dict(\n", + " type='RandomBBoxTransform',\n", + " shift_factor=0.0,\n", + " scale_factor=[0.75, 1.25],\n", + " rotate_factor=60),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='mmdet.YOLOXHSVRandomAug'),\n", + " dict(\n", + " type='Albumentation',\n", + " transforms=[\n", + " dict(type='Blur', p=0.1),\n", + " dict(type='MedianBlur', p=0.1),\n", + " dict(\n", + " type='CoarseDropout',\n", + " max_holes=1,\n", + " max_height=0.4,\n", + " max_width=0.4,\n", + " min_holes=1,\n", + " min_height=0.2,\n", + " min_width=0.2,\n", + " p=0.5)\n", + " ]),\n", + " dict(\n", + " type='GenerateTarget',\n", + " encoder=dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)),\n", + " dict(type='PackPoseInputs')\n", + "]\n", + "train_dataloader = dict(\n", + " batch_size=64,\n", + " num_workers=1,\n", + " persistent_workers=True,\n", + " sampler=dict(type='DefaultSampler', shuffle=True),\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(\n", + " dataset_name='Triangle_140_Keypoint',\n", + " classes='sjb_rect',\n", + " paper_info=dict(\n", + " author='Tongji Zihao',\n", + " title='Triangle Keypoints Detection',\n", + " container='OpenMMLab',\n", + " year='2023',\n", + " homepage='https://space.bilibili.com/1900783'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='angle_30', id=0, color=[255, 0, 0], type='',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='angle_60', id=1, color=[0, 255, 0], type='',\n", + " swap=''),\n", + " 2:\n", + " dict(\n", + " name='angle_90', id=2, color=[0, 0, 255], type='', swap='')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('angle_30', 'angle_60'), id=0, color=[100, 150,\n", + " 200]),\n", + " 1:\n", + " dict(\n", + " link=('angle_60', 'angle_90'), id=1, color=[200, 100,\n", + " 150]),\n", + " 2:\n", + " dict(\n", + " link=('angle_90', 'angle_30'), id=2, color=[150, 120, 100])\n", + " }),\n", + " joint_weights=[1.0, 1.0, 1.0],\n", + " sigmas=[0.026, 0.025, 0.025]),\n", + " data_mode='topdown',\n", + " ann_file='train_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " pipeline=[\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='RandomFlip', direction='horizontal'),\n", + " dict(\n", + " type='RandomBBoxTransform',\n", + " scale_factor=[0.8, 1.2],\n", + " rotate_factor=30),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='mmdet.YOLOXHSVRandomAug'),\n", + " dict(\n", + " type='Albumentation',\n", + " transforms=[\n", + " dict(type='ChannelShuffle', p=0.5),\n", + " dict(type='CLAHE', p=0.5),\n", + " dict(type='ColorJitter', p=0.5),\n", + " dict(\n", + " type='CoarseDropout',\n", + " max_holes=4,\n", + " max_height=0.3,\n", + " max_width=0.3,\n", + " min_holes=1,\n", + " min_height=0.2,\n", + " min_width=0.2,\n", + " p=0.5)\n", + " ]),\n", + " dict(\n", + " type='GenerateTarget',\n", + " encoder=dict(\n", + " type='SimCCLabel',\n", + " input_size=(256, 256),\n", + " sigma=(12, 12),\n", + " simcc_split_ratio=2.0,\n", + " normalize=False,\n", + " use_dark=False)),\n", + " dict(type='PackPoseInputs')\n", + " ]))\n", + "val_dataloader = dict(\n", + " batch_size=8,\n", + " num_workers=1,\n", + " persistent_workers=True,\n", + " drop_last=False,\n", + " sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(\n", + " dataset_name='Triangle_140_Keypoint',\n", + " classes='sjb_rect',\n", + " paper_info=dict(\n", + " author='Tongji Zihao',\n", + " title='Triangle Keypoints Detection',\n", + " container='OpenMMLab',\n", + " year='2023',\n", + " homepage='https://space.bilibili.com/1900783'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='angle_30', id=0, color=[255, 0, 0], type='',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='angle_60', id=1, color=[0, 255, 0], type='',\n", + " swap=''),\n", + " 2:\n", + " dict(\n", + " name='angle_90', id=2, color=[0, 0, 255], type='', swap='')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('angle_30', 'angle_60'), id=0, color=[100, 150,\n", + " 200]),\n", + " 1:\n", + " dict(\n", + " link=('angle_60', 'angle_90'), id=1, color=[200, 100,\n", + " 150]),\n", + " 2:\n", + " dict(\n", + " link=('angle_90', 'angle_30'), id=2, color=[150, 120, 100])\n", + " }),\n", + " joint_weights=[1.0, 1.0, 1.0],\n", + " sigmas=[0.026, 0.025, 0.025]),\n", + " data_mode='topdown',\n", + " ann_file='val_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " pipeline=[\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='PackPoseInputs')\n", + " ]))\n", + "test_dataloader = dict(\n", + " batch_size=8,\n", + " num_workers=1,\n", + " persistent_workers=True,\n", + " drop_last=False,\n", + " sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n", + " dataset=dict(\n", + " type='CocoDataset',\n", + " data_root='data/Triangle_140_Keypoint_Dataset/',\n", + " metainfo=dict(\n", + " dataset_name='Triangle_140_Keypoint',\n", + " classes='sjb_rect',\n", + " paper_info=dict(\n", + " author='Tongji Zihao',\n", + " title='Triangle Keypoints Detection',\n", + " container='OpenMMLab',\n", + " year='2023',\n", + " homepage='https://space.bilibili.com/1900783'),\n", + " keypoint_info=dict({\n", + " 0:\n", + " dict(\n", + " name='angle_30', id=0, color=[255, 0, 0], type='',\n", + " swap=''),\n", + " 1:\n", + " dict(\n", + " name='angle_60', id=1, color=[0, 255, 0], type='',\n", + " swap=''),\n", + " 2:\n", + " dict(\n", + " name='angle_90', id=2, color=[0, 0, 255], type='', swap='')\n", + " }),\n", + " skeleton_info=dict({\n", + " 0:\n", + " dict(\n", + " link=('angle_30', 'angle_60'), id=0, color=[100, 150,\n", + " 200]),\n", + " 1:\n", + " dict(\n", + " link=('angle_60', 'angle_90'), id=1, color=[200, 100,\n", + " 150]),\n", + " 2:\n", + " dict(\n", + " link=('angle_90', 'angle_30'), id=2, color=[150, 120, 100])\n", + " }),\n", + " joint_weights=[1.0, 1.0, 1.0],\n", + " sigmas=[0.026, 0.025, 0.025]),\n", + " data_mode='topdown',\n", + " ann_file='val_coco.json',\n", + " data_prefix=dict(img='images/'),\n", + " pipeline=[\n", + " dict(type='LoadImage', backend_args=dict(backend='local')),\n", + " dict(type='GetBBoxCenterScale'),\n", + " dict(type='TopdownAffine', input_size=(256, 256)),\n", + " dict(type='PackPoseInputs')\n", + " ]))\n", + "val_evaluator = [\n", + " dict(\n", + " type='CocoMetric',\n", + " ann_file='data/Triangle_140_Keypoint_Dataset/val_coco.json'),\n", + " dict(type='PCKAccuracy')\n", + "]\n", + "test_evaluator = [\n", + " dict(\n", + " type='CocoMetric',\n", + " ann_file='data/Triangle_140_Keypoint_Dataset/val_coco.json'),\n", + " dict(type='PCKAccuracy')\n", + "]\n", + "launcher = 'none'\n", + "work_dir = './work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192'\n", + "\n", + "04/03 19:53:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n", + "04/03 19:53:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_load_checkpoint:\n", + "(49 ) EMAHook \n", + " -------------------- \n", + "before_train:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) DistSamplerSeedHook \n", + "(NORMAL ) PipelineSwitchHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) IterTimerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) PoseVisualizationHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + "(LOW ) ParamSchedulerHook \n", + "(VERY_LOW ) CheckpointHook \n", + " -------------------- \n", + "before_save_checkpoint:\n", + "(49 ) EMAHook \n", + " -------------------- \n", + "before_test_epoch:\n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "before_test_iter:\n", + "(NORMAL ) IterTimerHook \n", + " -------------------- \n", + "after_test_iter:\n", + "(NORMAL ) IterTimerHook \n", + "(NORMAL ) PoseVisualizationHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_test_epoch:\n", + "(VERY_HIGH ) RuntimeInfoHook \n", + "(49 ) EMAHook \n", + "(NORMAL ) IterTimerHook \n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "after_run:\n", + "(BELOW_NORMAL) LoggerHook \n", + " -------------------- \n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.0.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.0.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.1.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.1.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.2.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stem.2.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.0.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.0.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.main_conv.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage1.1.main_conv.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - 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backbone.stage4.2.blocks.1.conv1.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.blocks.1.conv1.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.blocks.1.conv2.depthwise_conv.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.blocks.1.conv2.depthwise_conv.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.blocks.1.conv2.pointwise_conv.bn.weight:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.blocks.1.conv2.pointwise_conv.bn.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.stage4.2.attention.fc.bias:weight_decay=0.0\n", + "04/03 19:53:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.final_layer.bias:weight_decay=0.0\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "04/03 19:53:36 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - The prefix is not set in metric class PCKAccuracy.\n", + "04/03 19:53:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load backbone. in model from: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth\n", + "Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth\n", + "04/03 19:53:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"FileClient\" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io\n", + "04/03 19:53:39 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - \"HardDiskBackend\" is the alias of \"LocalBackend\" and the former will be deprecated in future.\n", + "04/03 19:53:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /home/featurize/work/关键点检测/mmpose/work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192.\n", + "04/03 19:54:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][1/4] lr: 4.000000e-08 eta: 5:21:52 time: 21.971296 data_time: 21.453711 memory: 5513 loss: 0.833019 loss_kpt: 0.833019 acc_pose: 0.021169\n", + "04/03 19:54:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][2/4] lr: 2.105642e-04 eta: 5:11:38 time: 21.296390 data_time: 20.827563 memory: 5671 loss: 0.827701 loss_kpt: 0.827701 acc_pose: 0.010755\n", + "04/03 19:54:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][3/4] lr: 4.210884e-04 eta: 5:11:07 time: 21.286129 data_time: 20.835113 memory: 5671 loss: 0.826783 loss_kpt: 0.826783 acc_pose: 0.026207\n", + "04/03 19:54:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 19:54:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][4/4] lr: 6.316126e-04 eta: 4:21:01 time: 17.878487 data_time: 17.484713 memory: 2632 loss: 0.708121 loss_kpt: 0.708121 acc_pose: 0.012346\n", + "04/03 19:55:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][1/4] lr: 8.421368e-04 eta: 4:29:39 time: 18.490512 data_time: 18.091605 memory: 5671 loss: 0.729177 loss_kpt: 0.729177 acc_pose: 0.042413\n", + "04/03 19:55:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][2/4] lr: 1.052661e-03 eta: 4:34:28 time: 18.842249 data_time: 18.440568 memory: 5671 loss: 0.745623 loss_kpt: 0.745623 acc_pose: 0.052249\n", + "04/03 19:55:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][3/4] lr: 1.263185e-03 eta: 4:39:27 time: 19.207044 data_time: 18.804249 memory: 5671 loss: 0.754027 loss_kpt: 0.754027 acc_pose: 0.031917\n", + "04/03 19:56:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 19:56:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][4/4] lr: 1.473709e-03 eta: 4:19:01 time: 17.822780 data_time: 17.444529 memory: 2632 loss: 0.702516 loss_kpt: 0.702516 acc_pose: 0.025641\n", + "04/03 19:58:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][3/4] lr: 2.947379e-03 eta: 4:26:16 time: 18.469785 data_time: 18.087790 memory: 5671 loss: 0.697578 loss_kpt: 0.697578 acc_pose: 0.125000\n", + "04/03 19:58:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 19:58:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [4][4/4] lr: 3.157903e-03 eta: 4:16:33 time: 17.816922 data_time: 17.445925 memory: 2632 loss: 0.672473 loss_kpt: 0.672473 acc_pose: 0.075024\n", + "04/03 19:58:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][1/4] lr: 3.368427e-03 eta: 4:20:23 time: 18.104053 data_time: 17.730164 memory: 5671 loss: 0.675240 loss_kpt: 0.675240 acc_pose: 0.062831\n", + "04/03 19:59:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][2/4] lr: 3.578952e-03 eta: 4:22:00 time: 18.237802 data_time: 17.861121 memory: 5671 loss: 0.676159 loss_kpt: 0.676159 acc_pose: 0.085865\n", + "04/03 19:59:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][3/4] lr: 3.789476e-03 eta: 4:23:16 time: 18.346841 data_time: 17.968899 memory: 5671 loss: 0.676750 loss_kpt: 0.676750 acc_pose: 0.099702\n", + "04/03 19:59:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 19:59:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [5][4/4] lr: 4.000000e-03 eta: 4:16:49 time: 17.917554 data_time: 17.547996 memory: 2632 loss: 0.656826 loss_kpt: 0.656826 acc_pose: 0.136752\n", + "04/03 19:59:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][1/4] lr: 4.000000e-03 eta: 4:19:18 time: 18.112402 data_time: 17.740284 memory: 5671 loss: 0.656873 loss_kpt: 0.656873 acc_pose: 0.128743\n", + "04/03 20:00:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][2/4] lr: 4.000000e-03 eta: 4:19:48 time: 18.168167 data_time: 17.794176 memory: 5671 loss: 0.659138 loss_kpt: 0.659138 acc_pose: 0.126323\n", + "04/03 20:00:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][3/4] lr: 4.000000e-03 eta: 4:21:01 time: 18.274660 data_time: 17.899079 memory: 5671 loss: 0.660547 loss_kpt: 0.660547 acc_pose: 0.161458\n", + "04/03 20:00:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:00:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [6][4/4] lr: 4.000000e-03 eta: 4:14:35 time: 17.844724 data_time: 17.465477 memory: 2632 loss: 0.645089 loss_kpt: 0.645089 acc_pose: 0.112061\n", + "04/03 20:01:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][1/4] lr: 4.000000e-03 eta: 4:17:08 time: 18.044771 data_time: 17.664377 memory: 5671 loss: 0.648676 loss_kpt: 0.648676 acc_pose: 0.130306\n", + "04/03 20:01:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][2/4] lr: 4.000000e-03 eta: 4:18:25 time: 18.156309 data_time: 17.774625 memory: 5671 loss: 0.649140 loss_kpt: 0.649140 acc_pose: 0.138590\n", + "04/03 20:01:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][3/4] lr: 4.000000e-03 eta: 4:19:57 time: 18.285789 data_time: 17.903112 memory: 5671 loss: 0.651752 loss_kpt: 0.651752 acc_pose: 0.125827\n", + "04/03 20:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:02:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [7][4/4] lr: 4.000000e-03 eta: 4:14:45 time: 17.940959 data_time: 17.564449 memory: 2632 loss: 0.639803 loss_kpt: 0.639803 acc_pose: 0.128205\n", + "04/03 20:02:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [8][1/4] lr: 4.000000e-03 eta: 4:16:19 time: 18.071889 data_time: 17.694249 memory: 5671 loss: 0.645515 loss_kpt: 0.645515 acc_pose: 0.154463\n", + "04/03 20:02:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [8][2/4] lr: 4.000000e-03 eta: 4:17:29 time: 18.175975 data_time: 17.795986 memory: 5671 loss: 0.647413 loss_kpt: 0.647413 acc_pose: 0.131035\n", + "04/03 20:03:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [8][3/4] lr: 4.000000e-03 eta: 4:18:05 time: 18.239828 data_time: 17.858492 memory: 5671 loss: 0.648090 loss_kpt: 0.648090 acc_pose: 0.186977\n", + "04/03 20:03:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:03:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [8][4/4] lr: 4.000000e-03 eta: 4:13:18 time: 17.922572 data_time: 17.546758 memory: 2632 loss: 0.636951 loss_kpt: 0.636951 acc_pose: 0.213675\n", + "04/03 20:03:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][1/4] lr: 4.000000e-03 eta: 4:15:14 time: 18.080844 data_time: 17.704105 memory: 5671 loss: 0.638969 loss_kpt: 0.638969 acc_pose: 0.143796\n", + "04/03 20:03:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][2/4] lr: 4.000000e-03 eta: 4:15:57 time: 18.152889 data_time: 17.774828 memory: 5671 loss: 0.641360 loss_kpt: 0.641360 acc_pose: 0.141286\n", + "04/03 20:04:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][3/4] lr: 4.000000e-03 eta: 4:16:41 time: 18.226773 data_time: 17.847878 memory: 5671 loss: 0.642345 loss_kpt: 0.642345 acc_pose: 0.143711\n", + "04/03 20:04:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:04:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [9][4/4] lr: 4.000000e-03 eta: 4:12:36 time: 17.958433 data_time: 17.584499 memory: 2632 loss: 0.632429 loss_kpt: 0.632429 acc_pose: 0.162393\n", + "04/03 20:04:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][1/4] lr: 4.000000e-03 eta: 4:13:38 time: 18.052765 data_time: 17.677866 memory: 5671 loss: 0.634757 loss_kpt: 0.634757 acc_pose: 0.132275\n", + "04/03 20:05:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][2/4] lr: 4.000000e-03 eta: 4:14:32 time: 18.138885 data_time: 17.763204 memory: 5671 loss: 0.637239 loss_kpt: 0.637239 acc_pose: 0.075040\n", + "04/03 20:05:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][3/4] lr: 4.000000e-03 eta: 4:15:53 time: 18.256739 data_time: 17.880323 memory: 5671 loss: 0.637969 loss_kpt: 0.637969 acc_pose: 0.182292\n", + "04/03 20:05:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:05:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [10][4/4] lr: 4.000000e-03 eta: 4:12:15 time: 18.018991 data_time: 17.646790 memory: 2632 loss: 0.628941 loss_kpt: 0.628941 acc_pose: 0.142678\n", + "04/03 20:05:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 10 epochs\n", + "04/03 20:06:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][1/4] lr: 4.000000e-03 eta: 4:13:14 time: 18.109893 data_time: 17.736685 memory: 5671 loss: 0.630768 loss_kpt: 0.630768 acc_pose: 0.094081\n", + "04/03 20:06:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][2/4] lr: 4.000000e-03 eta: 4:13:50 time: 18.174977 data_time: 17.800929 memory: 5671 loss: 0.632101 loss_kpt: 0.632101 acc_pose: 0.132531\n", + "04/03 20:06:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][3/4] lr: 4.000000e-03 eta: 4:13:58 time: 18.206111 data_time: 17.831358 memory: 5671 loss: 0.633114 loss_kpt: 0.633114 acc_pose: 0.146495\n", + "04/03 20:06:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:06:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [11][4/4] lr: 4.000000e-03 eta: 4:10:23 time: 17.970408 data_time: 17.599598 memory: 2632 loss: 0.624629 loss_kpt: 0.624629 acc_pose: 0.224122\n", + "04/03 20:07:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][1/4] lr: 4.000000e-03 eta: 4:11:11 time: 18.049941 data_time: 17.678143 memory: 5671 loss: 0.625696 loss_kpt: 0.625696 acc_pose: 0.139188\n", + "04/03 20:07:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][2/4] lr: 4.000000e-03 eta: 4:11:22 time: 18.084701 data_time: 17.712293 memory: 5671 loss: 0.626516 loss_kpt: 0.626516 acc_pose: 0.179233\n", + "04/03 20:07:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][3/4] lr: 4.000000e-03 eta: 4:12:05 time: 18.157665 data_time: 17.784176 memory: 5671 loss: 0.625770 loss_kpt: 0.625770 acc_pose: 0.242759\n", + "04/03 20:08:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:08:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [12][4/4] lr: 4.000000e-03 eta: 4:08:50 time: 17.945820 data_time: 17.576018 memory: 2632 loss: 0.618571 loss_kpt: 0.618571 acc_pose: 0.111111\n", + "04/03 20:08:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][1/4] lr: 4.000000e-03 eta: 4:09:38 time: 18.024314 data_time: 17.653717 memory: 5671 loss: 0.618625 loss_kpt: 0.618625 acc_pose: 0.189071\n", + "04/03 20:08:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][2/4] lr: 4.000000e-03 eta: 4:10:02 time: 18.074963 data_time: 17.703666 memory: 5671 loss: 0.618275 loss_kpt: 0.618275 acc_pose: 0.168839\n", + "04/03 20:09:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][3/4] lr: 4.000000e-03 eta: 4:10:26 time: 18.049096 data_time: 17.679966 memory: 5671 loss: 0.613651 loss_kpt: 0.613651 acc_pose: 0.225974\n", + "04/03 20:09:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:09:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [13][4/4] lr: 4.000000e-03 eta: 4:07:41 time: 17.814740 data_time: 17.449920 memory: 2632 loss: 0.602537 loss_kpt: 0.602537 acc_pose: 0.160494\n", + "04/03 20:09:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][1/4] lr: 4.000000e-03 eta: 4:08:15 time: 17.814911 data_time: 17.450324 memory: 5671 loss: 0.597918 loss_kpt: 0.597918 acc_pose: 0.246280\n", + "04/03 20:09:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][2/4] lr: 4.000000e-03 eta: 4:08:45 time: 18.084923 data_time: 17.716186 memory: 5671 loss: 0.602674 loss_kpt: 0.602674 acc_pose: 0.229595\n", + "04/03 20:10:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][3/4] lr: 4.000000e-03 eta: 4:08:53 time: 18.062428 data_time: 17.693751 memory: 5671 loss: 0.598279 loss_kpt: 0.598279 acc_pose: 0.202253\n", + "04/03 20:10:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:10:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [14][4/4] lr: 4.000000e-03 eta: 4:06:24 time: 17.833937 data_time: 17.469559 memory: 2632 loss: 0.586320 loss_kpt: 0.586320 acc_pose: 0.259639\n", + "04/03 20:10:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][1/4] lr: 4.000000e-03 eta: 4:06:55 time: 17.833643 data_time: 17.469127 memory: 5671 loss: 0.580672 loss_kpt: 0.580672 acc_pose: 0.290717\n", + "04/03 20:11:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][2/4] lr: 4.000000e-03 eta: 4:07:17 time: 18.086323 data_time: 17.717696 memory: 5671 loss: 0.585216 loss_kpt: 0.585216 acc_pose: 0.284226\n", + "04/03 20:11:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][3/4] lr: 4.000000e-03 eta: 4:07:35 time: 18.076092 data_time: 17.707575 memory: 5671 loss: 0.580304 loss_kpt: 0.580304 acc_pose: 0.305473\n", + "04/03 20:11:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:11:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [15][4/4] lr: 4.000000e-03 eta: 4:05:01 time: 17.826465 data_time: 17.462278 memory: 2632 loss: 0.569355 loss_kpt: 0.569355 acc_pose: 0.254815\n", + "04/03 20:11:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][1/4] lr: 4.000000e-03 eta: 4:05:17 time: 17.823162 data_time: 17.459209 memory: 5671 loss: 0.564831 loss_kpt: 0.564831 acc_pose: 0.259641\n", + "04/03 20:12:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][2/4] lr: 4.000000e-03 eta: 4:05:35 time: 18.057925 data_time: 17.689895 memory: 5671 loss: 0.569349 loss_kpt: 0.569349 acc_pose: 0.310020\n", + "04/03 20:12:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][3/4] lr: 4.000000e-03 eta: 4:06:00 time: 18.061113 data_time: 17.692947 memory: 5671 loss: 0.564872 loss_kpt: 0.564872 acc_pose: 0.336832\n", + "04/03 20:12:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:12:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [16][4/4] lr: 4.000000e-03 eta: 4:03:45 time: 17.827301 data_time: 17.463319 memory: 2632 loss: 0.554526 loss_kpt: 0.554526 acc_pose: 0.371795\n", + "04/03 20:13:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][1/4] lr: 4.000000e-03 eta: 4:04:11 time: 17.829090 data_time: 17.464755 memory: 5671 loss: 0.549969 loss_kpt: 0.549969 acc_pose: 0.368493\n", + "04/03 20:13:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][2/4] lr: 4.000000e-03 eta: 4:04:35 time: 18.097105 data_time: 17.728832 memory: 5671 loss: 0.554403 loss_kpt: 0.554403 acc_pose: 0.338267\n", + "04/03 20:13:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][3/4] lr: 4.000000e-03 eta: 4:04:31 time: 18.027225 data_time: 17.659325 memory: 5671 loss: 0.549773 loss_kpt: 0.549773 acc_pose: 0.350364\n", + "04/03 20:13:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:13:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [17][4/4] lr: 4.000000e-03 eta: 4:02:10 time: 17.771866 data_time: 17.408340 memory: 2632 loss: 0.539511 loss_kpt: 0.539511 acc_pose: 0.484568\n", + "04/03 20:14:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][1/4] lr: 4.000000e-03 eta: 4:02:35 time: 17.796387 data_time: 17.432316 memory: 5671 loss: 0.534959 loss_kpt: 0.534959 acc_pose: 0.453968\n", + "04/03 20:14:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][2/4] lr: 4.000000e-03 eta: 4:02:57 time: 18.028326 data_time: 17.660131 memory: 5671 loss: 0.538807 loss_kpt: 0.538807 acc_pose: 0.392372\n", + "04/03 20:15:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][3/4] lr: 4.000000e-03 eta: 4:03:10 time: 18.003033 data_time: 17.634986 memory: 5671 loss: 0.535447 loss_kpt: 0.535447 acc_pose: 0.375661\n", + "04/03 20:15:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:15:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [18][4/4] lr: 4.000000e-03 eta: 4:01:06 time: 17.788328 data_time: 17.424430 memory: 2632 loss: 0.525417 loss_kpt: 0.525417 acc_pose: 0.388889\n", + "04/03 20:15:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][1/4] lr: 4.000000e-03 eta: 4:01:22 time: 17.794435 data_time: 17.429878 memory: 5671 loss: 0.520608 loss_kpt: 0.520608 acc_pose: 0.392247\n", + "04/03 20:15:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][2/4] lr: 4.000000e-03 eta: 4:01:41 time: 18.063445 data_time: 17.699678 memory: 5671 loss: 0.524146 loss_kpt: 0.524146 acc_pose: 0.370517\n", + "04/03 20:16:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][3/4] lr: 4.000000e-03 eta: 4:01:54 time: 18.022668 data_time: 17.658981 memory: 5671 loss: 0.518125 loss_kpt: 0.518125 acc_pose: 0.510582\n", + "04/03 20:16:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:16:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [19][4/4] lr: 4.000000e-03 eta: 3:59:56 time: 17.776392 data_time: 17.416900 memory: 2632 loss: 0.508640 loss_kpt: 0.508640 acc_pose: 0.395062\n", + "04/03 20:16:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][1/4] lr: 4.000000e-03 eta: 4:00:07 time: 17.757215 data_time: 17.397567 memory: 5671 loss: 0.503287 loss_kpt: 0.503287 acc_pose: 0.443852\n", + "04/03 20:17:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][2/4] lr: 4.000000e-03 eta: 4:00:16 time: 17.994993 data_time: 17.631133 memory: 5671 loss: 0.506045 loss_kpt: 0.506045 acc_pose: 0.481567\n", + "04/03 20:17:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][3/4] lr: 4.000000e-03 eta: 4:00:25 time: 17.973289 data_time: 17.609429 memory: 5671 loss: 0.498552 loss_kpt: 0.498552 acc_pose: 0.492619\n", + "04/03 20:17:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:17:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [20][4/4] lr: 4.000000e-03 eta: 3:58:28 time: 17.712231 data_time: 17.353292 memory: 2632 loss: 0.488251 loss_kpt: 0.488251 acc_pose: 0.480000\n", + "04/03 20:17:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 20 epochs\n", + "04/03 20:17:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][1/7] eta: 0:00:13 time: 2.253704 data_time: 2.182140 memory: 360 \n", + "04/03 20:17:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][2/7] eta: 0:00:10 time: 2.093631 data_time: 2.033460 memory: 360 \n", + "04/03 20:17:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][3/7] eta: 0:00:08 time: 2.040577 data_time: 1.984201 memory: 360 \n", + "04/03 20:17:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][4/7] eta: 0:00:06 time: 2.047474 data_time: 1.994854 memory: 360 \n", + "04/03 20:17:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][5/7] eta: 0:00:04 time: 2.053111 data_time: 2.000979 memory: 360 \n", + "04/03 20:17:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][6/7] eta: 0:00:02 time: 2.019677 data_time: 1.968014 memory: 360 \n", + "04/03 20:17:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][7/7] eta: 0:00:00 time: 1.804504 data_time: 1.753482 memory: 287 \n", + "04/03 20:17:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating CocoMetric...\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.112\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.325\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.047\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.112\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.168\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.420\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.100\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.168\n", + "04/03 20:17:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating PCKAccuracy (normalized by ``\"bbox_size\"``)...\n", + "04/03 20:17:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [20][7/7] coco/AP: 0.111900 coco/AP .5: 0.324801 coco/AP .75: 0.046906 coco/AP (M): -1.000000 coco/AP (L): 0.111900 coco/AR: 0.168000 coco/AR .5: 0.420000 coco/AR .75: 0.100000 coco/AR (M): -1.000000 coco/AR (L): 0.168000 PCK: 0.486395data_time: 1.753482 time: 1.804504 \n", + "04/03 20:17:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.4864 PCK at 20 epoch is saved to best_PCK_epoch_20.pth.\n", + "04/03 20:18:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][1/4] lr: 4.000000e-03 eta: 3:58:48 time: 17.742676 data_time: 17.383873 memory: 5671 loss: 0.482981 loss_kpt: 0.482981 acc_pose: 0.550691\n", + "04/03 20:18:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][2/4] lr: 4.000000e-03 eta: 3:58:54 time: 17.988165 data_time: 17.624950 memory: 5671 loss: 0.486242 loss_kpt: 0.486242 acc_pose: 0.472801\n", + "04/03 20:18:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][3/4] lr: 4.000000e-03 eta: 3:59:07 time: 17.949530 data_time: 17.586387 memory: 5671 loss: 0.480452 loss_kpt: 0.480452 acc_pose: 0.537450\n", + "04/03 20:19:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:19:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [21][4/4] lr: 4.000000e-03 eta: 3:57:17 time: 17.704354 data_time: 17.345526 memory: 2632 loss: 0.469456 loss_kpt: 0.469456 acc_pose: 0.604938\n", + "04/03 20:19:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][1/4] lr: 4.000000e-03 eta: 3:57:43 time: 17.740844 data_time: 17.381797 memory: 5671 loss: 0.464252 loss_kpt: 0.464252 acc_pose: 0.502892\n", + "04/03 20:19:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][2/4] lr: 4.000000e-03 eta: 3:57:43 time: 17.968149 data_time: 17.604870 memory: 5671 loss: 0.466516 loss_kpt: 0.466516 acc_pose: 0.580688\n", + "04/03 20:20:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][3/4] lr: 4.000000e-03 eta: 3:57:54 time: 17.962801 data_time: 17.599544 memory: 5671 loss: 0.460349 loss_kpt: 0.460349 acc_pose: 0.574927\n", + "04/03 20:20:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:20:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [22][4/4] lr: 4.000000e-03 eta: 3:56:09 time: 17.701490 data_time: 17.342234 memory: 2632 loss: 0.449024 loss_kpt: 0.449024 acc_pose: 0.654321\n", + "04/03 20:20:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][1/4] lr: 4.000000e-03 eta: 3:56:21 time: 17.672466 data_time: 17.312823 memory: 5671 loss: 0.443723 loss_kpt: 0.443723 acc_pose: 0.566660\n", + "04/03 20:20:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [23][2/4] lr: 4.000000e-03 eta: 3:56:21 time: 17.896282 data_time: 17.532285 memory: 5671 loss: 0.446121 loss_kpt: 0.446121 acc_pose: 0.609375\n", + "04/03 20:21:19 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [34][3/4] lr: 4.000000e-03 eta: 3:45:04 time: 18.441850 data_time: 18.075298 memory: 5671 loss: 0.292128 loss_kpt: 0.292128 acc_pose: 0.820106\n", + "04/03 20:35:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:35:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [34][4/4] lr: 4.000000e-03 eta: 3:43:55 time: 18.221710 data_time: 17.859276 memory: 2632 loss: 0.286696 loss_kpt: 0.286696 acc_pose: 0.833333\n", + "04/03 20:35:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][1/4] lr: 4.000000e-03 eta: 3:43:53 time: 18.216559 data_time: 17.854166 memory: 5671 loss: 0.284287 loss_kpt: 0.284287 acc_pose: 0.857986\n", + "04/03 20:35:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][2/4] lr: 4.000000e-03 eta: 3:43:55 time: 18.487850 data_time: 18.121287 memory: 5671 loss: 0.286673 loss_kpt: 0.286673 acc_pose: 0.845963\n", + "04/03 20:36:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][3/4] lr: 4.000000e-03 eta: 3:43:50 time: 18.474491 data_time: 18.108371 memory: 5671 loss: 0.284113 loss_kpt: 0.284113 acc_pose: 0.863591\n", + "04/03 20:36:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:36:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [35][4/4] lr: 4.000000e-03 eta: 3:42:46 time: 18.265840 data_time: 17.904260 memory: 2632 loss: 0.278505 loss_kpt: 0.278505 acc_pose: 0.812441\n", + "04/03 20:36:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][1/4] lr: 4.000000e-03 eta: 3:42:50 time: 18.285405 data_time: 17.923604 memory: 5671 loss: 0.276587 loss_kpt: 0.276587 acc_pose: 0.836704\n", + "04/03 20:36:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][2/4] lr: 4.000000e-03 eta: 3:42:42 time: 18.520682 data_time: 18.154891 memory: 5671 loss: 0.279369 loss_kpt: 0.279369 acc_pose: 0.847636\n", + "04/03 20:37:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][3/4] lr: 4.000000e-03 eta: 3:42:34 time: 18.469601 data_time: 18.104136 memory: 5671 loss: 0.277178 loss_kpt: 0.277178 acc_pose: 0.857143\n", + "04/03 20:37:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:37:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [36][4/4] lr: 4.000000e-03 eta: 3:41:28 time: 18.220422 data_time: 17.859054 memory: 2632 loss: 0.271620 loss_kpt: 0.271620 acc_pose: 0.849478\n", + "04/03 20:37:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][1/4] lr: 4.000000e-03 eta: 3:41:28 time: 18.211363 data_time: 17.849876 memory: 5671 loss: 0.269829 loss_kpt: 0.269829 acc_pose: 0.850401\n", + "04/03 20:38:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][2/4] lr: 4.000000e-03 eta: 3:41:23 time: 18.453075 data_time: 18.087640 memory: 5671 loss: 0.272459 loss_kpt: 0.272459 acc_pose: 0.853588\n", + "04/03 20:38:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][3/4] lr: 4.000000e-03 eta: 3:41:20 time: 18.425617 data_time: 18.060826 memory: 5671 loss: 0.270421 loss_kpt: 0.270421 acc_pose: 0.813981\n", + "04/03 20:38:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:38:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [37][4/4] lr: 4.000000e-03 eta: 3:40:17 time: 18.154611 data_time: 17.789577 memory: 2632 loss: 0.265891 loss_kpt: 0.265891 acc_pose: 0.847104\n", + "04/03 20:39:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][1/4] lr: 4.000000e-03 eta: 3:40:18 time: 18.146857 data_time: 17.781731 memory: 5671 loss: 0.263719 loss_kpt: 0.263719 acc_pose: 0.895172\n", + "04/03 20:39:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][2/4] lr: 4.000000e-03 eta: 3:40:16 time: 18.393975 data_time: 18.025013 memory: 5671 loss: 0.266018 loss_kpt: 0.266018 acc_pose: 0.839188\n", + "04/03 20:39:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][3/4] lr: 4.000000e-03 eta: 3:40:14 time: 18.395220 data_time: 18.025851 memory: 5671 loss: 0.264381 loss_kpt: 0.264381 acc_pose: 0.859375\n", + "04/03 20:39:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:39:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [38][4/4] lr: 4.000000e-03 eta: 3:39:09 time: 18.128937 data_time: 17.763426 memory: 2632 loss: 0.259673 loss_kpt: 0.259673 acc_pose: 0.830731\n", + "04/03 20:40:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][1/4] lr: 4.000000e-03 eta: 3:39:05 time: 18.119718 data_time: 17.753392 memory: 5671 loss: 0.258458 loss_kpt: 0.258458 acc_pose: 0.837198\n", + "04/03 20:40:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][2/4] lr: 4.000000e-03 eta: 3:39:05 time: 18.368799 data_time: 17.998087 memory: 5671 loss: 0.260665 loss_kpt: 0.260665 acc_pose: 0.851718\n", + "04/03 20:40:55 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][3/4] lr: 4.000000e-03 eta: 3:39:00 time: 18.333765 data_time: 17.963233 memory: 5671 loss: 0.258369 loss_kpt: 0.258369 acc_pose: 0.913978\n", + "04/03 20:41:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:41:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [39][4/4] lr: 4.000000e-03 eta: 3:38:00 time: 18.113407 data_time: 17.746805 memory: 2632 loss: 0.253919 loss_kpt: 0.253919 acc_pose: 0.874644\n", + "04/03 20:41:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][1/4] lr: 4.000000e-03 eta: 3:37:55 time: 18.135464 data_time: 17.769104 memory: 5671 loss: 0.252633 loss_kpt: 0.252633 acc_pose: 0.849602\n", + "04/03 20:41:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][2/4] lr: 4.000000e-03 eta: 3:37:48 time: 18.369150 data_time: 17.998671 memory: 5671 loss: 0.254927 loss_kpt: 0.254927 acc_pose: 0.873264\n", + "04/03 20:42:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][3/4] lr: 4.000000e-03 eta: 3:37:40 time: 18.331456 data_time: 17.961603 memory: 5671 loss: 0.253300 loss_kpt: 0.253300 acc_pose: 0.866530\n", + "04/03 20:42:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:42:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [40][4/4] lr: 4.000000e-03 eta: 3:36:39 time: 18.074973 data_time: 17.710204 memory: 2632 loss: 0.249029 loss_kpt: 0.249029 acc_pose: 0.837607\n", + "04/03 20:42:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 40 epochs\n", + "04/03 20:42:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][1/7] eta: 0:00:12 time: 1.838023 data_time: 1.786485 memory: 360 \n", + "04/03 20:42:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][2/7] eta: 0:00:10 time: 1.850705 data_time: 1.798937 memory: 360 \n", + "04/03 20:42:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][3/7] eta: 0:00:07 time: 1.861372 data_time: 1.809765 memory: 360 \n", + "04/03 20:42:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][4/7] eta: 0:00:06 time: 1.876915 data_time: 1.825430 memory: 360 \n", + "04/03 20:42:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][5/7] eta: 0:00:03 time: 1.884779 data_time: 1.833482 memory: 360 \n", + "04/03 20:42:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][6/7] eta: 0:00:01 time: 1.881032 data_time: 1.829952 memory: 360 \n", + "04/03 20:42:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][7/7] eta: 0:00:00 time: 1.782412 data_time: 1.731655 memory: 287 \n", + "04/03 20:42:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating CocoMetric...\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.334\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.733\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.179\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.334\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.382\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.740\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.340\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.382\n", + "04/03 20:42:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating PCKAccuracy (normalized by ``\"bbox_size\"``)...\n", + "04/03 20:42:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [40][7/7] coco/AP: 0.334206 coco/AP .5: 0.733052 coco/AP .75: 0.178783 coco/AP (M): -1.000000 coco/AP (L): 0.334206 coco/AR: 0.382000 coco/AR .5: 0.740000 coco/AR .75: 0.340000 coco/AR (M): -1.000000 coco/AR (L): 0.382000 PCK: 0.783810data_time: 1.731655 time: 1.782412 \n", + "04/03 20:42:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmpose/work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192/best_PCK_epoch_20.pth is removed\n", + "04/03 20:42:34 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.7838 PCK at 40 epoch is saved to best_PCK_epoch_40.pth.\n", + "04/03 20:42:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][1/4] lr: 4.000000e-03 eta: 3:36:39 time: 18.073387 data_time: 17.708402 memory: 5671 loss: 0.248752 loss_kpt: 0.248752 acc_pose: 0.815860\n", + "04/03 20:43:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][2/4] lr: 4.000000e-03 eta: 3:36:33 time: 18.312587 data_time: 17.943421 memory: 5671 loss: 0.251831 loss_kpt: 0.251831 acc_pose: 0.851478\n", + "04/03 20:43:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][3/4] lr: 4.000000e-03 eta: 3:36:26 time: 18.286642 data_time: 17.917042 memory: 5671 loss: 0.249981 loss_kpt: 0.249981 acc_pose: 0.869355\n", + "04/03 20:43:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:43:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [41][4/4] lr: 4.000000e-03 eta: 3:35:29 time: 18.025335 data_time: 17.660274 memory: 2632 loss: 0.245344 loss_kpt: 0.245344 acc_pose: 0.909193\n", + "04/03 20:44:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][1/4] lr: 4.000000e-03 eta: 3:35:35 time: 18.076719 data_time: 17.711725 memory: 5671 loss: 0.244140 loss_kpt: 0.244140 acc_pose: 0.872248\n", + "04/03 20:44:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][2/4] lr: 4.000000e-03 eta: 3:35:32 time: 18.340685 data_time: 17.971460 memory: 5671 loss: 0.246770 loss_kpt: 0.246770 acc_pose: 0.861154\n", + "04/03 20:44:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][3/4] lr: 4.000000e-03 eta: 3:35:28 time: 18.317211 data_time: 17.947889 memory: 5671 loss: 0.246067 loss_kpt: 0.246067 acc_pose: 0.847470\n", + "04/03 20:45:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:45:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [42][4/4] lr: 4.000000e-03 eta: 3:34:27 time: 18.052173 data_time: 17.686979 memory: 2632 loss: 0.240807 loss_kpt: 0.240807 acc_pose: 0.901235\n", + "04/03 20:45:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][1/4] lr: 4.000000e-03 eta: 3:34:27 time: 18.054189 data_time: 17.689310 memory: 5671 loss: 0.240413 loss_kpt: 0.240413 acc_pose: 0.871480\n", + "04/03 20:45:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][2/4] lr: 4.000000e-03 eta: 3:34:19 time: 18.298634 data_time: 17.929312 memory: 5671 loss: 0.243291 loss_kpt: 0.243291 acc_pose: 0.867042\n", + "04/03 20:46:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][3/4] lr: 4.000000e-03 eta: 3:34:09 time: 18.272619 data_time: 17.903660 memory: 5671 loss: 0.241924 loss_kpt: 0.241924 acc_pose: 0.873760\n", + "04/03 20:46:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:46:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [43][4/4] lr: 4.000000e-03 eta: 3:33:14 time: 18.040964 data_time: 17.676156 memory: 2632 loss: 0.238029 loss_kpt: 0.238029 acc_pose: 0.831168\n", + "04/03 20:46:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][1/4] lr: 4.000000e-03 eta: 3:33:14 time: 18.071248 data_time: 17.706948 memory: 5671 loss: 0.236992 loss_kpt: 0.236992 acc_pose: 0.824390\n", + "04/03 20:46:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][2/4] lr: 4.000000e-03 eta: 3:33:08 time: 18.316204 data_time: 17.947940 memory: 5671 loss: 0.239603 loss_kpt: 0.239603 acc_pose: 0.847966\n", + "04/03 20:47:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][3/4] lr: 4.000000e-03 eta: 3:33:05 time: 18.312628 data_time: 17.944155 memory: 5671 loss: 0.238851 loss_kpt: 0.238851 acc_pose: 0.898777\n", + "04/03 20:47:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:47:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [44][4/4] lr: 4.000000e-03 eta: 3:32:10 time: 18.056496 data_time: 17.692391 memory: 2632 loss: 0.235219 loss_kpt: 0.235219 acc_pose: 0.837132\n", + "04/03 20:47:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][1/4] lr: 4.000000e-03 eta: 3:32:06 time: 18.066413 data_time: 17.702265 memory: 5671 loss: 0.234315 loss_kpt: 0.234315 acc_pose: 0.880208\n", + "04/03 20:48:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][2/4] lr: 4.000000e-03 eta: 3:31:58 time: 18.296078 data_time: 17.927784 memory: 5671 loss: 0.236869 loss_kpt: 0.236869 acc_pose: 0.815932\n", + "04/03 20:48:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][3/4] lr: 4.000000e-03 eta: 3:31:51 time: 18.277586 data_time: 17.908647 memory: 5671 loss: 0.235710 loss_kpt: 0.235710 acc_pose: 0.930355\n", + "04/03 20:48:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:48:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [45][4/4] lr: 4.000000e-03 eta: 3:30:52 time: 17.987362 data_time: 17.622404 memory: 2632 loss: 0.232151 loss_kpt: 0.232151 acc_pose: 0.864198\n", + "04/03 20:49:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [46][1/4] lr: 4.000000e-03 eta: 3:30:53 time: 18.051739 data_time: 17.686300 memory: 5671 loss: 0.231532 loss_kpt: 0.231532 acc_pose: 0.888446\n", + "04/03 20:49:23 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [50][2/4] lr: 4.000000e-03 eta: 3:26:04 time: 18.343405 data_time: 17.976290 memory: 5671 loss: 0.219941 loss_kpt: 0.219941 acc_pose: 0.930705\n", + "04/03 20:54:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [50][3/4] lr: 4.000000e-03 eta: 3:26:00 time: 18.351486 data_time: 17.984380 memory: 5671 loss: 0.219683 loss_kpt: 0.219683 acc_pose: 0.867440\n", + "04/03 20:54:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 20:54:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [50][4/4] lr: 4.000000e-03 eta: 3:25:10 time: 18.104051 data_time: 17.740837 memory: 2632 loss: 0.216338 loss_kpt: 0.216338 acc_pose: 0.924501\n", + "04/03 20:54:46 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 50 epochs\n", + "04/03 20:55:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [51][1/4] lr: 4.000000e-03 eta: 3:25:08 time: 18.130420 data_time: 17.767441 memory: 5671 loss: 0.215554 loss_kpt: 0.215554 acc_pose: 0.904762\n", + "04/03 20:55:34 - 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mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [57][2/4] lr: 4.000000e-03 eta: 3:17:59 time: 18.450860 data_time: 18.082499 memory: 5671 loss: 0.194391 loss_kpt: 0.194391 acc_pose: 0.956896\n", + "04/03 21:03:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [57][3/4] lr: 4.000000e-03 eta: 3:17:54 time: 18.471971 data_time: 18.103888 memory: 5671 loss: 0.193451 loss_kpt: 0.193451 acc_pose: 0.931289\n", + "04/03 21:03:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:03:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [57][4/4] lr: 4.000000e-03 eta: 3:17:08 time: 18.225394 data_time: 17.861274 memory: 2632 loss: 0.189959 loss_kpt: 0.189959 acc_pose: 0.962963\n", + "04/03 21:03:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [58][1/4] lr: 4.000000e-03 eta: 3:17:01 time: 18.250030 data_time: 17.886316 memory: 5671 loss: 0.189479 loss_kpt: 0.189479 acc_pose: 0.952712\n", + "04/03 21:04:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [58][2/4] lr: 4.000000e-03 eta: 3:16:54 time: 18.539596 data_time: 18.171774 memory: 5671 loss: 0.191450 loss_kpt: 0.191450 acc_pose: 0.937004\n", + "04/03 21:04:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [58][3/4] lr: 4.000000e-03 eta: 3:16:43 time: 18.491957 data_time: 18.124600 memory: 5671 loss: 0.190458 loss_kpt: 0.190458 acc_pose: 0.936833\n", + "04/03 21:04:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:04:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [58][4/4] lr: 4.000000e-03 eta: 3:15:59 time: 18.239604 data_time: 17.876470 memory: 2632 loss: 0.187599 loss_kpt: 0.187599 acc_pose: 0.935897\n", + "04/03 21:05:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [59][1/4] lr: 4.000000e-03 eta: 3:15:53 time: 18.260360 data_time: 17.897189 memory: 5671 loss: 0.186742 loss_kpt: 0.186742 acc_pose: 0.931878\n", + "04/03 21:05:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [59][2/4] lr: 4.000000e-03 eta: 3:15:42 time: 18.491870 data_time: 18.124424 memory: 5671 loss: 0.188409 loss_kpt: 0.188409 acc_pose: 0.928768\n", + "04/03 21:05:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [59][3/4] lr: 4.000000e-03 eta: 3:15:33 time: 18.493307 data_time: 18.126373 memory: 5671 loss: 0.187116 loss_kpt: 0.187116 acc_pose: 0.968414\n", + "04/03 21:05:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:05:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [59][4/4] lr: 4.000000e-03 eta: 3:14:49 time: 18.266077 data_time: 17.903187 memory: 2632 loss: 0.184021 loss_kpt: 0.184021 acc_pose: 0.938272\n", + "04/03 21:06:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [60][1/4] lr: 4.000000e-03 eta: 3:14:42 time: 18.280699 data_time: 17.917769 memory: 5671 loss: 0.183071 loss_kpt: 0.183071 acc_pose: 0.940596\n", + "04/03 21:06:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [60][2/4] lr: 4.000000e-03 eta: 3:14:30 time: 18.504829 data_time: 18.138015 memory: 5671 loss: 0.185183 loss_kpt: 0.185183 acc_pose: 0.936492\n", + "04/03 21:06:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [60][3/4] lr: 4.000000e-03 eta: 3:14:22 time: 18.493685 data_time: 18.127092 memory: 5671 loss: 0.184455 loss_kpt: 0.184455 acc_pose: 0.947090\n", + "04/03 21:07:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:07:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [60][4/4] lr: 4.000000e-03 eta: 3:13:40 time: 18.260249 data_time: 17.898270 memory: 2632 loss: 0.181744 loss_kpt: 0.181744 acc_pose: 0.910617\n", + "04/03 21:07:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 60 epochs\n", + "04/03 21:07:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][1/7] eta: 0:00:12 time: 1.802531 data_time: 1.751589 memory: 360 \n", + "04/03 21:07:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][2/7] eta: 0:00:10 time: 1.812718 data_time: 1.761698 memory: 360 \n", + "04/03 21:07:16 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][3/7] eta: 0:00:07 time: 1.819670 data_time: 1.768690 memory: 360 \n", + "04/03 21:07:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][4/7] eta: 0:00:06 time: 1.830850 data_time: 1.778693 memory: 360 \n", + "04/03 21:07:20 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][5/7] eta: 0:00:04 time: 1.841544 data_time: 1.788767 memory: 360 \n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][6/7] eta: 0:00:01 time: 1.842253 data_time: 1.789809 memory: 360 \n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][7/7] eta: 0:00:00 time: 1.782098 data_time: 1.729935 memory: 287 \n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating CocoMetric...\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *keypoints*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.00s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.754\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.980\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.929\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.754\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.786\n", + " Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.980\n", + " Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.940\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = -1.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.786\n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluating PCKAccuracy (normalized by ``\"bbox_size\"``)...\n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [60][7/7] coco/AP: 0.754073 coco/AP .5: 0.980198 coco/AP .75: 0.928837 coco/AP (M): -1.000000 coco/AP (L): 0.754073 coco/AR: 0.786000 coco/AR .5: 0.980000 coco/AR .75: 0.940000 coco/AR (M): -1.000000 coco/AR (L): 0.786000 PCK: 0.966395data_time: 1.729935 time: 1.782098 \n", + "04/03 21:07:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The previous best checkpoint /home/featurize/work/关键点检测/mmpose/work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192/best_PCK_epoch_40.pth is removed\n", + "04/03 21:07:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - The best checkpoint with 0.9664 PCK at 60 epoch is saved to best_PCK_epoch_60.pth.\n", + "04/03 21:07:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [61][1/4] lr: 4.000000e-03 eta: 3:13:31 time: 18.284825 data_time: 17.922822 memory: 5671 loss: 0.181433 loss_kpt: 0.181433 acc_pose: 0.942378\n", + "04/03 21:08:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [61][2/4] lr: 4.000000e-03 eta: 3:13:21 time: 18.543751 data_time: 18.178216 memory: 5671 loss: 0.183411 loss_kpt: 0.183411 acc_pose: 0.968080\n", + "04/03 21:08:29 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [61][3/4] lr: 4.000000e-03 eta: 3:13:10 time: 18.531533 data_time: 18.165707 memory: 5671 loss: 0.182385 loss_kpt: 0.182385 acc_pose: 0.942212\n", + "04/03 21:08:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:08:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [61][4/4] lr: 4.000000e-03 eta: 3:12:26 time: 18.274205 data_time: 17.912655 memory: 2632 loss: 0.179205 loss_kpt: 0.179205 acc_pose: 0.872745\n", + "04/03 21:08:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [62][1/4] lr: 4.000000e-03 eta: 3:12:16 time: 18.264589 data_time: 17.903320 memory: 5671 loss: 0.178771 loss_kpt: 0.178771 acc_pose: 0.941111\n", + "04/03 21:09:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [62][2/4] lr: 4.000000e-03 eta: 3:12:07 time: 18.527450 data_time: 18.162113 memory: 5671 loss: 0.180498 loss_kpt: 0.180498 acc_pose: 0.973372\n", + "04/03 21:09:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [62][3/4] lr: 4.000000e-03 eta: 3:11:54 time: 18.498503 data_time: 18.132890 memory: 5671 loss: 0.180314 loss_kpt: 0.180314 acc_pose: 0.925050\n", + "04/03 21:09:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:09:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [62][4/4] lr: 4.000000e-03 eta: 3:11:12 time: 18.244492 data_time: 17.882984 memory: 2632 loss: 0.177578 loss_kpt: 0.177578 acc_pose: 0.974359\n", + "04/03 21:10:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [63][1/4] lr: 4.000000e-03 eta: 3:11:05 time: 18.246790 data_time: 17.885219 memory: 5671 loss: 0.176828 loss_kpt: 0.176828 acc_pose: 0.941865\n", + "04/03 21:10:33 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [63][2/4] lr: 4.000000e-03 eta: 3:10:53 time: 18.486368 data_time: 18.116591 memory: 5671 loss: 0.178345 loss_kpt: 0.178345 acc_pose: 0.968750\n", + "04/03 21:10:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [63][3/4] lr: 4.000000e-03 eta: 3:10:42 time: 18.450108 data_time: 18.080428 memory: 5671 loss: 0.177604 loss_kpt: 0.177604 acc_pose: 0.936167\n", + "04/03 21:11:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:11:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [63][4/4] lr: 4.000000e-03 eta: 3:10:00 time: 18.193290 data_time: 17.827687 memory: 2632 loss: 0.174694 loss_kpt: 0.174694 acc_pose: 0.898386\n", + "04/03 21:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [64][1/4] lr: 4.000000e-03 eta: 3:09:49 time: 18.214705 data_time: 17.849104 memory: 5671 loss: 0.173701 loss_kpt: 0.173701 acc_pose: 0.936748\n", + "04/03 21:11:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [64][2/4] lr: 4.000000e-03 eta: 3:09:37 time: 18.458425 data_time: 18.088732 memory: 5671 loss: 0.175912 loss_kpt: 0.175912 acc_pose: 0.941628\n", + "04/03 21:12:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [64][3/4] lr: 4.000000e-03 eta: 3:09:25 time: 18.418581 data_time: 18.048628 memory: 5671 loss: 0.175418 loss_kpt: 0.175418 acc_pose: 0.942378\n", + "04/03 21:12:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:12:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [64][4/4] lr: 4.000000e-03 eta: 3:08:44 time: 18.164567 data_time: 17.798937 memory: 2632 loss: 0.172960 loss_kpt: 0.172960 acc_pose: 0.949668\n", + "04/03 21:12:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [65][1/4] lr: 4.000000e-03 eta: 3:08:37 time: 18.218279 data_time: 17.852542 memory: 5671 loss: 0.173166 loss_kpt: 0.173166 acc_pose: 0.939785\n", + "04/03 21:12:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [65][2/4] lr: 4.000000e-03 eta: 3:08:25 time: 18.451629 data_time: 18.081854 memory: 5671 loss: 0.175139 loss_kpt: 0.175139 acc_pose: 0.962622\n", + "04/03 21:13:18 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [65][3/4] lr: 4.000000e-03 eta: 3:08:14 time: 18.447272 data_time: 18.077219 memory: 5671 loss: 0.174785 loss_kpt: 0.174785 acc_pose: 0.952456\n", + "04/03 21:13:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:13:27 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [65][4/4] lr: 4.000000e-03 eta: 3:07:33 time: 18.195563 data_time: 17.829455 memory: 2632 loss: 0.171889 loss_kpt: 0.171889 acc_pose: 0.973333\n", + "04/03 21:13:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [66][1/4] lr: 4.000000e-03 eta: 3:07:23 time: 18.226192 data_time: 17.860105 memory: 5671 loss: 0.171504 loss_kpt: 0.171504 acc_pose: 0.941372\n", + "04/03 21:14:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [66][2/4] lr: 4.000000e-03 eta: 3:07:13 time: 18.483352 data_time: 18.113046 memory: 5671 loss: 0.173714 loss_kpt: 0.173714 acc_pose: 0.946493\n", + "04/03 21:14:31 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [66][3/4] lr: 4.000000e-03 eta: 3:07:00 time: 18.475362 data_time: 18.108858 memory: 5671 loss: 0.173458 loss_kpt: 0.173458 acc_pose: 0.963211\n", + "04/03 21:14:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:14:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [66][4/4] lr: 4.000000e-03 eta: 3:06:18 time: 18.223447 data_time: 17.861037 memory: 2632 loss: 0.170803 loss_kpt: 0.170803 acc_pose: 0.938272\n", + "04/03 21:15:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [67][1/4] lr: 4.000000e-03 eta: 3:06:08 time: 18.238617 data_time: 17.876158 memory: 5671 loss: 0.170848 loss_kpt: 0.170848 acc_pose: 0.910466\n", + "04/03 21:15:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [67][2/4] lr: 4.000000e-03 eta: 3:05:56 time: 18.468945 data_time: 18.102405 memory: 5671 loss: 0.173321 loss_kpt: 0.173321 acc_pose: 0.963038\n", + "04/03 21:15:42 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [67][3/4] lr: 4.000000e-03 eta: 3:05:44 time: 18.459840 data_time: 18.093202 memory: 5671 loss: 0.172724 loss_kpt: 0.172724 acc_pose: 0.947503\n", + "04/03 21:15:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:15:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [67][4/4] lr: 4.000000e-03 eta: 3:05:03 time: 18.209416 data_time: 17.846792 memory: 2632 loss: 0.169938 loss_kpt: 0.169938 acc_pose: 0.974321\n", + "04/03 21:16:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [68][1/4] lr: 4.000000e-03 eta: 3:04:52 time: 18.202378 data_time: 17.839758 memory: 5671 loss: 0.170112 loss_kpt: 0.170112 acc_pose: 0.926670\n", + "04/03 21:16:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [68][2/4] lr: 4.000000e-03 eta: 3:04:40 time: 18.440526 data_time: 18.074259 memory: 5671 loss: 0.172056 loss_kpt: 0.172056 acc_pose: 0.984210\n", + "04/03 21:16:53 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [68][3/4] lr: 4.000000e-03 eta: 3:04:27 time: 18.425107 data_time: 18.059038 memory: 5671 loss: 0.171184 loss_kpt: 0.171184 acc_pose: 0.947077\n", + "04/03 21:17:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:17:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [68][4/4] lr: 4.000000e-03 eta: 3:03:47 time: 18.149381 data_time: 17.787739 memory: 2632 loss: 0.168260 loss_kpt: 0.168260 acc_pose: 0.987654\n", + "04/03 21:17:23 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [69][1/4] lr: 4.000000e-03 eta: 3:03:36 time: 18.138077 data_time: 17.776297 memory: 5671 loss: 0.167639 loss_kpt: 0.167639 acc_pose: 0.958333\n", + "04/03 21:17:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [69][2/4] lr: 4.000000e-03 eta: 3:03:25 time: 18.360123 data_time: 17.994332 memory: 5671 loss: 0.169930 loss_kpt: 0.169930 acc_pose: 0.931630\n", + "04/03 21:18:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [69][3/4] lr: 4.000000e-03 eta: 3:03:12 time: 18.286977 data_time: 17.921954 memory: 5671 loss: 0.169583 loss_kpt: 0.169583 acc_pose: 0.935911\n", + "04/03 21:18:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:18:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [69][4/4] lr: 4.000000e-03 eta: 3:02:33 time: 18.006528 data_time: 17.645677 memory: 2632 loss: 0.167348 loss_kpt: 0.167348 acc_pose: 0.938272\n", + "04/03 21:18:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [70][1/4] lr: 4.000000e-03 eta: 3:02:24 time: 17.989898 data_time: 17.628955 memory: 5671 loss: 0.167052 loss_kpt: 0.167052 acc_pose: 0.931382\n", + "04/03 21:18:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [70][2/4] lr: 4.000000e-03 eta: 3:02:12 time: 18.248701 data_time: 17.883761 memory: 5671 loss: 0.168660 loss_kpt: 0.168660 acc_pose: 0.962277\n", + "04/03 21:19:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [70][3/4] lr: 4.000000e-03 eta: 3:01:58 time: 18.209518 data_time: 17.844643 memory: 5671 loss: 0.168032 loss_kpt: 0.168032 acc_pose: 0.958333\n", + "04/03 21:19:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:19:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [70][4/4] lr: 4.000000e-03 eta: 3:01:21 time: 17.947917 data_time: 17.587225 memory: 2632 loss: 0.165482 loss_kpt: 0.165482 acc_pose: 0.947654\n", + "04/03 21:19:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 70 epochs\n", + "04/03 21:19:50 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [71][1/4] lr: 4.000000e-03 eta: 3:01:09 time: 17.960185 data_time: 17.599444 memory: 5671 loss: 0.164938 loss_kpt: 0.164938 acc_pose: 0.958251\n", + "04/03 21:20:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [71][2/4] lr: 4.000000e-03 eta: 3:00:58 time: 18.207050 data_time: 17.842366 memory: 5671 loss: 0.166491 loss_kpt: 0.166491 acc_pose: 0.958085\n", + "04/03 21:20:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [71][3/4] lr: 4.000000e-03 eta: 3:00:44 time: 18.158367 data_time: 17.793774 memory: 5671 loss: 0.165932 loss_kpt: 0.165932 acc_pose: 0.957672\n", + "04/03 21:20:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:20:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [71][4/4] lr: 4.000000e-03 eta: 3:00:05 time: 17.903622 data_time: 17.543244 memory: 2632 loss: 0.163612 loss_kpt: 0.163612 acc_pose: 0.961975\n", + "04/03 21:21:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [72][1/4] lr: 4.000000e-03 eta: 2:59:53 time: 17.904971 data_time: 17.544704 memory: 5671 loss: 0.163325 loss_kpt: 0.163325 acc_pose: 0.968582\n", + "04/03 21:21:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [72][2/4] lr: 4.000000e-03 eta: 2:59:39 time: 18.129942 data_time: 17.765828 memory: 5671 loss: 0.164846 loss_kpt: 0.164846 acc_pose: 0.984042\n", + "04/03 21:21:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [72][3/4] lr: 4.000000e-03 eta: 2:59:24 time: 18.075209 data_time: 17.711241 memory: 5671 loss: 0.164136 loss_kpt: 0.164136 acc_pose: 0.968163\n", + "04/03 21:21:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:21:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [72][4/4] lr: 4.000000e-03 eta: 2:58:45 time: 17.827492 data_time: 17.467714 memory: 2632 loss: 0.161448 loss_kpt: 0.161448 acc_pose: 0.972222\n", + "04/03 21:22:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [73][1/4] lr: 4.000000e-03 eta: 2:58:34 time: 17.825777 data_time: 17.466110 memory: 5671 loss: 0.160816 loss_kpt: 0.160816 acc_pose: 0.924731\n", + "04/03 21:22:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [73][2/4] lr: 4.000000e-03 eta: 2:58:21 time: 18.047701 data_time: 17.683993 memory: 5671 loss: 0.162160 loss_kpt: 0.162160 acc_pose: 0.989247\n", + "04/03 21:22:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [73][3/4] lr: 4.000000e-03 eta: 2:58:07 time: 18.020991 data_time: 17.657473 memory: 5671 loss: 0.161515 loss_kpt: 0.161515 acc_pose: 0.978665\n", + "04/03 21:23:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:23:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [73][4/4] lr: 4.000000e-03 eta: 2:57:31 time: 17.776043 data_time: 17.416545 memory: 2632 loss: 0.159016 loss_kpt: 0.159016 acc_pose: 0.950617\n", + "04/03 21:23:22 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [74][1/4] lr: 4.000000e-03 eta: 2:57:19 time: 17.786012 data_time: 17.427083 memory: 5671 loss: 0.158128 loss_kpt: 0.158128 acc_pose: 0.968166\n", + "04/03 21:23:44 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [74][2/4] lr: 4.000000e-03 eta: 2:57:09 time: 18.056840 data_time: 17.693865 memory: 5671 loss: 0.159477 loss_kpt: 0.159477 acc_pose: 0.967998\n", + "04/03 21:24:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [74][3/4] lr: 4.000000e-03 eta: 2:56:56 time: 18.053821 data_time: 17.690790 memory: 5671 loss: 0.158765 loss_kpt: 0.158765 acc_pose: 0.947338\n", + "04/03 21:24:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:24:14 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [74][4/4] lr: 4.000000e-03 eta: 2:56:20 time: 17.798511 data_time: 17.439609 memory: 2632 loss: 0.156581 loss_kpt: 0.156581 acc_pose: 0.987654\n", + "04/03 21:24:36 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [75][1/4] lr: 4.000000e-03 eta: 2:56:09 time: 17.825605 data_time: 17.466922 memory: 5671 loss: 0.155506 loss_kpt: 0.155506 acc_pose: 0.947338\n", + "04/03 21:24:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [75][2/4] lr: 4.000000e-03 eta: 2:55:57 time: 18.078171 data_time: 17.715541 memory: 5671 loss: 0.157227 loss_kpt: 0.157227 acc_pose: 0.951605\n", + "04/03 21:25:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [75][3/4] lr: 4.000000e-03 eta: 2:55:42 time: 18.025159 data_time: 17.662750 memory: 5671 loss: 0.156599 loss_kpt: 0.156599 acc_pose: 0.958085\n", + "04/03 21:25:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:25:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [75][4/4] lr: 4.000000e-03 eta: 2:55:04 time: 17.762011 data_time: 17.407893 memory: 2632 loss: 0.154443 loss_kpt: 0.154443 acc_pose: 0.937797\n", + "04/03 21:25:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [76][1/4] lr: 4.000000e-03 eta: 2:54:55 time: 17.800555 data_time: 17.446472 memory: 5671 loss: 0.153668 loss_kpt: 0.153668 acc_pose: 0.968331\n", + "04/03 21:26:08 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [76][2/4] lr: 4.000000e-03 eta: 2:54:42 time: 18.045629 data_time: 17.687659 memory: 5671 loss: 0.154815 loss_kpt: 0.154815 acc_pose: 0.984210\n", + "04/03 21:26:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [76][3/4] lr: 4.000000e-03 eta: 2:54:27 time: 18.023922 data_time: 17.665844 memory: 5671 loss: 0.154081 loss_kpt: 0.154081 acc_pose: 0.968166\n", + "04/03 21:26:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: rtmpose-s_triangle_8xb256-420e_coco-256x192_20230403_195318\n", + "04/03 21:26:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [76][4/4] lr: 4.000000e-03 eta: 2:53:51 time: 17.775518 data_time: 17.421305 memory: 2632 loss: 0.151526 loss_kpt: 0.151526 acc_pose: 0.974359\n", + "04/03 21:26:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [77][1/4] lr: 4.000000e-03 eta: 2:53:41 time: 17.814159 data_time: 17.459963 memory: 5671 loss: 0.150983 loss_kpt: 0.150983 acc_pose: 0.962352\n" + ] + } + ], + "source": [ + "!python tools/train.py data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13c89ab9-89c8-4df0-a476-9a0cc33a481e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220E2\343\200\221RTMPose\346\235\203\351\207\215\346\226\207\344\273\266\350\275\273\351\207\217\345\214\226\345\244\204\347\220\206.ipynb" "b/2023/0404/\343\200\220E2\343\200\221RTMPose\346\235\203\351\207\215\346\226\207\344\273\266\350\275\273\351\207\217\345\214\226\345\244\204\347\220\206.ipynb" new file mode 100644 index 0000000..c5b51f5 --- /dev/null +++ "b/2023/0404/\343\200\220E2\343\200\221RTMPose\346\235\203\351\207\215\346\226\207\344\273\266\350\275\273\351\207\217\345\214\226\345\244\204\347\220\206.ipynb" @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d428e1e5-5b7b-4e16-880c-bdb3d3d1f6e4", + "metadata": {}, + "source": [ + "# RTMPose权重文件轻量化处理\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "06a39b7b-017c-4193-9387-dce43396f96c", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a1103085-107b-4ef7-9a04-cb2e5ce2a6f0", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "83e8f653-0f5d-46b1-85bb-f0e235badfba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "04/03 22:27:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Key `message_hub` will be removed because it is not in save_keys. If you want to keep it, please set --save-keys.\n", + "04/03 22:27:38 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Key `ema_state_dict` will be removed because it is not in save_keys. If you want to keep it, please set --save-keys.\n" + ] + } + ], + "source": [ + "!python tools/misc/publish_model.py \\\n", + " work_dirs/best_PCK_epoch_80.pth \\\n", + " work_dirs/rtmpose_slim.pth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c48d0c2b-bdb1-4f63-9f0a-f485f37b1e4b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2023/0404/\343\200\220F1\343\200\221RTMPose\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" "b/2023/0404/\343\200\220F1\343\200\221RTMPose\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" new file mode 100644 index 0000000..f5dd9d5 --- /dev/null +++ "b/2023/0404/\343\200\220F1\343\200\221RTMPose\344\270\211\350\247\222\346\235\277\345\205\263\351\224\256\347\202\271\346\243\200\346\265\213\351\242\204\346\265\213-\345\221\275\344\273\244\350\241\214.ipynb" @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "466a64ed-7068-4a92-9755-84ea56de799d", + "metadata": {}, + "source": [ + "# RTMPose三角板关键点检测预测-命令行\n", + "\n", + "同济子豪兄 2023-4-3" + ] + }, + { + "cell_type": "markdown", + "id": "90f3a192-6e82-493d-ad66-f3c8cf16ece5", + "metadata": { + "tags": [] + }, + "source": [ + "## 进入 mmpose 主目录" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "801da2ce-97c1-47a2-a211-86336b6f0679", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('mmpose')" + ] + }, + { + "cell_type": "markdown", + "id": "8ed4d058-f23c-40d1-a662-910cce1be52b", + "metadata": {}, + "source": [ + "## 单张图像-关键点检测预测" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cab9972f-3694-4cb1-87f2-5a55d5feca5d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by local backend from path: checkpoint/faster_r_cnn_triangle_epoch_50.pth\n", + "04/04 13:50:02 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - sjb_rect is not a meta file, simply parsed as meta information\n", + "Loads checkpoint by local backend from path: checkpoint/rtmpose_slim-9888686d_20230403.pth\n", + "04/04 13:50:10 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "/home/featurize/work/关键点检测/mmpose/mmpose/models/heads/coord_cls_heads/rtmcc_head.py:217: UserWarning: The predicted simcc values are normalized for visualization. This may cause discrepancy between the keypoint scores and the 1D heatmaps.\n", + " warnings.warn('The predicted simcc values are normalized for '\n" + ] + } + ], + "source": [ + "# RTMPose\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " data/faster_r_cnn_triangle.py \\\n", + " checkpoint/faster_r_cnn_triangle_epoch_50.pth \\\n", + " data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py \\\n", + " checkpoint/rtmpose_slim-9888686d_20230403.pth \\\n", + " --input data/test_triangle/triangle_3.jpg \\\n", + " --output-root outputs/F1_RTM \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.5 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 36 \\\n", + " --thickness 30 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "markdown", + "id": "dd73e683-9186-4868-ade0-ea8dfa5bacd8", + "metadata": {}, + "source": [ + "## 视频-关键点检测预测" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "22c18333-b46a-4072-bc2a-42a82edf9c3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loads checkpoint by local backend from path: checkpoint/faster_r_cnn_triangle_epoch_50.pth\n", + "04/04 17:03:09 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - sjb_rect is not a meta file, simply parsed as meta information\n", + "Loads checkpoint by local backend from path: checkpoint/rtmpose_slim-9888686d_20230403.pth\n", + "04/04 17:03:17 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n", + "[ ] 0/318, elapsed: 0s, ETA:/home/featurize/work/关键点检测/mmpose/mmpose/models/heads/coord_cls_heads/rtmcc_head.py:217: UserWarning: The predicted simcc values are normalized for visualization. This may cause discrepancy between the keypoint scores and the 1D heatmaps.\n", + " warnings.warn('The predicted simcc values are normalized for '\n", + "[ ] 2/318, 0.3 task/s, elapsed: 7s, ETA: 1053s/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:666: UserWarning: Warning: The circle is out of bounds, the drawn circle may not be in the image\n", + " ' the drawn circle may not be in the image', UserWarning)\n", + "/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:491: UserWarning: Warning: The text is out of bounds, the drawn text may not be in the image\n", + " ' the drawn text may not be in the image', UserWarning)\n", + "/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:743: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may not be in the image\n", + " ' the drawn bbox may not be in the image', UserWarning)\n", + "/environment/miniconda3/lib/python3.7/site-packages/mmengine/visualization/visualizer.py:814: UserWarning: Warning: The polygon is out of bounds, the drawn polygon may not be in the image\n", + " ' the drawn polygon may not be in the image', UserWarning)\n", + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 318/318, 1.5 task/s, elapsed: 215s, ETA: 0s" + ] + } + ], + "source": [ + "# RTMPose\n", + "!python demo/topdown_demo_with_mmdet.py \\\n", + " data/faster_r_cnn_triangle.py \\\n", + " checkpoint/faster_r_cnn_triangle_epoch_50.pth \\\n", + " data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py \\\n", + " checkpoint/rtmpose_slim-9888686d_20230403.pth \\\n", + " --input data/test_triangle/triangle_9.mp4 \\\n", + " --output-root outputs/F1_RTM \\\n", + " --device cuda:0 \\\n", + " --bbox-thr 0.5 \\\n", + " --kpt-thr 0.5 \\\n", + " --nms-thr 0.3 \\\n", + " --radius 16 \\\n", + " --thickness 10 \\\n", + " --draw-bbox \\\n", + " --draw-heatmap \\\n", + " --show-kpt-idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fec0673-0c19-47b9-965f-2eb145310179", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}