1stplace_final .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "304d9dc2-83a8-426e-b252-6cc437682b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -q kaggle\n",
    "# !mkdir -p ./kaggle\n",
    "# !cp kaggle.json ./kaggle/\n",
    "# !chmod 600 ./kaggle/kaggle.json\n",
    "# !cat ./kaggle/kaggle.json\n",
    "# ! kaggle datasets list\n",
    "# !kaggle datasets download -d haqishen/timm-20220211"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12eb849d-02b9-4c7d-a019-14c47354ee2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ./seg_model.zip\n",
      "  inflating: ./seg_model/timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep_fold0_best.pth  \n",
      "  inflating: ./seg_model/timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep_fold1_best.pth  \n",
      "  inflating: ./seg_model/timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep_fold2_best.pth  \n",
      "  inflating: ./seg_model/timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep_fold3_best.pth  \n",
      "  inflating: ./seg_model/timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep_fold4_best.pth  \n"
     ]
    }
   ],
   "source": [
    "!unzip ./seg_model.zip -d ./seg_model\n",
    "#!cp -r ./timm-20220211/pytorch-image-models-master/timm/ ./timm4smp\n",
    "#!cp -r ./timm-20220211/pytorch-image-models-master/timm/ ./timm4smp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bb8edf6d-2799-447e-98fa-bdc1ddbb218f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# # Specify the directory you want to delete\n",
    "directory_to_delete = './seg_model'\n",
    "\n",
    "\n",
    "# # Delete the directory and all its contents\n",
    "shutil.rmtree(directory_to_delete)\n",
    "\n",
    "#print(\"Directory deleted successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d469b59-c4d2-4e5d-b5f8-c7ab319e01ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: timm in /opt/conda/lib/python3.11/site-packages (0.9.16)\n",
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      "Using cached segmentation_models_pytorch-0.3.3-py3-none-any.whl (106 kB)\n",
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      "Installing collected packages: munch, efficientnet-pytorch, timm, pretrainedmodels, segmentation_models_pytorch\n",
      "  Attempting uninstall: timm\n",
      "    Found existing installation: timm 0.9.16\n",
      "    Uninstalling timm-0.9.16:\n",
      "      Successfully uninstalled timm-0.9.16\n",
      "Successfully installed efficientnet-pytorch-0.7.1 munch-4.0.0 pretrainedmodels-0.7.4 segmentation_models_pytorch-0.3.3 timm-0.9.2\n",
      "Collecting monai\n",
      "  Obtaining dependency information for monai from https://files.pythonhosted.org/packages/08/94/e8a7ba00dd0c7ce959648b562043bd22125d65f5e519e566c822f71bc437/monai-1.3.0-202310121228-py3-none-any.whl.metadata\n",
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      "Using cached monai-1.3.0-202310121228-py3-none-any.whl (1.3 MB)\n",
      "Installing collected packages: monai\n",
      "Successfully installed monai-1.3.0\n",
      "Requirement already satisfied: imageio in /opt/conda/lib/python3.11/site-packages (2.34.1)\n",
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      "Collecting pydicom\n",
      "  Obtaining dependency information for pydicom from https://files.pythonhosted.org/packages/35/2a/8c0f6fe243e6b6793868c6834203a44cc8f3f25abad780e1c7b21e15594d/pydicom-2.4.4-py3-none-any.whl.metadata\n",
      "  Using cached pydicom-2.4.4-py3-none-any.whl.metadata (7.8 kB)\n",
      "Using cached pydicom-2.4.4-py3-none-any.whl (1.8 MB)\n",
      "Installing collected packages: pydicom\n",
      "Successfully installed pydicom-2.4.4\n",
      "Collecting pandas\n",
      "  Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/fc/a5/4d82be566f069d7a9a702dcdf6f9106df0e0b042e738043c0cc7ddd7e3f6/pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
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      "Using cached pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
      "Installing collected packages: pandas\n",
      "Successfully installed pandas-2.2.2\n",
      "Collecting nibabel\n",
      "  Obtaining dependency information for nibabel from https://files.pythonhosted.org/packages/77/3f/ce43b8c2ccc4a7913a87c4d425aaf0080ea3abf947587e47dc2025981a17/nibabel-5.2.1-py3-none-any.whl.metadata\n",
      "  Using cached nibabel-5.2.1-py3-none-any.whl.metadata (8.8 kB)\n",
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      "Installing collected packages: nibabel\n",
      "Successfully installed nibabel-5.2.1\n",
      "Collecting matplotlib\n",
      "  Obtaining dependency information for matplotlib from https://files.pythonhosted.org/packages/80/3b/e363612ac1a514abfb5505aa209dd5b724b3232a6de98710d7759559706a/matplotlib-3.8.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached matplotlib-3.8.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)\n",
      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
      "  Obtaining dependency information for contourpy>=1.0.1 from https://files.pythonhosted.org/packages/ee/c0/9bd123d676eb61750e116a2cd915b06483fc406143cfc36c7f263f0f5368/contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
      "  Obtaining dependency information for cycler>=0.10 from https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl.metadata\n",
      "  Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
      "  Obtaining dependency information for fonttools>=4.22.0 from https://files.pythonhosted.org/packages/c6/b5/dc17e93f60567fa1b0fa3720c2f28e0df5293927e2356e066e87af9adaba/fonttools-4.51.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached fonttools-4.51.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB)\n",
      "Collecting kiwisolver>=1.3.1 (from matplotlib)\n",
      "  Obtaining dependency information for kiwisolver>=1.3.1 from https://files.pythonhosted.org/packages/17/ba/17a706b232308e65f57deeccae503c268292e6a091313f6ce833a23093ea/kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.4 kB)\n",
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      "Using cached kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB)\n",
      "Installing collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.2.1 cycler-0.12.1 fonttools-4.51.0 kiwisolver-1.4.5 matplotlib-3.8.4\n",
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      "Collecting pylibjpeg==1.4.0\n",
      "  Obtaining dependency information for pylibjpeg==1.4.0 from https://files.pythonhosted.org/packages/71/54/c9a15a05027316973686b5e6c5c6c306feb989810123928446f0ec588c2c/pylibjpeg-1.4.0-py3-none-any.whl.metadata\n",
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      "Collecting python_gdcm==3.0.17.1\n",
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      "Successfully installed python_gdcm-3.0.17.1\n",
      "Collecting pylibjpeg-libjpeg\n",
      "  Obtaining dependency information for pylibjpeg-libjpeg from https://files.pythonhosted.org/packages/3b/4a/8debd5c101f4e590c13d9a9f7e6ba9d63e01acdd0fef126fe9f3737b72ed/pylibjpeg_libjpeg-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Using cached pylibjpeg_libjpeg-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.8 kB)\n",
      "Requirement already satisfied: numpy<2.0,>=1.24 in /opt/conda/lib/python3.11/site-packages (from pylibjpeg-libjpeg) (1.26.4)\n",
      "Using cached pylibjpeg_libjpeg-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
      "Installing collected packages: pylibjpeg-libjpeg\n",
      "Successfully installed pylibjpeg-libjpeg-2.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install timm\n",
    "!pip install albumentations\n",
    "!pip install segmentation_models_pytorch\n",
    "!pip install monai\n",
    "!pip install imageio\n",
    "!pip install pydicom\n",
    "!pip install pandas\n",
    "!pip install nibabel\n",
    "!pip install matplotlib\n",
    "!pip install torch\n",
    "!pip install opencv-python\n",
    "!pip install opencv-python-headless\n",
    "!pip install pylibjpeg==1.4.0\n",
    "!pip install python_gdcm==3.0.17.1\n",
    "!pip install pylibjpeg-libjpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d853972-3b8b-44f6-bfb6-7eac38e4cb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0.9.16', '0.5.5')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import ast\n",
    "import cv2\n",
    "import time\n",
    "import timm\n",
    "import timm4smp\n",
    "import pickle\n",
    "import random\n",
    "import pydicom\n",
    "import argparse\n",
    "import warnings\n",
    "import threading\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "from glob import glob\n",
    "import albumentations\n",
    "import matplotlib.pyplot as plt\n",
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.cuda.amp as amp\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from pylab import rcParams\n",
    "\n",
    "%matplotlib inline\n",
    "device = torch.device('cuda')\n",
    "torch.backends.cudnn.benchmark = True\n",
    "\n",
    "timm.__version__, timm4smp.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1fd7d76-bc21-4742-ae87-d5f990c29a1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "753f139a-4c6d-49c9-9e2e-c634bac4f0d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './Dataset'\n",
    "image_size_seg = (128, 128, 128)\n",
    "msk_size = image_size_seg[0]\n",
    "image_size_cls = 224\n",
    "n_slice_per_c = 15\n",
    "n_ch = 5\n",
    "\n",
    "batch_size_seg = 1\n",
    "num_workers = 2\n",
    "DEBUG = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb8d231e-4af2-4fc3-a714-ae09207acd38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>StudyInstanceUID</th>\n",
       "      <th>prediction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2.826.0.1.3680043.10197_C1</td>\n",
       "      <td>1.2.826.0.1.3680043.10197</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2.826.0.1.3680043.10454_C1</td>\n",
       "      <td>1.2.826.0.1.3680043.10454</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.2.826.0.1.3680043.10690_C1</td>\n",
       "      <td>1.2.826.0.1.3680043.10690</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         row_id           StudyInstanceUID prediction_type\n",
       "0  1.2.826.0.1.3680043.10197_C1  1.2.826.0.1.3680043.10197              C1\n",
       "1  1.2.826.0.1.3680043.10454_C1  1.2.826.0.1.3680043.10454              C1\n",
       "2  1.2.826.0.1.3680043.10690_C1  1.2.826.0.1.3680043.10690              C1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"./Dataset/test.csv\")\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afe1b775-07b3-4438-8493-40b3ba746756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudyInstanceUID</th>\n",
       "      <th>image_folder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2.826.0.1.3680043.22327</td>\n",
       "      <td>./Dataset/test_images/1.2.826.0.1.3680043.22327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2.826.0.1.3680043.25399</td>\n",
       "      <td>./Dataset/test_images/1.2.826.0.1.3680043.25399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.2.826.0.1.3680043.5876</td>\n",
       "      <td>./Dataset/test_images/1.2.826.0.1.3680043.5876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            StudyInstanceUID                                     image_folder\n",
       "0  1.2.826.0.1.3680043.22327  ./Dataset/test_images/1.2.826.0.1.3680043.22327\n",
       "1  1.2.826.0.1.3680043.25399  ./Dataset/test_images/1.2.826.0.1.3680043.25399\n",
       "2   1.2.826.0.1.3680043.5876   ./Dataset/test_images/1.2.826.0.1.3680043.5876"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if DEBUG:\n",
    "    df = pd.read_csv(os.path.join(data_dir, 'train.csv')).head(1500)\n",
    "    df = pd.DataFrame({\n",
    "        'StudyInstanceUID': df['StudyInstanceUID'].unique().tolist()\n",
    "    })\n",
    "    df['image_folder'] = df['StudyInstanceUID'].apply(lambda x: os.path.join(data_dir, 'train_images', x))\n",
    "else:\n",
    "    df = pd.read_csv(os.path.join(data_dir, 'test.csv'))\n",
    "    if df.iloc[0].row_id == '1.2.826.0.1.3680043.10197_C1':\n",
    "        # test_images and test.csv are inconsistent in the dev dataset, fixing labels for the dev run.\n",
    "        df = pd.DataFrame({\n",
    "            \"row_id\": ['1.2.826.0.1.3680043.22327_C1', '1.2.826.0.1.3680043.25399_C1', '1.2.826.0.1.3680043.5876_C1'],\n",
    "            \"StudyInstanceUID\": ['1.2.826.0.1.3680043.22327', '1.2.826.0.1.3680043.25399', '1.2.826.0.1.3680043.5876'],\n",
    "            \"prediction_type\": [\"C1\", \"C1\", \"patient_overall\"]}\n",
    "        )\n",
    "    df = pd.DataFrame({\n",
    "        'StudyInstanceUID': df['StudyInstanceUID'].unique().tolist()\n",
    "    })\n",
    "    df['image_folder'] = df['StudyInstanceUID'].apply(lambda x: os.path.join(data_dir, 'test_images', x))\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad507dc-b8ef-4070-bd2b-28f6c2cdf20c",
   "metadata": {},
   "source": [
    "<h2>Dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5a1f1ab-14ef-4044-b510-75b970ceac71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dicom(path):\n",
    "    dicom = pydicom.read_file(path)\n",
    "    data = dicom.pixel_array\n",
    "    data = cv2.resize(data, (image_size_seg[0], image_size_seg[1]), interpolation = cv2.INTER_AREA)\n",
    "    return data\n",
    "\n",
    "\n",
    "def load_dicom_line_par(path):\n",
    "\n",
    "    t_paths = sorted(glob(os.path.join(path, \"*\")), key=lambda x: int(x.split('/')[-1].split(\".\")[0]))\n",
    "\n",
    "    n_scans = len(t_paths)\n",
    "#     print(n_scans)\n",
    "    indices = np.quantile(list(range(n_scans)), np.linspace(0., 1., image_size_seg[2])).round().astype(int)\n",
    "    t_paths = [t_paths[i] for i in indices]\n",
    "\n",
    "    images = []\n",
    "    for filename in t_paths:\n",
    "        images.append(load_dicom(filename))\n",
    "    images = np.stack(images, -1)\n",
    "    \n",
    "    images = images - np.min(images)\n",
    "    images = images / (np.max(images) + 1e-4)\n",
    "    images = (images * 255).astype(np.uint8)\n",
    "\n",
    "    return images\n",
    "\n",
    "\n",
    "class SegTestDataset(Dataset):\n",
    "\n",
    "    def __init__(self, df):\n",
    "        self.df = df.reset_index()\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.iloc[index]\n",
    "\n",
    "        image = load_dicom_line_par(row.image_folder)\n",
    "        if image.ndim < 4:\n",
    "            image = np.expand_dims(image, 0)\n",
    "        image = image.astype(np.float32).repeat(3, 0)  # to 3ch\n",
    "        image = image / 255.\n",
    "        return torch.tensor(image).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd432f2a-a490-4610-aa9f-79be81027800",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_seg = SegTestDataset(df)\n",
    "loader_seg = torch.utils.data.DataLoader(dataset_seg, batch_size=batch_size_seg, shuffle=False, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "80649d42-5649-4208-819c-196348bc871d",
   "metadata": {},
   "outputs": [],
   "source": [
    "if DEBUG:\n",
    "    rcParams['figure.figsize'] = 20,8\n",
    "    for i in range(2):\n",
    "        f, axarr = plt.subplots(1,4)\n",
    "        for p in range(4):\n",
    "            idx = i*4+p\n",
    "            img = dataset_seg[idx]\n",
    "            img = img[:, :, :, 60]\n",
    "            axarr[p].imshow(img.transpose(0, 1).transpose(1,2).squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "125ffcfa-757d-4769-8a85-56adbca5886a",
   "metadata": {},
   "outputs": [],
   "source": [
    "if DEBUG:\n",
    "    rcParams['figure.figsize'] = 20,8\n",
    "    for i in range(2):\n",
    "        f, axarr = plt.subplots(1,4)\n",
    "        for p in range(4):\n",
    "            idx = i*4+p\n",
    "            img = dataset_seg[idx]\n",
    "            img = img[:, :, 60, :]\n",
    "            axarr[p].imshow(img.transpose(0, 1).transpose(1,2).squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e18e651-83b7-4e7a-8c2b-18688915b592",
   "metadata": {},
   "outputs": [],
   "source": [
    "if DEBUG:\n",
    "    rcParams['figure.figsize'] = 20,8\n",
    "    for i in range(2):\n",
    "        f, axarr = plt.subplots(1,4)\n",
    "        for p in range(4):\n",
    "            idx = i*4+p\n",
    "            img = dataset_seg[idx]\n",
    "            img = img[:, 60, :, :]\n",
    "            axarr[p].imshow(img.transpose(0, 1).transpose(1,2).squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d890662-7ddb-4b79-9a68-5c50000f79bd",
   "metadata": {},
   "source": [
    "<h2>Models</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1877f514-c0fe-4cb4-bca3-8480a94f156f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Conv3d w/ Same Padding\n",
    "modified from:\n",
    "https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/conv2d_same.py\n",
    "https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/padding.py\n",
    "\"\"\"\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from typing import Tuple, Optional, List\n",
    "\n",
    "\n",
    "# Calculate symmetric padding for a convolution\n",
    "def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:\n",
    "    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2\n",
    "    return padding\n",
    "\n",
    "\n",
    "# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution\n",
    "def get_same_padding(x: int, k: int, s: int, d: int):\n",
    "    return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)\n",
    "\n",
    "\n",
    "# Can SAME padding for given args be done statically?\n",
    "def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):\n",
    "    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0\n",
    "\n",
    "\n",
    "# Dynamically pad input x with 'SAME' padding for conv with specified args\n",
    "def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1, 1), value: float = 0):\n",
    "    ih, iw, iz = x.size()[-3:]\n",
    "    pad_h = get_same_padding(ih, k[0], s[0], d[0])\n",
    "    pad_w = get_same_padding(iw, k[1], s[1], d[1])\n",
    "    pad_z = get_same_padding(iz, k[2], s[2], d[2])\n",
    "    if pad_h > 0 or pad_w > 0 or pad_z > 0:\n",
    "        x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2, pad_z // 2, pad_z - pad_z // 2], value=value)\n",
    "    return x\n",
    "\n",
    "\n",
    "def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:\n",
    "    dynamic = False\n",
    "    if isinstance(padding, str):\n",
    "        # for any string padding, the padding will be calculated for you, one of three ways\n",
    "        padding = padding.lower()\n",
    "        if padding == 'same':\n",
    "            # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact\n",
    "            if is_static_pad(kernel_size, **kwargs):\n",
    "                # static case, no extra overhead\n",
    "                padding = get_padding(kernel_size, **kwargs)\n",
    "            else:\n",
    "                # dynamic 'SAME' padding, has runtime/GPU memory overhead\n",
    "                padding = 0\n",
    "                dynamic = True\n",
    "        elif padding == 'valid':\n",
    "            # 'VALID' padding, same as padding=0\n",
    "            padding = 0\n",
    "        else:\n",
    "            # Default to PyTorch style 'same'-ish symmetric padding\n",
    "            padding = get_padding(kernel_size, **kwargs)\n",
    "    return padding, dynamic\n",
    "\n",
    "\n",
    "def conv3d_same(\n",
    "        x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int, int] = (1, 1, 1),\n",
    "        padding: Tuple[int, int, int] = (0, 0, 0), dilation: Tuple[int, int, int] = (1, 1, 1), groups: int = 1):\n",
    "    x = pad_same(x, weight.shape[-3:], stride, dilation)\n",
    "    return F.conv3d(x, weight, bias, stride, (0, 0, 0), dilation, groups)\n",
    "\n",
    "\n",
    "class Conv3dSame(nn.Conv3d):\n",
    "    \"\"\" Tensorflow like 'SAME' convolution wrapper for 3d convolutions\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n",
    "                 padding=0, dilation=1, groups=1, bias=True):\n",
    "        super(Conv3dSame, self).__init__(\n",
    "            in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return conv3d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n",
    "\n",
    "\n",
    "def create_conv3d_pad(in_chs, out_chs, kernel_size, **kwargs):\n",
    "    padding = kwargs.pop('padding', '')\n",
    "    kwargs.setdefault('bias', False)\n",
    "    padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)\n",
    "    if is_dynamic:\n",
    "        return Conv3dSame(in_chs, out_chs, kernel_size, **kwargs)\n",
    "    else:\n",
    "        return nn.Conv3d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "11125ffd-e4e2-4a87-9a80-06bcc75967d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timm4smp.models.layers.conv2d_same import Conv2dSame\n",
    "from timm4smp.models import create_model\n",
    "\n",
    "def convert_3d(module):\n",
    "\n",
    "    module_output = module\n",
    "    if isinstance(module, torch.nn.BatchNorm2d):\n",
    "        module_output = torch.nn.BatchNorm3d(\n",
    "            module.num_features,\n",
    "            module.eps,\n",
    "            module.momentum,\n",
    "            module.affine,\n",
    "            module.track_running_stats,\n",
    "        )\n",
    "        if module.affine:\n",
    "            with torch.no_grad():\n",
    "                module_output.weight = module.weight\n",
    "                module_output.bias = module.bias\n",
    "        module_output.running_mean = module.running_mean\n",
    "        module_output.running_var = module.running_var\n",
    "        module_output.num_batches_tracked = module.num_batches_tracked\n",
    "        if hasattr(module, \"qconfig\"):\n",
    "            module_output.qconfig = module.qconfig\n",
    "            \n",
    "    elif isinstance(module, Conv2dSame):\n",
    "        module_output = Conv3dSame(\n",
    "            in_channels=module.in_channels,\n",
    "            out_channels=module.out_channels,\n",
    "            kernel_size=module.kernel_size[0],\n",
    "            stride=module.stride[0],\n",
    "            padding=module.padding[0],\n",
    "            dilation=module.dilation[0],\n",
    "            groups=module.groups,\n",
    "            bias=module.bias is not None,\n",
    "        )\n",
    "        module_output.weight = torch.nn.Parameter(module.weight.unsqueeze(-1).repeat(1,1,1,1,module.kernel_size[0]))\n",
    "\n",
    "    elif isinstance(module, torch.nn.Conv2d):\n",
    "        module_output = torch.nn.Conv3d(\n",
    "            in_channels=module.in_channels,\n",
    "            out_channels=module.out_channels,\n",
    "            kernel_size=module.kernel_size[0],\n",
    "            stride=module.stride[0],\n",
    "            padding=module.padding[0],\n",
    "            dilation=module.dilation[0],\n",
    "            groups=module.groups,\n",
    "            bias=module.bias is not None,\n",
    "            padding_mode=module.padding_mode\n",
    "        )\n",
    "        module_output.weight = torch.nn.Parameter(module.weight.unsqueeze(-1).repeat(1,1,1,1,module.kernel_size[0]))\n",
    "\n",
    "    elif isinstance(module, torch.nn.MaxPool2d):\n",
    "        module_output = torch.nn.MaxPool3d(\n",
    "            kernel_size=module.kernel_size,\n",
    "            stride=module.stride,\n",
    "            padding=module.padding,\n",
    "            dilation=module.dilation,\n",
    "            ceil_mode=module.ceil_mode,\n",
    "        )\n",
    "    elif isinstance(module, torch.nn.AvgPool2d):\n",
    "        module_output = torch.nn.AvgPool3d(\n",
    "            kernel_size=module.kernel_size,\n",
    "            stride=module.stride,\n",
    "            padding=module.padding,\n",
    "            ceil_mode=module.ceil_mode,\n",
    "        )\n",
    "\n",
    "    for name, child in module.named_children():\n",
    "        module_output.add_module(\n",
    "            name, convert_3d(child)\n",
    "        )\n",
    "    del module\n",
    "\n",
    "    return module_output\n",
    "\n",
    "\n",
    "\n",
    "class TimmSegModel(nn.Module):\n",
    "    def __init__(self, backbone, segtype='unet', pretrained=False):\n",
    "        super(TimmSegModel, self).__init__()\n",
    "\n",
    "        self.encoder = timm4smp.models.create_model(\n",
    "            backbone,\n",
    "            in_chans=3,\n",
    "            features_only=True,\n",
    "            pretrained=pretrained\n",
    "        )\n",
    "\n",
    "        # num_input_channels = saved_model['encoder.bn1.running_mean'].shape[0]\n",
    "        # self.encoder.bn1 = nn.BatchNorm2d(num_features=64)\n",
    "        g = self.encoder(torch.rand(1, 3, 64, 64))\n",
    "        encoder_channels = [1] + [_.shape[1] for _ in g]\n",
    "        decoder_channels = [256, 128, 64, 32, 16]\n",
    "        if segtype == 'unet':\n",
    "            self.decoder = smp.decoders.unet.model.UnetDecoder(\n",
    "                encoder_channels=encoder_channels[:n_blocks+1],\n",
    "                decoder_channels=decoder_channels[:n_blocks],\n",
    "                n_blocks=n_blocks,\n",
    "            )\n",
    "        self.segmentation_head = nn.Conv2d(decoder_channels[n_blocks-1], 7, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "\n",
    "    def forward(self,x):\n",
    "        global_features = [0] + self.encoder(x)[:n_blocks]\n",
    "        seg_features = self.decoder(*global_features)\n",
    "        seg_features = self.segmentation_head(seg_features)\n",
    "        return seg_features\n",
    "    \n",
    "    \n",
    "class TimmModel(nn.Module):\n",
    "    def __init__(self, backbone, image_size, pretrained=False):\n",
    "        super(TimmModel, self).__init__()\n",
    "        self.image_size = image_size\n",
    "        self.encoder = timm.create_model(\n",
    "            backbone,\n",
    "            in_chans=in_chans,\n",
    "            num_classes=1,\n",
    "            features_only=False,\n",
    "            drop_rate=0,\n",
    "            drop_path_rate=0,\n",
    "            pretrained=pretrained\n",
    "        )\n",
    "\n",
    "        if 'resnet' in backbone:\n",
    "            hdim = self.encoder.conv_head.out_channels\n",
    "            self.encoder.classifier = nn.Identity()\n",
    "        elif 'convnext' in backbone or 'nfnet' in backbone:\n",
    "            hdim = self.encoder.head.fc.in_features\n",
    "            self.encoder.head.fc = nn.Identity()\n",
    "\n",
    "        self.lstm = nn.LSTM(hdim, 256, num_layers=2, dropout=0, bidirectional=True, batch_first=True)\n",
    "        self.head = nn.Sequential(\n",
    "            nn.Linear(512, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.Dropout(0),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(256, 1),\n",
    "        )\n",
    "        self.lstm2 = nn.LSTM(hdim, 256, num_layers=2, dropout=0, bidirectional=True, batch_first=True)\n",
    "        self.head2 = nn.Sequential(\n",
    "            nn.Linear(512, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.Dropout(0),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(256, 1),\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self, x):  # (bs, nc*7, ch, sz, sz)\n",
    "        bs = x.shape[0]\n",
    "        x = x.view(bs * n_slice_per_c * 7, in_chans, self.image_size, self.image_size)\n",
    "        feat = self.encoder(x)\n",
    "        feat = feat.view(bs, n_slice_per_c * 7, -1)\n",
    "        feat1, _ = self.lstm(feat)\n",
    "        feat1 = feat1.contiguous().view(bs * n_slice_per_c * 7, 512)\n",
    "        feat2, _ = self.lstm2(feat)\n",
    "\n",
    "        return self.head(feat1), self.head2(feat2[:, 0])\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "class Timm1BoneModel(nn.Module):\n",
    "    def __init__(self, backbone, image_size, pretrained=False):\n",
    "        super(Timm1BoneModel, self).__init__()\n",
    "        self.image_size = image_size\n",
    "\n",
    "        self.encoder = timm.create_model(\n",
    "            backbone,\n",
    "            in_chans=in_chans,\n",
    "            num_classes=1,\n",
    "            features_only=False,\n",
    "            drop_rate=0,\n",
    "            drop_path_rate=0,\n",
    "            pretrained=pretrained\n",
    "        )\n",
    "\n",
    "        if 'resnet' in backbone:\n",
    "            hdim = self.encoder.fc.out_features\n",
    "            self.encoder.classifier = nn.Identity()\n",
    "        elif 'convnext' in backbone or 'nfnet' in backbone:\n",
    "            hdim = self.encoder.head.fc.in_features\n",
    "            self.encoder.head.fc = nn.Identity()\n",
    "\n",
    "        self.lstm = nn.LSTM(hdim, 256, num_layers=2, dropout=0, bidirectional=True, batch_first=True)\n",
    "        self.head = nn.Sequential(\n",
    "            nn.Linear(512, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.Dropout(0),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(256, 1),\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self, x):  # (bs, nslice, ch, sz, sz)\n",
    "        bs = x.shape[0]\n",
    "        x = x.view(bs * n_slice_per_c, in_chans, self.image_size, self.image_size)\n",
    "        feat = self.encoder(x)\n",
    "        feat = feat.view(bs, n_slice_per_c, -1)\n",
    "        feat, _ = self.lstm(feat)\n",
    "        feat = feat.contiguous().view(bs * n_slice_per_c, -1)\n",
    "        feat = self.head(feat)\n",
    "        feat = feat.view(bs, n_slice_per_c).contiguous()\n",
    "\n",
    "        return feat\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f293b2c6-7f46-4c47-ba00-92f5e253d60a",
   "metadata": {},
   "source": [
    "<h2>Load Models</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d960514-fdcb-489b-b9cc-4189d6e16f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_seg = []\n",
    "\n",
    "kernel_type = 'timm3d_v2s_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_mixup1_lr1e3_20x50ep'\n",
    "backbone = 'tf_efficientnetv2_s_in21ft1k'\n",
    "model_dir_seg = './seg_model'\n",
    "n_blocks = 4\n",
    "for fold in range(5):\n",
    "    model = TimmSegModel(backbone, pretrained=False)\n",
    "    model = convert_3d(model)\n",
    "    model = model.to(device)\n",
    "    load_model_file = os.path.join(model_dir_seg, f'{kernel_type}_fold{fold}_best.pth')\n",
    "    sd = torch.load(load_model_file)\n",
    "    if 'model_state_dict' in sd.keys():\n",
    "        sd = sd['model_state_dict']\n",
    "    sd = {k[7:] if k.startswith('module.') else k: sd[k] for k in sd.keys()}\n",
    "    # Assuming `model` is your PyTorch model\n",
    "    # for name, param in model.named_parameters():\n",
    "    #     print(f\"Parameter name: {name}, Size: {param.size()}\")\n",
    "\n",
    "    model.load_state_dict(sd, strict=False)\n",
    "    model.eval()\n",
    "    models_seg.append(model)\n",
    "\n",
    "len(models_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb1e0ca-7a8d-4035-ac0b-320c548331e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cdf87694-d725-494c-aa65-5530d8c85527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_type = '0920_1bonev2_resnet18_224_15_6ch_augv2_mixupp5_drl3_rov1p2_bs8_lr23e5_eta23e6_50ep'\n",
    "model_dir_cls = './models'\n",
    "backbone = 'resnet18'\n",
    "in_chans = 6\n",
    "models_cls1 = []\n",
    "\n",
    "for fold in range(5):\n",
    "    model = Timm1BoneModel(backbone, image_size=224, pretrained=False)\n",
    "    load_model_file = os.path.join(model_dir_cls, f'{kernel_type}_fold{fold}_best.pth')\n",
    "    sd = torch.load(load_model_file, map_location='cpu')\n",
    "    if 'model_state_dict' in sd.keys():\n",
    "        sd = sd['model_state_dict']\n",
    "    sd = {k[7:] if k.startswith('module.') else k: sd[k] for k in sd.keys()}\n",
    "    model.load_state_dict(sd, strict=True)\n",
    "    model = model.to(device)\n",
    "    model.eval()\n",
    "    models_cls1.append(model)\n",
    "\n",
    "len(models_cls1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1a3cf89-dea6-433b-a27c-b34feb8c0825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_type = '0920_2d_lstmv22headv2_convnn_224_15_6ch_8flip_augv2_drl3_rov1p2_rov3p2_bs4_lr6e5_eta6e6_lw151_50ep'\n",
    "model_dir_cls = './conv_model'\n",
    "backbone = 'convnext_nano'\n",
    "in_chans = 6\n",
    "models_cls2 = []\n",
    "\n",
    "for fold in range(5):\n",
    "    model = TimmModel(backbone, image_size=224, pretrained=False)\n",
    "    model = model.to(device)\n",
    "    load_model_file = os.path.join(model_dir_cls, f'{kernel_type}_fold{fold}_best.pth')\n",
    "    sd = torch.load(load_model_file)\n",
    "    if 'model_state_dict' in sd.keys():\n",
    "        sd = sd['model_state_dict']\n",
    "    sd = {k[7:] if k.startswith('module.') else k: sd[k] for k in sd.keys()}\n",
    "    model.load_state_dict(sd, strict=True)\n",
    "    model.eval()\n",
    "    models_cls2.append(model)\n",
    "\n",
    "len(models_cls2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91bd6782-a7d9-4f9a-91d3-aa9f8b0bf485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri May 10 21:18:50 2024       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.60.13    Driver Version: 525.60.13    CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA A16          On   | 00000000:E6:00.0 Off |                    0 |\n",
      "|  0%   45C    P0    27W /  62W |   1612MiB / 15356MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "266997af-3f6d-4246-b6c6-14d6e605ba9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_bone(msk, cid, t_paths, cropped_images):\n",
    "    n_scans = len(t_paths)\n",
    "    bone = []\n",
    "    try:\n",
    "        msk_b = msk[cid] > 0.2\n",
    "        msk_c = msk[cid] > 0.05\n",
    "\n",
    "        x = np.where(msk_b.sum(1).sum(1) > 0)[0]\n",
    "        y = np.where(msk_b.sum(0).sum(1) > 0)[0]\n",
    "        z = np.where(msk_b.sum(0).sum(0) > 0)[0]\n",
    "\n",
    "        if len(x) == 0 or len(y) == 0 or len(z) == 0:\n",
    "            x = np.where(msk_c.sum(1).sum(1) > 0)[0]\n",
    "            y = np.where(msk_c.sum(0).sum(1) > 0)[0]\n",
    "            z = np.where(msk_c.sum(0).sum(0) > 0)[0]\n",
    "\n",
    "        x1, x2 = max(0, x[0] - 1), min(msk.shape[1], x[-1] + 1)\n",
    "        y1, y2 = max(0, y[0] - 1), min(msk.shape[2], y[-1] + 1)\n",
    "        z1, z2 = max(0, z[0] - 1), min(msk.shape[3], z[-1] + 1)\n",
    "        zz1, zz2 = int(z1 / msk_size * n_scans), int(z2 / msk_size * n_scans)\n",
    "\n",
    "        inds = np.linspace(zz1 ,zz2-1 ,n_slice_per_c).astype(int)\n",
    "        inds_ = np.linspace(z1 ,z2-1 ,n_slice_per_c).astype(int)\n",
    "        for sid, (ind, ind_) in enumerate(zip(inds, inds_)):\n",
    "\n",
    "            msk_this = msk[cid, :, :, ind_]\n",
    "\n",
    "            images = []\n",
    "            for i in range(-n_ch//2+1, n_ch//2+1):\n",
    "                try:\n",
    "                    dicom = pydicom.read_file(t_paths[ind+i])\n",
    "                    images.append(dicom.pixel_array)\n",
    "                except:\n",
    "                    images.append(np.zeros((512, 512)))\n",
    "\n",
    "            data = np.stack(images, -1)\n",
    "            data = data - np.min(data)\n",
    "            data = data / (np.max(data) + 1e-4)\n",
    "            data = (data * 255).astype(np.uint8)\n",
    "            msk_this = msk_this[x1:x2, y1:y2]\n",
    "            xx1 = int(x1 / msk_size * data.shape[0])\n",
    "            xx2 = int(x2 / msk_size * data.shape[0])\n",
    "            yy1 = int(y1 / msk_size * data.shape[1])\n",
    "            yy2 = int(y2 / msk_size * data.shape[1])\n",
    "            data = data[xx1:xx2, yy1:yy2]\n",
    "            data = np.stack([cv2.resize(data[:, :, i], (image_size_cls, image_size_cls), interpolation = cv2.INTER_LINEAR) for i in range(n_ch)], -1)\n",
    "            msk_this = (msk_this * 255).astype(np.uint8)\n",
    "            msk_this = cv2.resize(msk_this, (image_size_cls, image_size_cls), interpolation = cv2.INTER_LINEAR)\n",
    "\n",
    "            data = np.concatenate([data, msk_this[:, :, np.newaxis]], -1)\n",
    "\n",
    "            bone.append(torch.tensor(data))\n",
    "\n",
    "    except:\n",
    "        for sid in range(n_slice_per_c):\n",
    "            bone.append(torch.ones((image_size_cls, image_size_cls, n_ch+1)).int())\n",
    "\n",
    "    cropped_images[cid] = torch.stack(bone, 0)\n",
    "\n",
    "\n",
    "def load_cropped_images(msk, image_folder, n_ch=n_ch):\n",
    "\n",
    "    t_paths = sorted(glob(os.path.join(image_folder, \"*\")), key=lambda x: int(x.split('/')[-1].split(\".\")[0]))\n",
    "    for cid in range(7):\n",
    "        threads[cid] = threading.Thread(target=load_bone, args=(msk, cid, t_paths, cropped_images))\n",
    "        threads[cid].start()\n",
    "    for cid in range(7):\n",
    "        threads[cid].join()\n",
    "\n",
    "    return torch.cat(cropped_images, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fa563b-78a0-4969-a0cb-6efe2e879e86",
   "metadata": {},
   "source": [
    "<h2>Predict</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "19c1df18-fc88-4d8c-a532-40c42f69b06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:40<00:00, 13.52s/it]\n"
     ]
    }
   ],
   "source": [
    "outputs1 = []\n",
    "outputs2 = []\n",
    "\n",
    "bar = tqdm(loader_seg)\n",
    "with torch.no_grad():\n",
    "    for batch_id, (images) in enumerate(bar):\n",
    "        images = images.cuda()\n",
    "\n",
    "        # SEG\n",
    "        pred_masks = []\n",
    "        for model in models_seg:\n",
    "            pmask = model(images).sigmoid()\n",
    "            pred_masks.append(pmask)\n",
    "        pred_masks = torch.stack(pred_masks, 0).mean(0).cpu().numpy()\n",
    "\n",
    "        # Build cls input\n",
    "        cls_inp = []\n",
    "        threads = [None] * 7\n",
    "        cropped_images = [None] * 7\n",
    "\n",
    "        for i in range(pred_masks.shape[0]):\n",
    "            row = df.iloc[batch_id*batch_size_seg+i]\n",
    "            cropped_images = load_cropped_images(pred_masks[i], row.image_folder)\n",
    "            cls_inp.append(cropped_images.permute(0, 3, 1, 2).float() / 255.)\n",
    "        cls_inp = torch.stack(cls_inp, 0).to(device)  # (1, 105, 6, 224, 224)\n",
    "\n",
    "        pred_cls1, pred_cls2 = [], []\n",
    "        # CLS 2\n",
    "        for _, model in enumerate(models_cls2):\n",
    "            logits, logits2 = model(cls_inp)\n",
    "            pred_cls1.append(logits.sigmoid().view(-1, 7, n_slice_per_c))\n",
    "            pred_cls2.append(logits2.sigmoid())\n",
    "\n",
    "        # CLS 1\n",
    "        cls_inp = cls_inp.view(7, 15, 6, image_size_cls, image_size_cls).contiguous()\n",
    "        for _, model in enumerate(models_cls1):\n",
    "            logits = model(cls_inp)\n",
    "            pred_cls1.append(logits.sigmoid().view(-1, 7, n_slice_per_c))\n",
    "\n",
    "        pred_cls1 = torch.stack(pred_cls1, 0).mean(0)\n",
    "        pred_cls2 = torch.stack(pred_cls2, 0).mean(0)\n",
    "        outputs1.append(pred_cls1.cpu())\n",
    "        outputs2.append(pred_cls2.cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556b5d2c-fa95-4c98-a9d0-fa37406b7ee3",
   "metadata": {},
   "source": [
    "<h2>Outputs</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4a88052-a12d-4d52-855a-a4261a298b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs1 = torch.cat(outputs1)\n",
    "outputs2 = torch.cat(outputs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ef86c619-a505-446f-b13e-97786678b4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED1 = (outputs1.mean(-1)).clamp(0.0001, 0.9999)\n",
    "PRED2 = (outputs2.view(-1)).clamp(0.0001, 0.9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac219fac-dbe7-4352-81c2-2b32cb6b5e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_ids = []\n",
    "for _, row in df.iterrows():\n",
    "    for i in range(7):\n",
    "        row_ids.append(row.StudyInstanceUID + f'_C{i+1}')\n",
    "    row_ids.append(row.StudyInstanceUID + '_patient_overall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c28c4c0-e7db-4b14-8b79-956bb573db29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub = pd.DataFrame({\n",
    "    'row_id': row_ids,\n",
    "    'fractured': torch.cat([PRED1, PRED2.unsqueeze(1)], 1).view(-1),\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "abfcf21a-d1ef-468c-a8f3-afdf790e187e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub.to_csv('submissionresnet.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ccaa0867-ddc0-4aba-b3d7-69bafb5f12b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>fractured</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C1</td>\n",
       "      <td>0.033526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C2</td>\n",
       "      <td>0.050589</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C3</td>\n",
       "      <td>0.017206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C4</td>\n",
       "      <td>0.026012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C5</td>\n",
       "      <td>0.057979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C6</td>\n",
       "      <td>0.268392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_C7</td>\n",
       "      <td>0.441880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.2.826.0.1.3680043.22327_patient_overall</td>\n",
       "      <td>0.403687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C1</td>\n",
       "      <td>0.016853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C2</td>\n",
       "      <td>0.034683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C3</td>\n",
       "      <td>0.033408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C4</td>\n",
       "      <td>0.049175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C5</td>\n",
       "      <td>0.043538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C6</td>\n",
       "      <td>0.062341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_C7</td>\n",
       "      <td>0.120163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.2.826.0.1.3680043.25399_patient_overall</td>\n",
       "      <td>0.113701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C1</td>\n",
       "      <td>0.021906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C2</td>\n",
       "      <td>0.250863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C3</td>\n",
       "      <td>0.040769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C4</td>\n",
       "      <td>0.092129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C5</td>\n",
       "      <td>0.123581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C6</td>\n",
       "      <td>0.149740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_C7</td>\n",
       "      <td>0.200967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.2.826.0.1.3680043.5876_patient_overall</td>\n",
       "      <td>0.443938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       row_id  fractured\n",
       "0                1.2.826.0.1.3680043.22327_C1   0.033526\n",
       "1                1.2.826.0.1.3680043.22327_C2   0.050589\n",
       "2                1.2.826.0.1.3680043.22327_C3   0.017206\n",
       "3                1.2.826.0.1.3680043.22327_C4   0.026012\n",
       "4                1.2.826.0.1.3680043.22327_C5   0.057979\n",
       "5                1.2.826.0.1.3680043.22327_C6   0.268392\n",
       "6                1.2.826.0.1.3680043.22327_C7   0.441880\n",
       "7   1.2.826.0.1.3680043.22327_patient_overall   0.403687\n",
       "8                1.2.826.0.1.3680043.25399_C1   0.016853\n",
       "9                1.2.826.0.1.3680043.25399_C2   0.034683\n",
       "10               1.2.826.0.1.3680043.25399_C3   0.033408\n",
       "11               1.2.826.0.1.3680043.25399_C4   0.049175\n",
       "12               1.2.826.0.1.3680043.25399_C5   0.043538\n",
       "13               1.2.826.0.1.3680043.25399_C6   0.062341\n",
       "14               1.2.826.0.1.3680043.25399_C7   0.120163\n",
       "15  1.2.826.0.1.3680043.25399_patient_overall   0.113701\n",
       "16                1.2.826.0.1.3680043.5876_C1   0.021906\n",
       "17                1.2.826.0.1.3680043.5876_C2   0.250863\n",
       "18                1.2.826.0.1.3680043.5876_C3   0.040769\n",
       "19                1.2.826.0.1.3680043.5876_C4   0.092129\n",
       "20                1.2.826.0.1.3680043.5876_C5   0.123581\n",
       "21                1.2.826.0.1.3680043.5876_C6   0.149740\n",
       "22                1.2.826.0.1.3680043.5876_C7   0.200967\n",
       "23   1.2.826.0.1.3680043.5876_patient_overall   0.443938"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b20d748-ed00-491d-ba04-40c676a26e8a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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