From ca2f462733d7fdd8af25f3837e6f4baef2b57928 Mon Sep 17 00:00:00 2001 From: roomrys Date: Sat, 31 Aug 2024 16:51:44 +0000 Subject: [PATCH] deploy: b9bb300a23b3ad7a09d9f32eed74a9aaf9c9bc5b --- develop/.buildinfo | 2 +- develop/CODE_OF_CONDUCT.html | 2 +- develop/CONTRIBUTING.html | 2 +- develop/_sources/api.rst | 76 +- develop/_sources/guides/cli.md | 25 +- develop/_sources/guides/gui.md | 2 +- develop/_sources/installation.md | 6 +- .../_sources/notebooks/Data_structures.ipynb | 2 +- .../Interactive_and_realtime_inference.ipynb | 2 +- .../Interactive_and_resumable_training.ipynb | 2 +- .../_sources/notebooks/Model_evaluation.ipynb | 2 +- .../notebooks/Post_inference_tracking.ipynb | 2 +- ..._and_inference_on_an_example_dataset.ipynb | 2570 ++++++++--------- ...ing_and_inference_using_Google_Drive.ipynb | 726 ++--- develop/_static/documentation_options.js | 2 +- develop/api.html | 230 +- develop/api/sleap.info.align.html | 18 +- .../api/sleap.info.feature_suggestions.html | 50 +- 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bytes develop/overview.html | 2 +- develop/py-modindex.html | 2 +- develop/search.html | 2 +- develop/searchindex.js | 2 +- develop/tutorials/analysis.html | 2 +- develop/tutorials/assisted-labeling.html | 2 +- develop/tutorials/initial-labeling.html | 2 +- develop/tutorials/initial-training.html | 2 +- develop/tutorials/new-project.html | 2 +- develop/tutorials/proofreading.html | 2 +- develop/tutorials/tutorial.html | 2 +- 139 files changed, 3036 insertions(+), 3196 deletions(-) diff --git a/develop/.buildinfo b/develop/.buildinfo index ff92ce9d0..5fcb3d65d 100644 --- a/develop/.buildinfo +++ b/develop/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: a7b7f9800faa1bcc2746e1b3ba7ad6cf +config: ea14d6c449de46604b07382202b24124 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/develop/CODE_OF_CONDUCT.html b/develop/CODE_OF_CONDUCT.html index 1c50c1822..afa95fae3 100644 --- a/develop/CODE_OF_CONDUCT.html +++ b/develop/CODE_OF_CONDUCT.html @@ -9,7 +9,7 @@ - Contributor Covenant Code of Conduct — SLEAP (v1.4.1a2) + Contributor Covenant Code of Conduct — SLEAP (v1.3.4) diff --git a/develop/CONTRIBUTING.html b/develop/CONTRIBUTING.html index f247fa178..d21c88946 100644 --- a/develop/CONTRIBUTING.html +++ b/develop/CONTRIBUTING.html @@ -9,7 +9,7 @@ - Contributing to SLEAP — SLEAP (v1.4.1a2) + Contributing to SLEAP — SLEAP (v1.3.4) diff --git a/develop/_sources/api.rst b/develop/_sources/api.rst index e6af22307..dfb670913 100644 --- a/develop/_sources/api.rst +++ b/develop/_sources/api.rst @@ -19,38 +19,6 @@ Developer API sleap.message sleap.skeleton sleap.util - sleap.info.align - sleap.info.feature_suggestions - sleap.info.labels - sleap.info.metrics - sleap.info.summary - sleap.info.trackcleaner - sleap.info.write_tracking_h5 - sleap.io.asyncvideo - sleap.io.convert - sleap.io.dataset - sleap.io.legacy - sleap.io.pathutils - sleap.io.video - sleap.io.videowriter - sleap.io.visuals - sleap.io.format.adaptor - sleap.io.format.alphatracker - sleap.io.format.coco - sleap.io.format.csv - sleap.io.format.deeplabcut - sleap.io.format.deepposekit - sleap.io.format.dispatch - sleap.io.format.filehandle - sleap.io.format.genericjson - sleap.io.format.hdf5 - sleap.io.format.labels_json - sleap.io.format.leap_matlab - sleap.io.format.main - sleap.io.format.ndx_pose - sleap.io.format.nix - sleap.io.format.sleap_analysis - sleap.io.format.text sleap.nn.callbacks sleap.nn.evals sleap.nn.heads @@ -65,12 +33,6 @@ Developer API sleap.nn.training sleap.nn.utils sleap.nn.viz - sleap.nn.config.data - sleap.nn.config.model - sleap.nn.config.optimization - sleap.nn.config.outputs - sleap.nn.config.training_job - sleap.nn.config.utils sleap.nn.architectures.common sleap.nn.architectures.encoder_decoder sleap.nn.architectures.hourglass @@ -99,3 +61,41 @@ Developer API sleap.nn.data.resizing sleap.nn.data.training sleap.nn.data.utils + sleap.nn.config.data + sleap.nn.config.model + sleap.nn.config.optimization + sleap.nn.config.outputs + sleap.nn.config.training_job + sleap.nn.config.utils + sleap.info.align + sleap.info.feature_suggestions + sleap.info.labels + sleap.info.metrics + sleap.info.summary + sleap.info.trackcleaner + sleap.info.write_tracking_h5 + sleap.io.asyncvideo + sleap.io.convert + sleap.io.dataset + sleap.io.legacy + sleap.io.pathutils + sleap.io.video + sleap.io.videowriter + sleap.io.visuals + sleap.io.format.adaptor + sleap.io.format.alphatracker + sleap.io.format.coco + sleap.io.format.csv + sleap.io.format.deeplabcut + sleap.io.format.deepposekit + sleap.io.format.dispatch + sleap.io.format.filehandle + sleap.io.format.genericjson + sleap.io.format.hdf5 + sleap.io.format.labels_json + sleap.io.format.leap_matlab + sleap.io.format.main + sleap.io.format.ndx_pose + sleap.io.format.nix + sleap.io.format.sleap_analysis + sleap.io.format.text diff --git a/develop/_sources/guides/cli.md b/develop/_sources/guides/cli.md index 03b806903..35ea52171 100644 --- a/develop/_sources/guides/cli.md +++ b/develop/_sources/guides/cli.md @@ -36,8 +36,8 @@ optional arguments: ```none usage: sleap-train [-h] [--video-paths VIDEO_PATHS] [--val_labels VAL_LABELS] - [--test_labels TEST_LABELS] [--tensorboard] [--save_viz] - [--keep_viz] [--zmq] [--run_name RUN_NAME] [--prefix PREFIX] + [--test_labels TEST_LABELS] [--tensorboard] [--save_viz] + [--zmq] [--run_name RUN_NAME] [--prefix PREFIX] [--suffix SUFFIX] training_job_path [labels_path] @@ -68,8 +68,6 @@ optional arguments: --save_viz Enable saving of prediction visualizations to the run folder if not already specified in the training job config. - --keep_viz Keep prediction visualization images in the run - folder after training if --save_viz is enabled. --zmq Enable ZMQ logging (for GUI) if not already specified in the training job config. --run_name RUN_NAME Run name to use when saving file, overrides other run @@ -101,9 +99,9 @@ optional arguments: -e [EXPORT_PATH], --export_path [EXPORT_PATH] Path to output directory where the frozen model will be exported to. Defaults to a folder named 'exported_model'. - -r, --ragged RAGGED - Keep tensors ragged if present. If ommited, convert - ragged tensors into regular tensors with NaN padding. + -u, --unrag UNRAG + Convert ragged tensors into regular tensors with NaN padding. + Defaults to True. -n, --max_instances MAX_INSTANCES Limit maximum number of instances in multi-instance models. Not available for ID models. Defaults to None. @@ -138,10 +136,7 @@ usage: sleap-track [-h] [-m MODELS] [--frames FRAMES] [--only-labeled-frames] [- [data_path] positional arguments: - data_path Path to data to predict on. This can be one of the following: A .slp file containing labeled data; A folder containing multiple - video files in supported formats; An individual video file in a supported format; A CSV file with a column of video file paths. - If more than one column is provided in the CSV file, the first will be used for the input data paths and the next column will be - used as the output paths; A text file with a path to a video file on each line + data_path Path to data to predict on. This can be a labels (.slp) file or any supported video format. optional arguments: -h, --help show this help message and exit @@ -156,7 +151,7 @@ optional arguments: Only run inference on unlabeled suggested frames when running on labels dataset. This is useful for generating predictions for initialization during labeling. -o OUTPUT, --output OUTPUT - The output filename or directory path to use for the predicted data. If not provided, defaults to '[data_path].predictions.slp'. + The output filename to use for the predicted data. If not provided, defaults to '[data_path].predictions.slp'. --no-empty-frames Clear any empty frames that did not have any detected instances before saving to output. --verbosity {none,rich,json} Verbosity of inference progress reporting. 'none' does not output anything during inference, 'rich' displays an updating @@ -327,8 +322,7 @@ optional arguments: analysis file for the latter video is given a default name. --format FORMAT Output format. Default ('slp') is SLEAP dataset; 'analysis' results in analysis.h5 file; 'analysis.nix' results - in an analysis nix file; 'analysis.csv' results - in an analysis csv file; 'h5' or 'json' results in SLEAP dataset + in an analysis nix file; 'h5' or 'json' results in SLEAP dataset with specified file format. --video VIDEO Path to video (if needed for conversion). ``` @@ -395,9 +389,6 @@ optional arguments: --distinctly_color DISTINCTLY_COLOR Specify how to color instances. Options include: "instances", "edges", and "nodes" (default: "instances") - --background BACKGROUND - Specify the type of background to be used to save the videos. - Options: original, black, white and grey. (default: "original") ``` ## Debugging diff --git a/develop/_sources/guides/gui.md b/develop/_sources/guides/gui.md index 813ed68fa..88cf3f656 100644 --- a/develop/_sources/guides/gui.md +++ b/develop/_sources/guides/gui.md @@ -60,7 +60,7 @@ Note that many of the menu command have keyboard shortcuts which can be configur "**Edge Style**" controls whether edges are drawn as thin lines or as wedges which indicate the {ref}`orientation` of the instance (as well as the direction of the part affinity field which would be used to predict the connection between nodes when using a "bottom-up" approach). -"**Trail Length**" allows you to show a trail of where each instance was located in prior frames (the length of the trail is the number of prior frames). This can be useful when proofreading predictions since it can help you detect swaps in the identities of animals across frames. By default, you can only select trail lengths of up to 250 frames. You can use a custom trail length by modifying the default length in the `preferences.yaml` file. However, using trail lengths longer than about 500 frames can result in significant lag. +"**Trail Length**" allows you to show a trail of where each instance was located in prior frames (the length of the trail is the number of prior frames). This can be useful when proofreading predictions since it can help you detect swaps in the identities of animals across frames. "**Fit Instances to View**" allows you to toggle whether the view is auto-zoomed to the instances in each frame. This can be useful when proofreading predictions. diff --git a/develop/_sources/installation.md b/develop/_sources/installation.md index c0ab66580..ee2e7eec0 100644 --- a/develop/_sources/installation.md +++ b/develop/_sources/installation.md @@ -137,13 +137,13 @@ SLEAP can be installed three different ways: via {ref}`conda package=1.3.3\"\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"\n", "\n", "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap" diff --git a/develop/_sources/notebooks/Interactive_and_realtime_inference.ipynb b/develop/_sources/notebooks/Interactive_and_realtime_inference.ipynb index 4a3b612a2..94a20ea3b 100644 --- a/develop/_sources/notebooks/Interactive_and_realtime_inference.ipynb +++ b/develop/_sources/notebooks/Interactive_and_realtime_inference.ipynb @@ -60,7 +60,7 @@ "source": [ "# This should take care of all the dependencies on colab:\n", "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", - "!pip install -qqq \"sleap[pypi]>=1.3.3\"\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"\n", "\n", "\n", "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", diff --git a/develop/_sources/notebooks/Interactive_and_resumable_training.ipynb b/develop/_sources/notebooks/Interactive_and_resumable_training.ipynb index f30f036f3..68b4f0715 100644 --- a/develop/_sources/notebooks/Interactive_and_resumable_training.ipynb +++ b/develop/_sources/notebooks/Interactive_and_resumable_training.ipynb @@ -62,7 +62,7 @@ "source": [ "# This should take care of all the dependencies on colab:\n", "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", - "!pip install -qqq \"sleap[pypi]>=1.3.3\"\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"\n", "\n", "\n", "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", diff --git a/develop/_sources/notebooks/Model_evaluation.ipynb b/develop/_sources/notebooks/Model_evaluation.ipynb index 41ca6568c..af2b55d51 100644 --- a/develop/_sources/notebooks/Model_evaluation.ipynb +++ b/develop/_sources/notebooks/Model_evaluation.ipynb @@ -40,7 +40,7 @@ ], "source": [ "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", - "!pip install -qqq \"sleap[pypi]>=1.3.3\"\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"\n", "!apt -qq install tree\n", "!wget -q https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\n", "!unzip -qq -o -d \"td_fast.210505_012601.centered_instance.n=1800\" \"td_fast.210505_012601.centered_instance.n=1800.zip\"" diff --git a/develop/_sources/notebooks/Post_inference_tracking.ipynb b/develop/_sources/notebooks/Post_inference_tracking.ipynb index 239176bdb..e91ed002d 100644 --- a/develop/_sources/notebooks/Post_inference_tracking.ipynb +++ b/develop/_sources/notebooks/Post_inference_tracking.ipynb @@ -61,7 +61,7 @@ "source": [ "# This should take care of all the dependencies on colab:\n", "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", - "!pip install -qqq \"sleap[pypi]>=1.3.3\"\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"\n", "\n", "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap" diff --git a/develop/_sources/notebooks/Training_and_inference_on_an_example_dataset.ipynb b/develop/_sources/notebooks/Training_and_inference_on_an_example_dataset.ipynb index 4e26cb286..df101eb19 100644 --- a/develop/_sources/notebooks/Training_and_inference_on_an_example_dataset.ipynb +++ b/develop/_sources/notebooks/Training_and_inference_on_an_example_dataset.ipynb @@ -1,1302 +1,1302 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Training and inference on an example dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LlV70jDuWzea" - }, - "source": [ - "In this notebook we'll install SLEAP, download a sample dataset, run training and inference on that dataset using the SLEAP command-line interface, and then download the predictions." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "yX9noEb8m8re" - }, - "source": [ - "## Install SLEAP\n", - "Note: Before installing SLEAP check [SLEAP releases](https://github.com/talmolab/sleap/releases) page for the latest version." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] }, - "id": "DUfnkxMtLcK3", - "outputId": "a6340ef1-eaac-42ef-f8d4-bcc499feb57b" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n", - "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", - "!pip install -qqq \"sleap[pypi]>=1.3.3\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iq7jrgUksLtR" - }, - "source": [ - "## Download sample training data into Colab\n", - "Let's download a sample dataset from the SLEAP [sample datasets repository](https://github.com/talmolab/sleap-datasets) into Colab." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training and inference on an example dataset" + ] }, - "id": "fm3cU1Bc0tWc", - "outputId": "c0ac5677-e3c5-477c-a2f7-44d619208b22" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n", - "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n", - "--2023-09-01 13:30:33-- https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\n", - "Resolving github.com (github.com)... 192.30.255.113\n", - "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230901%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230901T203033Z&X-Amz-Expires=300&X-Amz-Signature=b9b0638744af3144affdc46668c749128bd6c4f23ca2a1313821c7bbcd54ccdd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=263375180&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream [following]\n", - "--2023-09-01 13:30:33-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230901%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230901T203033Z&X-Amz-Expires=300&X-Amz-Signature=b9b0638744af3144affdc46668c749128bd6c4f23ca2a1313821c7bbcd54ccdd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=263375180&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream\n", - "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 111973079 (107M) [application/octet-stream]\n", - "Saving to: ‘dataset.zip’\n", - "\n", - "dataset.zip 100%[===================>] 106.79M 63.0MB/s in 1.7s \n", - "\n", - "2023-09-01 13:30:35 (63.0 MB/s) - ‘dataset.zip’ saved [111973079/111973079]\n", - "\n", - "Archive: dataset.zip\n", - " creating: dataset/drosophila-melanogaster-courtship/\n", - " inflating: dataset/drosophila-melanogaster-courtship/.DS_Store \n", - " creating: dataset/__MACOSX/\n", - " creating: dataset/__MACOSX/drosophila-melanogaster-courtship/\n", - " inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._.DS_Store \n", - " inflating: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4 \n", - " inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._20190128_113421.mp4 \n", - " inflating: dataset/drosophila-melanogaster-courtship/courtship_labels.slp \n", - " inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._courtship_labels.slp \n", - " inflating: dataset/drosophila-melanogaster-courtship/example.jpg \n", - " inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._example.jpg \n", - "\u001b[01;34mdataset\u001b[00m\n", - "├── \u001b[01;34mdrosophila-melanogaster-courtship\u001b[00m\n", - "│   ├── \u001b[01;32m20190128_113421.mp4\u001b[00m\n", - "│   ├── \u001b[01;32mcourtship_labels.slp\u001b[00m\n", - "│   └── \u001b[01;35mexample.jpg\u001b[00m\n", - "└── \u001b[01;34m__MACOSX\u001b[00m\n", - " └── \u001b[01;34mdrosophila-melanogaster-courtship\u001b[00m\n", - "\n", - "3 directories, 3 files\n" - ] - } - ], - "source": [ - "!apt-get install tree\n", - "!wget -O dataset.zip https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\n", - "!mkdir dataset\n", - "!unzip dataset.zip -d dataset\n", - "!rm dataset.zip\n", - "!tree dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xZ-sr67av5uu" - }, - "source": [ - "## Train models\n", - "For the top-down pipeline, we'll need train two models: a centroid model and a centered-instance model.\n", - "\n", - "Using the command-line interface, we'll first train a model for centroids using the default **training profile**. The training profile determines the model architecture, the learning rate, and other parameters.\n", - "\n", - "When you start training, you'll first see the training parameters and then the training and validation loss for each training epoch. \n", - "\n", - "As soon as you're satisfied with the validation loss you see for an epoch during training, you're welcome to stop training by clicking the stop button. The version of the model with the lowest validation loss is saved during training, and that's what will be used for inference.\n", - "\n", - "If you don't stop training, it will run for 200 epochs or until validation loss fails to improve for some number of epochs (controlled by the `early_stopping` fields in the training profile)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "QKf6qzMqNBUi" - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LlV70jDuWzea" + }, + "source": [ + "In this notebook we'll install SLEAP, download a sample dataset, run training and inference on that dataset using the SLEAP command-line interface, and then download the predictions." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:sleap.nn.training:Versions:\n", - "SLEAP: 1.3.2\n", - "TensorFlow: 2.7.0\n", - "Numpy: 1.21.5\n", - "Python: 3.7.12\n", - "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n", - "INFO:sleap.nn.training:Training labels file: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n", - "INFO:sleap.nn.training:Training profile: /home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline.centroid.json\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Arguments:\n", - "INFO:sleap.nn.training:{\n", - " \"training_job_path\": \"baseline.centroid.json\",\n", - " \"labels_path\": \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\",\n", - " \"video_paths\": [\n", - " \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"\n", - " ],\n", - " \"val_labels\": null,\n", - " \"test_labels\": null,\n", - " \"base_checkpoint\": null,\n", - " \"tensorboard\": false,\n", - " \"save_viz\": false,\n", - " \"zmq\": false,\n", - " \"run_name\": \"courtship.centroid\",\n", - " \"prefix\": \"\",\n", - " \"suffix\": \"\",\n", - " \"cpu\": false,\n", - " \"first_gpu\": false,\n", - " \"last_gpu\": false,\n", - " \"gpu\": \"auto\"\n", - "}\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Training job:\n", - "INFO:sleap.nn.training:{\n", - " \"data\": {\n", - " \"labels\": {\n", - " \"training_labels\": null,\n", - " \"validation_labels\": null,\n", - " \"validation_fraction\": 0.1,\n", - " \"test_labels\": null,\n", - " \"split_by_inds\": false,\n", - " \"training_inds\": null,\n", - " \"validation_inds\": null,\n", - " \"test_inds\": null,\n", - " \"search_path_hints\": [],\n", - " \"skeletons\": []\n", - " },\n", - " \"preprocessing\": {\n", - " \"ensure_rgb\": false,\n", - " \"ensure_grayscale\": false,\n", - " \"imagenet_mode\": null,\n", - " \"input_scaling\": 0.5,\n", - " \"pad_to_stride\": null,\n", - " \"resize_and_pad_to_target\": true,\n", - " \"target_height\": null,\n", - " \"target_width\": null\n", - " },\n", - " \"instance_cropping\": {\n", - " \"center_on_part\": null,\n", - " \"crop_size\": null,\n", - " \"crop_size_detection_padding\": 16\n", - " }\n", - " },\n", - " \"model\": {\n", - " \"backbone\": {\n", - " \"leap\": null,\n", - " \"unet\": {\n", - " \"stem_stride\": null,\n", - " \"max_stride\": 16,\n", - " \"output_stride\": 2,\n", - " \"filters\": 16,\n", - " \"filters_rate\": 2.0,\n", - " \"middle_block\": true,\n", - " \"up_interpolate\": true,\n", - " \"stacks\": 1\n", - " },\n", - " \"hourglass\": null,\n", - " \"resnet\": null,\n", - " \"pretrained_encoder\": null\n", - " },\n", - " \"heads\": {\n", - " \"single_instance\": null,\n", - " \"centroid\": {\n", - " \"anchor_part\": null,\n", - " \"sigma\": 2.5,\n", - " \"output_stride\": 2,\n", - " \"loss_weight\": 1.0,\n", - " \"offset_refinement\": false\n", - " },\n", - " \"centered_instance\": null,\n", - " \"multi_instance\": null,\n", - " \"multi_class_bottomup\": null,\n", - " \"multi_class_topdown\": null\n", - " },\n", - " \"base_checkpoint\": null\n", - " },\n", - " \"optimization\": {\n", - " \"preload_data\": true,\n", - " \"augmentation_config\": {\n", - " \"rotate\": true,\n", - " \"rotation_min_angle\": -15.0,\n", - " \"rotation_max_angle\": 15.0,\n", - " \"translate\": false,\n", - " \"translate_min\": -5,\n", - " \"translate_max\": 5,\n", - " \"scale\": false,\n", - " \"scale_min\": 0.9,\n", - " \"scale_max\": 1.1,\n", - " \"uniform_noise\": false,\n", - " \"uniform_noise_min_val\": 0.0,\n", - " \"uniform_noise_max_val\": 10.0,\n", - " \"gaussian_noise\": false,\n", - " \"gaussian_noise_mean\": 5.0,\n", - " \"gaussian_noise_stddev\": 1.0,\n", - " \"contrast\": false,\n", - " \"contrast_min_gamma\": 0.5,\n", - " \"contrast_max_gamma\": 2.0,\n", - " \"brightness\": false,\n", - " \"brightness_min_val\": 0.0,\n", - " \"brightness_max_val\": 10.0,\n", - " \"random_crop\": false,\n", - " \"random_crop_height\": 256,\n", - " \"random_crop_width\": 256,\n", - " \"random_flip\": false,\n", - " \"flip_horizontal\": true\n", - " },\n", - " \"online_shuffling\": true,\n", - " \"shuffle_buffer_size\": 128,\n", - " \"prefetch\": true,\n", - " \"batch_size\": 4,\n", - " \"batches_per_epoch\": null,\n", - " \"min_batches_per_epoch\": 200,\n", - " \"val_batches_per_epoch\": null,\n", - " \"min_val_batches_per_epoch\": 10,\n", - " \"epochs\": 200,\n", - " \"optimizer\": \"adam\",\n", - " \"initial_learning_rate\": 0.0001,\n", - " \"learning_rate_schedule\": {\n", - " \"reduce_on_plateau\": true,\n", - " \"reduction_factor\": 0.5,\n", - " \"plateau_min_delta\": 1e-08,\n", - " \"plateau_patience\": 5,\n", - " \"plateau_cooldown\": 3,\n", - " \"min_learning_rate\": 1e-08\n", - " },\n", - " \"hard_keypoint_mining\": {\n", - " \"online_mining\": false,\n", - " \"hard_to_easy_ratio\": 2.0,\n", - " \"min_hard_keypoints\": 2,\n", - " \"max_hard_keypoints\": null,\n", - " \"loss_scale\": 5.0\n", - " },\n", - " \"early_stopping\": {\n", - " \"stop_training_on_plateau\": true,\n", - " \"plateau_min_delta\": 1e-08,\n", - " \"plateau_patience\": 20\n", - " }\n", - " },\n", - " \"outputs\": {\n", - " \"save_outputs\": true,\n", - " \"run_name\": \"courtship.centroid\",\n", - " \"run_name_prefix\": \"\",\n", - " \"run_name_suffix\": null,\n", - " \"runs_folder\": \"models\",\n", - " \"tags\": [],\n", - " \"save_visualizations\": true,\n", - " \"keep_viz_images\": true,\n", - " \"zip_outputs\": false,\n", - " \"log_to_csv\": true,\n", - " \"checkpointing\": {\n", - " \"initial_model\": false,\n", - " \"best_model\": true,\n", - " \"every_epoch\": false,\n", - " \"latest_model\": false,\n", - " \"final_model\": false\n", - " },\n", - " \"tensorboard\": {\n", - " \"write_logs\": false,\n", - " \"loss_frequency\": \"epoch\",\n", - " \"architecture_graph\": false,\n", - " \"profile_graph\": false,\n", - " \"visualizations\": true\n", - " },\n", - " \"zmq\": {\n", - " \"subscribe_to_controller\": false,\n", - " \"controller_address\": \"tcp://127.0.0.1:9000\",\n", - " \"controller_polling_timeout\": 10,\n", - " \"publish_updates\": false,\n", - " \"publish_address\": \"tcp://127.0.0.1:9001\"\n", - " }\n", - " },\n", - " \"name\": \"\",\n", - " \"description\": \"\",\n", - " \"sleap_version\": \"1.3.2\",\n", - " \"filename\": \"/home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline.centroid.json\"\n", - "}\n", - "INFO:sleap.nn.training:\n", - "2023-09-01 13:30:38.827290: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:38.831845: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:38.832633: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "INFO:sleap.nn.training:Auto-selected GPU 0 with 22980 MiB of free memory.\n", - "INFO:sleap.nn.training:Using GPU 0 for acceleration.\n", - "INFO:sleap.nn.training:Disabled GPU memory pre-allocation.\n", - "INFO:sleap.nn.training:System:\n", - "GPUs: 1/1 available\n", - " Device: /physical_device:GPU:0\n", - " Available: True\n", - " Initalized: False\n", - " Memory growth: True\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Initializing trainer...\n", - "INFO:sleap.nn.training:Loading training labels from: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n", - "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n", - "INFO:sleap.nn.training: Splits: Training = 134 / Validation = 15.\n", - "INFO:sleap.nn.training:Setting up for training...\n", - "INFO:sleap.nn.training:Setting up pipeline builders...\n", - "INFO:sleap.nn.training:Setting up model...\n", - "INFO:sleap.nn.training:Building test pipeline...\n", - "2023-09-01 13:30:39.755154: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-09-01 13:30:39.756024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:39.757213: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:39.758315: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:40.089801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:40.090652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:40.091464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:30:40.092164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21084 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n", - "INFO:sleap.nn.training:Loaded test example. [1.326s]\n", - "INFO:sleap.nn.training: Input shape: (512, 512, 3)\n", - "INFO:sleap.nn.training:Created Keras model.\n", - "INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=3, up_interpolate=True, block_contraction=False)\n", - "INFO:sleap.nn.training: Max stride: 16\n", - "INFO:sleap.nn.training: Parameters: 1,953,393\n", - "INFO:sleap.nn.training: Heads: \n", - "INFO:sleap.nn.training: [0] = CentroidConfmapsHead(anchor_part=None, sigma=2.5, output_stride=2, loss_weight=1.0)\n", - "INFO:sleap.nn.training: Outputs: \n", - "INFO:sleap.nn.training: [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32, name=None), name='CentroidConfmapsHead/BiasAdd:0', description=\"created by layer 'CentroidConfmapsHead'\")\n", - "INFO:sleap.nn.training:Training from scratch\n", - "INFO:sleap.nn.training:Setting up data pipelines...\n", - "INFO:sleap.nn.training:Training set: n = 134\n", - "INFO:sleap.nn.training:Validation set: n = 15\n", - "INFO:sleap.nn.training:Setting up optimization...\n", - "INFO:sleap.nn.training: Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-08, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n", - "INFO:sleap.nn.training: Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=20)\n", - "INFO:sleap.nn.training:Setting up outputs...\n", - "INFO:sleap.nn.training:Created run path: models/courtship.centroid\n", - "INFO:sleap.nn.training:Setting up visualization...\n", - "INFO:sleap.nn.training:Finished trainer set up. [3.5s]\n", - "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n", - "INFO:sleap.nn.training:Finished creating training datasets. [5.4s]\n", - "INFO:sleap.nn.training:Starting training loop...\n", - "Epoch 1/200\n", - "2023-09-01 13:30:49.814560: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n", - "2023-09-01 13:31:07.940585: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n", - "200/200 - 20s - loss: 2.5945e-04 - val_loss: 1.5190e-04 - lr: 1.0000e-04 - 20s/epoch - 99ms/step\n", - "Epoch 2/200\n", - "200/200 - 11s - loss: 1.2513e-04 - val_loss: 9.5694e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n", - "Epoch 3/200\n", - "200/200 - 11s - loss: 9.6987e-05 - val_loss: 6.8224e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n", - "Epoch 4/200\n", - "200/200 - 12s - loss: 8.1486e-05 - val_loss: 5.0657e-05 - lr: 1.0000e-04 - 12s/epoch - 58ms/step\n", - "Epoch 5/200\n", - "200/200 - 11s - loss: 7.2174e-05 - val_loss: 5.3859e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 6/200\n", - "200/200 - 11s - loss: 5.9181e-05 - val_loss: 7.0259e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 7/200\n", - "200/200 - 11s - loss: 4.9353e-05 - val_loss: 4.9832e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n", - "Epoch 8/200\n", - "200/200 - 11s - loss: 3.8997e-05 - val_loss: 4.4787e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 9/200\n", - "200/200 - 11s - loss: 3.5596e-05 - val_loss: 6.5150e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 10/200\n", - "200/200 - 12s - loss: 2.9256e-05 - val_loss: 3.8968e-05 - lr: 1.0000e-04 - 12s/epoch - 58ms/step\n", - "Epoch 11/200\n", - "200/200 - 11s - loss: 2.8572e-05 - val_loss: 3.5451e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 12/200\n", - "200/200 - 11s - loss: 2.2156e-05 - val_loss: 4.8602e-05 - lr: 1.0000e-04 - 11s/epoch - 53ms/step\n", - "Epoch 13/200\n", - "200/200 - 11s - loss: 1.7656e-05 - val_loss: 4.1905e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 14/200\n", - "200/200 - 11s - loss: 1.6440e-05 - val_loss: 3.6607e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 15/200\n", - "200/200 - 11s - loss: 1.4415e-05 - val_loss: 4.1699e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 16/200\n", - "200/200 - 11s - loss: 1.3589e-05 - val_loss: 3.5362e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n", - "Epoch 17/200\n", - "200/200 - 11s - loss: 1.0888e-05 - val_loss: 2.1600e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n", - "Epoch 18/200\n", - "200/200 - 11s - loss: 1.0426e-05 - val_loss: 3.6782e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 19/200\n", - "200/200 - 11s - loss: 9.9092e-06 - val_loss: 3.8284e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n", - "Epoch 20/200\n", - "200/200 - 11s - loss: 8.0018e-06 - val_loss: 2.9439e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n", - "Epoch 21/200\n", - "200/200 - 11s - loss: 7.7977e-06 - val_loss: 2.8703e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n", - "Epoch 22/200\n", - "\n", - "Epoch 00022: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n", - "200/200 - 11s - loss: 6.5981e-06 - val_loss: 3.6030e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n", - "Epoch 23/200\n", - "200/200 - 11s - loss: 4.6479e-06 - val_loss: 2.8081e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 24/200\n", - "200/200 - 11s - loss: 4.2579e-06 - val_loss: 3.7954e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 25/200\n", - "200/200 - 11s - loss: 3.9628e-06 - val_loss: 2.6399e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 26/200\n", - "200/200 - 11s - loss: 3.6915e-06 - val_loss: 1.9973e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 27/200\n", - "200/200 - 11s - loss: 3.4726e-06 - val_loss: 3.5831e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 28/200\n", - "200/200 - 11s - loss: 3.2110e-06 - val_loss: 2.7290e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 29/200\n", - "200/200 - 11s - loss: 3.3421e-06 - val_loss: 3.1827e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 30/200\n", - "200/200 - 11s - loss: 3.3472e-06 - val_loss: 3.4653e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 31/200\n", - "\n", - "Epoch 00031: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n", - "200/200 - 11s - loss: 3.1221e-06 - val_loss: 2.7741e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 32/200\n", - "200/200 - 11s - loss: 2.5739e-06 - val_loss: 3.2486e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 33/200\n", - "200/200 - 11s - loss: 2.5589e-06 - val_loss: 3.3135e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 34/200\n", - "200/200 - 11s - loss: 2.4215e-06 - val_loss: 2.8923e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 35/200\n", - "200/200 - 11s - loss: 2.4033e-06 - val_loss: 2.8776e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 36/200\n", - "200/200 - 11s - loss: 2.3358e-06 - val_loss: 2.5874e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n", - "Epoch 37/200\n", - "200/200 - 11s - loss: 2.2922e-06 - val_loss: 3.6051e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 38/200\n", - "\n", - "Epoch 00038: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n", - "200/200 - 11s - loss: 2.1278e-06 - val_loss: 2.4898e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n", - "Epoch 39/200\n", - "200/200 - 11s - loss: 2.0474e-06 - val_loss: 2.8901e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 40/200\n", - "200/200 - 11s - loss: 2.0612e-06 - val_loss: 3.7469e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 41/200\n", - "200/200 - 11s - loss: 1.8414e-06 - val_loss: 2.8496e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 42/200\n", - "200/200 - 11s - loss: 2.0196e-06 - val_loss: 3.5206e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 43/200\n", - "200/200 - 11s - loss: 1.8551e-06 - val_loss: 2.6483e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 44/200\n", - "200/200 - 11s - loss: 1.9705e-06 - val_loss: 2.4643e-05 - lr: 1.2500e-05 - 11s/epoch - 55ms/step\n", - "Epoch 45/200\n", - "\n", - "Epoch 00045: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n", - "200/200 - 11s - loss: 1.9136e-06 - val_loss: 2.8379e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n", - "Epoch 46/200\n", - "200/200 - 11s - loss: 1.7911e-06 - val_loss: 4.0055e-05 - lr: 6.2500e-06 - 11s/epoch - 56ms/step\n", - "Epoch 00046: early stopping\n", - "INFO:sleap.nn.training:Finished training loop. [8.7 min]\n", - "INFO:sleap.nn.training:Deleting visualization directory: models/courtship.centroid/viz\n", - "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n", - "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m33.7 FPS\u001b[0m31m51.9 FPS\u001b[0m31m52.6 FPS\u001b[0mFPS\u001b[0m\n", - "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.centroid/labels_pr.train.slp\n", - "INFO:sleap.nn.evals:Saved metrics: models/courtship.centroid/metrics.train.npz\n", - "INFO:sleap.nn.evals:OKS mAP: 0.725241\n", - "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m7.3 FPS\u001b[0m0:00:01\u001b[0m \u001b[31m184.6 FPS\u001b[0mm\n", - "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.centroid/labels_pr.val.slp\n", - "INFO:sleap.nn.evals:Saved metrics: models/courtship.centroid/metrics.val.npz\n", - "INFO:sleap.nn.evals:OKS mAP: 0.870526\n" - ] - } - ], - "source": [ - "!sleap-train baseline.centroid.json \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\" --run_name \"courtship.centroid\" --video-paths \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vm3i0ry04IMx" - }, - "source": [ - "Let's now train a centered-instance model." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "ufbULTDw4Hbh" - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yX9noEb8m8re" + }, + "source": [ + "## Install SLEAP\n", + "Note: Before installing SLEAP check [SLEAP releases](https://github.com/talmolab/sleap/releases) page for the latest version." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:sleap.nn.training:Versions:\n", - "SLEAP: 1.3.2\n", - "TensorFlow: 2.7.0\n", - "Numpy: 1.21.5\n", - "Python: 3.7.12\n", - "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n", - "INFO:sleap.nn.training:Training labels file: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n", - "INFO:sleap.nn.training:Training profile: /home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline_medium_rf.topdown.json\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Arguments:\n", - "INFO:sleap.nn.training:{\n", - " \"training_job_path\": \"baseline_medium_rf.topdown.json\",\n", - " \"labels_path\": \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\",\n", - " \"video_paths\": [\n", - " \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"\n", - " ],\n", - " \"val_labels\": null,\n", - " \"test_labels\": null,\n", - " \"base_checkpoint\": null,\n", - " \"tensorboard\": false,\n", - " \"save_viz\": false,\n", - " \"zmq\": false,\n", - " \"run_name\": \"courtship.topdown_confmaps\",\n", - " \"prefix\": \"\",\n", - " \"suffix\": \"\",\n", - " \"cpu\": false,\n", - " \"first_gpu\": false,\n", - " \"last_gpu\": false,\n", - " \"gpu\": \"auto\"\n", - "}\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Training job:\n", - "INFO:sleap.nn.training:{\n", - " \"data\": {\n", - " \"labels\": {\n", - " \"training_labels\": null,\n", - " \"validation_labels\": null,\n", - " \"validation_fraction\": 0.1,\n", - " \"test_labels\": null,\n", - " \"split_by_inds\": false,\n", - " \"training_inds\": null,\n", - " \"validation_inds\": null,\n", - " \"test_inds\": null,\n", - " \"search_path_hints\": [],\n", - " \"skeletons\": []\n", - " },\n", - " \"preprocessing\": {\n", - " \"ensure_rgb\": false,\n", - " \"ensure_grayscale\": false,\n", - " \"imagenet_mode\": null,\n", - " \"input_scaling\": 1.0,\n", - " \"pad_to_stride\": null,\n", - " \"resize_and_pad_to_target\": true,\n", - " \"target_height\": null,\n", - " \"target_width\": null\n", - " },\n", - " \"instance_cropping\": {\n", - " \"center_on_part\": null,\n", - " \"crop_size\": null,\n", - " \"crop_size_detection_padding\": 16\n", - " }\n", - " },\n", - " \"model\": {\n", - " \"backbone\": {\n", - " \"leap\": null,\n", - " \"unet\": {\n", - " \"stem_stride\": null,\n", - " \"max_stride\": 16,\n", - " \"output_stride\": 4,\n", - " \"filters\": 24,\n", - " \"filters_rate\": 2.0,\n", - " \"middle_block\": true,\n", - " \"up_interpolate\": true,\n", - " \"stacks\": 1\n", - " },\n", - " \"hourglass\": null,\n", - " \"resnet\": null,\n", - " \"pretrained_encoder\": null\n", - " },\n", - " \"heads\": {\n", - " \"single_instance\": null,\n", - " \"centroid\": null,\n", - " \"centered_instance\": {\n", - " \"anchor_part\": null,\n", - " \"part_names\": null,\n", - " \"sigma\": 2.5,\n", - " \"output_stride\": 4,\n", - " \"loss_weight\": 1.0,\n", - " \"offset_refinement\": false\n", - " },\n", - " \"multi_instance\": null,\n", - " \"multi_class_bottomup\": null,\n", - " \"multi_class_topdown\": null\n", - " },\n", - " \"base_checkpoint\": null\n", - " },\n", - " \"optimization\": {\n", - " \"preload_data\": true,\n", - " \"augmentation_config\": {\n", - " \"rotate\": true,\n", - " \"rotation_min_angle\": -15.0,\n", - " \"rotation_max_angle\": 15.0,\n", - " \"translate\": false,\n", - " \"translate_min\": -5,\n", - " \"translate_max\": 5,\n", - " \"scale\": false,\n", - " \"scale_min\": 0.9,\n", - " \"scale_max\": 1.1,\n", - " \"uniform_noise\": false,\n", - " \"uniform_noise_min_val\": 0.0,\n", - " \"uniform_noise_max_val\": 10.0,\n", - " \"gaussian_noise\": false,\n", - " \"gaussian_noise_mean\": 5.0,\n", - " \"gaussian_noise_stddev\": 1.0,\n", - " \"contrast\": false,\n", - " \"contrast_min_gamma\": 0.5,\n", - " \"contrast_max_gamma\": 2.0,\n", - " \"brightness\": false,\n", - " \"brightness_min_val\": 0.0,\n", - " \"brightness_max_val\": 10.0,\n", - " \"random_crop\": false,\n", - " \"random_crop_height\": 256,\n", - " \"random_crop_width\": 256,\n", - " \"random_flip\": false,\n", - " \"flip_horizontal\": true\n", - " },\n", - " \"online_shuffling\": true,\n", - " \"shuffle_buffer_size\": 128,\n", - " \"prefetch\": true,\n", - " \"batch_size\": 4,\n", - " \"batches_per_epoch\": null,\n", - " \"min_batches_per_epoch\": 200,\n", - " \"val_batches_per_epoch\": null,\n", - " \"min_val_batches_per_epoch\": 10,\n", - " \"epochs\": 200,\n", - " \"optimizer\": \"adam\",\n", - " \"initial_learning_rate\": 0.0001,\n", - " \"learning_rate_schedule\": {\n", - " \"reduce_on_plateau\": true,\n", - " \"reduction_factor\": 0.5,\n", - " \"plateau_min_delta\": 1e-08,\n", - " \"plateau_patience\": 5,\n", - " \"plateau_cooldown\": 3,\n", - " \"min_learning_rate\": 1e-08\n", - " },\n", - " \"hard_keypoint_mining\": {\n", - " \"online_mining\": false,\n", - " \"hard_to_easy_ratio\": 2.0,\n", - " \"min_hard_keypoints\": 2,\n", - " \"max_hard_keypoints\": null,\n", - " \"loss_scale\": 5.0\n", - " },\n", - " \"early_stopping\": {\n", - " \"stop_training_on_plateau\": true,\n", - " \"plateau_min_delta\": 1e-08,\n", - " \"plateau_patience\": 10\n", - " }\n", - " },\n", - " \"outputs\": {\n", - " \"save_outputs\": true,\n", - " \"run_name\": \"courtship.topdown_confmaps\",\n", - " \"run_name_prefix\": \"\",\n", - " \"run_name_suffix\": null,\n", - " \"runs_folder\": \"models\",\n", - " \"tags\": [],\n", - " \"save_visualizations\": true,\n", - " \"keep_viz_images\": true,\n", - " \"zip_outputs\": false,\n", - " \"log_to_csv\": true,\n", - " \"checkpointing\": {\n", - " \"initial_model\": false,\n", - " \"best_model\": true,\n", - " \"every_epoch\": false,\n", - " \"latest_model\": false,\n", - " \"final_model\": false\n", - " },\n", - " \"tensorboard\": {\n", - " \"write_logs\": false,\n", - " \"loss_frequency\": \"epoch\",\n", - " \"architecture_graph\": true,\n", - " \"profile_graph\": false,\n", - " \"visualizations\": true\n", - " },\n", - " \"zmq\": {\n", - " \"subscribe_to_controller\": false,\n", - " \"controller_address\": \"tcp://127.0.0.1:9000\",\n", - " \"controller_polling_timeout\": 10,\n", - " \"publish_updates\": false,\n", - " \"publish_address\": \"tcp://127.0.0.1:9001\"\n", - " }\n", - " },\n", - " \"name\": \"\",\n", - " \"description\": \"\",\n", - " \"sleap_version\": \"1.3.2\",\n", - " \"filename\": \"/home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline_medium_rf.topdown.json\"\n", - "}\n", - "INFO:sleap.nn.training:\n", - "2023-09-01 13:39:43.324520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:43.329181: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:43.329961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "INFO:sleap.nn.training:Auto-selected GPU 0 with 23056 MiB of free memory.\n", - "INFO:sleap.nn.training:Using GPU 0 for acceleration.\n", - "INFO:sleap.nn.training:Disabled GPU memory pre-allocation.\n", - "INFO:sleap.nn.training:System:\n", - "GPUs: 1/1 available\n", - " Device: /physical_device:GPU:0\n", - " Available: True\n", - " Initalized: False\n", - " Memory growth: True\n", - "INFO:sleap.nn.training:\n", - "INFO:sleap.nn.training:Initializing trainer...\n", - "INFO:sleap.nn.training:Loading training labels from: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n", - "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n", - "INFO:sleap.nn.training: Splits: Training = 134 / Validation = 15.\n", - "INFO:sleap.nn.training:Setting up for training...\n", - "INFO:sleap.nn.training:Setting up pipeline builders...\n", - "INFO:sleap.nn.training:Setting up model...\n", - "INFO:sleap.nn.training:Building test pipeline...\n", - "2023-09-01 13:39:44.254912: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-09-01 13:39:44.255468: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.256291: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.257158: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.546117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.546866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.547533: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:39:44.548184: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21151 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n", - "INFO:sleap.nn.training:Loaded test example. [1.684s]\n", - "INFO:sleap.nn.training: Input shape: (144, 144, 3)\n", - "INFO:sleap.nn.training:Created Keras model.\n", - "INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=24, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=True, block_contraction=False)\n", - "INFO:sleap.nn.training: Max stride: 16\n", - "INFO:sleap.nn.training: Parameters: 4,311,877\n", - "INFO:sleap.nn.training: Heads: \n", - "INFO:sleap.nn.training: [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part=None, sigma=2.5, output_stride=4, loss_weight=1.0)\n", - "INFO:sleap.nn.training: Outputs: \n", - "INFO:sleap.nn.training: [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 36, 36, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n", - "INFO:sleap.nn.training:Training from scratch\n", - "INFO:sleap.nn.training:Setting up data pipelines...\n", - "INFO:sleap.nn.training:Training set: n = 134\n", - "INFO:sleap.nn.training:Validation set: n = 15\n", - "INFO:sleap.nn.training:Setting up optimization...\n", - "INFO:sleap.nn.training: Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-08, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n", - "INFO:sleap.nn.training: Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=10)\n", - "INFO:sleap.nn.training:Setting up outputs...\n", - "INFO:sleap.nn.training:Created run path: models/courtship.topdown_confmaps\n", - "INFO:sleap.nn.training:Setting up visualization...\n", - "INFO:sleap.nn.training:Finished trainer set up. [3.2s]\n", - "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n", - "INFO:sleap.nn.training:Finished creating training datasets. [5.9s]\n", - "INFO:sleap.nn.training:Starting training loop...\n", - "Epoch 1/200\n", - "2023-09-01 13:39:54.940083: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n", - "2023-09-01 13:40:00.337645: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n", - "200/200 - 8s - loss: 0.0108 - head: 0.0073 - thorax: 0.0067 - abdomen: 0.0111 - wingL: 0.0125 - wingR: 0.0126 - forelegL4: 0.0111 - forelegR4: 0.0108 - midlegL4: 0.0127 - midlegR4: 0.0128 - hindlegL4: 0.0131 - hindlegR4: 0.0131 - eyeL: 0.0082 - eyeR: 0.0083 - val_loss: 0.0087 - val_head: 0.0033 - val_thorax: 0.0039 - val_abdomen: 0.0089 - val_wingL: 0.0105 - val_wingR: 0.0106 - val_forelegL4: 0.0091 - val_forelegR4: 0.0091 - val_midlegL4: 0.0123 - val_midlegR4: 0.0116 - val_hindlegL4: 0.0128 - val_hindlegR4: 0.0116 - val_eyeL: 0.0045 - val_eyeR: 0.0045 - lr: 1.0000e-04 - 8s/epoch - 38ms/step\n", - "Epoch 2/200\n", - "200/200 - 4s - loss: 0.0064 - head: 0.0019 - thorax: 0.0029 - abdomen: 0.0057 - wingL: 0.0061 - wingR: 0.0073 - forelegL4: 0.0075 - forelegR4: 0.0078 - midlegL4: 0.0092 - midlegR4: 0.0092 - hindlegL4: 0.0099 - hindlegR4: 0.0102 - eyeL: 0.0025 - eyeR: 0.0025 - val_loss: 0.0061 - val_head: 0.0015 - val_thorax: 0.0024 - val_abdomen: 0.0049 - val_wingL: 0.0056 - val_wingR: 0.0078 - val_forelegL4: 0.0079 - val_forelegR4: 0.0067 - val_midlegL4: 0.0086 - val_midlegR4: 0.0089 - val_hindlegL4: 0.0093 - val_hindlegR4: 0.0081 - val_eyeL: 0.0037 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 4s/epoch - 19ms/step\n", - "Epoch 3/200\n", - "200/200 - 3s - loss: 0.0048 - head: 8.9048e-04 - thorax: 0.0019 - abdomen: 0.0036 - wingL: 0.0041 - wingR: 0.0051 - forelegL4: 0.0063 - forelegR4: 0.0066 - midlegL4: 0.0076 - midlegR4: 0.0076 - hindlegL4: 0.0076 - hindlegR4: 0.0080 - eyeL: 0.0015 - eyeR: 0.0015 - val_loss: 0.0058 - val_head: 0.0014 - val_thorax: 0.0021 - val_abdomen: 0.0044 - val_wingL: 0.0051 - val_wingR: 0.0070 - val_forelegL4: 0.0072 - val_forelegR4: 0.0063 - val_midlegL4: 0.0088 - val_midlegR4: 0.0085 - val_hindlegL4: 0.0097 - val_hindlegR4: 0.0079 - val_eyeL: 0.0038 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 3s/epoch - 16ms/step\n", - "Epoch 4/200\n", - "200/200 - 3s - loss: 0.0041 - head: 7.6417e-04 - thorax: 0.0015 - abdomen: 0.0028 - wingL: 0.0035 - wingR: 0.0041 - forelegL4: 0.0058 - forelegR4: 0.0060 - midlegL4: 0.0066 - midlegR4: 0.0064 - hindlegL4: 0.0066 - hindlegR4: 0.0070 - eyeL: 0.0013 - eyeR: 0.0012 - val_loss: 0.0048 - val_head: 7.6555e-04 - val_thorax: 0.0013 - val_abdomen: 0.0034 - val_wingL: 0.0042 - val_wingR: 0.0065 - val_forelegL4: 0.0063 - val_forelegR4: 0.0064 - val_midlegL4: 0.0069 - val_midlegR4: 0.0071 - val_hindlegL4: 0.0080 - val_hindlegR4: 0.0062 - val_eyeL: 0.0028 - val_eyeR: 0.0026 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 5/200\n", - "200/200 - 3s - loss: 0.0034 - head: 6.1233e-04 - thorax: 0.0012 - abdomen: 0.0023 - wingL: 0.0028 - wingR: 0.0032 - forelegL4: 0.0052 - forelegR4: 0.0054 - midlegL4: 0.0052 - midlegR4: 0.0051 - hindlegL4: 0.0057 - hindlegR4: 0.0058 - eyeL: 0.0011 - eyeR: 0.0011 - val_loss: 0.0044 - val_head: 9.3809e-04 - val_thorax: 0.0012 - val_abdomen: 0.0027 - val_wingL: 0.0032 - val_wingR: 0.0048 - val_forelegL4: 0.0062 - val_forelegR4: 0.0053 - val_midlegL4: 0.0068 - val_midlegR4: 0.0063 - val_hindlegL4: 0.0067 - val_hindlegR4: 0.0065 - val_eyeL: 0.0035 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 6/200\n", - "200/200 - 3s - loss: 0.0028 - head: 5.5957e-04 - thorax: 9.3519e-04 - abdomen: 0.0019 - wingL: 0.0023 - wingR: 0.0025 - forelegL4: 0.0045 - forelegR4: 0.0045 - midlegL4: 0.0040 - midlegR4: 0.0040 - hindlegL4: 0.0047 - hindlegR4: 0.0048 - eyeL: 0.0010 - eyeR: 9.7287e-04 - val_loss: 0.0038 - val_head: 7.6837e-04 - val_thorax: 9.9723e-04 - val_abdomen: 0.0027 - val_wingL: 0.0025 - val_wingR: 0.0046 - val_forelegL4: 0.0058 - val_forelegR4: 0.0049 - val_midlegL4: 0.0054 - val_midlegR4: 0.0058 - val_hindlegL4: 0.0057 - val_hindlegR4: 0.0065 - val_eyeL: 0.0023 - val_eyeR: 0.0022 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 7/200\n", - "200/200 - 3s - loss: 0.0024 - head: 4.7941e-04 - thorax: 7.5772e-04 - abdomen: 0.0017 - wingL: 0.0020 - wingR: 0.0022 - forelegL4: 0.0039 - forelegR4: 0.0041 - midlegL4: 0.0033 - midlegR4: 0.0033 - hindlegL4: 0.0039 - hindlegR4: 0.0040 - eyeL: 9.3055e-04 - eyeR: 8.9191e-04 - val_loss: 0.0036 - val_head: 6.1078e-04 - val_thorax: 0.0010 - val_abdomen: 0.0023 - val_wingL: 0.0025 - val_wingR: 0.0039 - val_forelegL4: 0.0053 - val_forelegR4: 0.0058 - val_midlegL4: 0.0049 - val_midlegR4: 0.0056 - val_hindlegL4: 0.0054 - val_hindlegR4: 0.0049 - val_eyeL: 0.0026 - val_eyeR: 0.0024 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 8/200\n", - "200/200 - 3s - loss: 0.0020 - head: 4.4425e-04 - thorax: 6.8283e-04 - abdomen: 0.0014 - wingL: 0.0015 - wingR: 0.0017 - forelegL4: 0.0035 - forelegR4: 0.0035 - midlegL4: 0.0027 - midlegR4: 0.0026 - hindlegL4: 0.0033 - hindlegR4: 0.0033 - eyeL: 7.7111e-04 - eyeR: 7.2022e-04 - val_loss: 0.0035 - val_head: 7.1555e-04 - val_thorax: 9.1508e-04 - val_abdomen: 0.0022 - val_wingL: 0.0023 - val_wingR: 0.0033 - val_forelegL4: 0.0054 - val_forelegR4: 0.0049 - val_midlegL4: 0.0049 - val_midlegR4: 0.0052 - val_hindlegL4: 0.0052 - val_hindlegR4: 0.0051 - val_eyeL: 0.0025 - val_eyeR: 0.0025 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 9/200\n", - "200/200 - 3s - loss: 0.0017 - head: 3.8990e-04 - thorax: 5.4963e-04 - abdomen: 0.0012 - wingL: 0.0012 - wingR: 0.0014 - forelegL4: 0.0030 - forelegR4: 0.0031 - midlegL4: 0.0022 - midlegR4: 0.0022 - hindlegL4: 0.0027 - hindlegR4: 0.0027 - eyeL: 6.9041e-04 - eyeR: 6.7679e-04 - val_loss: 0.0034 - val_head: 5.6666e-04 - val_thorax: 7.9156e-04 - val_abdomen: 0.0023 - val_wingL: 0.0020 - val_wingR: 0.0041 - val_forelegL4: 0.0043 - val_forelegR4: 0.0048 - val_midlegL4: 0.0041 - val_midlegR4: 0.0051 - val_hindlegL4: 0.0053 - val_hindlegR4: 0.0052 - val_eyeL: 0.0024 - val_eyeR: 0.0026 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 10/200\n", - "200/200 - 3s - loss: 0.0015 - head: 3.6281e-04 - thorax: 5.2471e-04 - abdomen: 0.0010 - wingL: 0.0011 - wingR: 0.0012 - forelegL4: 0.0027 - forelegR4: 0.0028 - midlegL4: 0.0019 - midlegR4: 0.0019 - hindlegL4: 0.0023 - hindlegR4: 0.0024 - eyeL: 7.0986e-04 - eyeR: 6.9581e-04 - val_loss: 0.0024 - val_head: 4.8376e-04 - val_thorax: 6.2502e-04 - val_abdomen: 0.0016 - val_wingL: 0.0014 - val_wingR: 0.0027 - val_forelegL4: 0.0035 - val_forelegR4: 0.0033 - val_midlegL4: 0.0028 - val_midlegR4: 0.0041 - val_hindlegL4: 0.0036 - val_hindlegR4: 0.0038 - val_eyeL: 0.0015 - val_eyeR: 0.0016 - lr: 1.0000e-04 - 3s/epoch - 16ms/step\n", - "Epoch 11/200\n", - "200/200 - 3s - loss: 0.0013 - head: 3.1183e-04 - thorax: 4.7891e-04 - abdomen: 9.4567e-04 - wingL: 9.6811e-04 - wingR: 0.0011 - forelegL4: 0.0023 - forelegR4: 0.0025 - midlegL4: 0.0016 - midlegR4: 0.0016 - hindlegL4: 0.0020 - hindlegR4: 0.0021 - eyeL: 5.7635e-04 - eyeR: 5.3648e-04 - val_loss: 0.0028 - val_head: 5.2940e-04 - val_thorax: 6.6554e-04 - val_abdomen: 0.0020 - val_wingL: 0.0013 - val_wingR: 0.0024 - val_forelegL4: 0.0041 - val_forelegR4: 0.0041 - val_midlegL4: 0.0034 - val_midlegR4: 0.0042 - val_hindlegL4: 0.0047 - val_hindlegR4: 0.0040 - val_eyeL: 0.0025 - val_eyeR: 0.0022 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 12/200\n", - "200/200 - 3s - loss: 0.0011 - head: 2.8863e-04 - thorax: 4.2604e-04 - abdomen: 8.0488e-04 - wingL: 8.1238e-04 - wingR: 8.5798e-04 - forelegL4: 0.0021 - forelegR4: 0.0021 - midlegL4: 0.0014 - midlegR4: 0.0014 - hindlegL4: 0.0017 - hindlegR4: 0.0018 - eyeL: 5.1007e-04 - eyeR: 4.5654e-04 - val_loss: 0.0031 - val_head: 8.1802e-04 - val_thorax: 7.9789e-04 - val_abdomen: 0.0018 - val_wingL: 0.0014 - val_wingR: 0.0028 - val_forelegL4: 0.0040 - val_forelegR4: 0.0048 - val_midlegL4: 0.0057 - val_midlegR4: 0.0037 - val_hindlegL4: 0.0053 - val_hindlegR4: 0.0050 - val_eyeL: 0.0020 - val_eyeR: 0.0018 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n", - "Epoch 13/200\n", - "200/200 - 3s - loss: 0.0010 - head: 2.8818e-04 - thorax: 4.1018e-04 - abdomen: 7.8027e-04 - wingL: 7.8017e-04 - wingR: 8.4529e-04 - forelegL4: 0.0019 - forelegR4: 0.0019 - midlegL4: 0.0013 - midlegR4: 0.0013 - hindlegL4: 0.0015 - hindlegR4: 0.0016 - eyeL: 4.6272e-04 - eyeR: 4.3265e-04 - val_loss: 0.0026 - val_head: 3.5806e-04 - val_thorax: 6.6352e-04 - val_abdomen: 0.0017 - val_wingL: 0.0015 - val_wingR: 0.0037 - val_forelegL4: 0.0036 - val_forelegR4: 0.0042 - val_midlegL4: 0.0034 - val_midlegR4: 0.0032 - val_hindlegL4: 0.0041 - val_hindlegR4: 0.0047 - val_eyeL: 0.0013 - val_eyeR: 0.0013 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 14/200\n", - "200/200 - 3s - loss: 9.4029e-04 - head: 2.8339e-04 - thorax: 3.6739e-04 - abdomen: 7.0118e-04 - wingL: 7.4831e-04 - wingR: 7.1158e-04 - forelegL4: 0.0017 - forelegR4: 0.0017 - midlegL4: 0.0012 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0015 - eyeL: 4.2793e-04 - eyeR: 4.1400e-04 - val_loss: 0.0024 - val_head: 3.4292e-04 - val_thorax: 7.1119e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0028 - val_forelegL4: 0.0030 - val_forelegR4: 0.0043 - val_midlegL4: 0.0031 - val_midlegR4: 0.0030 - val_hindlegL4: 0.0039 - val_hindlegR4: 0.0038 - val_eyeL: 0.0017 - val_eyeR: 0.0015 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 15/200\n", - "200/200 - 3s - loss: 7.8295e-04 - head: 2.3028e-04 - thorax: 3.3006e-04 - abdomen: 5.9391e-04 - wingL: 5.8825e-04 - wingR: 6.0989e-04 - forelegL4: 0.0015 - forelegR4: 0.0015 - midlegL4: 9.6945e-04 - midlegR4: 9.3611e-04 - hindlegL4: 0.0011 - hindlegR4: 0.0012 - eyeL: 3.4493e-04 - eyeR: 3.1164e-04 - val_loss: 0.0019 - val_head: 4.4152e-04 - val_thorax: 5.4500e-04 - val_abdomen: 0.0013 - val_wingL: 0.0012 - val_wingR: 0.0026 - val_forelegL4: 0.0024 - val_forelegR4: 0.0037 - val_midlegL4: 0.0024 - val_midlegR4: 0.0024 - val_hindlegL4: 0.0030 - val_hindlegR4: 0.0030 - val_eyeL: 0.0011 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 16/200\n", - "200/200 - 3s - loss: 7.3208e-04 - head: 2.3573e-04 - thorax: 3.0631e-04 - abdomen: 5.5007e-04 - wingL: 5.3431e-04 - wingR: 5.9773e-04 - forelegL4: 0.0013 - forelegR4: 0.0014 - midlegL4: 9.1004e-04 - midlegR4: 8.7803e-04 - hindlegL4: 0.0010 - hindlegR4: 0.0011 - eyeL: 3.3279e-04 - eyeR: 2.9841e-04 - val_loss: 0.0023 - val_head: 3.5381e-04 - val_thorax: 7.0128e-04 - val_abdomen: 0.0015 - val_wingL: 0.0013 - val_wingR: 0.0022 - val_forelegL4: 0.0031 - val_forelegR4: 0.0041 - val_midlegL4: 0.0033 - val_midlegR4: 0.0028 - val_hindlegL4: 0.0036 - val_hindlegR4: 0.0033 - val_eyeL: 0.0017 - val_eyeR: 0.0014 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n", - "Epoch 17/200\n", - "200/200 - 3s - loss: 6.3161e-04 - head: 2.0100e-04 - thorax: 2.8088e-04 - abdomen: 4.9153e-04 - wingL: 4.7586e-04 - wingR: 4.9866e-04 - forelegL4: 0.0011 - forelegR4: 0.0012 - midlegL4: 7.6100e-04 - midlegR4: 8.0266e-04 - hindlegL4: 8.9697e-04 - hindlegR4: 8.9149e-04 - eyeL: 2.8189e-04 - eyeR: 2.7208e-04 - val_loss: 0.0018 - val_head: 2.8070e-04 - val_thorax: 5.1903e-04 - val_abdomen: 0.0011 - val_wingL: 9.8509e-04 - val_wingR: 0.0025 - val_forelegL4: 0.0022 - val_forelegR4: 0.0026 - val_midlegL4: 0.0025 - val_midlegR4: 0.0021 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0031 - val_eyeL: 0.0011 - val_eyeR: 9.7838e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 18/200\n", - "200/200 - 3s - loss: 5.7844e-04 - head: 1.9896e-04 - thorax: 2.9112e-04 - abdomen: 4.7495e-04 - wingL: 4.5591e-04 - wingR: 4.5877e-04 - forelegL4: 0.0011 - forelegR4: 0.0012 - midlegL4: 6.9042e-04 - midlegR4: 6.6195e-04 - hindlegL4: 7.9452e-04 - hindlegR4: 7.6819e-04 - eyeL: 2.5989e-04 - eyeR: 2.4763e-04 - val_loss: 0.0018 - val_head: 3.1925e-04 - val_thorax: 6.0394e-04 - val_abdomen: 0.0012 - val_wingL: 9.0835e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0022 - val_forelegR4: 0.0029 - val_midlegL4: 0.0026 - val_midlegR4: 0.0024 - val_hindlegL4: 0.0033 - val_hindlegR4: 0.0022 - val_eyeL: 0.0015 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 19/200\n", - "200/200 - 3s - loss: 5.1323e-04 - head: 1.8346e-04 - thorax: 2.5475e-04 - abdomen: 4.2159e-04 - wingL: 4.3027e-04 - wingR: 3.9814e-04 - forelegL4: 9.5814e-04 - forelegR4: 9.9765e-04 - midlegL4: 5.9968e-04 - midlegR4: 5.8423e-04 - hindlegL4: 6.7869e-04 - hindlegR4: 6.9121e-04 - eyeL: 2.4343e-04 - eyeR: 2.3077e-04 - val_loss: 0.0021 - val_head: 3.3346e-04 - val_thorax: 5.9007e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0031 - val_forelegL4: 0.0026 - val_forelegR4: 0.0036 - val_midlegL4: 0.0029 - val_midlegR4: 0.0021 - val_hindlegL4: 0.0037 - val_hindlegR4: 0.0036 - val_eyeL: 0.0011 - val_eyeR: 9.4254e-04 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n", - "Epoch 20/200\n", - "200/200 - 3s - loss: 4.7991e-04 - head: 1.7328e-04 - thorax: 2.2397e-04 - abdomen: 4.2417e-04 - wingL: 3.9313e-04 - wingR: 3.9871e-04 - forelegL4: 8.8547e-04 - forelegR4: 8.9704e-04 - midlegL4: 5.3515e-04 - midlegR4: 5.8294e-04 - hindlegL4: 6.5212e-04 - hindlegR4: 6.2828e-04 - eyeL: 2.2438e-04 - eyeR: 2.2012e-04 - val_loss: 0.0014 - val_head: 2.7034e-04 - val_thorax: 4.7978e-04 - val_abdomen: 9.7903e-04 - val_wingL: 8.6477e-04 - val_wingR: 0.0020 - val_forelegL4: 0.0018 - val_forelegR4: 0.0024 - val_midlegL4: 0.0019 - val_midlegR4: 0.0018 - val_hindlegL4: 0.0024 - val_hindlegR4: 0.0022 - val_eyeL: 9.9423e-04 - val_eyeR: 8.4541e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 21/200\n", - "200/200 - 3s - loss: 4.4100e-04 - head: 1.6076e-04 - thorax: 2.4080e-04 - abdomen: 3.8343e-04 - wingL: 3.6759e-04 - wingR: 3.7489e-04 - forelegL4: 8.1060e-04 - forelegR4: 8.1600e-04 - midlegL4: 4.7288e-04 - midlegR4: 5.2695e-04 - hindlegL4: 5.6401e-04 - hindlegR4: 6.3519e-04 - eyeL: 1.9033e-04 - eyeR: 1.8954e-04 - val_loss: 0.0018 - val_head: 2.5764e-04 - val_thorax: 5.8718e-04 - val_abdomen: 0.0011 - val_wingL: 9.6939e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0022 - val_forelegR4: 0.0026 - val_midlegL4: 0.0025 - val_midlegR4: 0.0026 - val_hindlegL4: 0.0032 - val_hindlegR4: 0.0028 - val_eyeL: 0.0014 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 22/200\n", - "200/200 - 3s - loss: 3.7738e-04 - head: 1.4725e-04 - thorax: 2.0905e-04 - abdomen: 3.2447e-04 - wingL: 3.2224e-04 - wingR: 3.0585e-04 - forelegL4: 6.2169e-04 - forelegR4: 6.7379e-04 - midlegL4: 4.5061e-04 - midlegR4: 4.3931e-04 - hindlegL4: 5.1129e-04 - hindlegR4: 5.2449e-04 - eyeL: 1.9372e-04 - eyeR: 1.8213e-04 - val_loss: 0.0015 - val_head: 2.2947e-04 - val_thorax: 5.4640e-04 - val_abdomen: 9.8293e-04 - val_wingL: 8.6663e-04 - val_wingR: 0.0013 - val_forelegL4: 0.0018 - val_forelegR4: 0.0027 - val_midlegL4: 0.0021 - val_midlegR4: 0.0019 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0022 - val_eyeL: 0.0013 - val_eyeR: 0.0010 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 23/200\n", - "200/200 - 3s - loss: 3.6084e-04 - head: 1.4440e-04 - thorax: 2.0277e-04 - abdomen: 3.0561e-04 - wingL: 3.0192e-04 - wingR: 2.8845e-04 - forelegL4: 6.3221e-04 - forelegR4: 6.7722e-04 - midlegL4: 3.9143e-04 - midlegR4: 4.3545e-04 - hindlegL4: 5.1985e-04 - hindlegR4: 4.5058e-04 - eyeL: 1.7636e-04 - eyeR: 1.6468e-04 - val_loss: 0.0015 - val_head: 2.9639e-04 - val_thorax: 4.6412e-04 - val_abdomen: 0.0011 - val_wingL: 9.0466e-04 - val_wingR: 0.0021 - val_forelegL4: 0.0015 - val_forelegR4: 0.0025 - val_midlegL4: 0.0018 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0029 - val_hindlegR4: 0.0022 - val_eyeL: 8.7357e-04 - val_eyeR: 7.0067e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 24/200\n", - "200/200 - 3s - loss: 3.4886e-04 - head: 1.4382e-04 - thorax: 1.9157e-04 - abdomen: 3.2551e-04 - wingL: 3.0634e-04 - wingR: 3.0727e-04 - forelegL4: 6.3863e-04 - forelegR4: 6.0904e-04 - midlegL4: 3.5949e-04 - midlegR4: 4.1201e-04 - hindlegL4: 4.2893e-04 - hindlegR4: 4.8121e-04 - eyeL: 1.6669e-04 - eyeR: 1.6464e-04 - val_loss: 0.0022 - val_head: 3.2159e-04 - val_thorax: 7.2743e-04 - val_abdomen: 0.0014 - val_wingL: 0.0011 - val_wingR: 0.0027 - val_forelegL4: 0.0025 - val_forelegR4: 0.0037 - val_midlegL4: 0.0033 - val_midlegR4: 0.0020 - val_hindlegL4: 0.0043 - val_hindlegR4: 0.0031 - val_eyeL: 0.0017 - val_eyeR: 0.0012 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n", - "Epoch 25/200\n", - "\n", - "Epoch 00025: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n", - "200/200 - 3s - loss: 3.0444e-04 - head: 1.2563e-04 - thorax: 1.7247e-04 - abdomen: 2.6934e-04 - wingL: 2.5754e-04 - wingR: 2.4728e-04 - forelegL4: 5.8390e-04 - forelegR4: 5.3959e-04 - midlegL4: 3.3003e-04 - midlegR4: 3.6432e-04 - hindlegL4: 4.0270e-04 - hindlegR4: 3.5518e-04 - eyeL: 1.5609e-04 - eyeR: 1.5365e-04 - val_loss: 0.0017 - val_head: 2.5420e-04 - val_thorax: 5.5809e-04 - val_abdomen: 0.0011 - val_wingL: 9.6708e-04 - val_wingR: 0.0022 - val_forelegL4: 0.0018 - val_forelegR4: 0.0033 - val_midlegL4: 0.0025 - val_midlegR4: 0.0017 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0031 - val_eyeL: 9.8718e-04 - val_eyeR: 8.0263e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n", - "Epoch 26/200\n", - "200/200 - 3s - loss: 2.3368e-04 - head: 1.1149e-04 - thorax: 1.5177e-04 - abdomen: 2.1763e-04 - wingL: 2.2159e-04 - wingR: 1.9396e-04 - forelegL4: 3.8234e-04 - forelegR4: 3.8248e-04 - midlegL4: 2.7555e-04 - midlegR4: 2.8653e-04 - hindlegL4: 2.7842e-04 - hindlegR4: 2.8074e-04 - eyeL: 1.3157e-04 - eyeR: 1.2374e-04 - val_loss: 0.0017 - val_head: 2.1815e-04 - val_thorax: 5.0063e-04 - val_abdomen: 0.0011 - val_wingL: 8.2248e-04 - val_wingR: 0.0020 - val_forelegL4: 0.0019 - val_forelegR4: 0.0035 - val_midlegL4: 0.0022 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0022 - val_eyeL: 0.0013 - val_eyeR: 9.8071e-04 - lr: 5.0000e-05 - 3s/epoch - 14ms/step\n", - "Epoch 27/200\n", - "200/200 - 3s - loss: 2.0711e-04 - head: 9.7513e-05 - thorax: 1.4018e-04 - abdomen: 2.0210e-04 - wingL: 1.8693e-04 - wingR: 1.7399e-04 - forelegL4: 3.1753e-04 - forelegR4: 3.7613e-04 - midlegL4: 2.2838e-04 - midlegR4: 2.4643e-04 - hindlegL4: 2.4471e-04 - hindlegR4: 2.4706e-04 - eyeL: 1.1696e-04 - eyeR: 1.1452e-04 - val_loss: 0.0011 - val_head: 1.7855e-04 - val_thorax: 3.7885e-04 - val_abdomen: 7.0074e-04 - val_wingL: 6.4821e-04 - val_wingR: 0.0012 - val_forelegL4: 0.0012 - val_forelegR4: 0.0017 - val_midlegL4: 0.0014 - val_midlegR4: 0.0013 - val_hindlegL4: 0.0019 - val_hindlegR4: 0.0018 - val_eyeL: 8.8941e-04 - val_eyeR: 7.0606e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 28/200\n", - "200/200 - 3s - loss: 1.9539e-04 - head: 9.4716e-05 - thorax: 1.3617e-04 - abdomen: 1.8547e-04 - wingL: 1.8173e-04 - wingR: 1.6716e-04 - forelegL4: 3.2783e-04 - forelegR4: 3.1060e-04 - midlegL4: 2.2172e-04 - midlegR4: 2.2648e-04 - hindlegL4: 2.3846e-04 - hindlegR4: 2.2823e-04 - eyeL: 1.1204e-04 - eyeR: 1.0944e-04 - val_loss: 0.0012 - val_head: 1.9505e-04 - val_thorax: 3.8105e-04 - val_abdomen: 7.7888e-04 - val_wingL: 6.8985e-04 - val_wingR: 0.0016 - val_forelegL4: 0.0015 - val_forelegR4: 0.0020 - val_midlegL4: 0.0017 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0022 - val_hindlegR4: 0.0019 - val_eyeL: 9.1223e-04 - val_eyeR: 7.0778e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 29/200\n", - "200/200 - 3s - loss: 1.8262e-04 - head: 9.2364e-05 - thorax: 1.3126e-04 - abdomen: 1.7625e-04 - wingL: 1.7494e-04 - wingR: 1.5998e-04 - forelegL4: 3.0159e-04 - forelegR4: 2.9470e-04 - midlegL4: 1.9773e-04 - midlegR4: 2.0446e-04 - hindlegL4: 2.0576e-04 - hindlegR4: 2.1560e-04 - eyeL: 1.1218e-04 - eyeR: 1.0720e-04 - val_loss: 0.0015 - val_head: 2.2535e-04 - val_thorax: 4.8031e-04 - val_abdomen: 9.5428e-04 - val_wingL: 7.7468e-04 - val_wingR: 0.0016 - val_forelegL4: 0.0017 - val_forelegR4: 0.0025 - val_midlegL4: 0.0021 - val_midlegR4: 0.0018 - val_hindlegL4: 0.0029 - val_hindlegR4: 0.0019 - val_eyeL: 0.0013 - val_eyeR: 9.6936e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 30/200\n", - "200/200 - 3s - loss: 1.7461e-04 - head: 8.9617e-05 - thorax: 1.2428e-04 - abdomen: 1.7234e-04 - wingL: 1.6780e-04 - wingR: 1.5580e-04 - forelegL4: 2.7324e-04 - forelegR4: 2.8042e-04 - midlegL4: 1.9090e-04 - midlegR4: 2.0420e-04 - hindlegL4: 1.9914e-04 - hindlegR4: 2.0318e-04 - eyeL: 1.0518e-04 - eyeR: 1.0386e-04 - val_loss: 0.0015 - val_head: 1.9058e-04 - val_thorax: 4.9603e-04 - val_abdomen: 0.0011 - val_wingL: 9.7566e-04 - val_wingR: 0.0018 - val_forelegL4: 0.0016 - val_forelegR4: 0.0028 - val_midlegL4: 0.0022 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0028 - val_hindlegR4: 0.0028 - val_eyeL: 9.9699e-04 - val_eyeR: 8.3721e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 31/200\n", - "200/200 - 3s - loss: 1.7064e-04 - head: 8.7373e-05 - thorax: 1.2365e-04 - abdomen: 1.6765e-04 - wingL: 1.5656e-04 - wingR: 1.4505e-04 - forelegL4: 2.7352e-04 - forelegR4: 2.6274e-04 - midlegL4: 1.9639e-04 - midlegR4: 1.9628e-04 - hindlegL4: 2.0323e-04 - hindlegR4: 1.9917e-04 - eyeL: 1.0639e-04 - eyeR: 1.0032e-04 - val_loss: 0.0011 - val_head: 1.7938e-04 - val_thorax: 3.6727e-04 - val_abdomen: 7.7820e-04 - val_wingL: 6.4437e-04 - val_wingR: 0.0014 - val_forelegL4: 0.0014 - val_forelegR4: 0.0020 - val_midlegL4: 0.0016 - val_midlegR4: 0.0010 - val_hindlegL4: 0.0021 - val_hindlegR4: 0.0016 - val_eyeL: 8.0607e-04 - val_eyeR: 6.6172e-04 - lr: 5.0000e-05 - 3s/epoch - 16ms/step\n", - "Epoch 32/200\n", - "\n", - "Epoch 00032: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n", - "200/200 - 4s - loss: 1.6547e-04 - head: 8.6407e-05 - thorax: 1.1578e-04 - abdomen: 1.6160e-04 - wingL: 1.5752e-04 - wingR: 1.4326e-04 - forelegL4: 2.5855e-04 - forelegR4: 2.8317e-04 - midlegL4: 1.7880e-04 - midlegR4: 1.8021e-04 - hindlegL4: 1.9743e-04 - hindlegR4: 1.8831e-04 - eyeL: 1.0074e-04 - eyeR: 9.9381e-05 - val_loss: 0.0012 - val_head: 1.9257e-04 - val_thorax: 3.7361e-04 - val_abdomen: 7.0451e-04 - val_wingL: 7.8240e-04 - val_wingR: 0.0015 - val_forelegL4: 0.0014 - val_forelegR4: 0.0020 - val_midlegL4: 0.0016 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0020 - val_hindlegR4: 0.0019 - val_eyeL: 8.9328e-04 - val_eyeR: 7.3886e-04 - lr: 5.0000e-05 - 4s/epoch - 18ms/step\n", - "Epoch 33/200\n", - "200/200 - 3s - loss: 1.4767e-04 - head: 8.0575e-05 - thorax: 1.1097e-04 - abdomen: 1.4927e-04 - wingL: 1.4112e-04 - wingR: 1.3113e-04 - forelegL4: 2.1913e-04 - forelegR4: 2.1998e-04 - midlegL4: 1.6045e-04 - midlegR4: 1.6535e-04 - hindlegL4: 1.8091e-04 - hindlegR4: 1.7343e-04 - eyeL: 9.5387e-05 - eyeR: 9.2035e-05 - val_loss: 0.0014 - val_head: 1.9046e-04 - val_thorax: 4.6921e-04 - val_abdomen: 9.4087e-04 - val_wingL: 7.5647e-04 - val_wingR: 0.0015 - val_forelegL4: 0.0015 - val_forelegR4: 0.0025 - val_midlegL4: 0.0020 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0026 - val_hindlegR4: 0.0021 - val_eyeL: 0.0013 - val_eyeR: 0.0010 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n", - "Epoch 34/200\n", - "200/200 - 3s - loss: 1.4506e-04 - head: 7.9790e-05 - thorax: 1.0771e-04 - abdomen: 1.5052e-04 - wingL: 1.4143e-04 - wingR: 1.2485e-04 - forelegL4: 2.2486e-04 - forelegR4: 2.1619e-04 - midlegL4: 1.6584e-04 - midlegR4: 1.6250e-04 - hindlegL4: 1.6521e-04 - hindlegR4: 1.6717e-04 - eyeL: 9.1550e-05 - eyeR: 8.8112e-05 - val_loss: 0.0013 - val_head: 1.8689e-04 - val_thorax: 3.7203e-04 - val_abdomen: 9.3770e-04 - val_wingL: 7.0190e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0015 - val_forelegR4: 0.0023 - val_midlegL4: 0.0016 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0025 - val_hindlegR4: 0.0022 - val_eyeL: 8.0213e-04 - val_eyeR: 6.5036e-04 - lr: 2.5000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 35/200\n", - "200/200 - 3s - loss: 1.3911e-04 - head: 7.9674e-05 - thorax: 1.0668e-04 - abdomen: 1.4330e-04 - wingL: 1.3906e-04 - wingR: 1.2752e-04 - forelegL4: 1.9657e-04 - forelegR4: 1.9577e-04 - midlegL4: 1.5228e-04 - midlegR4: 1.5642e-04 - hindlegL4: 1.6610e-04 - hindlegR4: 1.6394e-04 - eyeL: 9.1523e-05 - eyeR: 8.9620e-05 - val_loss: 0.0013 - val_head: 1.7511e-04 - val_thorax: 4.2162e-04 - val_abdomen: 9.5009e-04 - val_wingL: 6.7908e-04 - val_wingR: 0.0013 - val_forelegL4: 0.0015 - val_forelegR4: 0.0023 - val_midlegL4: 0.0018 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0019 - val_eyeL: 0.0012 - val_eyeR: 9.8818e-04 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n", - "Epoch 36/200\n", - "200/200 - 3s - loss: 1.3697e-04 - head: 7.5207e-05 - thorax: 1.0507e-04 - abdomen: 1.3913e-04 - wingL: 1.3497e-04 - wingR: 1.2511e-04 - forelegL4: 1.9152e-04 - forelegR4: 2.0264e-04 - midlegL4: 1.5207e-04 - midlegR4: 1.5519e-04 - hindlegL4: 1.6368e-04 - hindlegR4: 1.5869e-04 - eyeL: 9.0233e-05 - eyeR: 8.7055e-05 - val_loss: 0.0013 - val_head: 1.8066e-04 - val_thorax: 4.6591e-04 - val_abdomen: 9.9582e-04 - val_wingL: 7.2600e-04 - val_wingR: 0.0012 - val_forelegL4: 0.0015 - val_forelegR4: 0.0022 - val_midlegL4: 0.0019 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0028 - val_hindlegR4: 0.0018 - val_eyeL: 0.0012 - val_eyeR: 9.6224e-04 - lr: 2.5000e-05 - 3s/epoch - 15ms/step\n", - "Epoch 37/200\n", - "200/200 - 3s - loss: 1.3638e-04 - head: 7.6822e-05 - thorax: 1.0531e-04 - abdomen: 1.4107e-04 - wingL: 1.4047e-04 - wingR: 1.2177e-04 - forelegL4: 1.9564e-04 - forelegR4: 1.7970e-04 - midlegL4: 1.5364e-04 - midlegR4: 1.5089e-04 - hindlegL4: 1.6647e-04 - hindlegR4: 1.6322e-04 - eyeL: 9.0198e-05 - eyeR: 8.7722e-05 - val_loss: 0.0017 - val_head: 2.3218e-04 - val_thorax: 5.3881e-04 - val_abdomen: 0.0011 - val_wingL: 0.0010 - val_wingR: 0.0019 - val_forelegL4: 0.0021 - val_forelegR4: 0.0028 - val_midlegL4: 0.0025 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0033 - val_hindlegR4: 0.0029 - val_eyeL: 0.0015 - val_eyeR: 0.0012 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n", - "Epoch 00037: early stopping\n", - "INFO:sleap.nn.training:Finished training loop. [2.0 min]\n", - "INFO:sleap.nn.training:Deleting visualization directory: models/courtship.topdown_confmaps/viz\n", - "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n", - "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m39.3 FPS\u001b[0m31m48.8 FPS\u001b[0m31m49.5 FPS\u001b[0mFPS\u001b[0m\n", - "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.topdown_confmaps/labels_pr.train.slp\n", - "INFO:sleap.nn.evals:Saved metrics: models/courtship.topdown_confmaps/metrics.train.npz\n", - "INFO:sleap.nn.evals:OKS mAP: 0.899237\n", - "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m14.2 FPS\u001b[0m0:00:01\u001b[0m \u001b[31m270.2 FPS\u001b[0mm\n", - "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.topdown_confmaps/labels_pr.val.slp\n", - "INFO:sleap.nn.evals:Saved metrics: models/courtship.topdown_confmaps/metrics.val.npz\n", - "INFO:sleap.nn.evals:OKS mAP: 0.691378\n" - ] - } - ], - "source": [ - "!sleap-train baseline_medium_rf.topdown.json \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\" --run_name \"courtship.topdown_confmaps\" --video-paths \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "whOf8PaFxYbt" - }, - "source": [ - "The models (along with the profiles and ground truth data used to train and validate the model) are saved in the `models/` directory:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 306 + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "DUfnkxMtLcK3", + "outputId": "a6340ef1-eaac-42ef-f8d4-bcc499feb57b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", + "!pip install -qqq \"sleap[pypi]>=1.3.4\"" + ] }, - "id": "GBUTQ2Cm44En", - "outputId": "ca298981-af65-43b3-f0f6-573f423acba8" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[01;34mmodels/\u001b[00m\n", - "├── \u001b[01;34mcourtship.centroid\u001b[00m\n", - "│   ├── best_model.h5\n", - "│   ├── initial_config.json\n", - "│   ├── labels_gt.train.slp\n", - "│   ├── labels_gt.val.slp\n", - "│   ├── labels_pr.train.slp\n", - "│   ├── labels_pr.val.slp\n", - "│   ├── metrics.train.npz\n", - "│   ├── metrics.val.npz\n", - "│   ├── training_config.json\n", - "│   └── training_log.csv\n", - "└── \u001b[01;34mcourtship.topdown_confmaps\u001b[00m\n", - " ├── best_model.h5\n", - " ├── initial_config.json\n", - " ├── labels_gt.train.slp\n", - " ├── labels_gt.val.slp\n", - " ├── labels_pr.train.slp\n", - " ├── labels_pr.val.slp\n", - " ├── metrics.train.npz\n", - " ├── metrics.val.npz\n", - " ├── training_config.json\n", - " └── training_log.csv\n", - "\n", - "2 directories, 20 files\n" - ] - } - ], - "source": [ - "!tree models/" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nIsKUX661xFK" - }, - "source": [ - "## Inference\n", - "Let's run inference with our trained models for centroids and centered instances." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "CLtjtq9E1Znr" - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "iq7jrgUksLtR" + }, + "source": [ + "## Download sample training data into Colab\n", + "Let's download a sample dataset from the SLEAP [sample datasets repository](https://github.com/talmolab/sleap-datasets) into Colab." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started inference at: 2023-09-01 13:42:03.066840\n", - "Args:\n", - "\u001b[1m{\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'data_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'models'\u001b[0m: \u001b[1m[\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'models/courtship.centroid'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'models/courtship.topdown_confmaps'\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'frames'\u001b[0m: \u001b[32m'0-100'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'only_labeled_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'only_suggested_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'no_empty_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'verbosity'\u001b[0m: \u001b[32m'rich'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'video.dataset'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'video.input_format'\u001b[0m: \u001b[32m'channels_last'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'video.index'\u001b[0m: \u001b[32m''\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'cpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'first_gpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'last_gpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'gpu'\u001b[0m: \u001b[32m'auto'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'max_edge_length_ratio'\u001b[0m: \u001b[1;36m0.25\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'dist_penalty_weight'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'batch_size'\u001b[0m: \u001b[1;36m4\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'open_in_gui'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'peak_threshold'\u001b[0m: \u001b[1;36m0.2\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'max_instances'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.tracker'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.target_instance_count'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.pre_cull_to_target'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.pre_cull_iou_threshold'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.post_connect_single_breaks'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.clean_instance_count'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.clean_iou_threshold'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.similarity'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.match'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.robust'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.track_window'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.min_new_track_points'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.min_match_points'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.img_scale'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.of_window_size'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.of_max_levels'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.save_shifted_instances'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.kf_node_indices'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'tracking.kf_init_frame_count'\u001b[0m: \u001b[3;35mNone\u001b[0m\n", - "\u001b[1m}\u001b[0m\n", - "\n", - "2023-09-01 13:42:03.098811: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.103255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.103982: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "INFO:sleap.nn.inference:Auto-selected GPU 0 with 23050 MiB of free memory.\n", - "Versions:\n", - "SLEAP: 1.3.2\n", - "TensorFlow: 2.7.0\n", - "Numpy: 1.21.5\n", - "Python: 3.7.12\n", - "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n", - "\n", - "System:\n", - "GPUs: 1/1 available\n", - " Device: /physical_device:GPU:0\n", - " Available: True\n", - " Initalized: False\n", - " Memory growth: True\n", - "\n", - "Video: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\n", - "2023-09-01 13:42:03.157392: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-09-01 13:42:03.158019: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.158864: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.159656: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.455402: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.456138: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.456803: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-09-01 13:42:03.457464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21145 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n", - "\u001b[2KPredicting... \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 0%\u001b[0m ETA: \u001b[36m-:--:--\u001b[0m \u001b[31m?\u001b[0m2023-09-01 13:42:07.038687: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n", - "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m51.9 FPS\u001b[0m[0m \u001b[31m126.4 FPS\u001b[0m FPS\u001b[0mFPS\u001b[0m\n", - "\u001b[?25hFinished inference at: 2023-09-01 13:42:10.842469\n", - "Total runtime: 7.775644779205322 secs\n", - "Predicted frames: 101/101\n", - "Provenance:\n", - "\u001b[1m{\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'model_paths'\u001b[0m: \u001b[1m[\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'models/courtship.centroid/training_config.json'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'models/courtship.topdown_confmaps/training_config.json'\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'predictor'\u001b[0m: \u001b[32m'TopDownPredictor'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'sleap_version'\u001b[0m: \u001b[32m'1.3.2'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'platform'\u001b[0m: \u001b[32m'Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'command'\u001b[0m: \u001b[32m'/home/talmolab/micromamba/envs/s0/bin/sleap-track dataset/drosophila-melanogaster-courtship/20190128_113421.mp4 --frames 0-100 -m models/courtship.centroid -m models/courtship.topdown_confmaps'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'data_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'output_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'total_elapsed'\u001b[0m: \u001b[1;36m7.775644779205322\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'start_timestamp'\u001b[0m: \u001b[32m'2023-09-01 13:42:03.066840'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[32m'finish_timestamp'\u001b[0m: \u001b[32m'2023-09-01 13:42:10.842469'\u001b[0m\n", - "\u001b[1m}\u001b[0m\n", - "\n", - "Saved output: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp\n" - ] - } - ], - "source": [ - "!sleap-track \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\" --frames 0-100 -m \"models/courtship.centroid\" -m \"models/courtship.topdown_confmaps\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nzObCUToEqwA" - }, - "source": [ - "When inference is finished, predictions are saved in a file. Since we didn't specify a path, it will be saved as `