From 7d705638aa8d887656c6ff8c52a3a5c419bdba2d Mon Sep 17 00:00:00 2001 From: Andy Bonnetto <67779579+andybonnetto@users.noreply.github.com> Date: Mon, 6 Nov 2023 08:56:57 +0100 Subject: [PATCH] Fix notebook data loader and add some notes for windows user (#16) * notes for windows users * fix data loader and notes for windows users --- examples/demo_notebook.ipynb | 2515 +++++-------------------------- examples/minimal_notebook.ipynb | 21 +- 2 files changed, 410 insertions(+), 2126 deletions(-) diff --git a/examples/demo_notebook.ipynb b/examples/demo_notebook.ipynb index 9ba17cb..7f78b7c 100644 --- a/examples/demo_notebook.ipynb +++ b/examples/demo_notebook.ipynb @@ -20,7 +20,7 @@ "source": [ "To see how it works, we will experiment on a relatively small [publically available](https://github.com/ETHZ-INS/DLCAnalyzer/tree/master/data/OFT) dataset (Sturman, 2020). Run the code below to download the data.\n", "\n", - "Note that the results we are getting here are not optimal because we are using very small numbers of epochs and trials to make the execution time fit within a short tutorial." + "Note that the results that we will get in this notebook here are not optimal because we will use very small numbers of epochs and trials to fit the execution time within this short tutorial." ] }, { @@ -34,129 +34,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's download the data. Downloading the data and installing the packages can take up to about 5-10 minutes." + "Now let's download the data. Downloading the data and installing the packages can take up to about 5-10 minutes.
\n", + "*For Windows user*, you may need to install `wget` by downloading the .exe file [here](https://eternallybored.org/misc/wget/) (>1.10.0) and moving it to the System32 directory." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gdown in /home/liza/miniconda3/envs/DLC2Action/lib/python3.9/site-packages (4.5.3)\n", - "Requirement already satisfied: filelock in /home/liza/miniconda3/envs/DLC2Action/lib/python3.9/site-packages (from gdown) (3.8.0)\n", - "Requirement already satisfied: requests[socks] in /home/liza/miniconda3/envs/DLC2Action/lib/python3.9/site-packages (from gdown) (2.27.1)\n", - "Requirement already satisfied: six in /home/liza/miniconda3/envs/DLC2Action/lib/python3.9/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: beautifulsoup4 in /home/liza/miniconda3/envs/DLC2Action/lib/python3.9/site-packages (from gdown) (4.11.1)\n", - 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"Downloading...\n", - "From: https://drive.google.com/uc?id=1c-dX7MtRGPSGSrNp3Uaf3aOIzokzuj69\n", - "To: /home/liza/DLC2Action_minimal/examples/OFT.zip\n", - "100%|█████████████████████████████████████████| 327M/327M [00:02<00:00, 111MB/s]\n", - "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", - "Archive: OFT.zip\n", - " creating: OFT/OFT/\n", - " creating: OFT/OFT/Labels/\n", - " inflating: OFT/OFT/Labels/21_02_A_190507131119.csv \n", - " inflating: OFT/OFT/Labels/37_03_A_190507134213.csv \n", - " inflating: OFT/OFT/Labels/17_02_A_190507120259.csv \n", - " inflating: OFT/OFT/Labels/49_04_A_190507125609.csv \n", - " inflating: OFT/OFT/Labels/48_04_A_190507123817.csv \n", - " inflating: OFT/OFT/Labels/13_01_A_190507145949.csv \n", - " inflating: OFT/OFT/Labels/57_04_A_190507150030.csv \n", - " inflating: OFT/OFT/Labels/3_01_A_190507121945.csv \n", - 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" inflating: OFT/OFT/Output_DLC/11_01_A_190507142850DeepCut_resnet50_Blockcourse1May9shuffle1_1030000.csv \n", - " inflating: OFT/OFT/Output_DLC/31_03_A_190507120320DeepCut_resnet50_Blockcourse1May9shuffle1_1030000.csv \n", - " inflating: OFT/OFT/Output_DLC/18_02_A_190507122004DeepCut_resnet50_Blockcourse1May9shuffle1_1030000.csv \n" - ] - } - ], + "outputs": [], "source": [ - "#download the data\n", - "!curl https://transfer.sh/g8t8Vy/OFT.zip -o OFT.zip\n", + "!wget https://github.com/ETHZ-INS/DLCAnalyzer/archive/refs/heads/master.zip\n", "!apt-get install unzip\n", - "!unzip OFT.zip -d OFT\n", - "!mv OFT/OFT/OFT OFT_data" + "!unzip master.zip\n", + "!mv DLCAnalyzer-master/data/OFT OFT_data" ] }, { @@ -175,15 +66,6 @@ "!python -m pip install dlc2action" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!python -m pip install dlc2action" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -277,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -433,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -513,14 +395,31 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "There is no blanks left!\n" + "Before running experiments, please update all the blanks.\n", + "To do that, you can run this.\n", + "--------------------------------------------------------\n", + "project.update_parameters(\n", + " {\n", + " \"data\": {\n", + " \"data_suffix\": ..., # set; the data files should have the format of {video_id}{data_suffix}, e.g. video1_suffix.pickle, where video1 is the video is and _suffix.pickle is the suffix\n", + " \"canvas_shape\": ..., # list; the size of the canvas where the pose was defined\n", + " \"annotation_suffix\": ..., # str | set, optional the suffix or the set of suffices such that the annotation files are named {video_id}{annotation_suffix}, e.g, video1_suffix.pickle where video1 is the video id and _suffix.pickle is the suffix\n", + " \"fps\": ..., # int; fps (assuming the annotations are given in seconds, otherwise set 1)\n", + " },\n", + " \"general\": {\n", + " \"exclusive\": ..., # bool; if true, single-label classification is used; otherwise multi-label\n", + " },\n", + " }\n", + ")\n", + "--------------------------------------------------------\n", + "Replace ... with relevant values.\n" ] } ], @@ -537,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -572,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -587,7 +486,7 @@ " {\n", " \"general\": {\n", " \"model_name\": \"c2f_tcn\", # str; model name (run project.help(\"model\") for more info)\n", - " \"metric_functions\": ['f1', 'precision'], \n", + " \"metric_functions\": {'recall', 'f1', 'precision'}, # set; set of metric names (run project.help(\"metrics\") for more info)\n", " \"ignored_clips\": None, # list; a list of string clip ids (agent names) to be ignored\n", " \"len_segment\": 512, # int; the length of segments (in frames) to cut the videos into\n", " \"overlap\": 0.75, # int; the overlap (in frames) between neighboring segments\n", @@ -609,7 +508,7 @@ " \"training\": {\n", " \"lr\": 0.001, # float; learning rate\n", " \"device\": \"auto\", # str; device\n", - " \"num_epochs\": 10, # int; number of epochs\n", + " \"num_epochs\": 50, # int; number of epochs\n", " \"to_ram\": False, # bool; transfer the dataset to RAM for training (preferred if the dataset fits in working memory)\n", " \"batch_size\": 64, # int; batch size\n", " \"normalize\": True, # bool; if true, normalization statistics will be computed on the training set and applied to all data\n", @@ -635,6 +534,12 @@ " \"tag_average\": \"micro\", # ['micro', 'macro', 'none']; averaging method for meta tags (if given)\n", " \"threshold_value\": 0.5, # float; the probability threshold for positive samples\n", " },\n", + " \"recall\": {\n", + " \"average\": \"macro\", # ['macro', 'micro', 'none']; averaging method for classes\n", + " \"ignored_classes\": None, # set; a set of class ids to ignore in calculation\n", + " \"tag_average\": \"micro\", # ['micro', 'macro', 'none']; averaging method for meta tags (if given)\n", + " \"threshold_value\": 0.5, # float; the probability threshold for positive samples\n", + " },\n", " \"precision\": {\n", " \"average\": \"macro\", # ['macro', 'micro', 'none']; averaging method for classes\n", " \"ignored_classes\": None, # set; a set of class ids to ignore in calculation\n", @@ -685,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -784,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -940,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -958,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -998,1576 +903,252 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for model in [MODEL_NAME1, MODEL_NAME2]:\n", + " project.run_default_hyperparameter_search(\n", + " f\"{model}_search\",\n", + " model_name=model,\n", + " metric=METRICS[0],\n", + " num_epochs=3,\n", + " n_trials=10,\n", + " prune=True,\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those searches will optimize the hyperparameters to maximize the first metric in our list. Note how some trials stop before reaching 10 epochs. That is happening because we have set `prune=True` to interrupt experiments when they are unlikely to beat the best score.\n", + "\n", + "Generally, it is better to set both the number of trials and the number of epochs much higher (30-50 and around 150, respectively, is usually a good choice). We are setting them low here to save time but keep in mind that it does mean that the parameters those searches find are probably not actually optimal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The searches have created a bunch of datasets that we are not going to use again so it's a good idea to clean up the memory at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m[I 2022-11-24 09:23:12,875]\u001b[0m A new study created in memory with name: no-name-0f119bcc-546e-4f81-ac4a-5aba0ad1ff0c\u001b[0m\n" + "Removing datasets...\n", + "\n", + "\n" ] - }, + } + ], + "source": [ + "project.remove_saved_features()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`DLC2Action` needs pre-computed features to run an experiment. It keeps track of features that are stored on your machine and does not re-compute them if they have already been created in other experiments with the same data parameters. They can take up a lot of space, however, so it's good practice to remove them sometimes. \n", + "\n", + "Do not be afraid to run `project.remove_saved_features()`, you never lose any information when you do. The features will just be computed again if you need them. In addition, if you are running low on space, it might be more convenient to pass `remove_saved_features=True` to project methods to remove the features as soon as they are not needed anymore.\n", + "\n", + "Another function that helps clean up the memory is `project.remove_extra_checkpoints()`. DLC2Action saves a model checkpoint every 5 epochs by default (you can change this interval at `\"training/model_save_interval\"`). Running this method will remove all the checkpoints except for the last one in each episode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the searches are done, we can check out the results." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SEARCH c2f_tcn_search\n", - "Computing input features...\n" + "SEARCH RESULTS c2f_tcn_search\n", + "losses/ms_tcn/alpha: 1.0242787534938017e-05\n", + "losses/ms_tcn/focal: True\n", + "training/temporal_subsampling_size: 0.8418055756065875\n", + "model/num_f_maps: 79\n", + "general/len_segment: 512\n", + "\n", + "\n", + "SEARCH RESULTS transformer_search\n", + "losses/ms_tcn/alpha: 0.0014098664544016791\n", + "losses/ms_tcn/focal: False\n", + "training/temporal_subsampling_size: 0.9027582022903071\n", + "model/N: 9\n", + "model/heads: 8\n", + "model/num_pool: 3\n", + "model/add_batchnorm: False\n", + "general/len_segment: 128\n", + "\n", + "\n" ] - }, + } + ], + "source": [ + "for model in [MODEL_NAME1, MODEL_NAME2]:\n", + " _ = project.list_best_parameters(f\"{model}_search\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The searches can be used to update the default parameters with `project.update_parameters(load_search=search_name)` but in this case it's more convenient to load them in the relevant episodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can train models with the best hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for model in [MODEL_NAME1, MODEL_NAME2]:\n", + " project.run_episode(\n", + " f\"{model}_best\",\n", + " load_search=f\"{model}_search\", # loading the search\n", + " force=True, # when force=True, if an episode with this name already exists it will be overwritten -> use with caution!\n", + " parameters_update={\n", + " \"general\": {\"model_name\": model} # note that you do need to set the model explicitly, it is not loaded with the search\n", + " },\n", + " n_seeds=2 # we will repeat the experiment 2 times (same parameters, different random seed) to get an estimation for how stable our results are\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've trained our best models, we can analyze the results. Note that most statistics are aggregated over the 2 runs (random seeds) automatically. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "best_episodes = [f\"{model}_best\" for model in [MODEL_NAME1, MODEL_NAME2]]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:09<00:00, 2.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 585.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 2.83% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 43564, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 163547, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 467/467 [00:00<00:00, 720.60it/s]\n", - "100%|██████████| 467/467 [00:00<00:00, 667.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0143, f1 0.393\n", - "validation: loss 0.0157, f1 0.239\n", - "[epoch 2]: loss 0.0104, f1 0.546\n", - "validation: loss 0.0130, f1 0.521\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:23:57,725]\u001b[0m Trial 0 finished with value: 0.46422381202379864 and parameters: {'losses/ms_tcn/alpha': 0.0015883641533781837, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.8991999677679936, 'model/num_f_maps': 67, 'general/len_segment': 512}. Best is trial 0 with value: 0.46422381202379864.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0078, f1 0.663\n", - "validation: loss 0.0097, f1 0.632\n", - "Number of samples:\n", - " validation:\n", - " {-100: 43564, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 163547, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 467/467 [00:00<00:00, 495.41it/s]\n", - "100%|██████████| 467/467 [00:00<00:00, 594.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0162, f1 0.375\n", - "validation: loss 0.0173, f1 0.367\n", - "[epoch 2]: loss 0.0121, f1 0.517\n", - "validation: loss 0.0144, f1 0.626\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:05,560]\u001b[0m Trial 1 finished with value: 0.552939385175705 and parameters: {'losses/ms_tcn/alpha': 0.0007464967274536318, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.9541599916891434, 'model/num_f_maps': 34, 'general/len_segment': 512}. Best is trial 1 with value: 0.552939385175705.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0103, f1 0.597\n", - "validation: loss 0.0119, f1 0.666\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:07<00:00, 2.52it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 412.12it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {-100: 45612, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 170203, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 240/240 [00:00<00:00, 518.58it/s]\n", - "100%|██████████| 240/240 [00:00<00:00, 565.64it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.272, 0.035, 0.006, 0.009\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0096, f1 0.219\n", - "validation: loss 0.0087, f1 0.087\n", - "[epoch 2]: loss 0.0065, f1 0.539\n", - "validation: loss 0.0079, f1 0.313\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:18,784]\u001b[0m Trial 2 finished with value: 0.26326049367586773 and parameters: {'losses/ms_tcn/alpha': 1.3669781078806032e-05, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.7576554155976422, 'model/num_f_maps': 82, 'general/len_segment': 1024}. Best is trial 1 with value: 0.552939385175705.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0053, f1 0.551\n", - "validation: loss 0.0073, f1 0.389\n", - "Number of samples:\n", - " validation:\n", - " {-100: 43564, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 163547, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 467/467 [00:00<00:00, 637.18it/s]\n", - "100%|██████████| 467/467 [00:00<00:00, 600.70it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0168, f1 0.338\n", - "validation: loss 0.0175, f1 0.499\n", - "[epoch 2]: loss 0.0129, f1 0.544\n", - "validation: loss 0.0144, f1 0.537\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:26,077]\u001b[0m Trial 3 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:28,427]\u001b[0m Trial 4 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:30,473]\u001b[0m Trial 5 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {-100: 45612, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 170203, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 240/240 [00:00<00:00, 522.38it/s]\n", - "100%|██████████| 240/240 [00:00<00:00, 477.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.272, 0.035, 0.006, 0.009\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:33,047]\u001b[0m Trial 6 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {-100: 43564, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 163547, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 467/467 [00:00<00:00, 619.99it/s]\n", - "100%|██████████| 467/467 [00:00<00:00, 630.86it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0160, f1 0.344\n", - "validation: loss 0.0164, f1 0.171\n", - "[epoch 2]: loss 0.0121, f1 0.518\n", - "validation: loss 0.0134, f1 0.584\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:40,769]\u001b[0m Trial 7 finished with value: 0.46972566346327466 and parameters: {'losses/ms_tcn/alpha': 5.58263606003158e-05, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.9574566378704996, 'model/num_f_maps': 57, 'general/len_segment': 512}. Best is trial 1 with value: 0.552939385175705.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0105, f1 0.603\n", - "validation: loss 0.0100, f1 0.655\n", - "Initializing class weights:\n", - " 0.53, 0.068, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 1.0908, f1 0.379\n", - "validation: loss 1.1094, f1 0.359\n", - "[epoch 2]: loss 0.8620, f1 0.610\n", - "validation: loss 0.9522, f1 0.438\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:46,840]\u001b[0m Trial 8 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {-100: 45612, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 170203, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 240/240 [00:00<00:00, 536.56it/s]\n", - "100%|██████████| 240/240 [00:00<00:00, 527.51it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.272, 0.035, 0.006, 0.009\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:49,347]\u001b[0m Trial 9 pruned. \u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,029]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,031]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,031]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,032]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,033]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,034]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,035]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n", - "\u001b[33m[W 2022-11-24 09:24:51,035]\u001b[0m Param losses/ms_tcn/focal unique value length is less than 2.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best parameters: {'losses/ms_tcn/alpha': 0.0007464967274536318, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.9541599916891434, 'model/num_f_maps': 34, 'general/len_segment': 512}\n", - "\n", - "\n", - "SEARCH transformer_search\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:24:54,102]\u001b[0m A new study created in memory with name: no-name-02526333-755f-46ac-97a9-2ae2de08f854\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:08<00:00, 2.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 490.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 27.18% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 26668, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 105179, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1412/1412 [00:01<00:00, 882.29it/s]\n", - "100%|██████████| 1412/1412 [00:01<00:00, 947.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0020, f1 0.455\n", - "validation: loss 0.0009, f1 0.388\n", - "[epoch 2]: loss 0.0013, f1 0.426\n", - "validation: loss 0.0013, f1 0.409\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:25:16,950]\u001b[0m Trial 0 finished with value: 0.4149907131989797 and parameters: {'losses/ms_tcn/alpha': 5.215361992359534e-05, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.881360573093753, 'model/N': 9, 'model/heads': 4, 'model/num_pool': 1, 'model/add_batchnorm': True, 'general/len_segment': 128}. Best is trial 0 with value: 0.4149907131989797.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0008, f1 0.398\n", - "validation: loss 0.0012, f1 0.448\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:08<00:00, 2.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 334.68it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 42.53% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 16556, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 67675, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2238/2238 [00:02<00:00, 922.53it/s]\n", - "100%|██████████| 2238/2238 [00:02<00:00, 996.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 2.54, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0011, f1 0.246\n", - "validation: loss 0.0011, f1 0.194\n", - "[epoch 2]: loss 0.0010, f1 0.251\n", - "validation: loss 0.0009, f1 0.417\n", - "[epoch 3]: loss 0.0008, f1 0.372\n", - "validation: loss 0.0009, f1 0.422\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:25:47,308]\u001b[0m Trial 1 finished with value: 0.34459022184213 and parameters: {'losses/ms_tcn/alpha': 1.0798037911397853e-05, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.9351556627361312, 'model/N': 12, 'model/heads': 1, 'model/num_pool': 4, 'model/add_batchnorm': False, 'general/len_segment': 64}. Best is trial 0 with value: 0.4149907131989797.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {-100: 26668, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 105179, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1412/1412 [00:01<00:00, 776.64it/s]\n", - "100%|██████████| 1412/1412 [00:01<00:00, 948.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0012, f1 0.405\n", - "validation: loss 0.0022, f1 0.625\n", - "[epoch 2]: loss 0.0006, f1 0.554\n", - "validation: loss 0.0006, f1 0.642\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:26:00,880]\u001b[0m Trial 2 finished with value: 0.6384904980659485 and parameters: {'losses/ms_tcn/alpha': 0.0020648953506156916, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.9981787398682551, 'model/N': 5, 'model/heads': 8, 'model/num_pool': 1, 'model/add_batchnorm': False, 'general/len_segment': 128}. Best is trial 2 with value: 0.6384904980659485.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0005, f1 0.598\n", - "validation: loss 0.0006, f1 0.648\n", - "Number of samples:\n", - " validation:\n", - " {-100: 16556, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 67675, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2238/2238 [00:02<00:00, 845.92it/s]\n", - "100%|██████████| 2238/2238 [00:02<00:00, 997.75it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 2.54, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0142, f1 0.479\n", - "validation: loss 0.0100, f1 0.742\n", - "[epoch 2]: loss 0.0098, f1 0.629\n", - "validation: loss 0.0101, f1 0.690\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:26:20,537]\u001b[0m Trial 3 finished with value: 0.7395498951276144 and parameters: {'losses/ms_tcn/alpha': 0.0010662443105835437, 'losses/ms_tcn/focal': False, 'training/temporal_subsampling_size': 0.9271396506401881, 'model/N': 8, 'model/heads': 4, 'model/num_pool': 2, 'model/add_batchnorm': True, 'general/len_segment': 64}. Best is trial 3 with value: 0.7395498951276144.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0083, f1 0.695\n", - "validation: loss 0.0090, f1 0.786\n", - "Number of samples:\n", - " validation:\n", - " {-100: 26668, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 105179, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1412/1412 [00:01<00:00, 758.19it/s]\n", - "100%|██████████| 1412/1412 [00:01<00:00, 887.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0204, f1 0.436\n", - "validation: loss 0.0159, f1 0.500\n", - "[epoch 2]: loss 0.0225, f1 0.567\n", - "validation: loss 0.0153, f1 0.688\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:26:35,277]\u001b[0m Trial 4 finished with value: 0.5780942936738332 and parameters: {'losses/ms_tcn/alpha': 0.00966421743737705, 'losses/ms_tcn/focal': False, 'training/temporal_subsampling_size': 0.954978497585141, 'model/N': 9, 'model/heads': 2, 'model/num_pool': 1, 'model/add_batchnorm': True, 'general/len_segment': 128}. Best is trial 3 with value: 0.7395498951276144.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0306, f1 0.561\n", - "validation: loss 0.0150, f1 0.547\n", - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0007, f1 0.443\n", - "validation: loss 0.0007, f1 0.669\n", - "[epoch 2]: loss 0.0006, f1 0.635\n", - "validation: loss 0.0005, f1 0.711\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:26:46,861]\u001b[0m Trial 5 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0221, f1 0.431\n", - "validation: loss 0.0146, f1 0.597\n", - "[epoch 2]: loss 0.0171, f1 0.506\n", - "validation: loss 0.0179, f1 0.645\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:26:58,364]\u001b[0m Trial 6 finished with value: 0.6152208844820658 and parameters: {'losses/ms_tcn/alpha': 5.192942355098905e-05, 'losses/ms_tcn/focal': False, 'training/temporal_subsampling_size': 0.9151174665799751, 'model/N': 9, 'model/heads': 8, 'model/num_pool': 2, 'model/add_batchnorm': True, 'general/len_segment': 128}. Best is trial 3 with value: 0.7395498951276144.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0201, f1 0.497\n", - "validation: loss 0.0142, f1 0.603\n", - "Number of samples:\n", - " validation:\n", - " {-100: 16556, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 67675, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2238/2238 [00:02<00:00, 853.24it/s]\n", - "100%|██████████| 2238/2238 [00:02<00:00, 945.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 2.54, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0008, f1 0.490\n", - "validation: loss 0.0005, f1 0.702\n", - "[epoch 2]: loss 0.0005, f1 0.603\n", - "validation: loss 0.0005, f1 0.696\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:27:18,930]\u001b[0m Trial 7 finished with value: 0.7142450213432312 and parameters: {'losses/ms_tcn/alpha': 3.5021638730081896e-05, 'losses/ms_tcn/focal': True, 'training/temporal_subsampling_size': 0.8364818656563426, 'model/N': 10, 'model/heads': 1, 'model/num_pool': 1, 'model/add_batchnorm': False, 'general/len_segment': 64}. Best is trial 3 with value: 0.7395498951276144.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 3]: loss 0.0005, f1 0.645\n", - "validation: loss 0.0006, f1 0.744\n", - "Number of samples:\n", - " validation:\n", - " {-100: 26668, 2: 9289, 0: 166, 3: 4768, 1: 1605}\n", - " training:\n", - " {-100: 105179, 0: 881, 3: 25277, 2: 42486, 1: 6913}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1412/1412 [00:01<00:00, 760.63it/s]\n", - "100%|██████████| 1412/1412 [00:01<00:00, 919.98it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:27:26,718]\u001b[0m Trial 8 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 1.603, 0.204, 0.033, 0.056\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[I 2022-11-24 09:27:31,423]\u001b[0m Trial 9 pruned. \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best parameters: {'losses/ms_tcn/alpha': 0.0010662443105835437, 'losses/ms_tcn/focal': False, 'training/temporal_subsampling_size': 0.9271396506401881, 'model/N': 8, 'model/heads': 4, 'model/num_pool': 2, 'model/add_batchnorm': True, 'general/len_segment': 64}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for model in [MODEL_NAME1, MODEL_NAME2]:\n", - " project.run_default_hyperparameter_search(\n", - " f\"{model}_search\",\n", - " model_name=model,\n", - " metric=METRICS[0],\n", - " num_epochs=3,\n", - " n_trials=10,\n", - " prune=True,\n", - " )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Those searches will optimize the hyperparameters to maximize the first metric in our list. Note how some trials stop before reaching 10 epochs. That is happening because we have set `prune=True` to interrupt experiments when they are unlikely to beat the best score.\n", - "\n", - "Generally, it is better to set both the number of trials and the number of epochs much higher (30-50 and around 150, respectively, is usually a good choice). We are setting them low here to save time but keep in mind that it does mean that the parameters those searches find are probably not actually optimal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The searches have created a bunch of datasets that we are not going to use again so it's a good idea to clean up the memory at this point." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing datasets...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 18.59it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "project.remove_saved_features()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`DLC2Action` needs pre-computed features to run an experiment. It keeps track of features that are stored on your machine and does not re-compute them if they have already been created in other experiments with the same data parameters. They can take up a lot of space, however, so it's good practice to remove them sometimes. \n", - "\n", - "Do not be afraid to run `project.remove_saved_features()`, you never lose any information when you do. The features will just be computed again if you need them. In addition, if you are running low on space, it might be more convenient to pass `remove_saved_features=True` to project methods to remove the features as soon as they are not needed anymore.\n", - "\n", - "Another function that helps clean up the memory is `project.remove_extra_checkpoints()`. DLC2Action saves a model checkpoint every 5 epochs by default (you can change this interval at `\"training/model_save_interval\"`). Running this method will remove all the checkpoints except for the last one in each episode." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When the searches are done, we can check out the results." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SEARCH RESULTS c2f_tcn_search\n", - "losses/ms_tcn/alpha: 0.0007464967274536318\n", - "losses/ms_tcn/focal: True\n", - "training/temporal_subsampling_size: 0.9541599916891434\n", - "model/num_f_maps: 34\n", - "general/len_segment: 512\n", - "\n", - "\n", - "SEARCH RESULTS transformer_search\n", - "losses/ms_tcn/alpha: 0.0010662443105835437\n", - "losses/ms_tcn/focal: False\n", - "training/temporal_subsampling_size: 0.9271396506401881\n", - "model/N: 8\n", - "model/heads: 4\n", - "model/num_pool: 2\n", - "model/add_batchnorm: True\n", - "general/len_segment: 64\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for model in [MODEL_NAME1, MODEL_NAME2]:\n", - " _ = project.list_best_parameters(f\"{model}_search\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The searches can be used to update the default parameters with `project.update_parameters(load_search=search_name)` but in this case it's more convenient to load them in the relevant episodes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can train models with the best hyperparameters." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TRAINING c2f_tcn_best::0\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:13<00:00, 1.51it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 600.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 4.34% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 168275, 2: 36710, 0: 475, 3: 19032, 1: 6420}\n", - " training:\n", - " {-100: 642109, 0: 2606, 3: 100892, 2: 168967, 1: 27506}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1840/1840 [00:02<00:00, 690.92it/s]\n", - "100%|██████████| 1840/1840 [00:02<00:00, 729.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.706, 0.067, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0141, f1 0.423, precision 0.410\n", - "validation: loss 0.0094, f1 0.675, precision 0.631\n", - "[epoch 2]: loss 0.0075, f1 0.659, precision 0.623\n", - "validation: loss 0.0073, f1 0.754, precision 0.742\n", - "[epoch 3]: loss 0.0048, f1 0.737, precision 0.691\n", - "validation: loss 0.0076, f1 0.791, precision 0.856\n", - "[epoch 4]: loss 0.0037, f1 0.803, precision 0.763\n", - "validation: loss 0.0077, f1 0.757, precision 0.740\n", - "[epoch 5]: loss 0.0031, f1 0.817, precision 0.777\n", - "validation: loss 0.0063, f1 0.798, precision 0.786\n", - "[epoch 6]: loss 0.0025, f1 0.802, precision 0.750\n", - "validation: loss 0.0075, f1 0.783, precision 0.789\n", - "[epoch 7]: loss 0.0017, f1 0.869, precision 0.825\n", - "validation: loss 0.0049, f1 0.849, precision 0.850\n", - "[epoch 8]: loss 0.0019, f1 0.869, precision 0.833\n", - "validation: loss 0.0037, f1 0.828, precision 0.821\n", - "[epoch 9]: loss 0.0018, f1 0.856, precision 0.810\n", - "validation: loss 0.0053, f1 0.790, precision 0.776\n", - "[epoch 10]: loss 0.0013, f1 0.900, precision 0.865\n", - "validation: loss 0.0046, f1 0.849, precision 0.838\n", - "\n", - "\n", - "TRAINING c2f_tcn_best::1\n", - "Number of samples:\n", - " validation:\n", - " {-100: 168275, 2: 36710, 0: 475, 3: 19032, 1: 6420}\n", - " training:\n", - " {-100: 642109, 0: 2606, 3: 100892, 2: 168967, 1: 27506}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1840/1840 [00:03<00:00, 604.49it/s]\n", - "100%|██████████| 1840/1840 [00:02<00:00, 746.76it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.706, 0.067, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0121, f1 0.463, precision 0.489\n", - "validation: loss 0.0088, f1 0.651, precision 0.653\n", - "[epoch 2]: loss 0.0074, f1 0.648, precision 0.622\n", - "validation: loss 0.0098, f1 0.535, precision 0.556\n", - "[epoch 3]: loss 0.0052, f1 0.712, precision 0.669\n", - "validation: loss 0.0062, f1 0.761, precision 0.735\n", - "[epoch 4]: loss 0.0040, f1 0.781, precision 0.746\n", - "validation: loss 0.0073, f1 0.729, precision 0.737\n", - "[epoch 5]: loss 0.0047, f1 0.759, precision 0.728\n", - "validation: loss 0.0039, f1 0.832, precision 0.815\n", - "[epoch 6]: loss 0.0035, f1 0.794, precision 0.753\n", - "validation: loss 0.0038, f1 0.754, precision 0.734\n", - "[epoch 7]: loss 0.0024, f1 0.825, precision 0.778\n", - "validation: loss 0.0035, f1 0.861, precision 0.840\n", - "[epoch 8]: loss 0.0020, f1 0.842, precision 0.799\n", - "validation: loss 0.0068, f1 0.820, precision 0.816\n", - "[epoch 9]: loss 0.0018, f1 0.861, precision 0.820\n", - "validation: loss 0.0051, f1 0.778, precision 0.764\n", - "[epoch 10]: loss 0.0023, f1 0.832, precision 0.790\n", - "validation: loss 0.0042, f1 0.808, precision 0.773\n", - "\n", - "\n", - "TRAINING transformer_best::0\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:10<00:00, 1.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 416.06it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 42.57% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 64244, 2: 37156, 0: 660, 3: 19072, 1: 6420}\n", - " training:\n", - " {-100: 270676, 0: 3484, 3: 101108, 2: 169944, 1: 27652}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8951/8951 [00:10<00:00, 840.73it/s]\n", - "100%|██████████| 8951/8951 [00:10<00:00, 887.48it/s]\n", - "/home/liza/DLC2Action_minimal/dlc2action/project/meta.py:666: FutureWarning:\n", - "\n", - "Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 2.569, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0101, f1 0.602, precision 0.567\n", - "validation: loss 0.0088, f1 0.703, precision 0.689\n", - "[epoch 2]: loss 0.0061, f1 0.752, precision 0.698\n", - "validation: loss 0.0096, f1 0.763, precision 0.727\n", - "[epoch 3]: loss 0.0048, f1 0.803, precision 0.750\n", - "validation: loss 0.2655, f1 0.731, precision 0.728\n", - "[epoch 4]: loss 0.0038, f1 0.825, precision 0.774\n", - "validation: loss 0.2466, f1 0.708, precision 0.703\n", - "[epoch 5]: loss 0.0034, f1 0.849, precision 0.801\n", - "validation: loss 0.0174, f1 0.736, precision 0.738\n", - "[epoch 6]: loss 0.0029, f1 0.877, precision 0.835\n", - "validation: loss 0.0066, f1 0.801, precision 0.758\n", - "[epoch 7]: loss 0.0027, f1 0.879, precision 0.834\n", - "validation: loss 0.0122, f1 0.755, precision 0.758\n", - "[epoch 8]: loss 0.0019, f1 0.921, precision 0.890\n", - "validation: loss 0.0148, f1 0.772, precision 0.754\n", - "[epoch 9]: loss 0.0023, f1 0.893, precision 0.853\n", - "validation: loss 0.0123, f1 0.798, precision 0.812\n", - "[epoch 10]: loss 0.0018, f1 0.933, precision 0.909\n", - "validation: loss 0.0131, f1 0.854, precision 0.852\n", - "\n", - "\n", - "TRAINING transformer_best::1\n", - "Number of samples:\n", - " validation:\n", - " {-100: 64244, 2: 37156, 0: 660, 3: 19072, 1: 6420}\n", - " training:\n", - " {-100: 270676, 0: 3484, 3: 101108, 2: 169944, 1: 27652}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8951/8951 [00:10<00:00, 880.57it/s]\n", - "100%|██████████| 8951/8951 [00:09<00:00, 982.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 2.569, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0099, f1 0.611, precision 0.573\n", - "validation: loss 0.0093, f1 0.779, precision 0.748\n", - "[epoch 2]: loss 0.0060, f1 0.766, precision 0.712\n", - "validation: loss 0.0088, f1 0.737, precision 0.690\n", - "[epoch 3]: loss 0.0045, f1 0.813, precision 0.762\n", - "validation: loss 0.0075, f1 0.816, precision 0.805\n", - "[epoch 4]: loss 0.0035, f1 0.863, precision 0.821\n", - "validation: loss 0.0065, f1 0.801, precision 0.765\n", - "[epoch 5]: loss 0.0039, f1 0.819, precision 0.766\n", - "validation: loss 0.0089, f1 0.767, precision 0.751\n", - "[epoch 6]: loss 0.0031, f1 0.860, precision 0.813\n", - "validation: loss 0.0309, f1 0.659, precision 0.660\n", - "[epoch 7]: loss 0.0029, f1 0.876, precision 0.833\n", - "validation: loss 0.0994, f1 0.695, precision 0.701\n", - "[epoch 8]: loss 0.0023, f1 0.893, precision 0.852\n", - "validation: loss 0.0738, f1 0.718, precision 0.724\n", - "[epoch 9]: loss 0.0020, f1 0.913, precision 0.880\n", - "validation: loss 0.2885, f1 0.758, precision 0.746\n", - "[epoch 10]: loss 0.0016, f1 0.937, precision 0.913\n", - "validation: loss 0.0234, f1 0.801, precision 0.776\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for model in [MODEL_NAME1, MODEL_NAME2]:\n", - " project.run_episode(\n", - " f\"{model}_best\",\n", - " load_search=f\"{model}_search\", # loading the search\n", - " force=True, # when force=True, if an episode with this name already exists it will be overwritten -> use with caution!\n", - " parameters_update={\n", - " \"general\": {\"model_name\": model} # note that you do need to set the model explicitly, it is not loaded with the search\n", - " },\n", - " n_seeds=2 # we will repeat the experiment 2 times (same parameters, different random seed) to get an estimation for how stable our results are\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've trained our best models, we can analyze the results. Note that most statistics are aggregated over the 2 runs (random seeds) automatically. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "best_episodes = [f\"{model}_best\" for model in [MODEL_NAME1, MODEL_NAME2]]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "project.plot_episodes(\n", - " best_episodes,\n", - " metrics=METRICS, \n", - " episode_labels=[\"model_1\", \"model_2\"], \n", - " add_hlines=[(0.42, \"a line\")], # we'll add a random horizontal line here but you can use this parameter to mark important thresholds\n", - " title=\"Best model training curves\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bold lines here are the means over the two runs of each episode and the transparent lines are the individual runs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also check out more metrics now." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EVALUATION c2f_tcn_best\n", - "episode c2f_tcn_best::0\n", - "Number of samples:\n", - " validation:\n", - " {-100: 168275, 2: 36710, 0: 475, 3: 19032, 1: 6420}\n", - " training:\n", - " {-100: 642109, 0: 2606, 3: 100892, 2: 168967, 1: 27506}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Setting loaded normalization statistics...\n", - "Initializing class weights:\n", - " 0.706, 0.067, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:01<00:00, 6.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "f1_0 0.776, f1_1 0.805, f1_2 0.949, f1_3 0.866, mAP 0.117, segmental_f1 0.188\n", - "\n", - "\n", - "AGGREGATED:\n", - "f1_0 0.776, f1_1 0.805, f1_2 0.949, f1_3 0.866, mAP 0.117, segmental_f1 0.188\n", - "Inference time: 0:00:01\n", - "\n", - "\n", - "EVALUATION transformer_best\n", - "episode transformer_best::0\n", - "Number of samples:\n", - " validation:\n", - " {-100: 64244, 2: 37156, 0: 660, 3: 19072, 1: 6420}\n", - " training:\n", - " {-100: 270676, 0: 3484, 3: 101108, 2: 169944, 1: 27652}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Setting loaded normalization statistics...\n", - "Initializing class weights:\n", - " 2.569, 0.324, 0.053, 0.089\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 32/32 [00:02<00:00, 10.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "f1_0 0.771, f1_1 0.848, f1_2 0.941, f1_3 0.855, mAP 0.327, segmental_f1 0.306\n", - "\n", - "\n", - "AGGREGATED:\n", - "f1_0 0.771, f1_1 0.848, f1_2 0.941, f1_3 0.855, mAP 0.327, segmental_f1 0.306\n", - "Inference time: 0:00:02\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for episode in best_episodes:\n", - " project.evaluate(\n", - " [episode],\n", - " parameters_update={\n", - " \"general\": {\"metric_functions\": [\"segmental_f1\", \"mAP\", \"f1\"]},\n", - " \"metrics\": {\n", - " \"f1\": {\"average\": \"none\"} # you can also update parameters for metrics (check project.list_basic_parameters() for options)\n", - " }\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are several ways to summarize the results." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "project.plot_episodes(\n", + " best_episodes,\n", + " metrics=METRICS, \n", + " episode_labels=[\"model_1\", \"model_2\"], \n", + " add_hlines=[(0.42, \"a line\")], # we'll add a random horizontal line here but you can use this parameter to mark important thresholds\n", + " title=\"Best model training curves\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bold lines here are the means over the two runs of each episode and the transparent lines are the individual runs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also check out more metrics now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for episode in best_episodes:\n", + " project.evaluate(\n", + " [episode],\n", + " parameters_update={\n", + " \"general\": {\"metric_functions\": [\"segmental_f1\", \"mAP\", \"f1\"]},\n", + " \"metrics\": {\n", + " \"f1\": {\"average\": \"none\"} # you can also update parameters for metrics (check project.list_basic_parameters() for options)\n", + " }\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several ways to summarize the results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RESULTS:\n", " c2f_tcn_best precision c2f_tcn_best f1 transformer_best precision \\\n", - "average 0.805574 0.828555 0.814109 \n", + "average 0.545091 0.575021 0.648036 \n", "\n", " transformer_best f1 \n", - "average 0.827372 \n", + "average 0.681451 \n", "\n", "\n" ] @@ -2602,10 +1183,10 @@ " \n", " \n", " average\n", - " 0.805574\n", - " 0.828555\n", - " 0.814109\n", - " 0.827372\n", + " 0.545091\n", + " 0.575021\n", + " 0.648036\n", + " 0.681451\n", " \n", " \n", "\n", @@ -2613,13 +1194,13 @@ ], "text/plain": [ " c2f_tcn_best precision c2f_tcn_best f1 transformer_best precision \\\n", - "average 0.805574 0.828555 0.814109 \n", + "average 0.545091 0.575021 0.648036 \n", "\n", " transformer_best f1 \n", - "average 0.827372 " + "average 0.681451 " ] }, - "execution_count": 32, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2637,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2645,15 +1226,15 @@ "output_type": "stream", "text": [ "SUMMARY ['c2f_tcn_best']\n", - "loss: mean 0.004, std 0.000\n", - "f1: mean 0.829, std 0.020\n", - "precision: mean 0.805, std 0.032\n", + "loss: mean 0.011, std 0.002\n", + "f1: mean 0.575, std 0.090\n", + "precision: mean 0.545, std 0.110\n", "\n", "\n", "SUMMARY ['transformer_best']\n", - "loss: mean 0.018, std 0.005\n", - "f1: mean 0.828, std 0.026\n", - "precision: mean 0.814, std 0.038\n", + "loss: mean 0.013, std 0.001\n", + "f1: mean 0.681, std 0.013\n", + "precision: mean 0.648, std 0.005\n", "\n", "\n" ] @@ -2682,56 +1263,24 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "TRAINING c2f_tcn_best::0\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/20 [07:09 853\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_items\u001b[39m.\u001b[39;49mpopleft()\n\u001b[1;32m 854\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mIndexError\u001b[39;00m:\n", - "\u001b[0;31mIndexError\u001b[0m: pop from an empty deque", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m project\u001b[39m.\u001b[39;49mcontinue_episode(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m{\u001b[39;49;00mMODEL_NAME1\u001b[39m}\u001b[39;49;00m\u001b[39m_best\u001b[39;49m\u001b[39m\"\u001b[39;49m, num_epochs\u001b[39m=\u001b[39;49m\u001b[39m20\u001b[39;49m, n_seeds\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m)\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/project/project.py:956\u001b[0m, in \u001b[0;36mProject.continue_episode\u001b[0;34m(self, episode_name, num_epochs, task, n_seeds, remove_saved_features, device, num_cpus)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[39mcontinue\u001b[39;00m\n\u001b[1;32m 949\u001b[0m parameters_update \u001b[39m=\u001b[39m {\n\u001b[1;32m 950\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtraining\u001b[39m\u001b[39m\"\u001b[39m: {\n\u001b[1;32m 951\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnum_epochs\u001b[39m\u001b[39m\"\u001b[39m: num_epochs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 954\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mgeneral\u001b[39m\u001b[39m\"\u001b[39m: {\u001b[39m\"\u001b[39m\u001b[39mnum_cpus\u001b[39m\u001b[39m\"\u001b[39m: num_cpus},\n\u001b[1;32m 955\u001b[0m }\n\u001b[0;32m--> 956\u001b[0m task, parameters \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_task_training(\n\u001b[1;32m 957\u001b[0m run,\n\u001b[1;32m 958\u001b[0m load_episode\u001b[39m=\u001b[39;49mrun,\n\u001b[1;32m 959\u001b[0m parameters_update\u001b[39m=\u001b[39;49mparameters_update,\n\u001b[1;32m 960\u001b[0m continuing\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 961\u001b[0m task\u001b[39m=\u001b[39;49mtask,\n\u001b[1;32m 962\u001b[0m )\n\u001b[1;32m 963\u001b[0m time_start \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[1;32m 964\u001b[0m logs \u001b[39m=\u001b[39m task\u001b[39m.\u001b[39mtrain()\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/project/project.py:428\u001b[0m, in \u001b[0;36mProject._make_task_training\u001b[0;34m(self, episode_name, load_episode, parameters_update, load_epoch, load_search, load_parameters, round_to_binary, load_strict, continuing, task, mask_name, throwaway)\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[39mif\u001b[39;00m throwaway:\n\u001b[1;32m 425\u001b[0m parameters_update \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update(\n\u001b[1;32m 426\u001b[0m parameters_update, {\u001b[39m\"\u001b[39m\u001b[39mtraining\u001b[39m\u001b[39m\"\u001b[39m: {\u001b[39m\"\u001b[39m\u001b[39mnormalize\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mFalse\u001b[39;00m, \u001b[39m\"\u001b[39m\u001b[39mdevice\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m}}\n\u001b[1;32m 427\u001b[0m )\n\u001b[0;32m--> 428\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_task(\n\u001b[1;32m 429\u001b[0m episode_name,\n\u001b[1;32m 430\u001b[0m load_episode,\n\u001b[1;32m 431\u001b[0m parameters_update,\n\u001b[1;32m 432\u001b[0m parameters_update_second,\n\u001b[1;32m 433\u001b[0m load_epoch,\n\u001b[1;32m 434\u001b[0m load_search,\n\u001b[1;32m 435\u001b[0m load_parameters,\n\u001b[1;32m 436\u001b[0m round_to_binary,\n\u001b[1;32m 437\u001b[0m purpose,\n\u001b[1;32m 438\u001b[0m task,\n\u001b[1;32m 439\u001b[0m load_strict\u001b[39m=\u001b[39;49mload_strict,\n\u001b[1;32m 440\u001b[0m )\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/project/project.py:611\u001b[0m, in \u001b[0;36mProject._make_task\u001b[0;34m(self, episode_name, load_episode, parameters_update, parameters_update_second, load_epoch, load_search, load_parameters, round_to_binary, purpose, task, load_strict, behaviors)\u001b[0m\n\u001b[1;32m 607\u001b[0m parameters \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update(\n\u001b[1;32m 608\u001b[0m parameters, {\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m: {\u001b[39m\"\u001b[39m\u001b[39mbehavior_dictionary\u001b[39m\u001b[39m\"\u001b[39m: behaviors}}\n\u001b[1;32m 609\u001b[0m )\n\u001b[1;32m 610\u001b[0m \u001b[39mif\u001b[39;00m task \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m task \u001b[39m=\u001b[39m TaskDispatcher(parameters)\n\u001b[1;32m 612\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 613\u001b[0m task\u001b[39m.\u001b[39mupdate_task(parameters)\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/task/task_dispatcher.py:60\u001b[0m, in \u001b[0;36mTaskDispatcher.__init__\u001b[0;34m(self, parameters)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblanks \u001b[39m=\u001b[39m {blank: [] \u001b[39mfor\u001b[39;00m blank \u001b[39min\u001b[39;00m options\u001b[39m.\u001b[39mblanks}\n\u001b[1;32m 59\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtask \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_initialize_task()\n\u001b[1;32m 61\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_print_behaviors()\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/task/task_dispatcher.py:628\u001b[0m, in \u001b[0;36mTaskDispatcher._initialize_task\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_initialize_task\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 624\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 625\u001b[0m \u001b[39m Create a `dlc2action.task.universal_task.Task` instance\u001b[39;00m\n\u001b[1;32m 626\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 628\u001b[0m dataset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_construct_dataset()\n\u001b[1;32m 629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_data_blanks(dataset)\n\u001b[1;32m 630\u001b[0m model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_construct_model()\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/task/task_dispatcher.py:289\u001b[0m, in \u001b[0;36mTaskDispatcher._construct_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 282\u001b[0m pars \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcomplete_dataset_parameters(\n\u001b[1;32m 283\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_parameters,\n\u001b[1;32m 284\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeneral_parameters,\n\u001b[1;32m 285\u001b[0m data_type\u001b[39m=\u001b[39mdata_type,\n\u001b[1;32m 286\u001b[0m annotation_type\u001b[39m=\u001b[39mannotation_type,\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m pars[\u001b[39m\"\u001b[39m\u001b[39mfeature_extraction_pars\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m feature_extraction_pars\n\u001b[0;32m--> 289\u001b[0m dataset \u001b[39m=\u001b[39m BehaviorDataset(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpars)\n\u001b[1;32m 291\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeneral_parameters, \u001b[39m\"\u001b[39m\u001b[39msave_dataset\u001b[39m\u001b[39m\"\u001b[39m, default\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m 292\u001b[0m save_data_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_parameters\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39msaved_data_path\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/data/dataset.py:142\u001b[0m, in \u001b[0;36mBehaviorDataset.__init__\u001b[0;34m(self, data_type, annotation_type, ssl_transformations, saved_data_path, input_store, annotation_store, only_load_annotated, recompute_annotation, ids, **data_parameters)\u001b[0m\n\u001b[1;32m 140\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\u001b[39m\"\u001b[39m\u001b[39mLoading input store from key objects failed\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 141\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m ok:\n\u001b[0;32m--> 142\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_store \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_input_store(\n\u001b[1;32m 143\u001b[0m data_type, deepcopy(data_parameters)\n\u001b[1;32m 144\u001b[0m )\n\u001b[1;32m 145\u001b[0m \u001b[39m# get the objects needed to create the annotation store (like a clip length dictionary)\u001b[39;00m\n\u001b[1;32m 146\u001b[0m annotation_objects \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_store\u001b[39m.\u001b[39mget_annotation_objects()\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/data/dataset.py:951\u001b[0m, in \u001b[0;36mBehaviorDataset._get_input_store\u001b[0;34m(self, data_type, data_parameters)\u001b[0m\n\u001b[1;32m 946\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 947\u001b[0m \u001b[39mCreate input store from parameters\u001b[39;00m\n\u001b[1;32m 948\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 950\u001b[0m data_parameters[\u001b[39m\"\u001b[39m\u001b[39mkey_objects\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 951\u001b[0m input_store \u001b[39m=\u001b[39m options\u001b[39m.\u001b[39;49minput_stores[data_type](\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdata_parameters)\n\u001b[1;32m 952\u001b[0m \u001b[39mreturn\u001b[39;00m input_store\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/data/input_store.py:199\u001b[0m, in \u001b[0;36mGeneralInputStore.__init__\u001b[0;34m(self, video_order, data_path, file_paths, data_suffix, data_prefix, feature_suffix, convert_int_indices, feature_save_path, canvas_shape, len_segment, overlap, feature_extraction, ignored_clips, ignored_bodyparts, default_agent_name, key_objects, likelihood_threshold, num_cpus, frame_limit, normalize, feature_extraction_pars, centered, transpose_features, *args, **kwargs)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[39mif\u001b[39;00m key_objects \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvideo_order \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 198\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mComputing input features...\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_load_data()\n\u001b[1;32m 200\u001b[0m \u001b[39melif\u001b[39;00m key_objects \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mload_from_key_objects(key_objects)\n", - "File \u001b[0;32m~/DLC2Action_minimal/dlc2action/data/input_store.py:1149\u001b[0m, in \u001b[0;36mFileInputStore._load_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1146\u001b[0m names, lengths, coords \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_trimmed_data(data_dict)\n\u001b[1;32m 1147\u001b[0m \u001b[39mreturn\u001b[39;00m names, lengths, coords, bp_dict, min_frames, max_frames, video_tag\n\u001b[0;32m-> 1149\u001b[0m dict_list \u001b[39m=\u001b[39m p_map(make_data_dictionary, files, num_cpus\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnum_cpus)\n\u001b[1;32m 1150\u001b[0m \u001b[39m# dict_list = tqdm([make_data_dictionary(f) for f in files])\u001b[39;00m\n\u001b[1;32m 1152\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvisibility \u001b[39m=\u001b[39m {}\n", - "File \u001b[0;32m~/miniconda3/envs/DLC2Action/lib/python3.9/site-packages/p_tqdm/__init__.py:87\u001b[0m, in \u001b[0;36mp_map\u001b[0;34m(function, *arrays, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m ordered \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 86\u001b[0m iterator \u001b[39m=\u001b[39m _parallel(ordered, function, \u001b[39m*\u001b[39marrays, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m---> 87\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39;49m(iterator)\n\u001b[1;32m 89\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/miniconda3/envs/DLC2Action/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1192\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[1;32m 1194\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[1;32m 1196\u001b[0m \u001b[39myield\u001b[39;00m obj\n\u001b[1;32m 1197\u001b[0m \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1198\u001b[0m \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/DLC2Action/lib/python3.9/site-packages/multiprocess/pool.py:858\u001b[0m, in \u001b[0;36mIMapIterator.next\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 856\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pool \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 857\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m--> 858\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_cond\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m 859\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 860\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_items\u001b[39m.\u001b[39mpopleft()\n", - "File \u001b[0;32m~/miniconda3/envs/DLC2Action/lib/python3.9/threading.py:312\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[39mtry\u001b[39;00m: \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m 311\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m 313\u001b[0m gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 314\u001b[0m \u001b[39melse\u001b[39;00m:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "project.continue_episode(f\"{MODEL_NAME1}_best\", num_epochs=20, n_seeds=3)" + "# project.continue_episode(f\"{MODEL_NAME1}_best\", num_epochs=20, n_seeds=3)\n", + "import torch\n", + "torch.cuda.is_available()" ] }, { @@ -2743,149 +1292,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/liza/DLC2Action_minimal/dlc2action/project/project.py:3703: UserWarning:\n", - "\n", - "The partitioning parameters in the loaded experiment ({'partition_method': 'file'}) are not equal to the current partitioning parameters ({'val_frac': 0.2, 'test_frac': 0, 'partition_method': 'random', 'only_load_annotated': True, 'len_segment': 512, 'overlap': 0.75}). The current parameters are replaced.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TRAINING c2f_tcn_best_lr1e-5::0\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:11<00:00, 1.73it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 486.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 4.34% of samples\n", - "Number of samples:\n", - " validation:\n", - " {-100: 168275, 2: 36710, 0: 475, 3: 19032, 1: 6420}\n", - " training:\n", - " {-100: 642109, 0: 2606, 3: 100892, 2: 168967, 1: 27506}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1840/1840 [00:06<00:00, 304.70it/s]\n", - "100%|██████████| 1840/1840 [00:03<00:00, 574.80it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.706, 0.067, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/liza/DLC2Action_minimal/dlc2action/project/meta.py:666: FutureWarning:\n", - "\n", - "Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[epoch 1]: loss 0.0009, f1 0.924, precision 0.893\n", - "validation: loss 0.0045, f1 0.840, precision 0.821\n", - "[epoch 2]: loss 0.0009, f1 0.930, precision 0.902\n", - "validation: loss 0.0048, f1 0.835, precision 0.818\n", - "[epoch 3]: loss 0.0008, f1 0.939, precision 0.913\n", - "validation: loss 0.0047, f1 0.855, precision 0.843\n", - "[epoch 4]: loss 0.0008, f1 0.936, precision 0.909\n", - "validation: loss 0.0047, f1 0.851, precision 0.837\n", - "[epoch 5]: loss 0.0007, f1 0.943, precision 0.920\n", - "validation: loss 0.0049, f1 0.857, precision 0.847\n", - "\n", - "\n", - "TRAINING c2f_tcn_best_lr1e-5::1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/liza/DLC2Action_minimal/dlc2action/project/project.py:3703: UserWarning:\n", - "\n", - "The partitioning parameters in the loaded experiment ({'partition_method': 'file'}) are not equal to the current partitioning parameters ({'val_frac': 0.2, 'test_frac': 0, 'partition_method': 'random', 'only_load_annotated': True, 'len_segment': 512, 'overlap': 0.75}). The current parameters are replaced.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.706, 0.067, 0.011, 0.018\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0020, f1 0.834, precision 0.802\n", - "validation: loss 0.0034, f1 0.848, precision 0.819\n", - "[epoch 2]: loss 0.0018, f1 0.853, precision 0.826\n", - "validation: loss 0.0032, f1 0.855, precision 0.826\n", - "[epoch 3]: loss 0.0017, f1 0.856, precision 0.824\n", - "validation: loss 0.0031, f1 0.864, precision 0.839\n", - "[epoch 4]: loss 0.0016, f1 0.867, precision 0.835\n", - "validation: loss 0.0031, f1 0.865, precision 0.839\n", - "[epoch 5]: loss 0.0015, f1 0.876, precision 0.843\n", - "validation: loss 0.0031, f1 0.871, precision 0.846\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "project.run_episode(\n", " f\"{MODEL_NAME1}_best_lr1e-5\",\n", @@ -2908,12 +1317,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2939,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2947,12 +1356,12 @@ "output_type": "stream", "text": [ "TRAINING EPISODES\n", - " general meta results\n", - " model_name training_time time f1\n", - "c2f_tcn_best::0 c2f_tcn 0:01:12 2022-11-24 09:27:52 0.848955\n", - "c2f_tcn_best::1 c2f_tcn 0:01:12 2022-11-24 09:29:11 0.808155\n", - "transformer_best::0 transformer 0:03:13 2022-11-24 09:30:56 0.853692\n", - "transformer_best::1 transformer 0:03:14 2022-11-24 09:34:30 0.801052\n", + " general meta results\n", + " model_name training_time time f1\n", + "c2f_tcn_best#0 c2f_tcn 0:06:49 2023-10-27 11:16:22 0.485448\n", + "c2f_tcn_best#1 c2f_tcn 0:06:48 2023-10-27 11:23:23 0.664595\n", + "transformer_best#0 transformer 0:07:23 2023-10-27 11:32:20 0.668324\n", + "transformer_best#1 transformer 0:07:07 2023-10-27 11:40:10 0.694578\n", "\n", "\n" ] @@ -2974,7 +1383,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -3034,10 +1443,10 @@ " \n", " \n", " c2f_tcn_search\n", - " 2022-11-24 09:24:53\n", + " 2023-10-27 11:03:58\n", " f1\n", - " {'losses/ms_tcn/alpha': 0.0007464967274536318,...\n", - " 0.552939\n", + " {'losses/ms_tcn/alpha': 1.0242787534938017e-05...\n", + " 0.410043\n", " DeepCut_resnet50_Blockcourse1May9shuffle1_1030...\n", " None\n", " None\n", @@ -3058,10 +1467,10 @@ " \n", " \n", " transformer_search\n", - " 2022-11-24 09:27:33\n", + " 2023-10-27 11:14:16\n", " f1\n", - " {'losses/ms_tcn/alpha': 0.0010662443105835437,...\n", - " 0.739550\n", + " {'losses/ms_tcn/alpha': 0.0014098664544016791,...\n", + " 0.530371\n", " DeepCut_resnet50_Blockcourse1May9shuffle1_1030...\n", " None\n", " None\n", @@ -3088,18 +1497,18 @@ "text/plain": [ " meta \\\n", " time objective \n", - "c2f_tcn_search 2022-11-24 09:24:53 f1 \n", - "transformer_search 2022-11-24 09:27:33 f1 \n", + "c2f_tcn_search 2023-10-27 11:03:58 f1 \n", + "transformer_search 2023-10-27 11:14:16 f1 \n", "\n", " results \\\n", " best_params \n", - "c2f_tcn_search {'losses/ms_tcn/alpha': 0.0007464967274536318,... \n", - "transformer_search {'losses/ms_tcn/alpha': 0.0010662443105835437,... \n", + "c2f_tcn_search {'losses/ms_tcn/alpha': 1.0242787534938017e-05... \n", + "transformer_search {'losses/ms_tcn/alpha': 0.0014098664544016791,... \n", "\n", " \\\n", " best_value \n", - "c2f_tcn_search 0.552939 \n", - "transformer_search 0.739550 \n", + "c2f_tcn_search 0.410043 \n", + "transformer_search 0.530371 \n", "\n", " data \\\n", " data_suffix \n", @@ -3134,7 +1543,7 @@ "[2 rows x 109 columns]" ] }, - "execution_count": 38, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -3162,7 +1571,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -3170,29 +1579,36 @@ "output_type": "stream", "text": [ "PREDICTION c2f_tcn_best_prediction\n", - "episode c2f_tcn_best_lr1e-5::0\n", + "episode c2f_tcn_best_lr1e-5#0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Number of samples:\n", " validation:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", + " {1: 0, 2: 0, 3: 0, 4: 0, 0: 0}\n", " training:\n", - " {-100: 810384, 2: 205677, 0: 3081, 3: 119924, 1: 33926}\n", + " {-100: 761178, 3: 210654, 2: 1434, 0: 3663, 4: 160229, 1: 49658}\n", " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", + " {1: 0, 2: 0, 3: 0, 4: 0, 0: 0}\n", "Setting loaded normalization statistics...\n", "Initializing class weights:\n", - " 0.744, 0.068, 0.011, 0.019\n", + " 0.633, 0.047, 1.616, 0.011, 0.014\n", "Behavior indices:\n", " 0: other\n", " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n" + " 2: Start/End\n", + " 3: Supported\n", + " 4: Unsupported\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 36/36 [00:26<00:00, 1.36it/s]\n" + "100%|██████████| 37/37 [02:50<00:00, 4.61s/it]\n" ] }, { @@ -3216,15 +1632,15 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "ind0: torch.Size([4, 15244])\n", - "The mean probability of Unsupported between frames 50 and 70 is 0.012615837156772614\n" + "ind0: torch.Size([5, 15242])\n", + "The mean probability of Unsupported between frames 50 and 70 is 0.10572336614131927\n" ] } ], @@ -3258,39 +1674,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing datasets...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 4.70it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "project.remove_saved_features()\n", "project.remove_extra_checkpoints()" @@ -3344,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -3362,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -3399,80 +1785,80 @@ " \n", " \n", " 0\n", - " 3713\n", - " OFT_12_OS.csv\n", - " Oliver\n", - " 2.002\n", - " 2.419\n", - " StartEnd\n", - " OFT_12\n", - " 21_02_A_190507131119DeepCut_resnet50_Blockcour...\n", + " 1029\n", + " OFT_44_Jin.csv\n", + " Jin\n", + " 4.023\n", + " 5.115\n", + " Start/End\n", + " OFT_44\n", + " 12_01_A_190507144429DeepCut_resnet50_Blockcour...\n", " \n", " \n", " 1\n", - " 3714\n", - " OFT_12_OS.csv\n", - " Oliver\n", - " 7.481\n", - " 9.856\n", + " 1030\n", + " OFT_44_Jin.csv\n", + " Jin\n", + " 8.190\n", + " 8.981\n", " Supported\n", - " OFT_12\n", - " 21_02_A_190507131119DeepCut_resnet50_Blockcour...\n", + " OFT_44\n", + " 12_01_A_190507144429DeepCut_resnet50_Blockcour...\n", " \n", " \n", " 2\n", - " 3715\n", - " OFT_12_OS.csv\n", - " Oliver\n", - " 16.940\n", - " 18.544\n", + " 1031\n", + " OFT_44_Jin.csv\n", + " Jin\n", + " 12.836\n", + " 13.690\n", " Supported\n", - " OFT_12\n", - " 21_02_A_190507131119DeepCut_resnet50_Blockcour...\n", + " OFT_44\n", + " 12_01_A_190507144429DeepCut_resnet50_Blockcour...\n", " \n", " \n", " 3\n", - " 3716\n", - " OFT_12_OS.csv\n", - " Oliver\n", - " 28.315\n", - " 29.606\n", - " Unsupported\n", - " OFT_12\n", - " 21_02_A_190507131119DeepCut_resnet50_Blockcour...\n", + " 1032\n", + " OFT_44_Jin.csv\n", + " Jin\n", + " 20.836\n", + " 22.627\n", + " Supported\n", + " OFT_44\n", + " 12_01_A_190507144429DeepCut_resnet50_Blockcour...\n", " \n", " \n", " 4\n", - " 3717\n", - " OFT_12_OS.csv\n", - " Oliver\n", - " 32.231\n", - " 33.669\n", + " 1033\n", + " OFT_44_Jin.csv\n", + " Jin\n", + " 27.231\n", + " 28.815\n", " Supported\n", - " OFT_12\n", - " 21_02_A_190507131119DeepCut_resnet50_Blockcour...\n", + " OFT_44\n", + " 12_01_A_190507144429DeepCut_resnet50_Blockcour...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Unnamed: 0 file Experimenter from to type \\\n", - "0 3713 OFT_12_OS.csv Oliver 2.002 2.419 StartEnd \n", - "1 3714 OFT_12_OS.csv Oliver 7.481 9.856 Supported \n", - "2 3715 OFT_12_OS.csv Oliver 16.940 18.544 Supported \n", - "3 3716 OFT_12_OS.csv Oliver 28.315 29.606 Unsupported \n", - "4 3717 OFT_12_OS.csv Oliver 32.231 33.669 Supported \n", + " Unnamed: 0 file Experimenter from to type ID \\\n", + "0 1029 OFT_44_Jin.csv Jin 4.023 5.115 Start/End OFT_44 \n", + "1 1030 OFT_44_Jin.csv Jin 8.190 8.981 Supported OFT_44 \n", + "2 1031 OFT_44_Jin.csv Jin 12.836 13.690 Supported OFT_44 \n", + "3 1032 OFT_44_Jin.csv Jin 20.836 22.627 Supported OFT_44 \n", + "4 1033 OFT_44_Jin.csv Jin 27.231 28.815 Supported OFT_44 \n", "\n", - " ID DLCFile \n", - "0 OFT_12 21_02_A_190507131119DeepCut_resnet50_Blockcour... \n", - "1 OFT_12 21_02_A_190507131119DeepCut_resnet50_Blockcour... \n", - "2 OFT_12 21_02_A_190507131119DeepCut_resnet50_Blockcour... \n", - "3 OFT_12 21_02_A_190507131119DeepCut_resnet50_Blockcour... \n", - "4 OFT_12 21_02_A_190507131119DeepCut_resnet50_Blockcour... " + " DLCFile \n", + "0 12_01_A_190507144429DeepCut_resnet50_Blockcour... \n", + "1 12_01_A_190507144429DeepCut_resnet50_Blockcour... \n", + "2 12_01_A_190507144429DeepCut_resnet50_Blockcour... \n", + "3 12_01_A_190507144429DeepCut_resnet50_Blockcour... \n", + "4 12_01_A_190507144429DeepCut_resnet50_Blockcour... " ] }, - "execution_count": 43, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -3494,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -3531,31 +1917,7 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "tensors used as indices must be long, byte or bool tensors", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [48], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m pr \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mones((\u001b[39m1161\u001b[39m, \u001b[39m5\u001b[39m))\n\u001b[1;32m 3\u001b[0m t_mask \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mones((\u001b[39m1161\u001b[39m))\n\u001b[0;32m----> 4\u001b[0m pr[\u001b[39mrange\u001b[39;49m(\u001b[39m1161\u001b[39;49m), t_mask]\n", - "\u001b[0;31mIndexError\u001b[0m: tensors used as indices must be long, byte or bool tensors" - ] - } - ], - "source": [ - "import torch\n", - "pr = torch.ones((1161, 5))\n", - "t_mask = torch.ones((1161))\n", - "pr[range(1161), t_mask]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3573,7 +1935,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3594,88 +1956,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TRAINING test\n", - "Computing input features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:11<00:00, 1.80it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing annotation arrays...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 6072.98it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples:\n", - " validation:\n", - " {0: 178514, 2: 36710, 3: 19032, 1: 6432, -100: 4048}\n", - " training:\n", - " {0: 665603, 3: 100892, 2: 168967, 1: 27506, -100: 18536}\n", - " test:\n", - " {1: 0, 2: 0, 3: 0, 0: 0}\n", - "Computing normalization statistics...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1917/1917 [00:02<00:00, 669.84it/s]\n", - "100%|██████████| 1917/1917 [00:02<00:00, 727.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing class weights:\n", - " 0.003, 0.07, 0.011, 0.019\n", - "Behavior indices:\n", - " 0: other\n", - " 1: Grooming\n", - " 2: Supported\n", - " 3: Unsupported\n", - "[epoch 1]: loss 0.0046, f1 0.364, precision 0.398, recall 0.529\n", - "validation: loss 0.0049, f1 0.419, precision 0.465, recall 0.530\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "project.run_episode(\"test\", force=True, parameters_update={\"training\": {\"num_epochs\": 1}})" ] @@ -3737,7 +2020,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.18" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/minimal_notebook.ipynb b/examples/minimal_notebook.ipynb index 1882847..94a6711 100644 --- a/examples/minimal_notebook.ipynb +++ b/examples/minimal_notebook.ipynb @@ -47,7 +47,8 @@ "source": [ "Downloading the data and installing the packages can take up to about 5-10 minutes.\n", "\n", - "First, let's download the data." + "First, let's download the data.
\n", + "*For Windows user*, you may need to install `wget` by downloading the .exe file [here](https://eternallybored.org/misc/wget/) (>1.10.0) and moving it to the System32 directory." ] }, { @@ -112,7 +113,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then, we need to parse the data for it to be in a format that can be read by `dlc2action` (one annotation file per video)." + "Then, we need to parse the data to make it compatible with `dlc2action` (one annotation file per video)." ] }, { @@ -144,7 +145,7 @@ "source": [ "Then we can move on to `DLC2Action`.\n", "\n", - "High-level methods in DLC2Action are almost exclusively accessed through the `dlc2action.project.Project` class. A project instance should loosely correspond to a specific goal (e.g. generating automatic annotations for dataset A with input format X). You can use it to optimize hyperparameters, run experiments, analyze results and generate new data.\n", + "High-level methods in DLC2Action are almost exclusively accessed through the `dlc2action.project.Project` class. A project instance should loosely correspond to a specific goal (e.g. generating automatic annotations for dataset A with input format X). You can use it to optimize hyperparameters, run experiments, analyze results and generate new data. On the other hand, if you want to test different models, different model parameters, augmentations or types of extracted features it is better to work in the same project instance to compare these experiments.\n", "\n", "**Best practices**\n", "- When you need to do something with a different data type or unrelated files, it's better to create a new project to keep the experiment history easy to understand.\n", @@ -258,7 +259,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are many hyperparameters in model training, like the number of layers in a model or loss coefficients. The default settings for those parameters should generate reasonable results on most datasets but in order to get the most out of our data we can run a hyperparameter search.\n", + "There are many hyperparameters in model training, like the number of layers in a model or loss coefficients. The default settings for those parameters should generate reasonable results on most datasets but in order to get the most out of our data we can run a hyperparameter search. The default model is called C2F-TCN and is a temporal convolution neural network and can also be changed while updating the parameters.\n", "\n", "The easiest way to find a good set of hyperparameters for your data is to run `project.run_default_hyperparameter_search()`." ] @@ -287,7 +288,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can train models with the best hyperparameters." + "Now we can train a model with the best hyperparameters. " ] }, { @@ -314,7 +315,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we've trained our best models, we can analyze the results." + "Now that we've trained our best models, we can analyze the results. In action segmentation tasks, the F1 score is given by the ratio of the product between precision and recall of a given class divided by their sum. Here we plot the evolution of F1 score during the model training. It can gives many indication on whether to stop the training or continue experimenting. " ] }, { @@ -365,16 +366,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When you find that you are happy with the results, you can use the model to generate predictions for new data. \n", + "When you are happy with the results, you can use the model to generate predictions for new data.\n", "\n", - "Predictions here are probabilities of each behavior being seen in each frame while suggestions are suggested intervals generated from those probabilities." + "Predictions here are given by the probabilities of each behavior being seen in each frame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's generate a prediction with one of our models and look at one of the resulting files. Note that you can use multiple models and average over their predictions." + "Let's generate a prediction using our trained model and look at one of the resulting files. Note that you can use multiple models and average over their predictions." ] }, { @@ -457,7 +458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" }, "orig_nbformat": 4, "vscode": {