4_3_RESNET_101,80_20.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "4.3 RESNET-101,80:20.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VrcdtLPTZvzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04b4c77d-e8ef-4ecb-f3a9-b24b3f34a212"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting msgpack-numpy>=0.4.4.2\n",
            "  Downloading msgpack_numpy-0.4.7.1-py2.py3-none-any.whl (6.7 kB)\n",
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            "Building wheels for collected packages: filterpy\n",
            "  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=3c5a9db88a52c07c32b0036984231e5ec0dd6d84ac9e1bc9fdbe35fe0e384634\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/e0/ee/a2b3c5caab3418c1ccd8c4de573d4cbe13315d7e8b0a55fbc2\n",
            "Successfully built filterpy\n",
            "Installing collected packages: scikit-image, ruamel.yaml.clib, msgpack-numpy, tf-slim, tensorpack, statsmodels, ruamel.yaml, opencv-python-headless, imgaug, filterpy, deeplabcut\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.18.3\n",
            "    Uninstalling scikit-image-0.18.3:\n",
            "      Successfully uninstalled scikit-image-0.18.3\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "  Attempting uninstall: imgaug\n",
            "    Found existing installation: imgaug 0.2.9\n",
            "    Uninstalling imgaug-0.2.9:\n",
            "      Successfully uninstalled imgaug-0.2.9\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
            "Successfully installed deeplabcut-2.2.0.3 filterpy-1.4.5 imgaug-0.4.0 msgpack-numpy-0.4.7.1 opencv-python-headless-4.5.4.60 ruamel.yaml-0.17.19 ruamel.yaml.clib-0.2.6 scikit-image-0.18.1 statsmodels-0.13.1 tensorpack-0.11 tf-slim-1.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install deeplabcut"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 1.x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeSLdU4wm8-0",
        "outputId": "b8d1ff34-ccb7-4474-bfad-c0b3995efa78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow 1.x selected.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"DLClight\"]=\"True\""
      ],
      "metadata": {
        "id": "dqddxMKhm_uk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import deeplabcut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrlt1BJdnBFF",
        "outputId": "64cec428-5bac-4a9f-cc59-9f6a2704ed99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWfhxZhInDPp",
        "outputId": "7d0a22c0-c3de-4f25-a6ad-864312b90955"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls \"/content/drive/MyDrive\"\n",
        "ProjectFolderName = 'largewithaug-amir-2021-12-20'\n",
        "\n",
        "VideoType = 'mp4' "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPhJst5kn4ly",
        "outputId": "354f028b-1902-48e3-eced-ba8c6d6400f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " AYAMFINAL-AMIR-2021-10-07      MULTITEST1-AMIR-2021-11-03\n",
            "'belum di trained'\t        temp\n",
            " CHICKEN5-AMIR-2021-10-09      'temp 2'\n",
            "'Colab Notebooks'\t       'TRAINED BACKUP1'\n",
            "'Copy of learning_stats.csv'   'UNTRAINED BU1 AND 2'\n",
            " DMANIP\t\t\t        VIDDATASET\n",
            " largewithaug-amir-2021-12-20   VIDDATASET2\n",
            "'MESSED UP'\t\t        VIDDATASET3\n",
            " MISC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "project_path = '/content/drive/MyDrive/'+ ProjectFolderName\n",
        "videofile_path = ['/content/drive/MyDrive/'+ProjectFolderName+'/videos/']\n",
        "path_config_file = '/content/drive/MyDrive/'+ProjectFolderName+'/config.yaml'\n",
        "\n",
        "print('NEW project_path: ' + project_path)\n",
        "print('NEW path_config_file: ' + path_config_file)\n",
        "print('NEW videofile_path: ' + videofile_path[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3Rcv1SZn576",
        "outputId": "44161966-2729-4d7f-a993-4f88e5165864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NEW project_path: /content/drive/MyDrive/largewithaug-amir-2021-12-20\n",
            "NEW path_config_file: /content/drive/MyDrive/largewithaug-amir-2021-12-20/config.yaml\n",
            "NEW videofile_path: /content/drive/MyDrive/largewithaug-amir-2021-12-20/videos/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHANGE EVERYTIME U START A NEW PROJECT\n",
        "!ls '/content/drive/MyDrive/largewithaug-amir-2021-12-20'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTck0T4poKKd",
        "outputId": "d6474f73-09a1-428f-bc31-7b86b39b8593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "config.yaml  evaluation-results  training-datasets\n",
            "dlc-models   labeled-data\t videos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.create_training_dataset(path_config_file, net_type='resnet_101', augmenter_type='imgaug')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eZ7b47coNDx",
        "outputId": "281b4efc-c54a-46cd-c172-2f0614281a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It appears that the images were labeled on a Windows system, but you are currently trying to create a training set on a Unix system. \n",
            " In this case the paths should be converted. Do you want to proceed with the conversion?\n",
            "yes/noyes\n",
            "Annotation data converted to unix format...\n",
            "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.8,\n",
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              "          214,   0,  31, 191,  41,  78,  48, 132, 231,  95,  27,  44,  21])))]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HBdr0vMhoaLQ",
        "outputId": "6067a5be-2a86-48e4-cc28-f52b41d38ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'clahe': True,\n",
            " 'claheratio': 0.1,\n",
            " 'crop_pad': 0,\n",
            " 'crop_sampling': 'hybrid',\n",
            " 'crop_size': [400, 400],\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'edge': False,\n",
            " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'histeq': True,\n",
            " 'histeqratio': 0.1,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'max_shift': 0.4,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.005, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'pre_resize': [],\n",
            " 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'sharpen': False,\n",
            " 'sharpenratio': 0.3,\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selecting single-animal trainer\n",
            "Batch Size is 1\n",
            "Loading ImageNet-pretrained resnet_101\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 100\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_101', 'num_joints': 5, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "iteration: 100 loss: 0.0907 lr: 0.005\n",
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            "Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n",
            "\n",
            "Unfortunately, your original traceback can not be constructed.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-11-8ef3aacc5628>\", line 1, in <module>\n",
            "    deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 187, in train_network\n",
            "    raise e\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 183, in train_network\n",
            "    allow_growth=allow_growth,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 294, in train\n",
            "    saver.save(sess, model_name, global_step=it)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py\", line 1203, in save\n",
            "    save_debug_info=save_debug_info)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py\", line 1246, in export_meta_graph\n",
            "    graph_def=ops.get_default_graph().as_graph_def(add_shapes=True),\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\", line 3238, in as_graph_def\n",
            "    result, _ = self._as_graph_def(from_version, add_shapes)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\", line 3163, in _as_graph_def\n",
            "    c_api.TF_GraphToGraphDef(self._c_graph, buf)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 733, in getmodule\n",
            "    if ismodule(module) and hasattr(module, '__file__'):\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow/__init__.py\", line 50, in __getattr__\n",
            "    module = self._load()\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow/__init__.py\", line 44, in _load\n",
            "    module = _importlib.import_module(self.__name__)\n",
            "  File \"/usr/lib/python3.7/importlib/__init__.py\", line 127, in import_module\n",
            "    return _bootstrap._gcd_import(name[level:], package, level)\n",
            "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
            "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
            "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
            "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
            "  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\n",
            "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/__init__.py\", line 47, in <module>\n",
            "    from tensorflow.contrib import distributions\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/__init__.py\", line 29, in <module>\n",
            "    from tensorflow.contrib.distributions.python.ops import bijectors\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/__init__.py\", line 44, in <module>\n",
            "    from tensorflow.contrib.distributions.python.ops.estimator import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/python/ops/estimator.py\", line 21, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators.head import _compute_weighted_loss\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/__init__.py\", line 93, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/__init__.py\", line 28, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/__init__.py\", line 30, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import estimators\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/__init__.py\", line 302, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/dnn.py\", line 34, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/dnn_linear_combined.py\", line 36, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators import estimator\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/estimator.py\", line 52, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.learn_io import data_feeder\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/learn_io/__init__.py\", line 26, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.learn_io.dask_io import extract_dask_data\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/learn_io/dask_io.py\", line 33, in <module>\n",
            "    import dask.dataframe as dd\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/dataframe/__init__.py\", line 13, in <module>\n",
            "    from .io import (\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/dataframe/io/__init__.py\", line 13, in <module>\n",
            "    from .csv import read_csv, to_csv, read_table, read_fwf\n",
            "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
            "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
            "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
            "  File \"<frozen importlib._bootstrap_external>\", line 724, in exec_module\n",
            "  File \"<frozen importlib._bootstrap_external>\", line 818, in get_code\n",
            "  File \"<frozen importlib._bootstrap_external>\", line 917, in get_data\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1xVr30sq0SIm",
        "outputId": "889f852d-f0f5-45ac-af41-70a5caa88315"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'clahe': True,\n",
            " 'claheratio': 0.1,\n",
            " 'crop_pad': 0,\n",
            " 'crop_sampling': 'hybrid',\n",
            " 'crop_size': [400, 400],\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'edge': False,\n",
            " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'histeq': True,\n",
            " 'histeqratio': 0.1,\n",
            " 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-110200',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'max_shift': 0.4,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.02, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'pre_resize': [],\n",
            " 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'sharpen': False,\n",
            " 'sharpenratio': 0.3,\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selecting single-animal trainer\n",
            "Batch Size is 1\n",
            "Loading already trained DLC with backbone: resnet_101\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 100\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-110200', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.02, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_101', 'num_joints': 5, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-8ef3aacc5628>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_config_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplayiters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaveiters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\u001b[0m in \u001b[0;36mtrain_network\u001b[0;34m(config, shuffle, trainingsetindex, max_snapshots_to_keep, displayiters, saveiters, maxiters, allow_growth, gputouse, autotune, keepdeconvweights, modelprefix)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\u001b[0m in \u001b[0;36mtrain_network\u001b[0;34m(config, shuffle, trainingsetindex, max_snapshots_to_keep, displayiters, saveiters, maxiters, allow_growth, gputouse, autotune, keepdeconvweights, modelprefix)\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0mmax_to_keep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_snapshots_to_keep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mkeepdeconvweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeepdeconvweights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m                 \u001b[0mallow_growth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_growth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m             )  # pass on path and file name for pose_cfg.yaml!\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(config_yaml, displayiters, saveiters, maxiters, max_to_keep, keepdeconvweights, allow_growth)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         [_, loss_val, summary] = sess.run(\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;34m[\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmerged_summaries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m         )\n\u001b[1;32m    277\u001b[0m         \u001b[0mcum_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss_val\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1180\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1181\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1357\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1359\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1360\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1363\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1366\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1350\u001b[0;31m                                       target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1441\u001b[0m     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m   1442\u001b[0m                                             \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m                                             run_metadata)\n\u001b[0m\u001b[1;32m   1444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1445\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**FROM 214 800 TO 300K TRAINING ITE**"
      ],
      "metadata": {
        "id": "lgrlQfxeMirU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Tuu4kNTZNFWD",
        "outputId": "83829a8c-cf72-48bf-a3d5-43b798dfc55e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting single-animal trainer\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'clahe': True,\n",
            " 'claheratio': 0.1,\n",
            " 'crop_pad': 0,\n",
            " 'crop_sampling': 'hybrid',\n",
            " 'crop_size': [400, 400],\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'edge': False,\n",
            " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'histeq': True,\n",
            " 'histeqratio': 0.1,\n",
            " 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-214800',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'max_shift': 0.4,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.02, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'pre_resize': [],\n",
            " 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'sharpen': False,\n",
            " 'sharpenratio': 0.3,\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Size is 1\n",
            "Loading already trained DLC with backbone: resnet_101\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 100\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-214800', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.02, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_101', 'num_joints': 5, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "iteration: 214900 loss: 0.0035 lr: 0.02\n",
            "iteration: 215000 loss: 0.0031 lr: 0.02\n",
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            "iteration: 300300 loss: 0.0028 lr: 0.02\n",
            "NumExpr defaulting to 2 threads.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-8ef3aacc5628>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_config_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplayiters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaveiters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\u001b[0m in \u001b[0;36mtrain_network\u001b[0;34m(config, shuffle, trainingsetindex, max_snapshots_to_keep, displayiters, saveiters, maxiters, allow_growth, gputouse, autotune, keepdeconvweights, modelprefix)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\u001b[0m in \u001b[0;36mtrain_network\u001b[0;34m(config, shuffle, trainingsetindex, max_snapshots_to_keep, displayiters, saveiters, maxiters, allow_growth, gputouse, autotune, keepdeconvweights, modelprefix)\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0mmax_to_keep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_snapshots_to_keep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mkeepdeconvweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeepdeconvweights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m                 \u001b[0mallow_growth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_growth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m             )  # pass on path and file name for pose_cfg.yaml!\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(config_yaml, displayiters, saveiters, maxiters, max_to_keep, keepdeconvweights, allow_growth)\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msave_iters\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mstart_iter\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"snapshot_prefix\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m             \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0mlrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, sess, save_path, global_step, latest_filename, meta_graph_suffix, write_meta_graph, write_state, strip_default_attrs, save_debug_info)\u001b[0m\n\u001b[1;32m   1174\u001b[0m           model_checkpoint_path = sess.run(\n\u001b[1;32m   1175\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_tensor_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m               {self.saver_def.filename_tensor_name: checkpoint_file})\n\u001b[0m\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[0mmodel_checkpoint_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_checkpoint_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1180\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1181\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1357\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1359\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1360\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1363\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1366\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1350\u001b[0;31m                                       target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1441\u001b[0m     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m   1442\u001b[0m                                             \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m                                             run_metadata)\n\u001b[0m\u001b[1;32m   1444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1445\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EVAL AT 100K, 200K, 300K**"
      ],
      "metadata": {
        "id": "EN7gXinLoi4t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#TAKEN FOR 200K\n",
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "aepNA-a_8l5l",
        "outputId": "816e0002-00f9-48ac-ffbf-0fd3e40e627e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet101_largewithaugDec20shuffle1_214800  with # of training iterations: 214800\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:33,  7.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-214800\n",
            "Results for 214800  training iterations: 80 1 train error: 3.23 pixels. Test error: 23.44  pixels.\n",
            "With pcutoff of 0.6  train error: 3.22 pixels. Test error: 11.62 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#100K\n",
        "matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "wHWc1uHqAgjF",
        "outputId": "183930dc-2d26-4ae4-e8cd-5b0f8f665085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet101_largewithaugDec20shuffle1_100000  with # of training iterations: 100000\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:32,  7.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-100000\n",
            "Results for 100000  training iterations: 80 1 train error: 7.96 pixels. Test error: 22.84  pixels.\n",
            "With pcutoff of 0.6  train error: 7.56 pixels. Test error: 16.77 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#resnet101 at 300,300\n",
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aPAZpfYCaYk",
        "outputId": "bd5fb177-3dbc-4e3e-ad02-263319ff2a45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet101_largewithaugDec20shuffle1_300300  with # of training iterations: 300300\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:55,  4.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-300300\n",
            "Results for 300300  training iterations: 80 1 train error: 2.92 pixels. Test error: 16.12  pixels.\n",
            "With pcutoff of 0.6  train error: 2.92 pixels. Test error: 10.52 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#resenet101 at 300,900\n",
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-rE48t2Dr3n",
        "outputId": "954e0a5b-06c0-4bd9-a950-dd98d0bacdce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_101.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_101',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet101_largewithaugDec20shuffle1_300000  with # of training iterations: 300000\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:30,  8.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-300000\n",
            "Results for 300000  training iterations: 80 1 train error: 3.0 pixels. Test error: 20.88  pixels.\n",
            "With pcutoff of 0.6  train error: 3.0 pixels. Test error: 10.89 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        }
      ]
    }
  ]
}