IT416_Fusion_Model_Augmentation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Df58j5DDSOJJ",
        "outputId": "7eccb08c-1663-4f19-b013-ab9b91aaf0a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-Ct301PLSg7m"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tqdm.notebook import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "from IPython.display import display\n",
        "from keras.utils.vis_utils import plot_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VfIMZ4RwSnsu"
      },
      "outputs": [],
      "source": [
        "if not os.path.isfile('processed_dataset.zip'):\n",
        "  os.system(' '.join(['cp', 'drive/MyDrive/IT416/processed_dataset.zip', 'processed_dataset.zip']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "86yQ1B5DS2WE"
      },
      "outputs": [],
      "source": [
        "if not os.path.isdir('processed_dataset'):\n",
        "  os.system(' '.join(['unzip', 'processed_dataset.zip']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "zI1Kv-SoS50G"
      },
      "outputs": [],
      "source": [
        "def get_dataset(num_people, faces, gait_styles):\n",
        "  x1 = []\n",
        "  x2 = []\n",
        "  y  = []\n",
        "  for i in tqdm(range(num_people)):\n",
        "\n",
        "      for j in faces:\n",
        "          path = 'processed_dataset' + '/' + 'person' + str(i+1) + '/' + 'face' + '/' + str(j+1) + '.pgm'\n",
        "          image = cv2.imread(path, -1)\n",
        "          image = cv2.resize(image, (12, 12))\n",
        "          face = image.reshape(12, 12, 1)\n",
        "\n",
        "          gait_folder = 'processed_dataset' + '/' + 'person' + str(i+1) + '/' + 'gait' + '/'\n",
        "\n",
        "          for style in gait_styles:\n",
        "              \n",
        "              style_folder = gait_folder + style + '/'\n",
        "              angles = os.listdir(style_folder)\n",
        "              angles.sort()\n",
        "              x = []\n",
        "              for angle in angles:\n",
        "                  angle_folder = style_folder + angle + '/'\n",
        "                  images = os.listdir(angle_folder)\n",
        "                  random.shuffle(images)\n",
        "                  images = images[: 16]\n",
        "                  images.sort()\n",
        "                  xx = []\n",
        "                  for image in images:\n",
        "                      path = angle_folder + image\n",
        "                      img = cv2.imread(path, -1)\n",
        "                      img = cv2.resize(img, (32, 32))\n",
        "                      img = np.array(img, dtype=np.float16)\n",
        "                      xx.append(img)\n",
        "                  xx = np.array(xx, dtype=np.float16)\n",
        "                  x.append(xx)\n",
        "              gait = np.stack(x, axis=3)\n",
        "\n",
        "              x1.append(face)\n",
        "              x2.append(gait)\n",
        "              y.append(i)\n",
        "\n",
        "  x1 = np.array(x1, dtype = np.float32)\n",
        "  x2 = np.array(x2, dtype = np.float32)\n",
        "  y = tf.keras.utils.to_categorical(y)\n",
        "  x1, x2, y = shuffle(x1, x2, y)\n",
        "  return x1, x2, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191,
          "referenced_widgets": [
            "77ee2f8513884af9ad5eafb860f301fa",
            "a7962212fcf24118a3117e3fb58bc854",
            "64b8aac201fe4dc98d590ed8d91898f5",
            "985c17b49eba49a0915b5e3dee13d572",
            "8eec4aa07cd143aebbee034d55ad7833",
            "8d6b575060d64f7bb8f46a794ec06045",
            "a45bd30484bf403f91c4601e113bbe58",
            "1ff23eb63b73498f92ccf6675f7bb37d",
            "4fd0939001874b358daafd71e0b584e7",
            "cbb72d9f338449c29a524bce2fae8ebc",
            "9f8fa6af380846dcb2aa0267d1f1a8d6",
            "a6d81540fce1443aa1df8bf187964509",
            "da11b594adef4d4e90f855e52475cdea",
            "feb7aa3401294059af61d282d8b5a4cf",
            "395cb0ffc8094672832e39e322a4bffa",
            "2b3a212e3b7d44cf9f0479f490544ce9",
            "47d8ad7f1eae4c2cb595398ea5619367",
            "3e90d852d97c4bf4b6a36d2b76bd5bf6",
            "e4a9246eb06345d7bc2e041b48d5309f",
            "e00379b47710415c90fefd53905700d0",
            "9ba5ed7f467f4c6daea9fdb9bde0e847",
            "f13624c0ce0d4b4bb7ec1aa76330f4c1"
          ]
        },
        "id": "Y8BXapYfTNxT",
        "outputId": "f07a1a11-f6b0-4cee-e21d-2eede1771776"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77ee2f8513884af9ad5eafb860f301fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_face: (1960, 12, 12, 1)\n",
            "x_gait: (1960, 16, 32, 32, 11)\n",
            "y     : (1960, 40)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6d81540fce1443aa1df8bf187964509"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_face_val: (360, 12, 12, 1)\n",
            "x_gait_val: (360, 16, 32, 32, 11)\n",
            "y_val     : (360, 40)\n"
          ]
        }
      ],
      "source": [
        "num_people = 40\n",
        "\n",
        "faces = [0, 1, 2, 3, 4, 5, 6]\n",
        "gait_styles = ['bg-01', 'cl-01', 'nm-01', 'nm-02', 'nm-03', 'nm-04', 'nm-05' ]\n",
        "x_face, x_gait, y = get_dataset(num_people, faces, gait_styles)\n",
        "\n",
        "print('x_face:', x_face.shape)\n",
        "print('x_gait:', x_gait.shape)\n",
        "print('y     :', y.shape)\n",
        "\n",
        "faces_val = [7, 8, 9]\n",
        "gait_styles_val = ['bg-02', 'cl-02', 'nm-06']\n",
        "x_face_val, x_gait_val, y_val = get_dataset(num_people, faces_val, gait_styles_val)\n",
        "\n",
        "print('x_face_val:', x_face_val.shape)\n",
        "print('x_gait_val:', x_gait_val.shape)\n",
        "print('y_val     :', y_val.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6I7HJaJTbM-",
        "outputId": "1e3ef55d-e8d6-44a2-eb70-25336378096b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " gait_input (InputLayer)        [(None, 16, 32, 32,  0           []                               \n",
            "                                 11)]                                                             \n",
            "                                                                                                  \n",
            " gait_convlstm_1 (ConvLSTM2D)   (None, 16, 30, 30,   15616       ['gait_input[0][0]']             \n",
            "                                16)                                                               \n",
            "                                                                                                  \n",
            " gait_dropout_1 (Dropout)       (None, 16, 30, 30,   0           ['gait_convlstm_1[0][0]']        \n",
            "                                16)                                                               \n",
            "                                                                                                  \n",
            " gait_convlstm_2 (ConvLSTM2D)   (None, 16, 28, 28,   6944        ['gait_dropout_1[0][0]']         \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " face_input (InputLayer)        [(None, 12, 12, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " gait_dropout_2 (Dropout)       (None, 16, 28, 28,   0           ['gait_convlstm_2[0][0]']        \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " face_conv_1 (Conv2D)           (None, 10, 10, 32)   320         ['face_input[0][0]']             \n",
            "                                                                                                  \n",
            " gait_convlstm_3 (ConvLSTM2D)   (None, 26, 26, 1)    328         ['gait_dropout_2[0][0]']         \n",
            "                                                                                                  \n",
            " face_dropout_1 (Dropout)       (None, 10, 10, 32)   0           ['face_conv_1[0][0]']            \n",
            "                                                                                                  \n",
            " gait_dropout_3 (Dropout)       (None, 26, 26, 1)    0           ['gait_convlstm_3[0][0]']        \n",
            "                                                                                                  \n",
            " face_flatten_1 (Flatten)       (None, 3200)         0           ['face_dropout_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_flatten_1 (Flatten)       (None, 676)          0           ['gait_dropout_3[0][0]']         \n",
            "                                                                                                  \n",
            " face_dense_1 (Dense)           (None, 64)           204864      ['face_flatten_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_dense_1 (Dense)           (None, 64)           43328       ['gait_flatten_1[0][0]']         \n",
            "                                                                                                  \n",
            " face_dropout_2 (Dropout)       (None, 64)           0           ['face_dense_1[0][0]']           \n",
            "                                                                                                  \n",
            " gait_dropout_4 (Dropout)       (None, 64)           0           ['gait_dense_1[0][0]']           \n",
            "                                                                                                  \n",
            " face_norm_1 (BatchNormalizatio  (None, 64)          256         ['face_dropout_2[0][0]']         \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " gait_norm_1 (BatchNormalizatio  (None, 64)          256         ['gait_dropout_4[0][0]']         \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " face_gait_weighted_average (La  (None, 64)          0           ['face_norm_1[0][0]',            \n",
            " mbda)                                                            'gait_norm_1[0][0]']            \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 40)           2600        ['face_gait_weighted_average[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 274,512\n",
            "Trainable params: 274,256\n",
            "Non-trainable params: 256\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "face_input = tf.keras.layers.Input(shape = (12, 12, 1), name = 'face_input')\n",
        "\n",
        "face_encoder = tf.keras.layers.Conv2D(32, kernel_size = 3, activation = 'relu', name = 'face_conv_1')(face_input)\n",
        "face_encoder = tf.keras.layers.Dropout(0.2, name = 'face_dropout_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Flatten(name = 'face_flatten_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Dense(64, activation = 'relu', name = 'face_dense_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Dropout(0.2, name = 'face_dropout_2')(face_encoder)\n",
        "face_encoder = tf.keras.layers.BatchNormalization(name = 'face_norm_1')(face_encoder)\n",
        "\n",
        "gait_input = tf.keras.layers.Input(shape = (16, 32, 32, 11), name = 'gait_input')\n",
        "\n",
        "gait_encoder = tf.keras.layers.ConvLSTM2D(16, kernel_size = 3, return_sequences = True, name = 'gait_convlstm_1')(gait_input)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.ConvLSTM2D(8, kernel_size = 3, return_sequences = True, name = 'gait_convlstm_2')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_2')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.ConvLSTM2D(1, kernel_size = 3, name = 'gait_convlstm_3')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_3')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Flatten(name = 'gait_flatten_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dense(64, activation = 'relu', name = 'gait_dense_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_4')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.BatchNormalization(name = 'gait_norm_1')(gait_encoder)\n",
        "\n",
        "def weighted_average(tensors):\n",
        "  alpha = 0.5\n",
        "  face = tensors[0]\n",
        "  gait = tensors[1]\n",
        "  weighted_average = alpha * face + (1 - alpha) * gait\n",
        "  return weighted_average\n",
        "\n",
        "decoder = tf.keras.layers.Lambda(weighted_average, name = 'face_gait_weighted_average')([face_encoder, gait_encoder])\n",
        "\n",
        "decoder = tf.keras.layers.Dense(40, activation = 'softmax', name = 'output')(decoder)\n",
        "\n",
        "model = tf.keras.models.Model(\n",
        "      inputs = [face_input, gait_input], \n",
        "      outputs = decoder)\n",
        "\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(), \n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rY6ced4-Vv-_",
        "outputId": "e9ad5813-e8bb-47f8-a91f-310c05edaae8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot = plot_model(model, show_shapes = True, to_file = 'model.png', show_layer_names = True)\n",
        "display(plot)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mB-791cqUL_h",
        "outputId": "e1c90c8a-a0ca-448a-ade5-ab33e24aa7d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "123/123 [==============================] - 52s 299ms/step - loss: 3.3663 - accuracy: 0.1357 - val_loss: 3.1711 - val_accuracy: 0.3778\n",
            "Epoch 2/10\n",
            "123/123 [==============================] - 35s 286ms/step - loss: 2.6470 - accuracy: 0.4571 - val_loss: 2.5767 - val_accuracy: 0.5222\n",
            "Epoch 3/10\n",
            "123/123 [==============================] - 35s 285ms/step - loss: 1.6253 - accuracy: 0.7974 - val_loss: 1.3124 - val_accuracy: 0.8806\n",
            "Epoch 4/10\n",
            "123/123 [==============================] - 35s 282ms/step - loss: 0.8291 - accuracy: 0.9607 - val_loss: 0.7621 - val_accuracy: 0.9667\n",
            "Epoch 5/10\n",
            "123/123 [==============================] - 35s 284ms/step - loss: 0.4606 - accuracy: 0.9852 - val_loss: 0.5341 - val_accuracy: 0.9611\n",
            "Epoch 6/10\n",
            "123/123 [==============================] - 35s 282ms/step - loss: 0.2569 - accuracy: 0.9929 - val_loss: 0.3574 - val_accuracy: 0.9667\n",
            "Epoch 7/10\n",
            "123/123 [==============================] - 35s 284ms/step - loss: 0.1680 - accuracy: 0.9964 - val_loss: 0.2969 - val_accuracy: 0.9750\n",
            "Epoch 8/10\n",
            "123/123 [==============================] - 35s 282ms/step - loss: 0.1105 - accuracy: 0.9995 - val_loss: 0.2758 - val_accuracy: 0.9639\n",
            "Epoch 9/10\n",
            "123/123 [==============================] - 35s 286ms/step - loss: 0.0863 - accuracy: 0.9990 - val_loss: 0.2226 - val_accuracy: 0.9694\n",
            "Epoch 10/10\n",
            "123/123 [==============================] - 35s 284ms/step - loss: 0.0641 - accuracy: 0.9995 - val_loss: 0.2115 - val_accuracy: 0.9639\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "BATCH_SIZE = 16\n",
        "\n",
        "history = model.fit([x_face, x_gait], y, batch_size = BATCH_SIZE, epochs = EPOCHS,\n",
        "                    validation_data = ([x_face_val, x_gait_val], y_val), validation_batch_size = BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "pVB-S0I2VXd4",
        "outputId": "e27a8ff4-f633-40e4-d684-bd47d143d2c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (20, 6)\n",
        "plt.rcParams['font.size'] = 14\n",
        "\n",
        "fig, ax = plt.subplots(1, 2)\n",
        "\n",
        "ax[0].plot(history.history['accuracy'])\n",
        "ax[0].plot(history.history['val_accuracy'])\n",
        "ax[0].set_title('Model Accuracy')\n",
        "ax[0].set_ylabel('Accuracy')\n",
        "ax[0].set_xlabel('Epoch')\n",
        "ax[0].legend(['Train', 'Test'], loc='upper left')\n",
        "ax[0].grid()\n",
        "\n",
        "ax[1].plot(history.history['loss'])\n",
        "ax[1].plot(history.history['val_loss'])\n",
        "ax[1].set_title('Model Loss')\n",
        "ax[1].set_ylabel('Loss')\n",
        "ax[1].set_xlabel('Epoch')\n",
        "ax[1].legend(['Train', 'Test'], loc='upper left')\n",
        "ax[1].grid()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wdrujuYPmxKG"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "IT416_Fusion_Model_With_Augmentation_And_BatchNorm.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "77ee2f8513884af9ad5eafb860f301fa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_a7962212fcf24118a3117e3fb58bc854",
              "IPY_MODEL_64b8aac201fe4dc98d590ed8d91898f5",
              "IPY_MODEL_985c17b49eba49a0915b5e3dee13d572"
            ],
            "layout": "IPY_MODEL_8eec4aa07cd143aebbee034d55ad7833"
          }
        },
        "a7962212fcf24118a3117e3fb58bc854": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8d6b575060d64f7bb8f46a794ec06045",
            "placeholder": "​",
            "style": "IPY_MODEL_a45bd30484bf403f91c4601e113bbe58",
            "value": "100%"
          }
        },
        "64b8aac201fe4dc98d590ed8d91898f5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1ff23eb63b73498f92ccf6675f7bb37d",
            "max": 40,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_4fd0939001874b358daafd71e0b584e7",
            "value": 40
          }
        },
        "985c17b49eba49a0915b5e3dee13d572": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cbb72d9f338449c29a524bce2fae8ebc",
            "placeholder": "​",
            "style": "IPY_MODEL_9f8fa6af380846dcb2aa0267d1f1a8d6",
            "value": " 40/40 [01:22&lt;00:00,  2.04s/it]"
          }
        },
        "8eec4aa07cd143aebbee034d55ad7833": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8d6b575060d64f7bb8f46a794ec06045": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a45bd30484bf403f91c4601e113bbe58": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "1ff23eb63b73498f92ccf6675f7bb37d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4fd0939001874b358daafd71e0b584e7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cbb72d9f338449c29a524bce2fae8ebc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f8fa6af380846dcb2aa0267d1f1a8d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a6d81540fce1443aa1df8bf187964509": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_da11b594adef4d4e90f855e52475cdea",
              "IPY_MODEL_feb7aa3401294059af61d282d8b5a4cf",
              "IPY_MODEL_395cb0ffc8094672832e39e322a4bffa"
            ],
            "layout": "IPY_MODEL_2b3a212e3b7d44cf9f0479f490544ce9"
          }
        },
        "da11b594adef4d4e90f855e52475cdea": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_47d8ad7f1eae4c2cb595398ea5619367",
            "placeholder": "​",
            "style": "IPY_MODEL_3e90d852d97c4bf4b6a36d2b76bd5bf6",
            "value": "100%"
          }
        },
        "feb7aa3401294059af61d282d8b5a4cf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e4a9246eb06345d7bc2e041b48d5309f",
            "max": 40,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e00379b47710415c90fefd53905700d0",
            "value": 40
          }
        },
        "395cb0ffc8094672832e39e322a4bffa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9ba5ed7f467f4c6daea9fdb9bde0e847",
            "placeholder": "​",
            "style": "IPY_MODEL_f13624c0ce0d4b4bb7ec1aa76330f4c1",
            "value": " 40/40 [00:17&lt;00:00,  2.35it/s]"
          }
        },
        "2b3a212e3b7d44cf9f0479f490544ce9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "47d8ad7f1eae4c2cb595398ea5619367": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3e90d852d97c4bf4b6a36d2b76bd5bf6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e4a9246eb06345d7bc2e041b48d5309f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e00379b47710415c90fefd53905700d0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "9ba5ed7f467f4c6daea9fdb9bde0e847": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f13624c0ce0d4b4bb7ec1aa76330f4c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}