119. TransformerUnet_brain_tumor .ipynb

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      "name": "python3",
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      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "KTk2Ukmc5w-F",
        "outputId": "6369061a-fc5d-43a6-bf92-5821ab029fc2"
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      "outputs": [
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          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "!pip install keras-unet-collection==0.1.13"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown --id 11hcgbRcs_9mV1QkiDq8Eb6FKENQClI2f\n",
        "!pip install patool\n",
        "import patoolib\n",
        "patoolib.extract_archive('archive.zip')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
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        "id": "Yiifv3Lz534T",
        "outputId": "64174496-ba67-4b66-af3e-540c8d1d6fd5"
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            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
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            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n",
            "patool: Extracting archive.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack__ofvp4yc -- archive.zip\n",
            "patool: ... archive.zip extracted to `archive' (multiple files in root).\n"
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        },
        {
          "output_type": "execute_result",
          "data": {
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              "'archive'"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
        "\n",
        "import numpy as np\n",
        "import cv2\n",
        "from glob import glob\n",
        "from sklearn.utils import shuffle\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.metrics import Recall, Precision\n",
        "\n",
        "\n",
        "\"\"\" Global parameters \"\"\"\n",
        "H = 128\n",
        "W = 128\n",
        "\n",
        "def create_dir(path):\n",
        "    if not os.path.exists(path):\n",
        "        os.makedirs(path)\n",
        "\n",
        "\n",
        "def load_dataset(path, split=0.2):\n",
        "    images = sorted(glob(os.path.join(path, \"images\", \"*.png\")))\n",
        "    masks = sorted(glob(os.path.join(path, \"masks\", \"*.png\")))\n",
        "\n",
        "    split_size = int(len(images) * split)\n",
        "\n",
        "    train_x, valid_x = train_test_split(images, test_size=split_size, random_state=42)\n",
        "    train_y, valid_y = train_test_split(masks, test_size=split_size, random_state=42)\n",
        "\n",
        "    train_x, test_x = train_test_split(train_x, test_size=split_size, random_state=42)\n",
        "    train_y, test_y = train_test_split(train_y, test_size=split_size, random_state=42)\n",
        "\n",
        "    return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)\n",
        "def read_image(path):\n",
        "    path = path.decode()\n",
        "    x = cv2.imread(path, cv2.IMREAD_COLOR)\n",
        "    x = cv2.resize(x, (W, H))\n",
        "    x = x / 255.0\n",
        "    x = x.astype(np.float32)\n",
        "    return x\n",
        "\n",
        "def read_mask(path):\n",
        "    path = path.decode()\n",
        "    x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)  ## (h, w)\n",
        "    x = cv2.resize(x, (W, H))   ## (h, w)\n",
        "    x = x / 255.0               ## (h, w)\n",
        "    x = x.astype(np.float32)    ## (h, w)\n",
        "    x = np.expand_dims(x, axis=-1)## (h, w, 1)\n",
        "    return x\n",
        "\n",
        "def tf_parse(x, y):\n",
        "    def _parse(x, y):\n",
        "        x = read_image(x)\n",
        "        y = read_mask(y)\n",
        "        return x, y\n",
        "\n",
        "    x, y = tf.numpy_function(_parse, [x, y], [tf.float32, tf.float32])\n",
        "    x.set_shape([H, W, 3])\n",
        "    y.set_shape([H, W, 1])\n",
        "    return x, y\n",
        "\n",
        "def tf_dataset(X, Y, batch=2):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((X, Y))\n",
        "    dataset = dataset.map(tf_parse)\n",
        "    dataset = dataset.batch(batch)\n",
        "    dataset = dataset.prefetch(10)\n",
        "    return dataset\n",
        "     "
      ],
      "metadata": {
        "id": "EW1zPkBp56Z2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.layers import Reshape,Input\n",
        "from tensorflow import float32\n",
        "\n",
        "\n",
        "def iou(y_true, y_pred):\n",
        "    def f(y_true, y_pred):\n",
        "        intersection = (y_true * y_pred).sum()\n",
        "        union = y_true.sum() + y_pred.sum() - intersection\n",
        "        x = (intersection + 1e-15) / (union + 1e-15)\n",
        "        x = x.astype(np.float32)\n",
        "        return x\n",
        "    return tf.numpy_function(f, [y_true, y_pred], tf.float32)\n",
        "\n",
        "smooth = 1e-15\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    y_pred = tf.keras.layers.Flatten()(y_pred)\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "    return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)\n",
        "\n",
        "def dice_loss(y_true, y_pred):\n",
        "    return 1.0 - dice_coef(y_true, y_pred)"
      ],
      "metadata": {
        "id": "Xej3u0Gj59w6"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras_unet_collection import models\n",
        "help(models.transunet_2d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZIU_buZ0jv3",
        "outputId": "29bc3a78-8297-415b-df6b-1a7eccba3db5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function transunet_2d in module keras_unet_collection._model_transunet_2d:\n",
            "\n",
            "transunet_2d(input_size, filter_num, n_labels, stack_num_down=2, stack_num_up=2, embed_dim=768, num_mlp=3072, num_heads=12, num_transformer=12, activation='ReLU', mlp_activation='GELU', output_activation='Softmax', batch_norm=False, pool=True, unpool=True, backbone=None, weights='imagenet', freeze_backbone=True, freeze_batch_norm=True, name='transunet')\n",
            "    TransUNET with an optional ImageNet-trained bakcbone.\n",
            "    \n",
            "    \n",
            "    ----------\n",
            "    Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L. and Zhou, Y., 2021. \n",
            "    Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.\n",
            "    \n",
            "    Input\n",
            "    ----------\n",
            "        input_size: the size/shape of network input, e.g., `(128, 128, 3)`.\n",
            "        filter_num: a list that defines the number of filters for each                     down- and upsampling levels. e.g., `[64, 128, 256, 512]`.\n",
            "                    The depth is expected as `len(filter_num)`.\n",
            "        n_labels: number of output labels.\n",
            "        stack_num_down: number of convolutional layers per downsampling level/block. \n",
            "        stack_num_up: number of convolutional layers (after concatenation) per upsampling level/block.\n",
            "        activation: one of the `tensorflow.keras.layers` or `keras_unet_collection.activations` interfaces, e.g., 'ReLU'.\n",
            "        output_activation: one of the `tensorflow.keras.layers` or `keras_unet_collection.activations` interface or 'Sigmoid'.\n",
            "                           Default option is 'Softmax'.\n",
            "                           if None is received, then linear activation is applied.\n",
            "        batch_norm: True for batch normalization.\n",
            "        pool: True or 'max' for MaxPooling2D.\n",
            "              'ave' for AveragePooling2D.\n",
            "              False for strided conv + batch norm + activation.\n",
            "        unpool: True or 'bilinear' for Upsampling2D with bilinear interpolation.\n",
            "                'nearest' for Upsampling2D with nearest interpolation.\n",
            "                False for Conv2DTranspose + batch norm + activation.                 \n",
            "        name: prefix of the created keras model and its layers.\n",
            "        \n",
            "        ---------- (keywords of ViT) ----------\n",
            "        embed_dim: number of embedded dimensions.\n",
            "        num_mlp: number of MLP nodes.\n",
            "        num_heads: number of attention heads.\n",
            "        num_transformer: number of stacked ViTs.\n",
            "        mlp_activation: activation of MLP nodes.\n",
            "        \n",
            "        ---------- (keywords of backbone options) ----------\n",
            "        backbone_name: the bakcbone model name. Should be one of the `tensorflow.keras.applications` class.\n",
            "                       None (default) means no backbone. \n",
            "                       Currently supported backbones are:\n",
            "                       (1) VGG16, VGG19\n",
            "                       (2) ResNet50, ResNet101, ResNet152\n",
            "                       (3) ResNet50V2, ResNet101V2, ResNet152V2\n",
            "                       (4) DenseNet121, DenseNet169, DenseNet201\n",
            "                       (5) EfficientNetB[0-7]\n",
            "        weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), \n",
            "                 or the path to the weights file to be loaded.\n",
            "        freeze_backbone: True for a frozen backbone.\n",
            "        freeze_batch_norm: False for not freezing batch normalization layers.\n",
            "        \n",
            "    Output\n",
            "    ----------\n",
            "        model: a keras model.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    \"\"\" Seeding \"\"\"\n",
        "    np.random.seed(42)\n",
        "    tf.random.set_seed(42)\n",
        "\n",
        "    \"\"\" Directory for storing files \"\"\"\n",
        "    create_dir(\"files\")\n",
        "\n",
        "    \"\"\" Hyperparameters \"\"\"\n",
        "    batch_size = 16\n",
        "    lr = 1e-3\n",
        "    num_epochs = 30\n",
        "    model_path = os.path.join(\"files\", \"trans_Unet_model.h5\")\n",
        "    csv_path = os.path.join(\"files\", \"trans_UNET_data.csv\")\n",
        "\n",
        "    \"\"\" Dataset \"\"\"\n",
        "    dataset_path = \"/content/archive\"\n",
        "    (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)\n",
        "\n",
        "    print(f\"Train: {len(train_x)} - {len(train_y)}\")\n",
        "    print(f\"Valid: {len(valid_x)} - {len(valid_y)}\")\n",
        "    print(f\"Test : {len(test_x)} - {len(test_y)}\")\n",
        "   \n",
        "\n",
        "    train_dataset = tf_dataset(train_x, train_y, batch=batch_size)\n",
        "    valid_dataset = tf_dataset(valid_x, valid_y, batch=batch_size)\n",
        "    model_tran_unet = models.transunet_2d((128, 128, 3), filter_num=[64, 128, 256, 512, 1024],\n",
        "                         n_labels=1, stack_num_down=2,\n",
        "                         stack_num_up=2,\n",
        "                         embed_dim=768, num_mlp = 3072, num_heads=12, num_transformer=12,\n",
        "                         activation='ReLU', mlp_activation='GELU', output_activation='Sigmoid', batch_norm=True, pool=False, unpool=False, \n",
        "                         backbone='VGG19', weights='imagenet', freeze_backbone=True, freeze_batch_norm=True, name='transunet')\n",
        "\n",
        "    model_tran_unet.compile(loss=dice_loss, optimizer=Adam(lr =lr),   #dice_loss can be used instead of binary_crossentropy\n",
        "                         metrics=['accuracy', dice_coef,iou])\n",
        "    print(model_tran_unet.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPx_sFX75_53",
        "outputId": "570d80b1-9b37-4e0a-8a53-5d1a3955f8bc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 1840 - 1840\n",
            "Valid: 612 - 612\n",
            "Test : 612 - 612\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80134624/80134624 [==============================] - 2s 0us/step\n",
            "Model: \"transunet_model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " VGG19_backbone (Functional)    [(None, 128, 128, 6  20024384    ['input_1[0][0]']                \n",
            "                                4),                                                               \n",
            "                                 (None, 64, 64, 128                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 32, 32, 256                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 16, 16, 512                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, 8, 512)]                                               \n",
            "                                                                                                  \n",
            " transunet_conv_trans_before (C  (None, 8, 8, 1024)  524288      ['VGG19_backbone[0][4]']         \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " patch_extract (patch_extract)  (None, 64, 1024)     0           ['transunet_conv_trans_before[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " patch_embedding (patch_embeddi  (None, 64, 768)     836352      ['patch_extract[0][0]']          \n",
            " ng)                                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_0_layer_norm_1 (  (None, 64, 768)     1536        ['patch_embedding[0][0]']        \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_0_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_0_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_0_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_0_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_0_atten[0][0]',  \n",
            "                                                                  'patch_embedding[0][0]']        \n",
            "                                                                                                  \n",
            " transunet_ViT_0_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_0_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_0_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_0_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_0_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_0_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_0_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_0_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_0_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_0_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_0_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_0_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_0_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_1_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_0_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_1_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_1_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_1_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_1_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_1_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_0_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_1_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_1_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_1_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_1_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_1_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_1_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_1_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_1_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_1_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_1_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_1_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_1_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_1_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_2_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_1_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_2_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_2_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_2_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_2_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_2_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_1_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_2_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_2_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_2_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_2_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_2_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_2_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_2_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_2_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_2_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_2_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_2_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_2_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_2_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_3_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_2_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_3_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_3_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_3_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_3_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_3_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_2_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_3_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_3_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_3_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_3_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_3_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_3_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_3_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_3_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_3_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_3_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_3_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_3_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_3_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_4_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_3_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_4_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_4_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_4_layer_norm_1[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_4_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_4_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_3_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_4_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_4_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_4_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_4_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_4_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_4_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_4_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_4_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_4_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_4_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_4_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_4_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_4_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_5_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_4_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_5_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_5_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_5_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_5_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_5_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_4_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_5_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_5_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_5_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_5_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_5_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_5_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_5_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_5_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_5_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_5_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_5_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_5_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_5_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_6_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_5_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_6_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_6_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_6_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_6_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_6_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_5_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_6_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_6_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_6_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_6_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_6_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_6_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_6_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_6_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_6_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_6_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_6_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_6_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_6_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_7_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_6_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_7_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_7_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_7_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_7_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_7_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_6_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_7_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_7_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_7_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_7_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_7_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_7_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_7_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_7_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_7_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_7_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_7_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_7_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_7_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_8_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_7_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_8_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_8_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_8_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_8_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_8_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_7_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_8_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_8_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_8_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_8_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_8_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_8_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_8_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_8_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_8_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_8_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_8_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_8_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_8_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_9_layer_norm_1 (  (None, 64, 768)     1536        ['transunet_ViT_8_skip_2[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_9_atten (MultiHe  (None, 64, 768)     28339968    ['transunet_ViT_9_layer_norm_1[0]\n",
            " adAttention)                                                    [0]',                            \n",
            "                                                                  'transunet_ViT_9_layer_norm_1[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_9_skip_1 (Add)   (None, 64, 768)      0           ['transunet_ViT_9_atten[0][0]',  \n",
            "                                                                  'transunet_ViT_8_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_9_layer_norm_2 (  (None, 64, 768)     1536        ['transunet_ViT_9_skip_1[0][0]'] \n",
            " LayerNormalization)                                                                              \n",
            "                                                                                                  \n",
            " transunet_ViT_9_mlp_dense_0 (D  (None, 64, 3072)    2362368     ['transunet_ViT_9_layer_norm_2[0]\n",
            " ense)                                                           [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_9_mlp_activation  (None, 64, 3072)    0           ['transunet_ViT_9_mlp_dense_0[0][\n",
            " _0 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_9_mlp_dense_1 (D  (None, 64, 768)     2360064     ['transunet_ViT_9_mlp_activation_\n",
            " ense)                                                           0[0][0]']                        \n",
            "                                                                                                  \n",
            " transunet_ViT_9_mlp_activation  (None, 64, 768)     0           ['transunet_ViT_9_mlp_dense_1[0][\n",
            " _1 (GELU)                                                       0]']                             \n",
            "                                                                                                  \n",
            " transunet_ViT_9_skip_2 (Add)   (None, 64, 768)      0           ['transunet_ViT_9_mlp_activation_\n",
            "                                                                 1[0][0]',                        \n",
            "                                                                  'transunet_ViT_9_skip_1[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_10_layer_norm_1   (None, 64, 768)     1536        ['transunet_ViT_9_skip_2[0][0]'] \n",
            " (LayerNormalization)                                                                             \n",
            "                                                                                                  \n",
            " transunet_ViT_10_atten (MultiH  (None, 64, 768)     28339968    ['transunet_ViT_10_layer_norm_1[0\n",
            " eadAttention)                                                   ][0]',                           \n",
            "                                                                  'transunet_ViT_10_layer_norm_1[0\n",
            "                                                                 ][0]']                           \n",
            "                                                                                                  \n",
            " transunet_ViT_10_skip_1 (Add)  (None, 64, 768)      0           ['transunet_ViT_10_atten[0][0]', \n",
            "                                                                  'transunet_ViT_9_skip_2[0][0]'] \n",
            "                                                                                                  \n",
            " transunet_ViT_10_layer_norm_2   (None, 64, 768)     1536        ['transunet_ViT_10_skip_1[0][0]']\n",
            " (LayerNormalization)                                                                             \n",
            "                                                                                                  \n",
            " transunet_ViT_10_mlp_dense_0 (  (None, 64, 3072)    2362368     ['transunet_ViT_10_layer_norm_2[0\n",
            " Dense)                                                          ][0]']                           \n",
            "                                                                                                  \n",
            " transunet_ViT_10_mlp_activatio  (None, 64, 3072)    0           ['transunet_ViT_10_mlp_dense_0[0]\n",
            " n_0 (GELU)                                                      [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_10_mlp_dense_1 (  (None, 64, 768)     2360064     ['transunet_ViT_10_mlp_activation\n",
            " Dense)                                                          _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_ViT_10_mlp_activatio  (None, 64, 768)     0           ['transunet_ViT_10_mlp_dense_1[0]\n",
            " n_1 (GELU)                                                      [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_10_skip_2 (Add)  (None, 64, 768)      0           ['transunet_ViT_10_mlp_activation\n",
            "                                                                 _1[0][0]',                       \n",
            "                                                                  'transunet_ViT_10_skip_1[0][0]']\n",
            "                                                                                                  \n",
            " transunet_ViT_11_layer_norm_1   (None, 64, 768)     1536        ['transunet_ViT_10_skip_2[0][0]']\n",
            " (LayerNormalization)                                                                             \n",
            "                                                                                                  \n",
            " transunet_ViT_11_atten (MultiH  (None, 64, 768)     28339968    ['transunet_ViT_11_layer_norm_1[0\n",
            " eadAttention)                                                   ][0]',                           \n",
            "                                                                  'transunet_ViT_11_layer_norm_1[0\n",
            "                                                                 ][0]']                           \n",
            "                                                                                                  \n",
            " transunet_ViT_11_skip_1 (Add)  (None, 64, 768)      0           ['transunet_ViT_11_atten[0][0]', \n",
            "                                                                  'transunet_ViT_10_skip_2[0][0]']\n",
            "                                                                                                  \n",
            " transunet_ViT_11_layer_norm_2   (None, 64, 768)     1536        ['transunet_ViT_11_skip_1[0][0]']\n",
            " (LayerNormalization)                                                                             \n",
            "                                                                                                  \n",
            " transunet_ViT_11_mlp_dense_0 (  (None, 64, 3072)    2362368     ['transunet_ViT_11_layer_norm_2[0\n",
            " Dense)                                                          ][0]']                           \n",
            "                                                                                                  \n",
            " transunet_ViT_11_mlp_activatio  (None, 64, 3072)    0           ['transunet_ViT_11_mlp_dense_0[0]\n",
            " n_0 (GELU)                                                      [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_11_mlp_dense_1 (  (None, 64, 768)     2360064     ['transunet_ViT_11_mlp_activation\n",
            " Dense)                                                          _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_ViT_11_mlp_activatio  (None, 64, 768)     0           ['transunet_ViT_11_mlp_dense_1[0]\n",
            " n_1 (GELU)                                                      [0]']                            \n",
            "                                                                                                  \n",
            " transunet_ViT_11_skip_2 (Add)  (None, 64, 768)      0           ['transunet_ViT_11_mlp_activation\n",
            "                                                                 _1[0][0]',                       \n",
            "                                                                  'transunet_ViT_11_skip_1[0][0]']\n",
            "                                                                                                  \n",
            " tf.reshape (TFOpLambda)        (None, 8, 8, 768)    0           ['transunet_ViT_11_skip_2[0][0]']\n",
            "                                                                                                  \n",
            " transunet_conv_trans_after (Co  (None, 8, 8, 1024)  786432      ['tf.reshape[0][0]']             \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " transunet_up0_decode_trans_con  (None, 16, 16, 512)  4719104    ['transunet_conv_trans_after[0][0\n",
            " v (Conv2DTranspose)                                             ]']                              \n",
            "                                                                                                  \n",
            " transunet_up0_decode_bn (Batch  (None, 16, 16, 512)  2048       ['transunet_up0_decode_trans_conv\n",
            " Normalization)                                                  [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up0_decode_activatio  (None, 16, 16, 512)  0          ['transunet_up0_decode_bn[0][0]']\n",
            " n (ReLU)                                                                                         \n",
            "                                                                                                  \n",
            " transunet_up0_conv_before_conc  (None, 16, 16, 512)  2359296    ['transunet_up0_decode_activation\n",
            " at_0 (Conv2D)                                                   [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up0_conv_before_conc  (None, 16, 16, 512)  2048       ['transunet_up0_conv_before_conca\n",
            " at_0_bn (BatchNormalization)                                    t_0[0][0]']                      \n",
            "                                                                                                  \n",
            " transunet_up0_conv_before_conc  (None, 16, 16, 512)  0          ['transunet_up0_conv_before_conca\n",
            " at_0_activation (ReLU)                                          t_0_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " transunet_up0_concat (Concaten  (None, 16, 16, 1024  0          ['transunet_up0_conv_before_conca\n",
            " ate)                           )                                t_0_activation[0][0]',           \n",
            "                                                                  'VGG19_backbone[0][3]']         \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  4718592    ['transunet_up0_concat[0][0]']   \n",
            " t_0 (Conv2D)                                                                                     \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  2048       ['transunet_up0_conv_after_concat\n",
            " t_0_bn (BatchNormalization)                                     _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  0          ['transunet_up0_conv_after_concat\n",
            " t_0_activation (ReLU)                                           _0_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  2359296    ['transunet_up0_conv_after_concat\n",
            " t_1 (Conv2D)                                                    _0_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  2048       ['transunet_up0_conv_after_concat\n",
            " t_1_bn (BatchNormalization)                                     _1[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up0_conv_after_conca  (None, 16, 16, 512)  0          ['transunet_up0_conv_after_concat\n",
            " t_1_activation (ReLU)                                           _1_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up1_decode_trans_con  (None, 32, 32, 256)  1179904    ['transunet_up0_conv_after_concat\n",
            " v (Conv2DTranspose)                                             _1_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up1_decode_bn (Batch  (None, 32, 32, 256)  1024       ['transunet_up1_decode_trans_conv\n",
            " Normalization)                                                  [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up1_decode_activatio  (None, 32, 32, 256)  0          ['transunet_up1_decode_bn[0][0]']\n",
            " n (ReLU)                                                                                         \n",
            "                                                                                                  \n",
            " transunet_up1_conv_before_conc  (None, 32, 32, 256)  589824     ['transunet_up1_decode_activation\n",
            " at_0 (Conv2D)                                                   [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up1_conv_before_conc  (None, 32, 32, 256)  1024       ['transunet_up1_conv_before_conca\n",
            " at_0_bn (BatchNormalization)                                    t_0[0][0]']                      \n",
            "                                                                                                  \n",
            " transunet_up1_conv_before_conc  (None, 32, 32, 256)  0          ['transunet_up1_conv_before_conca\n",
            " at_0_activation (ReLU)                                          t_0_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " transunet_up1_concat (Concaten  (None, 32, 32, 512)  0          ['transunet_up1_conv_before_conca\n",
            " ate)                                                            t_0_activation[0][0]',           \n",
            "                                                                  'VGG19_backbone[0][2]']         \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  1179648    ['transunet_up1_concat[0][0]']   \n",
            " t_0 (Conv2D)                                                                                     \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  1024       ['transunet_up1_conv_after_concat\n",
            " t_0_bn (BatchNormalization)                                     _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  0          ['transunet_up1_conv_after_concat\n",
            " t_0_activation (ReLU)                                           _0_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  589824     ['transunet_up1_conv_after_concat\n",
            " t_1 (Conv2D)                                                    _0_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  1024       ['transunet_up1_conv_after_concat\n",
            " t_1_bn (BatchNormalization)                                     _1[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up1_conv_after_conca  (None, 32, 32, 256)  0          ['transunet_up1_conv_after_concat\n",
            " t_1_activation (ReLU)                                           _1_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up2_decode_trans_con  (None, 64, 64, 128)  295040     ['transunet_up1_conv_after_concat\n",
            " v (Conv2DTranspose)                                             _1_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up2_decode_bn (Batch  (None, 64, 64, 128)  512        ['transunet_up2_decode_trans_conv\n",
            " Normalization)                                                  [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up2_decode_activatio  (None, 64, 64, 128)  0          ['transunet_up2_decode_bn[0][0]']\n",
            " n (ReLU)                                                                                         \n",
            "                                                                                                  \n",
            " transunet_up2_conv_before_conc  (None, 64, 64, 128)  147456     ['transunet_up2_decode_activation\n",
            " at_0 (Conv2D)                                                   [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up2_conv_before_conc  (None, 64, 64, 128)  512        ['transunet_up2_conv_before_conca\n",
            " at_0_bn (BatchNormalization)                                    t_0[0][0]']                      \n",
            "                                                                                                  \n",
            " transunet_up2_conv_before_conc  (None, 64, 64, 128)  0          ['transunet_up2_conv_before_conca\n",
            " at_0_activation (ReLU)                                          t_0_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " transunet_up2_concat (Concaten  (None, 64, 64, 256)  0          ['transunet_up2_conv_before_conca\n",
            " ate)                                                            t_0_activation[0][0]',           \n",
            "                                                                  'VGG19_backbone[0][1]']         \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  294912     ['transunet_up2_concat[0][0]']   \n",
            " t_0 (Conv2D)                                                                                     \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  512        ['transunet_up2_conv_after_concat\n",
            " t_0_bn (BatchNormalization)                                     _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  0          ['transunet_up2_conv_after_concat\n",
            " t_0_activation (ReLU)                                           _0_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  147456     ['transunet_up2_conv_after_concat\n",
            " t_1 (Conv2D)                                                    _0_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  512        ['transunet_up2_conv_after_concat\n",
            " t_1_bn (BatchNormalization)                                     _1[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up2_conv_after_conca  (None, 64, 64, 128)  0          ['transunet_up2_conv_after_concat\n",
            " t_1_activation (ReLU)                                           _1_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up3_decode_trans_con  (None, 128, 128, 64  73792      ['transunet_up2_conv_after_concat\n",
            " v (Conv2DTranspose)            )                                _1_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up3_decode_bn (Batch  (None, 128, 128, 64  256        ['transunet_up3_decode_trans_conv\n",
            " Normalization)                 )                                [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up3_decode_activatio  (None, 128, 128, 64  0          ['transunet_up3_decode_bn[0][0]']\n",
            " n (ReLU)                       )                                                                 \n",
            "                                                                                                  \n",
            " transunet_up3_conv_before_conc  (None, 128, 128, 64  36864      ['transunet_up3_decode_activation\n",
            " at_0 (Conv2D)                  )                                [0][0]']                         \n",
            "                                                                                                  \n",
            " transunet_up3_conv_before_conc  (None, 128, 128, 64  256        ['transunet_up3_conv_before_conca\n",
            " at_0_bn (BatchNormalization)   )                                t_0[0][0]']                      \n",
            "                                                                                                  \n",
            " transunet_up3_conv_before_conc  (None, 128, 128, 64  0          ['transunet_up3_conv_before_conca\n",
            " at_0_activation (ReLU)         )                                t_0_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " transunet_up3_concat (Concaten  (None, 128, 128, 12  0          ['transunet_up3_conv_before_conca\n",
            " ate)                           8)                               t_0_activation[0][0]',           \n",
            "                                                                  'VGG19_backbone[0][0]']         \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  73728      ['transunet_up3_concat[0][0]']   \n",
            " t_0 (Conv2D)                   )                                                                 \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  256        ['transunet_up3_conv_after_concat\n",
            " t_0_bn (BatchNormalization)    )                                _0[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  0          ['transunet_up3_conv_after_concat\n",
            " t_0_activation (ReLU)          )                                _0_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  36864      ['transunet_up3_conv_after_concat\n",
            " t_1 (Conv2D)                   )                                _0_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  256        ['transunet_up3_conv_after_concat\n",
            " t_1_bn (BatchNormalization)    )                                _1[0][0]']                       \n",
            "                                                                                                  \n",
            " transunet_up3_conv_after_conca  (None, 128, 128, 64  0          ['transunet_up3_conv_after_concat\n",
            " t_1_activation (ReLU)          )                                _1_bn[0][0]']                    \n",
            "                                                                                                  \n",
            " transunet_output (Conv2D)      (None, 128, 128, 1)  65          ['transunet_up3_conv_after_concat\n",
            "                                                                 _1_activation[0][0]']            \n",
            "                                                                                                  \n",
            " transunet_output_activation (A  (None, 128, 128, 1)  0          ['transunet_output[0][0]']       \n",
            " ctivation)                                                                                       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 437,774,145\n",
            "Trainable params: 417,742,081\n",
            "Non-trainable params: 20,032,064\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "    callbacks = [\n",
        "        ModelCheckpoint(model_path, verbose=1, save_best_only=True),\n",
        "        ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1),\n",
        "        CSVLogger(csv_path),\n",
        "        EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=False),\n",
        "    ]\n",
        "\n",
        "    model_tran_unet.fit(\n",
        "        train_dataset,\n",
        "        epochs=num_epochs,\n",
        "        validation_data=valid_dataset,\n",
        "        callbacks=callbacks\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nh5bHUQX6B7l",
        "outputId": "faa0afe6-a55e-4ddd-e7b6-07d193d22603"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.6333 - accuracy: 0.9420 - dice_coef: 0.3667 - iou: 0.2391\n",
            "Epoch 1: val_loss improved from inf to 0.95529, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 191s 1s/step - loss: 0.6333 - accuracy: 0.9420 - dice_coef: 0.3667 - iou: 0.2391 - val_loss: 0.9553 - val_accuracy: 0.3552 - val_dice_coef: 0.0446 - val_iou: 0.0228 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.3591 - accuracy: 0.9878 - dice_coef: 0.6409 - iou: 0.4756\n",
            "Epoch 2: val_loss improved from 0.95529 to 0.47236, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 162s 1s/step - loss: 0.3591 - accuracy: 0.9878 - dice_coef: 0.6409 - iou: 0.4756 - val_loss: 0.4724 - val_accuracy: 0.9758 - val_dice_coef: 0.5277 - val_iou: 0.3608 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.3082 - accuracy: 0.9895 - dice_coef: 0.6918 - iou: 0.5327\n",
            "Epoch 3: val_loss improved from 0.47236 to 0.42734, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.3082 - accuracy: 0.9895 - dice_coef: 0.6918 - iou: 0.5327 - val_loss: 0.4273 - val_accuracy: 0.9792 - val_dice_coef: 0.5728 - val_iou: 0.4036 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2685 - accuracy: 0.9908 - dice_coef: 0.7315 - iou: 0.5802\n",
            "Epoch 4: val_loss improved from 0.42734 to 0.28037, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.2685 - accuracy: 0.9908 - dice_coef: 0.7315 - iou: 0.5802 - val_loss: 0.2804 - val_accuracy: 0.9895 - val_dice_coef: 0.7198 - val_iou: 0.5651 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2481 - accuracy: 0.9914 - dice_coef: 0.7519 - iou: 0.6060\n",
            "Epoch 5: val_loss improved from 0.28037 to 0.27276, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.2481 - accuracy: 0.9914 - dice_coef: 0.7519 - iou: 0.6060 - val_loss: 0.2728 - val_accuracy: 0.9907 - val_dice_coef: 0.7264 - val_iou: 0.5745 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2383 - accuracy: 0.9917 - dice_coef: 0.7617 - iou: 0.6186\n",
            "Epoch 6: val_loss improved from 0.27276 to 0.26787, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.2383 - accuracy: 0.9917 - dice_coef: 0.7617 - iou: 0.6186 - val_loss: 0.2679 - val_accuracy: 0.9908 - val_dice_coef: 0.7304 - val_iou: 0.5797 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2224 - accuracy: 0.9922 - dice_coef: 0.7776 - iou: 0.6395\n",
            "Epoch 7: val_loss did not improve from 0.26787\n",
            "115/115 [==============================] - 137s 1s/step - loss: 0.2224 - accuracy: 0.9922 - dice_coef: 0.7776 - iou: 0.6395 - val_loss: 0.2953 - val_accuracy: 0.9897 - val_dice_coef: 0.7040 - val_iou: 0.5462 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2065 - accuracy: 0.9927 - dice_coef: 0.7935 - iou: 0.6607\n",
            "Epoch 8: val_loss improved from 0.26787 to 0.25113, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.2065 - accuracy: 0.9927 - dice_coef: 0.7935 - iou: 0.6607 - val_loss: 0.2511 - val_accuracy: 0.9917 - val_dice_coef: 0.7476 - val_iou: 0.6006 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1876 - accuracy: 0.9934 - dice_coef: 0.8124 - iou: 0.6867\n",
            "Epoch 9: val_loss improved from 0.25113 to 0.24553, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1876 - accuracy: 0.9934 - dice_coef: 0.8124 - iou: 0.6867 - val_loss: 0.2455 - val_accuracy: 0.9919 - val_dice_coef: 0.7530 - val_iou: 0.6073 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1906 - accuracy: 0.9933 - dice_coef: 0.8094 - iou: 0.6824\n",
            "Epoch 10: val_loss did not improve from 0.24553\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1906 - accuracy: 0.9933 - dice_coef: 0.8094 - iou: 0.6824 - val_loss: 0.2462 - val_accuracy: 0.9919 - val_dice_coef: 0.7519 - val_iou: 0.6058 - lr: 0.0010\n",
            "Epoch 11/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.9936 - dice_coef: 0.8187 - iou: 0.6955\n",
            "Epoch 11: val_loss did not improve from 0.24553\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1813 - accuracy: 0.9936 - dice_coef: 0.8187 - iou: 0.6955 - val_loss: 0.3039 - val_accuracy: 0.9910 - val_dice_coef: 0.6945 - val_iou: 0.5374 - lr: 0.0010\n",
            "Epoch 12/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1823 - accuracy: 0.9935 - dice_coef: 0.8177 - iou: 0.6939\n",
            "Epoch 12: val_loss did not improve from 0.24553\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1823 - accuracy: 0.9935 - dice_coef: 0.8177 - iou: 0.6939 - val_loss: 0.2660 - val_accuracy: 0.9897 - val_dice_coef: 0.7322 - val_iou: 0.5809 - lr: 0.0010\n",
            "Epoch 13/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1674 - accuracy: 0.9940 - dice_coef: 0.8326 - iou: 0.7152\n",
            "Epoch 13: val_loss did not improve from 0.24553\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1674 - accuracy: 0.9940 - dice_coef: 0.8326 - iou: 0.7152 - val_loss: 0.2496 - val_accuracy: 0.9902 - val_dice_coef: 0.7475 - val_iou: 0.6001 - lr: 0.0010\n",
            "Epoch 14/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1601 - accuracy: 0.9942 - dice_coef: 0.8399 - iou: 0.7260\n",
            "Epoch 14: val_loss did not improve from 0.24553\n",
            "\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "115/115 [==============================] - 137s 1s/step - loss: 0.1601 - accuracy: 0.9942 - dice_coef: 0.8399 - iou: 0.7260 - val_loss: 0.2635 - val_accuracy: 0.9917 - val_dice_coef: 0.7345 - val_iou: 0.5845 - lr: 0.0010\n",
            "Epoch 15/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1476 - accuracy: 0.9946 - dice_coef: 0.8524 - iou: 0.7447\n",
            "Epoch 15: val_loss improved from 0.24553 to 0.22040, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1476 - accuracy: 0.9946 - dice_coef: 0.8524 - iou: 0.7447 - val_loss: 0.2204 - val_accuracy: 0.9921 - val_dice_coef: 0.7786 - val_iou: 0.6408 - lr: 1.0000e-04\n",
            "Epoch 16/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1377 - accuracy: 0.9949 - dice_coef: 0.8623 - iou: 0.7596\n",
            "Epoch 16: val_loss improved from 0.22040 to 0.21741, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1377 - accuracy: 0.9949 - dice_coef: 0.8623 - iou: 0.7596 - val_loss: 0.2174 - val_accuracy: 0.9924 - val_dice_coef: 0.7815 - val_iou: 0.6448 - lr: 1.0000e-04\n",
            "Epoch 17/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1339 - accuracy: 0.9951 - dice_coef: 0.8661 - iou: 0.7655\n",
            "Epoch 17: val_loss improved from 0.21741 to 0.21547, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.1339 - accuracy: 0.9951 - dice_coef: 0.8661 - iou: 0.7655 - val_loss: 0.2155 - val_accuracy: 0.9924 - val_dice_coef: 0.7834 - val_iou: 0.6473 - lr: 1.0000e-04\n",
            "Epoch 18/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1310 - accuracy: 0.9951 - dice_coef: 0.8690 - iou: 0.7700\n",
            "Epoch 18: val_loss improved from 0.21547 to 0.21490, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.1310 - accuracy: 0.9951 - dice_coef: 0.8690 - iou: 0.7700 - val_loss: 0.2149 - val_accuracy: 0.9924 - val_dice_coef: 0.7840 - val_iou: 0.6481 - lr: 1.0000e-04\n",
            "Epoch 19/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1289 - accuracy: 0.9952 - dice_coef: 0.8711 - iou: 0.7732\n",
            "Epoch 19: val_loss improved from 0.21490 to 0.21435, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.1289 - accuracy: 0.9952 - dice_coef: 0.8711 - iou: 0.7732 - val_loss: 0.2144 - val_accuracy: 0.9925 - val_dice_coef: 0.7845 - val_iou: 0.6488 - lr: 1.0000e-04\n",
            "Epoch 20/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1269 - accuracy: 0.9953 - dice_coef: 0.8731 - iou: 0.7763\n",
            "Epoch 20: val_loss improved from 0.21435 to 0.21392, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1269 - accuracy: 0.9953 - dice_coef: 0.8731 - iou: 0.7763 - val_loss: 0.2139 - val_accuracy: 0.9925 - val_dice_coef: 0.7849 - val_iou: 0.6492 - lr: 1.0000e-04\n",
            "Epoch 21/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1251 - accuracy: 0.9953 - dice_coef: 0.8749 - iou: 0.7792\n",
            "Epoch 21: val_loss improved from 0.21392 to 0.21369, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1251 - accuracy: 0.9953 - dice_coef: 0.8749 - iou: 0.7792 - val_loss: 0.2137 - val_accuracy: 0.9925 - val_dice_coef: 0.7851 - val_iou: 0.6495 - lr: 1.0000e-04\n",
            "Epoch 22/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1234 - accuracy: 0.9954 - dice_coef: 0.8766 - iou: 0.7818\n",
            "Epoch 22: val_loss improved from 0.21369 to 0.21300, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 160s 1s/step - loss: 0.1234 - accuracy: 0.9954 - dice_coef: 0.8766 - iou: 0.7818 - val_loss: 0.2130 - val_accuracy: 0.9925 - val_dice_coef: 0.7858 - val_iou: 0.6504 - lr: 1.0000e-04\n",
            "Epoch 23/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9955 - dice_coef: 0.8783 - iou: 0.7845\n",
            "Epoch 23: val_loss did not improve from 0.21300\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1217 - accuracy: 0.9955 - dice_coef: 0.8783 - iou: 0.7845 - val_loss: 0.2135 - val_accuracy: 0.9925 - val_dice_coef: 0.7853 - val_iou: 0.6497 - lr: 1.0000e-04\n",
            "Epoch 24/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1201 - accuracy: 0.9955 - dice_coef: 0.8799 - iou: 0.7870\n",
            "Epoch 24: val_loss did not improve from 0.21300\n",
            "115/115 [==============================] - 137s 1s/step - loss: 0.1201 - accuracy: 0.9955 - dice_coef: 0.8799 - iou: 0.7870 - val_loss: 0.2139 - val_accuracy: 0.9925 - val_dice_coef: 0.7849 - val_iou: 0.6492 - lr: 1.0000e-04\n",
            "Epoch 25/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1187 - accuracy: 0.9955 - dice_coef: 0.8813 - iou: 0.7893\n",
            "Epoch 25: val_loss improved from 0.21300 to 0.21297, saving model to files/trans_Unet_model.h5\n",
            "115/115 [==============================] - 161s 1s/step - loss: 0.1187 - accuracy: 0.9955 - dice_coef: 0.8813 - iou: 0.7893 - val_loss: 0.2130 - val_accuracy: 0.9925 - val_dice_coef: 0.7859 - val_iou: 0.6504 - lr: 1.0000e-04\n",
            "Epoch 26/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1172 - accuracy: 0.9956 - dice_coef: 0.8828 - iou: 0.7917\n",
            "Epoch 26: val_loss did not improve from 0.21297\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1172 - accuracy: 0.9956 - dice_coef: 0.8828 - iou: 0.7917 - val_loss: 0.2147 - val_accuracy: 0.9925 - val_dice_coef: 0.7842 - val_iou: 0.6483 - lr: 1.0000e-04\n",
            "Epoch 27/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1158 - accuracy: 0.9956 - dice_coef: 0.8842 - iou: 0.7939\n",
            "Epoch 27: val_loss did not improve from 0.21297\n",
            "\n",
            "Epoch 27: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1158 - accuracy: 0.9956 - dice_coef: 0.8842 - iou: 0.7939 - val_loss: 0.2137 - val_accuracy: 0.9925 - val_dice_coef: 0.7851 - val_iou: 0.6495 - lr: 1.0000e-04\n",
            "Epoch 28/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.9957 - dice_coef: 0.8864 - iou: 0.7974\n",
            "Epoch 28: val_loss did not improve from 0.21297\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1136 - accuracy: 0.9957 - dice_coef: 0.8864 - iou: 0.7974 - val_loss: 0.2151 - val_accuracy: 0.9926 - val_dice_coef: 0.7836 - val_iou: 0.6476 - lr: 1.0000e-05\n",
            "Epoch 29/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9957 - dice_coef: 0.8870 - iou: 0.7983\n",
            "Epoch 29: val_loss did not improve from 0.21297\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1130 - accuracy: 0.9957 - dice_coef: 0.8870 - iou: 0.7983 - val_loss: 0.2149 - val_accuracy: 0.9926 - val_dice_coef: 0.7838 - val_iou: 0.6478 - lr: 1.0000e-05\n",
            "Epoch 30/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1126 - accuracy: 0.9957 - dice_coef: 0.8874 - iou: 0.7989\n",
            "Epoch 30: val_loss did not improve from 0.21297\n",
            "115/115 [==============================] - 138s 1s/step - loss: 0.1126 - accuracy: 0.9957 - dice_coef: 0.8874 - iou: 0.7989 - val_loss: 0.2150 - val_accuracy: 0.9925 - val_dice_coef: 0.7837 - val_iou: 0.6478 - lr: 1.0000e-05\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdd8403ad00>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files #this files are available in keras unet collections repo.\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XwCJn-Gg6GpW",
        "outputId": "70548335-be95-48d0-9f24-9a3c43855d95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1ad20920-03ef-4c2f-891c-8f2ab180e380\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1ad20920-03ef-4c2f-891c-8f2ab180e380\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving activations.py to activations.py\n",
            "Saving transformer_layers.py to transformer_layers.py\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'activations.py': b\"from tensorflow import math\\r\\nfrom tensorflow.keras.layers import Layer\\r\\nimport tensorflow.keras.backend as K\\r\\n\\r\\n\\r\\ndef gelu_(X):\\r\\n\\r\\n    return 0.5*X*(1.0 + math.tanh(0.7978845608028654*(X + 0.044715*math.pow(X, 3))))\\r\\n\\r\\ndef snake_(X, beta):\\r\\n\\r\\n    return X + (1/beta)*math.square(math.sin(beta*X))\\r\\n\\r\\n\\r\\nclass GELU(Layer):\\r\\n    '''\\r\\n    Gaussian Error Linear Unit (GELU), an alternative of ReLU\\r\\n    \\r\\n    Y = GELU()(X)\\r\\n    \\r\\n    ----------\\r\\n    Hendrycks, D. and Gimpel, K., 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415.\\r\\n    \\r\\n    Usage: use it as a tf.keras.Layer\\r\\n    \\r\\n    \\r\\n    '''\\r\\n    def __init__(self, trainable=False, **kwargs):\\r\\n        super(GELU, self).__init__(**kwargs)\\r\\n        self.supports_masking = True\\r\\n        self.trainable = trainable\\r\\n\\r\\n    def build(self, input_shape):\\r\\n        super(GELU, self).build(input_shape)\\r\\n\\r\\n    def call(self, inputs, mask=None):\\r\\n        return gelu_(inputs)\\r\\n\\r\\n    def get_config(self):\\r\\n        config = {'trainable': self.trainable}\\r\\n        base_config = super(GELU, self).get_config()\\r\\n        return dict(list(base_config.items()) + list(config.items()))\\r\\n    def compute_output_shape(self, input_shape):\\r\\n        return input_shape\\r\\n\\r\\n    \\r\\nclass Snake(Layer):\\r\\n    '''\\r\\n    Snake activation function $X + (1/b)*sin^2(b*X)$. Proposed to learn periodic targets.\\r\\n    \\r\\n    Y = Snake(beta=0.5, trainable=False)(X)\\r\\n    \\r\\n    ----------\\r\\n    Ziyin, L., Hartwig, T. and Ueda, M., 2020. Neural networks fail to learn periodic functions \\r\\n    and how to fix it. arXiv preprint arXiv:2006.08195.\\r\\n    \\r\\n    '''\\r\\n    def __init__(self, beta=0.5, trainable=False, **kwargs):\\r\\n        super(Snake, self).__init__(**kwargs)\\r\\n        self.supports_masking = True\\r\\n        self.beta = beta\\r\\n        self.trainable = trainable\\r\\n\\r\\n    def build(self, input_shape):\\r\\n        self.beta_factor = K.variable(self.beta, dtype=K.floatx(), name='beta_factor')\\r\\n        if self.trainable:\\r\\n            self._trainable_weights.append(self.beta_factor)\\r\\n\\r\\n        super(Snake, self).build(input_shape)\\r\\n\\r\\n    def call(self, inputs, mask=None):\\r\\n        return snake_(inputs, self.beta_factor)\\r\\n\\r\\n    def get_config(self):\\r\\n        config = {'beta': self.get_weights()[0] if self.trainable else self.beta, 'trainable': self.trainable}\\r\\n        base_config = super(Snake, self).get_config()\\r\\n        return dict(list(base_config.items()) + list(config.items()))\\r\\n\\r\\n    def compute_output_shape(self, input_shape):\\r\\n        return \",\n",
              " 'transformer_layers.py': b\"from __future__ import absolute_import\\r\\n\\r\\nimport numpy as np\\r\\nimport tensorflow as tf\\r\\nfrom tensorflow.nn import depth_to_space\\r\\nfrom tensorflow.image import extract_patches\\r\\nfrom tensorflow.keras.layers import Conv2D, Layer, Dense, Embedding, Dropout, Conv2D, LayerNormalization\\r\\nfrom tensorflow.keras.activations import softmax\\r\\n\\r\\nclass patch_extract(Layer):\\r\\n    '''\\r\\n    Extract patches from the input feature map.\\r\\n    \\r\\n    patches = patch_extract(patch_size)(feature_map)\\r\\n    \\r\\n    ----------\\r\\n    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, \\r\\n    T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., 2020. \\r\\n    An image is worth 16x16 words: Transformers for image recognition at scale. \\r\\n    arXiv preprint arXiv:2010.11929.\\r\\n    \\r\\n    Input\\r\\n    ----------\\r\\n        feature_map: a four-dimensional tensor of (num_sample, width, height, channel)\\r\\n        patch_size: size of split patches (width=height)\\r\\n        \\r\\n    Output\\r\\n    ----------\\r\\n        patches: a two-dimensional tensor of (num_sample*num_patch, patch_size*patch_size)\\r\\n                 where `num_patch = (width // patch_size) * (height // patch_size)`\\r\\n                 \\r\\n    For further information see: https://www.tensorflow.org/api_docs/python/tf/image/extract_patches\\r\\n        \\r\\n    '''\\r\\n    \\r\\n    def __init__(self, patch_size, **kwargs):\\r\\n        super(patch_extract, self).__init__(**kwargs)\\r\\n        self.patch_size = patch_size\\r\\n        self.patch_size_x = patch_size[0]\\r\\n        self.patch_size_y = patch_size[0]\\r\\n    \\r\\n    def call(self, images):\\r\\n        \\r\\n        batch_size = tf.shape(images)[0]\\r\\n        \\r\\n        patches = extract_patches(images=images,\\r\\n                                  sizes=(1, self.patch_size_x, self.patch_size_y, 1),\\r\\n                                  strides=(1, self.patch_size_x, self.patch_size_y, 1),\\r\\n                                  rates=(1, 1, 1, 1), padding='VALID',)\\r\\n        # patches.shape = (num_sample, patch_num, patch_num, patch_size*channel)\\r\\n        \\r\\n        patch_dim = patches.shape[-1]\\r\\n        patch_num = patches.shape[1]\\r\\n        patches = tf.reshape(patches, (batch_size, patch_num*patch_num, patch_dim))\\r\\n        # patches.shape = (num_sample, patch_num*patch_num, patch_size*channel)\\r\\n        \\r\\n        return patches\\r\\n    \\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({'patch_size': self.patch_size,})\\r\\n        return config\\r\\n    \\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\nclass patch_embedding(Layer):\\r\\n    '''\\r\\n    Embed patches to tokens.\\r\\n    \\r\\n    patches_embed = patch_embedding(num_patch, embed_dim)(pathes)\\r\\n    \\r\\n    ----------\\r\\n    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, \\r\\n    T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., 2020. \\r\\n    An image is worth 16x16 words: Transformers for image recognition at scale. \\r\\n    arXiv preprint arXiv:2010.11929.\\r\\n    \\r\\n    Input\\r\\n    ----------\\r\\n        num_patch: number of patches to be embedded.\\r\\n        embed_dim: number of embedded dimensions. \\r\\n        \\r\\n    Output\\r\\n    ----------\\r\\n        embed: Embedded patches.\\r\\n    \\r\\n    For further information see: https://keras.io/api/layers/core_layers/embedding/\\r\\n    \\r\\n    '''\\r\\n    \\r\\n    def __init__(self, num_patch, embed_dim, **kwargs):\\r\\n        \\r\\n        super(patch_embedding, self).__init__(**kwargs)\\r\\n        self.num_patch = num_patch\\r\\n        self.embed_dim = embed_dim\\r\\n        self.proj = Dense(embed_dim)\\r\\n        self.pos_embed = Embedding(input_dim=num_patch, output_dim=embed_dim)\\r\\n\\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'num_patch': self.num_patch,\\r\\n            'embed_dim': self.embed_dim,\\r\\n        })\\r\\n        return config\\r\\n    \\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def call(self, patch):\\r\\n        pos = tf.range(start=0, limit=self.num_patch, delta=1)\\r\\n        embed = self.proj(patch) + self.pos_embed(pos)\\r\\n        return embed\\r\\n\\r\\nclass patch_merging(tf.keras.layers.Layer):\\r\\n    '''\\r\\n    Downsample embedded patches; it halfs the number of patches\\r\\n    and double the embedded dimensions (c.f. pooling layers).\\r\\n    \\r\\n    Input\\r\\n    ----------\\r\\n        num_patch: number of patches to be embedded.\\r\\n        embed_dim: number of embedded dimensions. \\r\\n        \\r\\n    Output\\r\\n    ----------\\r\\n        x: downsampled patches.\\r\\n    \\r\\n    '''\\r\\n    def __init__(self, num_patch, embed_dim, name='', **kwargs):\\r\\n        super(patch_merging, self).__init__(**kwargs)\\r\\n        \\r\\n        self.num_patch = num_patch\\r\\n        self.embed_dim = embed_dim\\r\\n        \\r\\n        # A linear transform that doubles the channels \\r\\n        self.linear_trans = Dense(2*embed_dim, use_bias=False, name='{}_linear_trans'.format(name))\\r\\n\\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'num_patch': self.num_patch,\\r\\n            'embed_dim': self.embed_dim,\\r\\n            'name':self.name\\r\\n        })\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def call(self, x):\\r\\n        \\r\\n        H, W = self.num_patch\\r\\n        B, L, C = x.get_shape().as_list()\\r\\n        \\r\\n        assert (L == H * W), 'input feature has wrong size'\\r\\n        assert (H % 2 == 0 and W % 2 == 0), '{}-by-{} patches received, they are not even.'.format(H, W)\\r\\n        \\r\\n        # Convert the patch sequence to aligned patches\\r\\n        x = tf.reshape(x, shape=(-1, H, W, C))\\r\\n        \\r\\n        # Downsample\\r\\n        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\\r\\n        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\\r\\n        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\\r\\n        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\\r\\n        x = tf.concat((x0, x1, x2, x3), axis=-1)\\r\\n        \\r\\n        # Convert to the patch squence\\r\\n        x = tf.reshape(x, shape=(-1, (H//2)*(W//2), 4*C))\\r\\n       \\r\\n        # Linear transform\\r\\n        x = self.linear_trans(x)\\r\\n\\r\\n        return x\\r\\n\\r\\nclass patch_expanding(tf.keras.layers.Layer):\\r\\n    '''\\r\\n    Upsample embedded patches with a given rate (e.g., x2, x4, x8, ...) \\r\\n    the number of patches is increased, and the embedded dimensions are reduced.\\r\\n    \\r\\n    Input\\r\\n    ----------\\r\\n        num_patch: number of patches.\\r\\n        embed_dim: number of embedded dimensions.\\r\\n        upsample_rate: the factor of patches expanding, \\r\\n                       e.g., upsample_rate=2 doubles input patches and halfs embedded dimensions.\\r\\n        return_vector: the indicator of returning a sequence of tokens (return_vector=True)  \\r\\n                       or two-dimentional, spatially aligned tokens (return_vector=False)\\r\\n                       \\r\\n    For further information see: https://www.tensorflow.org/api_docs/python/tf/nn/depth_to_space\\r\\n    '''\\r\\n\\r\\n    def __init__(self, num_patch, embed_dim, upsample_rate, return_vector=True, name='patch_expand', **kwargs):\\r\\n        super(patch_expanding, self).__init__(**kwargs)\\r\\n        \\r\\n        self.num_patch = num_patch\\r\\n        self.embed_dim = embed_dim\\r\\n        self.upsample_rate = upsample_rate\\r\\n        self.return_vector = return_vector\\r\\n        \\r\\n        # Linear transformations that doubles the channels \\r\\n        self.linear_trans1 = Conv2D(upsample_rate*embed_dim, kernel_size=1, use_bias=False, name='{}_linear_trans1'.format(name))\\r\\n        # \\r\\n        self.linear_trans2 = Conv2D(upsample_rate*embed_dim, kernel_size=1, use_bias=False, name='{}_linear_trans1'.format(name))\\r\\n        self.prefix = name\\r\\n        \\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'num_patch': self.num_patch,\\r\\n            'embed_dim': self.embed_dim,\\r\\n            'upsample_rate': self.upsample_rate,\\r\\n            'return_vector': self.return_vector,\\r\\n            'name':self.name,\\r\\n        })\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def call(self, x):\\r\\n        \\r\\n        H, W = self.num_patch\\r\\n        B, L, C = x.get_shape().as_list()\\r\\n        \\r\\n        assert (L == H * W), 'input feature has wrong size'\\r\\n\\r\\n        x = tf.reshape(x, (-1, H, W, C))\\r\\n        \\r\\n        x = self.linear_trans1(x)\\r\\n        \\r\\n        # rearange depth to number of patches\\r\\n        x = depth_to_space(x, self.upsample_rate, data_format='NHWC', name='{}_d_to_space'.format(self.prefix))\\r\\n        \\r\\n        if self.return_vector:\\r\\n            # Convert aligned patches to a patch sequence\\r\\n            x = tf.reshape(x, (-1, L*self.upsample_rate*self.upsample_rate, C//2))\\r\\n\\r\\n        return x\\r\\n    \\r\\n# ========== Swin-Transformer only ========== #\\r\\n# TODO: documentation.\\r\\n\\r\\ndef window_partition(x, window_size):\\r\\n    \\r\\n    # Get the static shape of the input tensor\\r\\n    # (Sample, Height, Width, Channel)\\r\\n    _, H, W, C = x.get_shape().as_list()\\r\\n    \\r\\n    # Subset tensors to patches\\r\\n    patch_num_H = H//window_size\\r\\n    patch_num_W = W//window_size\\r\\n    x = tf.reshape(x, shape=(-1, patch_num_H, window_size, patch_num_W, window_size, C))\\r\\n    x = tf.transpose(x, (0, 1, 3, 2, 4, 5))\\r\\n    \\r\\n    # Reshape patches to a patch sequence\\r\\n    windows = tf.reshape(x, shape=(-1, window_size, window_size, C))\\r\\n    \\r\\n    return windows\\r\\n\\r\\ndef window_reverse(windows, window_size, H, W, C):\\r\\n    \\r\\n    # Reshape a patch sequence to aligned patched \\r\\n    patch_num_H = H//window_size\\r\\n    patch_num_W = W//window_size\\r\\n    x = tf.reshape(windows, shape=(-1, patch_num_H, patch_num_W, window_size, window_size, C))\\r\\n    x = tf.transpose(x, perm=(0, 1, 3, 2, 4, 5))\\r\\n    \\r\\n    # Merge patches to spatial frames\\r\\n    x = tf.reshape(x, shape=(-1, H, W, C))\\r\\n    \\r\\n    return x\\r\\n\\r\\ndef drop_path_(inputs, drop_prob, is_training):\\r\\n    \\r\\n    # Bypass in non-training mode\\r\\n    if (not is_training) or (drop_prob == 0.):\\r\\n        return inputs\\r\\n\\r\\n    # Compute keep_prob\\r\\n    keep_prob = 1.0 - drop_prob\\r\\n\\r\\n    # Compute drop_connect tensor\\r\\n    input_shape = tf.shape(inputs)\\r\\n    batch_num = input_shape[0]; rank = len(input_shape)\\r\\n    \\r\\n    shape = (batch_num,) + (1,) * (rank - 1)\\r\\n    random_tensor = keep_prob + tf.random.uniform(shape, dtype=inputs.dtype)\\r\\n    path_mask = tf.floor(random_tensor)\\r\\n    output = tf.math.divide(inputs, keep_prob) * path_mask\\r\\n    return output\\r\\n\\r\\nclass drop_path(Layer):\\r\\n    def __init__(self, drop_prob=None, **kwargs):\\r\\n        super(drop_path, self).__init__(**kwargs)\\r\\n        self.drop_prob = drop_prob\\r\\n\\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({'drop_prob': self.drop_prob})\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def call(self, x, training=None):\\r\\n        return drop_path_(x, self.drop_prob, training)\\r\\n\\r\\nclass Mlp(tf.keras.layers.Layer):\\r\\n    def __init__(self, filter_num, drop=0., name='mlp', **kwargs):\\r\\n        \\r\\n        super(Mlp, self).__init__(**kwargs)\\r\\n        \\r\\n        self.filter_num = filter_num\\r\\n        self.drop = drop\\r\\n        \\r\\n        # MLP layers\\r\\n        self.fc1 = Dense(filter_num[0], name='{}_mlp_0'.format(name))\\r\\n        self.fc2 = Dense(filter_num[1], name='{}_mlp_1'.format(name))\\r\\n        \\r\\n        # Dropout layer\\r\\n        self.drop = Dropout(drop)\\r\\n        \\r\\n        # GELU activation\\r\\n        self.activation = tf.keras.activations.gelu\\r\\n        \\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'filter_num': self.filter_num,\\r\\n            'drop': self.drop,\\r\\n            'name': self.name,\\r\\n        })\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def call(self, x):\\r\\n        \\r\\n        # MLP --> GELU --> Drop --> MLP --> Drop\\r\\n        x = self.fc1(x)\\r\\n        x = self.activation(x)\\r\\n        x = self.drop(x)\\r\\n        x = self.fc2(x)\\r\\n        x = self.drop(x)\\r\\n        \\r\\n        return x\\r\\n\\r\\nclass WindowAttention(tf.keras.layers.Layer):\\r\\n    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, \\r\\n                 attn_drop=0, proj_drop=0., name='swin_atten', **kwargs):\\r\\n        super(WindowAttention, self).__init__(**kwargs)\\r\\n        \\r\\n        self.dim = dim # number of input dimensions\\r\\n        self.window_size = window_size # size of the attention window\\r\\n        self.num_heads = num_heads # number of self-attention heads\\r\\n        self.qkv_bias = qkv_bias\\r\\n        self.qk_scale = qk_scale\\r\\n        self.attn_drop = attn_drop\\r\\n        self.proj_drop = proj_drop\\r\\n        \\r\\n        head_dim = dim // num_heads\\r\\n        self.scale = qk_scale or head_dim ** -0.5 # query scaling factor\\r\\n        \\r\\n        self.prefix = name\\r\\n        \\r\\n        # Layers\\r\\n        self.qkv = Dense(dim * 3, use_bias=qkv_bias, name='{}_attn_qkv'.format(self.prefix))\\r\\n        self.attn_drop = Dropout(attn_drop)\\r\\n        self.proj = Dense(dim, name='{}_attn_proj'.format(self.prefix))\\r\\n        self.proj_drop = Dropout(proj_drop)\\r\\n\\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'dim':self.dim, \\r\\n            'window_size':self.window_size, \\r\\n            'num_heads':self.num_heads, \\r\\n            'qkv_bias':self.qkv_bias, \\r\\n            'qk_scale':self.qk_scale, \\r\\n            'attn_drop':self.attn_drop, \\r\\n            'proj_drop':self.proj_drop, \\r\\n            'name':self.prefix\\r\\n        })\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n        \\r\\n    def build(self, input_shape):\\r\\n        \\r\\n        # zero initialization\\r\\n        num_window_elements = (2*self.window_size[0] - 1) * (2*self.window_size[1] - 1)\\r\\n        self.relative_position_bias_table = self.add_weight('{}_attn_pos'.format(self.prefix),\\r\\n                                                            shape=(num_window_elements, self.num_heads),\\r\\n                                                            initializer=tf.initializers.Zeros(), trainable=True)\\r\\n        \\r\\n        # Indices of relative positions\\r\\n        coords_h = np.arange(self.window_size[0])\\r\\n        coords_w = np.arange(self.window_size[1])\\r\\n        coords_matrix = np.meshgrid(coords_h, coords_w, indexing='ij')\\r\\n        coords = np.stack(coords_matrix)\\r\\n        coords_flatten = coords.reshape(2, -1)\\r\\n        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]\\r\\n        relative_coords = relative_coords.transpose([1, 2, 0])\\r\\n        relative_coords[:, :, 0] += self.window_size[0] - 1\\r\\n        relative_coords[:, :, 1] += self.window_size[1] - 1\\r\\n        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\\r\\n        relative_position_index = relative_coords.sum(-1)\\r\\n        \\r\\n        # convert to the tf variable\\r\\n        self.relative_position_index = tf.Variable(\\r\\n            initial_value=tf.convert_to_tensor(relative_position_index), trainable=False, name='{}_attn_pos_ind'.format(self.prefix))\\r\\n        \\r\\n        self.built = True\\r\\n\\r\\n    def call(self, x, mask=None):\\r\\n        \\r\\n        # Get input tensor static shape\\r\\n        _, N, C = x.get_shape().as_list()\\r\\n        head_dim = C//self.num_heads\\r\\n        \\r\\n        x_qkv = self.qkv(x)\\r\\n        x_qkv = tf.reshape(x_qkv, shape=(-1, N, 3, self.num_heads, head_dim))\\r\\n        x_qkv = tf.transpose(x_qkv, perm=(2, 0, 3, 1, 4))\\r\\n        q, k, v = x_qkv[0], x_qkv[1], x_qkv[2]\\r\\n        \\r\\n        # Query rescaling\\r\\n        q = q * self.scale\\r\\n        \\r\\n        # multi-headed self-attention\\r\\n        k = tf.transpose(k, perm=(0, 1, 3, 2))\\r\\n        attn = (q @ k)\\r\\n        \\r\\n        # Shift window\\r\\n        num_window_elements = self.window_size[0] * self.window_size[1]\\r\\n        relative_position_index_flat = tf.reshape(self.relative_position_index, shape=(-1,))\\r\\n        relative_position_bias = tf.gather(self.relative_position_bias_table, relative_position_index_flat)\\r\\n        relative_position_bias = tf.reshape(relative_position_bias, shape=(num_window_elements, num_window_elements, -1))\\r\\n        relative_position_bias = tf.transpose(relative_position_bias, perm=(2, 0, 1))\\r\\n        attn = attn + tf.expand_dims(relative_position_bias, axis=0)\\r\\n\\r\\n        if mask is not None:\\r\\n            nW = mask.get_shape()[0]\\r\\n            mask_float = tf.cast(tf.expand_dims(tf.expand_dims(mask, axis=1), axis=0), tf.float32)\\r\\n            attn = tf.reshape(attn, shape=(-1, nW, self.num_heads, N, N)) + mask_float\\r\\n            attn = tf.reshape(attn, shape=(-1, self.num_heads, N, N))\\r\\n            attn = softmax(attn, axis=-1)\\r\\n        else:\\r\\n            attn = softmax(attn, axis=-1)\\r\\n        \\r\\n        # Dropout after attention\\r\\n        attn = self.attn_drop(attn)\\r\\n        \\r\\n        # Merge qkv vectors\\r\\n        x_qkv = (attn @ v)\\r\\n        x_qkv = tf.transpose(x_qkv, perm=(0, 2, 1, 3))\\r\\n        x_qkv = tf.reshape(x_qkv, shape=(-1, N, C))\\r\\n        \\r\\n        # Linear projection\\r\\n        x_qkv = self.proj(x_qkv)\\r\\n        \\r\\n        # Dropout after projection\\r\\n        x_qkv = self.proj_drop(x_qkv)\\r\\n        \\r\\n        return x_qkv\\r\\n\\r\\nclass SwinTransformerBlock(tf.keras.layers.Layer):\\r\\n    def __init__(self, dim, num_patch, num_heads, window_size=7, shift_size=0, \\r\\n                 num_mlp=1024, qkv_bias=True, qk_scale=None, mlp_drop=0, attn_drop=0, \\r\\n                 proj_drop=0, drop_path_prob=0, name='swin_block', **kwargs):\\r\\n        \\r\\n        super(SwinTransformerBlock, self).__init__(**kwargs)\\r\\n        \\r\\n        self.dim = dim # number of input dimensions\\r\\n        self.num_patch = num_patch # number of embedded patches; a tuple of  (heigh, width)\\r\\n        self.num_heads = num_heads # number of attention heads\\r\\n        self.window_size = window_size # size of window\\r\\n        self.shift_size = shift_size # size of window shift\\r\\n        self.num_mlp = num_mlp # number of MLP nodes\\r\\n        self.qkv_bias = qkv_bias\\r\\n        self.qk_scale = qk_scale\\r\\n        self.mlp_drop = mlp_drop\\r\\n        self.attn_drop = attn_drop\\r\\n        self.proj_drop = proj_drop\\r\\n        self.drop_path_prob = drop_path_prob\\r\\n        \\r\\n        self.prefix = name\\r\\n        \\r\\n        # Layers\\r\\n        self.norm1 = LayerNormalization(epsilon=1e-5, name='{}_norm1'.format(self.prefix))\\r\\n        self.attn = WindowAttention(dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,\\r\\n                                    qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=proj_drop, name=self.prefix)\\r\\n        self.drop_path = drop_path(drop_path_prob)\\r\\n        self.norm2 = LayerNormalization(epsilon=1e-5, name='{}_norm2'.format(self.prefix))\\r\\n        self.mlp = Mlp([num_mlp, dim], drop=mlp_drop, name=self.prefix)\\r\\n        \\r\\n        # Assertions\\r\\n        assert 0 <= self.shift_size, 'shift_size >= 0 is required'\\r\\n        assert self.shift_size < self.window_size, 'shift_size < window_size is required'\\r\\n        \\r\\n        # <---!!!\\r\\n        # Handling too-small patch numbers\\r\\n        if min(self.num_patch) < self.window_size:\\r\\n            self.shift_size = 0\\r\\n            self.window_size = min(self.num_patch)\\r\\n    \\r\\n    def get_config(self):\\r\\n        config = super().get_config().copy()\\r\\n        config.update({\\r\\n            'dim':self.dim, \\r\\n            'num_patch':self.num_patch, \\r\\n            'num_heads':self.num_heads, \\r\\n            'window_size':self.window_size, \\r\\n            'shift_size':self.shift_size, \\r\\n            'num_mlp':self.num_mlp,\\r\\n            'qkv_bias':self.qkv_bias, \\r\\n            'qk_scale':self.qk_scale, \\r\\n            'mlp_drop':self.mlp_drop, \\r\\n            'attn_drop':self.attn_drop, \\r\\n            'proj_drop':self.proj_drop, \\r\\n            'drop_path_prob':self.drop_path_prob, \\r\\n            'name':self.prefix\\r\\n        })\\r\\n        return config\\r\\n\\r\\n    @classmethod\\r\\n    def from_config(cls, config):\\r\\n        return cls(**config)\\r\\n    \\r\\n    def build(self, input_shape):\\r\\n        if self.shift_size > 0:\\r\\n            H, W = self.num_patch\\r\\n            h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))\\r\\n            w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))\\r\\n            \\r\\n            # attention mask\\r\\n            mask_array = np.zeros((1, H, W, 1))\\r\\n            \\r\\n            ## initialization\\r\\n            count = 0\\r\\n            for h in h_slices:\\r\\n                for w in w_slices:\\r\\n                    mask_array[:, h, w, :] = count\\r\\n                    count += 1\\r\\n            mask_array = tf.convert_to_tensor(mask_array)\\r\\n            \\r\\n            # mask array to windows\\r\\n            mask_windows = window_partition(mask_array, self.window_size)\\r\\n            mask_windows = tf.reshape(mask_windows, shape=[-1, self.window_size * self.window_size])\\r\\n            attn_mask = tf.expand_dims(mask_windows, axis=1) - tf.expand_dims(mask_windows, axis=2)\\r\\n            attn_mask = tf.where(attn_mask != 0, -100.0, attn_mask)\\r\\n            attn_mask = tf.where(attn_mask == 0, 0.0, attn_mask)\\r\\n            self.attn_mask = tf.Variable(initial_value=attn_mask, trainable=False, name='{}_attn_mask'.format(self.prefix))\\r\\n        else:\\r\\n            self.attn_mask = None\\r\\n\\r\\n        self.built = True\\r\\n\\r\\n    def call(self, x):\\r\\n        H, W = self.num_patch\\r\\n        B, L, C = x.get_shape().as_list()\\r\\n        \\r\\n        # Checking num_path and tensor sizes\\r\\n        assert L == H * W, 'Number of patches before and after Swin-MSA are mismatched.'\\r\\n        \\r\\n        # Skip connection I (start)\\r\\n        x_skip = x\\r\\n        \\r\\n        # Layer normalization\\r\\n        x = self.norm1(x)\\r\\n        \\r\\n        # Convert to aligned patches\\r\\n        x = tf.reshape(x, shape=(-1, H, W, C))\\r\\n\\r\\n        # Cyclic shift\\r\\n        if self.shift_size > 0:\\r\\n            shifted_x = tf.roll(x, shift=[-self.shift_size, -self.shift_size], axis=[1, 2])\\r\\n        else:\\r\\n            shifted_x = x\\r\\n\\r\\n        # Window partition \\r\\n        x_windows = window_partition(shifted_x, self.window_size)\\r\\n        x_windows = tf.reshape(x_windows, shape=(-1, self.window_size * self.window_size, C))\\r\\n\\r\\n        # Window-based multi-headed self-attention\\r\\n        attn_windows = self.attn(x_windows, mask=self.attn_mask)\\r\\n\\r\\n        # Merge windows\\r\\n        attn_windows = tf.reshape(attn_windows, shape=(-1, self.window_size, self.window_size, C))\\r\\n        shifted_x = window_reverse(attn_windows, self.window_size, H, W, C)\\r\\n\\r\\n        # Reverse cyclic shift\\r\\n        if self.shift_size > 0:\\r\\n            x = tf.roll(shifted_x, shift=[self.shift_size, self.shift_size], axis=[1, 2])\\r\\n        else:\\r\\n            x = shifted_x\\r\\n            \\r\\n        # Convert back to the patch sequence\\r\\n        x = tf.reshape(x, shape=(-1, H*W, C))\\r\\n\\r\\n        # Drop-path\\r\\n        ## if drop_path_prob = 0, it will not drop\\r\\n        x = self.drop_path(x)\\r\\n        \\r\\n        # Skip connection I (end)\\r\\n        x = x_skip +  x\\r\\n        \\r\\n        # Skip connection II (start)\\r\\n        x_skip = x\\r\\n        \\r\\n        x = self.norm2(x)\\r\\n        x = self.mlp(x)\\r\\n        x = self.drop_path(x)\\r\\n        \\r\\n        # Skip connection II (end)\\r\\n        x = x_skip + x\\r\\n\\r\\n        return x\"}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
        "import numpy as np\n",
        "import cv2\n",
        "from glob import glob\n",
        "from tqdm import tqdm\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import CustomObjectScope\n",
        "from transformer_layers import patch_extract, patch_embedding\n",
        "from activations import GELU, Snake\n",
        " \n",
        "if __name__ == \"__main__\":\n",
        " \n",
        "\n",
        "    # with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):\n",
        "    #     model = tf.keras.models.load_model(\"files/trans_Unet_model.h5\")\n",
        "\n",
        "    with CustomObjectScope({'patch_extract': patch_extract,'patch_embedding':patch_embedding,'GELU':GELU}):\n",
        "       model = tf.keras.models.load_model(\"/content/files/trans_Unet_model.h5\",compile=False)\n",
        "\n",
        "\n",
        "\n",
        "    create_dir(\"output\")\n",
        "    i=0\n",
        "    for x, y  in tqdm(zip(test_x,test_y), total=len(test_x)):\n",
        "\n",
        "        \"\"\" Reading the image \"\"\"\n",
        "        img = cv2.imread(x, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (128, 128))\n",
        "        x = img/255.0\n",
        "        x = x.astype(np.float32)\n",
        "        x = np.expand_dims(x, axis=0)\n",
        "\n",
        "        \"\"\" Reading the mask \"\"\"\n",
        "        img_y = cv2.imread(y, cv2.IMREAD_GRAYSCALE)\n",
        "        img_y = cv2.resize(img_y, (128, 128))\n",
        "        img_y = np.expand_dims(img_y, axis=-1)  \n",
        "        img_y = np.concatenate([img_y, img_y, img_y], axis=-1)  \n",
        "\n",
        "        \"\"\" Predicting the mask. \"\"\"\n",
        "        y_pred = model.predict(x)[0] > 0.5\n",
        "        y_pred = y_pred.astype(np.int32)\n",
        "\n",
        "        \"\"\" Saving the predicted mask along with the image and GT \"\"\"\n",
        "        y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)\n",
        "\n",
        "        sep_line = np.ones((128, 10, 3)) * 255\n",
        "        output_image = np.concatenate([img, sep_line, img_y, sep_line, y_pred*255], axis=1)\n",
        "        cv2.imwrite(f\"/content/output/{i}.png\", output_image)\n",
        "        i=i+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-4nMNPwWiy0",
        "outputId": "994bd25a-dcd8-440f-9e34-8a33241dcf47"
      },
      "execution_count": null,
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x=cv2.imread(\"/content/output/10.png\")\n",
        "x=cv2.cvtColor(x, cv2.COLOR_RGB2BGR)\n",
        "plt.imshow(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "LdAeLO06Wo5H",
        "outputId": "a3b461f6-5666-4d3e-cab3-d17f52315949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f94364daaf0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "x=pd.read_csv(\"/content/files/trans_UNET_data.csv\")\n",
        "epoch=x.iloc[:,0:1].values\n",
        "regular_iou=x.iloc[:,3:4].values\n",
        "val_iou=x.iloc[:,8:9].values \n",
        "plt.plot(epoch,regular_iou,label='regular iou')\n",
        "plt.plot(epoch,val_iou,label='validation iou')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "SOW1we3zWsGO",
        "outputId": "8e686060-86b3-4956-89ec-de8ebc57220b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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V5BdXtgV5Zb2rbZvYyHDqmrqf/T3K3WOlrqmZr104nm9fOtGGrTUDlg29a7zWX9+Pq7mFwxW17CupZr/H41hlPRFhQni4EBEWRniYOMunPIeBOJNFfHqyvSkpMSaCScMSmTQ8kcnDE5nofj0kLgpXcwuV9S6PuTQ95tV0z7VZ0+Bi4YxM5mZ3P9WgMX3Nht41A1pBWQ07j5xkX0k1B0qq2VdSRUFZLY3N7bP0jEiO4YyMBKaMSEJRXC1Kc4v7uVlxtbS0r2tWmlWZNy6VicOcAJ80PJERyTFdtn1HhIcxNN6Zjs2YYOdVqIvIFcDPcGY+elJVf9Th/Z8CF7kX44AMVT29WyFNSNtSUMEv3zrAm3nONLYiMHpoHBMyErhocgYTMhI5IyOB8enxJMZEBri0xgSPHkNdRMKBJ4AFQBGwWUTWqmpbXzxV/bbH9t8Aej2Pmqpab4IByB/NdKrKW/ml/PKt/WwuOE5KXCTfWTCRS3OGMS493sY8McYPvKmpzwX2q+pBABFZBVwDdNXBejFwf28KExMTQ3l5OampqRbsA4iqUl5eTkxM70YNdDW38MrOT/nVWwfIK65iZHIM9189hZvmZBEXZS2AxviTN/+iMoFCj+Ui4OzONhSRMUA28GYX7y8DlgGMHj36M++PGjWKoqIiSktLvSiW6U8xMTGMGjXKp8/UNzWzZmsRKzce5HBFLWdkJPDoDTO45qyRRIZb7xFj+oK/q0mLgDWq2mkfMVVdCawEp/dLx/cjIyPJzs72c5GMPzW3aNtwsq3PTR2WG10tfHT4BL975xPKqhuYkTWEf/98DgtyhhEWZn+BGdOXvAn1I0CWx/Io97rOLAK+frqFMoGjqpTXNPJJWQ2flNVQ4H7+pKyGQ+W1Pfbp9jR/Qhpfu/AszhlnzWnG9BdvQn0zMEFEsnHCfBHwLx03EpHJQArwvl9LaPrU3mNVvLLj01NCvKqh/eaciDBhdGoc2anxnDs+jSFxkUSGh7UNJRsVLkRFhDnrWteHh5GR5HRDNMb0rx5DXVVdIrIcWI/TpfEpVd0lIg8BW1R1rXvTRcAqDdTdTMYnh8tr+enre/nLtiMIkJkSy9jUeK6blcnYtHjGpsUzLi2ezCGxRFj7tzFBw6s2dVVdB6zrsO6+DssP+K9Ypq+UVNbz8zf3seqDQsLDhGXnj+OO88eTYjfeGBMSrD/ZIHGitpFf/eMAz75XgKtZWTQ3i29cPIFhSTa5sTGhxEI9xNU0uHjqnU9YufEg1Y0uvnhWJt+6dAJjUuMDXTRjBp6WFqg8AhUHoPwAnDgE8RmQPhkyJkNSpnP78wBmoR6iGlzN/OGfh3liw37KaxpZMGUY371sIpOHJwW6aCZYNLuguQFcDRAWAZGxED4AhmxodkFTDTS2Pqqd56Y6J3DDozp5REJEdPvrpjontCsOQPl+9+uDzsPlMadAWAS0tHccICoB0ic5Ie/5nDwawgbGtScL9RD03oEyvvfCDgor6jh3fCr/dvkkZo5OCXSxTG+pQuVRQEHCOnlI+2vECbm641B3wv18HOo9Xreub6hyAszV0B7ernpwNTrPnd1uIuEQEQORMc5zRIwT9hHRENEa+uqUue2ZDss+Pre42oO7sebU0PWH8ChIyYbU8TD+Yuc59QwYOh4SR0BdBZTmQ2le+/P+N2DbH9r3ERELyZnt30Hr99L2mlPXf+47MGWhf8+jtSh9slcTELWNLn78ah7Pvn+I7LR4nvvKXOZPSA90sczpUIWX7oAdq05/XxIGMckQmwIxQyA6EeKGumuw0e5gjnE/u1+31nRbXO7Qr3Oem+rcPwD10FTf/rqxGie8pPPn1tpsV+939hwW4dSQo+Ldj65ex4O2QHOj83A1tr9uezQ55Y+IhqHjnABPzoKwbsYdik9zHmPPO3V93XEo3dse9lVHT/0x0pb271BbTl0f0XfXsizUQ8SWggru+vN2CsprufW8sdx9+WRio2yArKD33i+cQJ/zVRgxwwmEtod2WHY/ohKc4I4d0h7gsSkQnTRgmghCQmwKjD7beQwgFupBrr6pmf96LZ8n3/mEUSmxrFo2j3njUgNdLOMPB96E1++HKdfAVY8O+At0ZmCwUA9i2wpP8N3V2zhQWsPNZ4/m3qtyiI8Owa+0oRrefhSKtsCkK2Hq9ZA4PNCl6lvHC2DN/3EuxF3zSwt047UQTIDQ1+Bq5udv7ONXbx1geFJM6Ladq8LONfD3H0DVp5A6AdbfC699H8ZdCNNvgslfgOgQG46gsQZW3ew0pSz6Q+idn+lTFupB5uMjJ7nrz9vJK67ixtxRfP8LU0gKxZmBij+GV++GQ+/CiLPgxucga45zQWrHati5Gl66HSLjnGCffpMT9OEB/F+67rjzI1R/Aub9X+fCna9U4eXlcGwX3LzGuZhnjA8G1MTTg1mjq4WKmkbKqhsor2mkvLqB8upGymqc5wr3ul1HKxkaH8WPrp/GxZOHBbrY/ld3HDb8J2x+0rnAd+n9MPNfP9s7oaUFCjfBjj/BrpecII1Ph6lfguk3wsiZ/dNk0dIMB99yurft+avTNRCcvyq+9BSMmO7b/t79Gfz9Prjkfpj/Hb8X1wS/niaetlAPsI8OH+e7f97OwdKaTt+PCg8jNSHKecRHc0ZGAt+4+AyGxIXYWC0tLfDRc/DGg06w534FLrrX6XLXE1cD7Pu7E/B7/+Z0XcuYArOXOjX42D6YLrfiE9j2vPOoLHJ+gKbfBDNvdvqBv7jM6d+84GE4+3bvfmAOvAm/vx5yFsINz1g7uumUhfoApar87p1P+NGreQxPjuGG2VmkJTrBnZYQRWpCNKkJUSRGRwTfWORVxXDoPYhJghjPrnXJnfcHLtoC6+6Cox/B6HPgqkdg+LTeHbvuhFNz//BZZ38RsTD1OifgR805vaBsrIU9a+Gj30PB24DAGZfAWTfDpKucG3Ja1ZTDy//X+ZGZcDl88ZdOX+euVHwCKy90bkP/ymvWjm66ZKE+AJ2obeSuP2/n9T0lXDZlGI98aQbJcQFqF29pdm4a6U37b0eNtfD+f8M7jzu3cX+GOEEfm9L+QODAG5AwHC57GKbd4L8a6tFtsPVpp527sRoyzoTcW53mmZjk7j+r6vw4le6Bkjwo3uE0rzRWOXcfzrwZZiyG5G6m+FOFD37rXNiNTYHrfuO0+3fUWANPLnDGHFn2Fgy12b9M1yzUB5ith45z5x8/oqSqnnuvymHpuWP7vybe0gJFH8DHL8Lul6G2zGmLPufrvrcBt+7v4zXw+gNOMOUshPO+6fxgdHWLeuujoQomXg4X3O3c4dgXGqqcYN/6NHy63V17v94J+MzZUFMKJXucOwNL9rS/rj/Rvo+4VKfGPfMWGHOubz88xTud7oll++Bz34KL/r19DBVVWHOr8z3cvMap+RvTDQv1AaKlRfnt2wd5ZH0+I4bE8N+LZzEjqw/aeruiCke2uoP8L074hkfDhAVOn+9tf3Rq19nnwznL4YwF3t19eHgTrL/H2feIs+Dy//zs7dQDyZEPYeszTsg31Th3XzZWt78fMwQycpxHeo4zMl96DiScZpfRxhr42z1Os1DmbLj+d06N/J3HnRuMLn3QCXxjeuCXUBeRK4Cf4cx89KSq/qiTbW4EHgAU2K6qn5nyztNgCvXjNY1898/beTOvhCunDudH108nObYXzS1NdU4tMjrRaT6ISXbGsOiKKny6zQnyXX+Bk4edcTzGX+K0M0+8wmkOAacG/eGz8M9fO2NYpE10uuXNWOQM2PSZkzrkhNGul5xBjy65D6YvCp7b0OsrYeefna6DaRPcQ6vmQMKwvr1Auesv8L93On/dzPmK09vlzGudnjLBdu3EBMRph7qIhAN7gQVAEc6cpYtVdbfHNhOA1cDFqnpcRDJUtaS7/Q6WUN96qILlz39EeXUj3/9CDv86b4zvzS3NTfDh/8DGR5ybcDxFxLQHfEyyU9OMSXaCuOAdOP6JMyDS+Iud8Jh0Vfe9QZqbnKB+7xdOO3JcqjPuyJzbnNpqfSW88xi8/0tngKjzvgnn3emfNvnB4sRheOE2KPwnDJvqXBi1/37GS/4I9XOAB1T1cvfyPQCq+v88tvkJsFdVn/S2YKEe6i0tykp3c8uolFj+e/Espo3q4eLcZ3cCH78AG37ohHPW2U73OFWnvbf+pFPDrj/p8WhdrnR6kEy9zrk5x5uugZ5UnR+F95+Ava86TTU5X4BPNjpt0NMXObXz5Ezf9msczS7nL4VxF0LSiECXxgSRnkLdm9vvMoFCj+UioOOwZBPdB3sXp4nmAVX9WyeFWQYsAxg9erQXhw5Omwsq+OEre9hWeILPTxvB/7t+mm93farC3vXw5sNw7GOnNrf4T84Fxf76E10Esuc7j7J9TrhvXwUjz4J/+ZPTLmx6LzwCzloc6FKYEOSve6ojgAnAhcAoYKOITFPVE54bqepKYCU4NXU/HXvAOFhazY//lsf6XccYlhTNozfM4PpZmb41txS8C2885PxpnpLtXFA787rAtlWnTYCrH4cv/NTafY0Z4LwJ9SNAlsfyKPc6T0XAJlVtAj4Rkb04Ib/ZL6Uc4CpqGvn5G/v4/T8PER0RxncXTOQr87OJi/LhN/PT7U6Y73/dufD4hZ86t8cPhOnDWlmgGzPgeZM6m4EJIpKNE+aLgI49W/4CLAaeFpE0nOaYg/4s6EBU39TM0+8W8MsN+6ltambRnCy+delE0hO76JHS2hZ+ohBOFjoXzE4UOn2iD7zh3KCy4GGYe1vnPU6MMaYHPYa6qrpEZDmwHqe9/ClV3SUiDwFbVHWt+73LRGQ30Az8m6qW92XBA6mlRXl5+xEe+Vs+R0/Wc2lOBiuunMwZGR43z9SUw/Y/OrORn3AH+MlCaKg8dWeRcTBkNJx/N5y7vOc7HY0xpht285GPNh0s5+FXdvPxkUqmZSZz71U5nDO+k5mGXn8A3vmpM4XYkNHOPIhDRsOQLI/lMU6vFGvWMMZ4yR+9X4zby9uO8O0/bWNEciyP33QWC2eMJCysi0A+vMnpIXLbm/1bSGPMoGah7qW/fHSE76zextzsofxuyZzup41zNcLRD53hY40xph9ZqHvhpY+K+O7q7Zydncrvlub23KuleKcz8mHW3P4poDHGuFmo9+DFD4v47p+3My87laeWziE2qpPxwDsq3OQ8Z3W8R8sYY/pWkIy+FBgvbHUC/ZxxPgQ6OMPaJmfZ7d/GmH5nod6FNVuLuGvNds4bn8bvlvgQ6ACFH1jTizEmIKz5pROrtxTyvRd28Lkz0vjtl3OJifQh0E8WOWOVW9OLMSYALNQ7WL25kO+92MtAB4/2dKupG2P6n4W6hz9tPsyKF3cyf0I6K/91tu+BDlC42ZkubdhU/xfQGGN6YG3qbqs+OMz3XtjJ+acT6ODU1DNnD6yBuIwxg4aFOvDKjk9Z8eJOLpyUzm9OJ9Aba53ZgqzpxRgTIIM+1CtqGvnByx8zI2sIv77lNAId4OhH0OKyi6TGmIAZ9KH+w1f2UFnXxI+vn3Z6gQ5O/3SAUXNOv2DGGNMLgzrU391fxgsfFnH7BeOYPDzp9HdY+AGkngHxnYzaaIwx/WDQhnp9UzP3vrST7LR4vnHxhNPfoapzkdSaXowxATRouzT+7I19HCqv5fnbzj79ZheAioNQW24XSY0xAeVVTV1ErhCRfBHZLyIrOnl/qYiUisg29+Or/i+q/+z5tJKVGw9yw+xRnDs+zT87LWxtT7dQN8YETo81dREJB54AFuBMML1ZRNaq6u4Om/5JVZf3QRn9qrlFWfHiTobERnLvVTn+23HhJmeWo/TJ/tunMcb4yJua+lxgv6oeVNVGYBVwTd8Wq+88934B2wtPcN/VU0iJj/Lfjgs/cHq9hA3ayxTGmAHAmwTKBAo9lovc6zq6XkR2iMgaEcnyS+n87OiJOh5Zn88FE9NZOGOk/3ZcfxJKdttFUmNMwPmrWvm/wFhVnQ78HXi2s41EZJmIbBGRLaWlpX46tHdUlfte/pgWhf/44lTEn5M9H9kKKGRZ/3RjTGB5E+pHAM+a9yj3ujaqWq6qDe7FJ4HZne1IVVeqaq6q5qanp/emvL32t4+LeX1PCd9ZMJGsoeXi8o0AABJkSURBVHH+3XnhB4BAZpcTfBtjTL/wJtQ3AxNEJFtEooBFwFrPDUTEc4qfhcAe/xXx9J2sa+L+tbuYmpnEreeN9f8BCjfBsDMhxg83MBljzGnosfeLqrpEZDmwHggHnlLVXSLyELBFVdcCd4rIQsAFVABL+7DMPvvx3/Ioq27gqaVziAj384XMlhYo2gLTvuTf/RpjTC94dfORqq4D1nVYd5/H63uAe/xbNP/YXFDB85sOc9v8bKZmJvv/AKV50FBp/dONMQNCSPe/a3A1c8+LO8kcEsu3F0zsm4PYTEfGmAEkpIcJ+PVbB9lfUs3Tt84hLqqPTrXwA4hLg6Hj+mb/xhjjg5CtqbuaW/j1Pw5w1bThXDQpo+8OVLjJqaX7s4ukMcb0UsiGekF5DXVNzVyaM6zvDlJTDhUHrOnFGDNghGyo5xVXATBpeGLfHaR1Ugy7k9QYM0CEbKjnF1cRHiackZHQdwcp3ARhETByZt8dwxhjfBCyoZ5XXEV2WjzREX4YK70rhR/A8OkQGdt3xzDGGB+EbKjnF1f1bdNLcxMc+dCaXowxA0pIhnpNg4vDFbVMHtaHoV68E1x1dpHUGDOghGSo7z3WDxdJC+0iqTFm4AnJUM9393yZPLwPB9gq3ARJmZDc2dDyxhgTGCEZ6nnFVcRFhTMqpQ8vYBZttqYXY8yAE6KhXsnEYYmEhfXRXZ4nj8DJQmt6McYMOCEX6qpKfnEVk/vlpiOrqRtjBpaQC/XSqgaO1zb5dpG0/ABs+E+oPOrd9oUfQEQMDJvWu0IaY0wfCblQ79XwAFufgX/8GH4+C954yJlIujuFm2DkLIiI6n1BjTGmD4RcqPeq50tpPqSMhZwvwNv/BT+fCZt+A67Gz27bVAef7rCmF2PMgORVqIvIFSKSLyL7RWRFN9tdLyIqIgGbgTmvuIqMxGiGxvtQiy7Ld8Zvuf5JWPYWZEyBV++GJ+bCrpdAtX3bo9ugpckukhpjBqQeQ11EwoEngCuBKcBiEZnSyXaJwDeBTf4upC/yj1X61vTSVAfHD0H6ZGd55ExY8r9w8xpnTJc/L4UnL4GCd533W2c6GjXHr+U2xhh/8KamPhfYr6oHVbURWAVc08l2DwM/Bur9WD6fNLco+45V+9bzpWwfoJDmMd2dCExYAHe8A9c8AZWfwjNXwfOLIH+dM8tRQrrfy2+MMafLm1DPBAo9lovc69qIyCwgS1Vf6W5HIrJMRLaIyJbS0lKfC9uTgvIaGlwtTPK1PR3aa+qewsJh5i3wja1wyf1w6F33TEfW9GKMGZhOe+JOEQkDHgOW9rStqq4EVgLk5uZqD5v7rP0iqS819XyQMEgd3/U2UXEw/zswawlsfQomff40S2qMMX3Dm1A/AmR5LI9yr2uVCEwF3hJnns7hwFoRWaiqW/xVUG/kFVcRJvg2MUZpvtOcEhHd87bxqXD+v/W+gMYY08e8aX7ZDEwQkWwRiQIWAWtb31TVk6qapqpjVXUs8E+g3wMdIL+4krFp8cRE+jAxRmk+pE3qu0IZY0w/6jHUVdUFLAfWA3uA1aq6S0QeEpGFfV1AX+T5OjxAc5MzcXT6xJ63NcaYIOBVm7qqrgPWdVh3XxfbXnj6xfJdbaMzMcZ1M0d5/6GKg9Di6vwiqTHGBKGQuaN077FqVH0cHqC150ua1dSNMaEhZEI9v7gS8LHni4W6MSbEhEyo5xVXERsZzuihcd5/qCwfkrMg2ofeMsYYM4CFTKjnF1cxcbiPE2OU5kG69XwxxoSOkAr1ycN8aHppaYGy/dad0RgTUkIi1EurGiivafTtIunJw+Cqs5q6MSakhESo92p4gLYxXyzUjTGhIyRCPc/d88W6MxpjBruQCPX84irSEqJJTfBi/JZWpfkQnwFxQ/uuYMYY089CI9SP+Tg8ADjdGa3pxRgTYoI+1JtblPziKt+aXlSdmrqFujEmxAR9qB9qmxjDh1CvKoaGSuvOaIwJOUEf6r3r+ZLnPFtN3RgTYoI+1POKq5wpRTN8me1or/NsoW6MCTFBH+r5xVWMTY0nNsrHiTFikiFhWN8VzBhjAiD4Q/1YFZN8GR4A2mc7Eh/GiTHGmCDgVaiLyBUiki8i+0VkRSfv3yEiO0Vkm4i8IyJT/F/Uz6prbKagvIbJI3rTndFuOjLGhJ4eQ11EwoEngCuBKcDiTkL7eVWdpqpnAT8BHvN7STuxr6QKVR8vktZWQE2pzXZkjAlJ3tTU5wL7VfWgqjYCq4BrPDdQ1UqPxXhA/VfEruW5e75MGp7k/Yfahgewi6TGmNDjzRylmUChx3IRcHbHjUTk68B3gCjg4s52JCLLgGUAo0eP9rWsn5FfXEVMZJhvE2NYd0ZjTAjz24VSVX1CVccD3wO+38U2K1U1V1Vz09PTT/uY+cVVTByWSLgvE2OU7YXIOGfGI2OMCTHehPoRwDMBR7nXdWUV8MXTKZS38op72/NlAoQFfccfY4z5DG+SbTMwQUSyRSQKWASs9dxARCZ4LH4e2Oe/InaurLqBsuoG34YHgPbujMYYE4J6bFNXVZeILAfWA+HAU6q6S0QeArao6lpguYhcCjQBx4ElfVlo8BwewIeLpA1VUFlk7enGmJDlzYVSVHUdsK7Duvs8Xn/Tz+XqUXvPFxsewBhjWgVtw3J+cSWp8VGkJ/oyMUZrqFsfdWNMaAriUPdxDHVwujOGRUJKdt8UyhhjAiwoQ72lRdl7rNr3UC/bC6njIdyrVidjjAk6QRnqhytqqWtq9n0Ku9I8a083xoS0oAz1Xg0P0FQPxwusO6MxJqQFZajnuyfGmDgswfsPVRwAbbGaujEmpAVnqB+rZMzQOOKifGgbtzFfjDGDQFCGel6ver7sBQmD1DP6plDGGDMABF2o1zc1U1BW41t7Ojg19SFjIDK2bwpmjDEDQNCF+r5j1bT4OjEGON0Z7aYjY0yIC7pQzyt25uPwqfml2QVl+2wKO2NMyAu6UBcRJg1LZGxqvPcfOl4ALU3WndEYE/KC7tbKL80exZdmj/LtQ209X6z5xRgT2oKupt4rZa3zkk7ofjtjjAlygyPUS/dCUibE+NhjxhhjgswgCfU8SLOLpMaY0OdVqIvIFSKSLyL7RWRFJ+9/R0R2i8gOEXlDRMb4v6i91NLi7vli7enGmNDXY6iLSDjwBHAlMAVYLCJTOmz2EZCrqtOBNcBP/F3QXqssgqYa685ojBkUvKmpzwX2q+pBVW0EVgHXeG6gqhtUtda9+E/Ax+4pfchmOzLGDCLehHomUOixXORe15WvAK929oaILBORLSKypbS01PtSno7W7ozWR90YMwj49UKpiNwC5AKPdPa+qq5U1VxVzU1PT/fnobtWlg9xaRCf2j/HM8aYAPLm5qMjQJbH8ij3ulOIyKXAvwMXqGqDf4rnB6X5NtyuMWbQ8KamvhmYICLZIhIFLALWem4gIjOB3wALVbXE/8XsJVUn1K07ozFmkOgx1FXVBSwH1gN7gNWquktEHhKRhe7NHgESgD+LyDYRWdvF7vpXTSnUn7CLpMaYQcOrsV9UdR2wrsO6+zxeX+rncvlH25gvVlM3xgwOoX1Haal7zBerqRtjBonQD/WoREgcEeiSGGNMvwjtUC9z93wRCXRJjDGmX4RuqKtCSZ51ZzTGDCqhG+qleVBTApmzA10SY4zpN6Eb6nmvOM+TrgpsOYwxph+Fbqjnr4ORsyDJLpIaYwaP0Az1yk/hyFaYbLV0Y8zgEpqhvtc9SOTkLwS2HMYY089CM9Tz1kFKtt10ZIwZdEIv1Buq4JN/wOTPW/90Y8ygE3qhvv91aG60Xi/GmEEp9EI9bx3EDoWsswNdEmOM6XehFerNTbBvPUy8AsK9GoDSGGNCSmiF+qH3oP6kdWU0xgxaoRXq+esgIgbGXxzokhhjTEB4FeoicoWI5IvIfhFZ0cn754vIhyLiEpEv+b+YXlB12tPHXQhR8QEpgjHGBFqPoS4i4cATwJXAFGCxiEzpsNlhYCnwvL8L6LVjH8PJw05XRmOMGaS8uZo4F9ivqgcBRGQVcA2wu3UDVS1wv9fSB2X0Tt4rgDgXSY0xZpDypvklEyj0WC5yr/OZiCwTkS0isqW0tLQ3u+ha3iuQNRcSMvy7X2OMCSL9eqFUVVeqaq6q5qanp/tvxycKoXiH3XBkjBn0vAn1I0CWx/Io97qBI791AC9rTzfGDG7ehPpmYIKIZItIFLAIWNu3xfJR/iuQOgHSJgS6JMYYE1A9hrqquoDlwHpgD7BaVXeJyEMishBAROaISBFwA/AbEdnVl4U+Rd0JKHjHbjgyxhi86/2Cqq4D1nVYd5/H6804zTL9b//r0OKCSdb0YowxwX9Had4rEJ8Oo3IDXRJjjAm44A51VwPs+ztMuhLCwgNdGmOMCbjgDvWCt6GxyppejDHGLbhDPW8dRMbBuAsCXRJjjBkQgjfUVZ3+6eMvhsjYQJfGGGMGhOAN9aMfQdVRu+HIGGM8BG+o568DCYMJlwe6JMYYM2AEb6jnrYPR50B8aqBLYowxA0ZwhnrFJ1CyywbwMsaYDoIz1PPdN7fa0ADGGHOK4Az1vHWQMQWGjgt0SYwxZkAJvlCvrYDD71nTizHGdCL4Qn3vetAWa3oxxphOBF+oxyQ7wwKMmBnokhhjzIDj1dC7A8rkq6yWbowxXQi+mroxxpgueRXqInKFiOSLyH4RWdHJ+9Ei8if3+5tEZKy/C2qMMaZnPYa6iIQDTwBXAlOAxSIypcNmXwGOq+oZwE+BH/u7oMYYY3rmTU19LrBfVQ+qaiOwCrimwzbXAM+6X68BLhER8V8xjTHGeMObUM8ECj2Wi9zrOt3GPVH1ScAGZTHGmH7WrxdKRWSZiGwRkS2lpaX9eWhjjBkUvAn1I0CWx/Io97pOtxGRCCAZKO+4I1Vdqaq5qpqbnp7euxIbY4zpkjehvhmYICLZIhIFLALWdthmLbDE/fpLwJuqqv4rpjHGGG+IN9krIlcBjwPhwFOq+kMReQjYoqprRSQGeA6YCVQAi1T1YA/7LAUO9bLcaUBZLz87UIXaOYXa+UDonVOonQ+E3jl1dj5jVLXLpg6vQn2gEZEtqpob6HL4U6idU6idD4TeOYXa+UDonVNvzsfuKDXGmBBioW6MMSEkWEN9ZaAL0AdC7ZxC7Xwg9M4p1M4HQu+cfD6foGxTN8YY07lgrakbY4zphIW6McaEkKAL9Z6GAQ42IlIgIjtFZJuIbAl0eXpDRJ4SkRIR+dhj3VAR+buI7HM/pwSyjL7o4nweEJEj7u9pm/vejaAhIlkiskFEdovILhH5pnt9UH5P3ZxP0H5PIhIjIh+IyHb3OT3oXp/tHtJ8v3uI86hu9xNMberuYYD3AgtwBhbbDCxW1d0BLdhpEJECIFdVg/aGCRE5H6gG/kdVp7rX/QSoUNUfuX98U1T1e4Esp7e6OJ8HgGpVfTSQZestERkBjFDVD0UkEdgKfBFYShB+T92cz40E6ffkHtk2XlWrRSQSeAf4JvAd4EVVXSUivwa2q+qvutpPsNXUvRkG2PQzVd2IcyexJ8/hmJ/F+QcXFLo4n6Cmqp+q6ofu11XAHpzRVYPye+rmfIKWOqrdi5HuhwIX4wxpDl58R8EW6t4MAxxsFHhNRLaKyLJAF8aPhqnqp+7XxcCwQBbGT5aLyA5380xQNFN0xj0z2UxgEyHwPXU4Hwji70lEwkVkG1AC/B04AJxwD2kOXmResIV6KPqcqs7CmVnq6+4//UOKe3C34Gnn69yvgPHAWcCnwH8Ftji9IyIJwAvAt1S10vO9YPyeOjmfoP6eVLVZVc/CGQ13LjDZ130EW6h7MwxwUFHVI+7nEuAlnC8yFBxzt3u2tn+WBLg8p0VVj7n/wbUAvyUIvyd3O+0LwB9U9UX36qD9njo7n1D4ngBU9QSwATgHGOIe0hy8yLxgC3VvhgEOGiIS777Ig4jEA5cBH3f/qaDhORzzEuDlAJbltLUGn9u1BNn35L4I9ztgj6o+5vFWUH5PXZ1PMH9PIpIuIkPcr2NxOoTswQn3L7k36/E7CqreL9D5MMABLlKvicg4nNo5QATwfDCej4j8EbgQZ5jQY8D9wF+A1cBonCGWb1TVoLj42MX5XIjzJ70CBcDtHm3RA56IfA54G9gJtLhX34vTDh1031M357OYIP2eRGQ6zoXQcJwK92pVfcidE6uAocBHwC2q2tDlfoIt1I0xxnQt2JpfjDHGdMNC3RhjQoiFujHGhBALdWOMCSEW6sYYE0Is1I0xJoRYqBtjTAj5/4/Rqin4MUMvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6PZ9J4qfWsLj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}