104. DoubleUnet_brain_tumor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0TRxG_H96lC",
        "outputId": "c61816f5-e87d-4e86-dedf-8aa6b0453916"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "\n",
        "!pip install segmentation-models\n",
        "!pip install tensorflow\n",
        "     "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Reshape, Dense, Multiply, AveragePooling2D, UpSampling2D\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications import VGG19,VGG16,ResNet50,InceptionV3\n",
        "\n",
        "def squeeze_excite_block(inputs, ratio=8):\n",
        "    init = inputs       ## (b, 128, 128, 32)\n",
        "    channel_axis = -1\n",
        "    filters = init.shape[channel_axis]\n",
        "    se_shape = (1, 1, filters)\n",
        "\n",
        "    se = GlobalAveragePooling2D()(init)     ## (b, 32)   -> (b, 1, 1, 32)\n",
        "    se = Reshape(se_shape)(se)\n",
        "    se = Dense(filters//ratio, activation=\"relu\", use_bias=False)(se)\n",
        "    se = Dense(filters, activation=\"sigmoid\", use_bias=False)(se)\n",
        "\n",
        "    x = Multiply()([inputs, se])\n",
        "    return x\n",
        "\n",
        "def ASPP(x, filter):\n",
        "    shape = x.shape\n",
        "\n",
        "    y1 = AveragePooling2D(pool_size=(shape[1], shape[2]))(x)\n",
        "    y1 = Conv2D(filter, 1, padding=\"same\")(y1)\n",
        "    y1 = BatchNormalization()(y1)\n",
        "    y1 = Activation(\"relu\")(y1)\n",
        "    y1 = UpSampling2D((shape[1], shape[2]), interpolation=\"bilinear\")(y1)\n",
        "\n",
        "    y2 = Conv2D(filter, 1, dilation_rate=1, padding=\"same\", use_bias=False)(x)\n",
        "    y2 = BatchNormalization()(y2)\n",
        "    y2 = Activation(\"relu\")(y2)\n",
        "\n",
        "    y3 = Conv2D(filter, 3, dilation_rate=6, padding=\"same\", use_bias=False)(x)\n",
        "    y3 = BatchNormalization()(y3)\n",
        "    y3 = Activation(\"relu\")(y3)\n",
        "\n",
        "    y4 = Conv2D(filter, 3, dilation_rate=12, padding=\"same\", use_bias=False)(x)\n",
        "    y4 = BatchNormalization()(y4)\n",
        "    y4 = Activation(\"relu\")(y4)\n",
        "\n",
        "    y5 = Conv2D(filter, 3, dilation_rate=18, padding=\"same\", use_bias=False)(x)\n",
        "    y5 = BatchNormalization()(y5)\n",
        "    y5 = Activation(\"relu\")(y5)\n",
        "\n",
        "    y = Concatenate()([y1, y2, y3, y4, y5])\n",
        "\n",
        "    y = Conv2D(filter, 1, dilation_rate=1, padding=\"same\", use_bias=False)(y)\n",
        "    y = BatchNormalization()(y)\n",
        "    y = Activation(\"relu\")(y)\n",
        "\n",
        "    return y\n",
        "\n",
        "def conv_block(x, filters):\n",
        "    x = Conv2D(filters, 3, padding=\"same\")(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    x = Conv2D(filters, 3, padding=\"same\")(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    x = squeeze_excite_block(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "def encoder1(inputs):\n",
        "    skip_connections = []\n",
        "\n",
        "    model = VGG19(include_top=False, weights=\"imagenet\", input_tensor=inputs)\n",
        "    names = [\"block1_conv2\", \"block2_conv2\", \"block3_conv4\", \"block4_conv4\"]\n",
        "    for name in names:\n",
        "        skip_connections.append(model.get_layer(name).output)\n",
        "\n",
        "    output = model.get_layer(\"block5_conv4\").output\n",
        "    return output, skip_connections\n",
        "\n",
        "def decoder1(inputs, skip_connections):\n",
        "    num_filters = [256, 128, 64, 32]\n",
        "    skip_connections.reverse()\n",
        "\n",
        "    x = inputs\n",
        "    for i, f in enumerate(num_filters):\n",
        "        x = UpSampling2D((2, 2), interpolation=\"bilinear\")(x)\n",
        "        x = Concatenate()([x, skip_connections[i]])\n",
        "        x = conv_block(x, f)\n",
        "\n",
        "    return x\n",
        "\n",
        "def output_block(inputs):\n",
        "    x = Conv2D(1, 1, padding=\"same\")(inputs)\n",
        "    x = Activation(\"sigmoid\")(x)\n",
        "    return x\n",
        "\n",
        "def encoder2(inputs):\n",
        "    num_filters = [32, 64, 128, 256]\n",
        "    skip_connections = []\n",
        "\n",
        "    x = inputs\n",
        "    for i, f in enumerate(num_filters):\n",
        "        x = conv_block(x, f)\n",
        "        skip_connections.append(x)\n",
        "        x = MaxPool2D((2, 2))(x)\n",
        "\n",
        "    return x, skip_connections\n",
        "\n",
        "def decoder2(inputs, skip_1, skip_2):\n",
        "    num_filters = [256, 128, 64, 32]\n",
        "    skip_2.reverse()\n",
        "\n",
        "    x = inputs\n",
        "    for i, f in enumerate(num_filters):\n",
        "        x = UpSampling2D((2, 2), interpolation=\"bilinear\")(x)\n",
        "        x = Concatenate()([x, skip_1[i], skip_2[i]])\n",
        "        x = conv_block(x, f)\n",
        "\n",
        "    return x\n",
        "\n",
        "def build_model(input_shape):\n",
        "    inputs = Input(input_shape)\n",
        "    x, skip_1 = encoder1(inputs)\n",
        "    x = ASPP(x, 64)\n",
        "    x = decoder1(x, skip_1)\n",
        "    output1 = output_block(x)\n",
        "\n",
        "    x = inputs * output1\n",
        "\n",
        "    x, skip_2 = encoder2(x)\n",
        "    x = ASPP(x, 64)\n",
        "    x = decoder2(x, skip_1, skip_2)\n",
        "    output2 = output_block(x)\n",
        "\n",
        "    outputs = Concatenate()([output1, output2])\n",
        "    model = Model(inputs, outputs)\n",
        "    return model\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    input_shape = (256, 256, 3)\n",
        "    model = build_model(input_shape)\n",
        "    model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIvp73ei-MqB",
        "outputId": "5c624a9d-d27b-4df6-cc91-cb7bd43afa0e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_5 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " block1_conv1 (Conv2D)          (None, 256, 256, 64  1792        ['input_5[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1_conv2 (Conv2D)          (None, 256, 256, 64  36928       ['block1_conv1[0][0]']           \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1_pool (MaxPooling2D)     (None, 128, 128, 64  0           ['block1_conv2[0][0]']           \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block2_conv1 (Conv2D)          (None, 128, 128, 12  73856       ['block1_pool[0][0]']            \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " block2_conv2 (Conv2D)          (None, 128, 128, 12  147584      ['block2_conv1[0][0]']           \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " block2_pool (MaxPooling2D)     (None, 64, 64, 128)  0           ['block2_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " block3_conv1 (Conv2D)          (None, 64, 64, 256)  295168      ['block2_pool[0][0]']            \n",
            "                                                                                                  \n",
            " block3_conv2 (Conv2D)          (None, 64, 64, 256)  590080      ['block3_conv1[0][0]']           \n",
            "                                                                                                  \n",
            " block3_conv3 (Conv2D)          (None, 64, 64, 256)  590080      ['block3_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " block3_conv4 (Conv2D)          (None, 64, 64, 256)  590080      ['block3_conv3[0][0]']           \n",
            "                                                                                                  \n",
            " block3_pool (MaxPooling2D)     (None, 32, 32, 256)  0           ['block3_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " block4_conv1 (Conv2D)          (None, 32, 32, 512)  1180160     ['block3_pool[0][0]']            \n",
            "                                                                                                  \n",
            " block4_conv2 (Conv2D)          (None, 32, 32, 512)  2359808     ['block4_conv1[0][0]']           \n",
            "                                                                                                  \n",
            " block4_conv3 (Conv2D)          (None, 32, 32, 512)  2359808     ['block4_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " block4_conv4 (Conv2D)          (None, 32, 32, 512)  2359808     ['block4_conv3[0][0]']           \n",
            "                                                                                                  \n",
            " block4_pool (MaxPooling2D)     (None, 16, 16, 512)  0           ['block4_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " block5_conv1 (Conv2D)          (None, 16, 16, 512)  2359808     ['block4_pool[0][0]']            \n",
            "                                                                                                  \n",
            " block5_conv2 (Conv2D)          (None, 16, 16, 512)  2359808     ['block5_conv1[0][0]']           \n",
            "                                                                                                  \n",
            " block5_conv3 (Conv2D)          (None, 16, 16, 512)  2359808     ['block5_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " block5_conv4 (Conv2D)          (None, 16, 16, 512)  2359808     ['block5_conv3[0][0]']           \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (AveragePo  (None, 1, 1, 512)   0           ['block5_conv4[0][0]']           \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_152 (Conv2D)            (None, 1, 1, 64)     32832       ['average_pooling2d_8[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_144 (Batch  (None, 1, 1, 64)    256         ['conv2d_152[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " conv2d_153 (Conv2D)            (None, 16, 16, 64)   32768       ['block5_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_154 (Conv2D)            (None, 16, 16, 64)   294912      ['block5_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_155 (Conv2D)            (None, 16, 16, 64)   294912      ['block5_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_156 (Conv2D)            (None, 16, 16, 64)   294912      ['block5_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " activation_152 (Activation)    (None, 1, 1, 64)     0           ['batch_normalization_144[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_145 (Batch  (None, 16, 16, 64)  256         ['conv2d_153[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_146 (Batch  (None, 16, 16, 64)  256         ['conv2d_154[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_147 (Batch  (None, 16, 16, 64)  256         ['conv2d_155[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_148 (Batch  (None, 16, 16, 64)  256         ['conv2d_156[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " up_sampling2d_40 (UpSampling2D  (None, 16, 16, 64)  0           ['activation_152[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " activation_153 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_145[0][0]']\n",
            "                                                                                                  \n",
            " activation_154 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_146[0][0]']\n",
            "                                                                                                  \n",
            " activation_155 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_147[0][0]']\n",
            "                                                                                                  \n",
            " activation_156 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_148[0][0]']\n",
            "                                                                                                  \n",
            " concatenate_42 (Concatenate)   (None, 16, 16, 320)  0           ['up_sampling2d_40[0][0]',       \n",
            "                                                                  'activation_153[0][0]',         \n",
            "                                                                  'activation_154[0][0]',         \n",
            "                                                                  'activation_155[0][0]',         \n",
            "                                                                  'activation_156[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_157 (Conv2D)            (None, 16, 16, 64)   20480       ['concatenate_42[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_149 (Batch  (None, 16, 16, 64)  256         ['conv2d_157[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_157 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_149[0][0]']\n",
            "                                                                                                  \n",
            " up_sampling2d_41 (UpSampling2D  (None, 32, 32, 64)  0           ['activation_157[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " concatenate_43 (Concatenate)   (None, 32, 32, 576)  0           ['up_sampling2d_41[0][0]',       \n",
            "                                                                  'block4_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_158 (Conv2D)            (None, 32, 32, 256)  1327360     ['concatenate_43[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_150 (Batch  (None, 32, 32, 256)  1024       ['conv2d_158[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_158 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_150[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_159 (Conv2D)            (None, 32, 32, 256)  590080      ['activation_158[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_151 (Batch  (None, 32, 32, 256)  1024       ['conv2d_159[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_159 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_151[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_48 (G  (None, 256)         0           ['activation_159[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_48 (Reshape)           (None, 1, 1, 256)    0           ['global_average_pooling2d_48[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_96 (Dense)               (None, 1, 1, 32)     8192        ['reshape_48[0][0]']             \n",
            "                                                                                                  \n",
            " dense_97 (Dense)               (None, 1, 1, 256)    8192        ['dense_96[0][0]']               \n",
            "                                                                                                  \n",
            " multiply_48 (Multiply)         (None, 32, 32, 256)  0           ['activation_159[0][0]',         \n",
            "                                                                  'dense_97[0][0]']               \n",
            "                                                                                                  \n",
            " up_sampling2d_42 (UpSampling2D  (None, 64, 64, 256)  0          ['multiply_48[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " concatenate_44 (Concatenate)   (None, 64, 64, 512)  0           ['up_sampling2d_42[0][0]',       \n",
            "                                                                  'block3_conv4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_160 (Conv2D)            (None, 64, 64, 128)  589952      ['concatenate_44[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_152 (Batch  (None, 64, 64, 128)  512        ['conv2d_160[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_160 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_152[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_161 (Conv2D)            (None, 64, 64, 128)  147584      ['activation_160[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_153 (Batch  (None, 64, 64, 128)  512        ['conv2d_161[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_161 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_153[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_49 (G  (None, 128)         0           ['activation_161[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_49 (Reshape)           (None, 1, 1, 128)    0           ['global_average_pooling2d_49[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_98 (Dense)               (None, 1, 1, 16)     2048        ['reshape_49[0][0]']             \n",
            "                                                                                                  \n",
            " dense_99 (Dense)               (None, 1, 1, 128)    2048        ['dense_98[0][0]']               \n",
            "                                                                                                  \n",
            " multiply_49 (Multiply)         (None, 64, 64, 128)  0           ['activation_161[0][0]',         \n",
            "                                                                  'dense_99[0][0]']               \n",
            "                                                                                                  \n",
            " up_sampling2d_43 (UpSampling2D  (None, 128, 128, 12  0          ['multiply_49[0][0]']            \n",
            " )                              8)                                                                \n",
            "                                                                                                  \n",
            " concatenate_45 (Concatenate)   (None, 128, 128, 25  0           ['up_sampling2d_43[0][0]',       \n",
            "                                6)                                'block2_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_162 (Conv2D)            (None, 128, 128, 64  147520      ['concatenate_45[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_154 (Batch  (None, 128, 128, 64  256        ['conv2d_162[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_162 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_154[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_163 (Conv2D)            (None, 128, 128, 64  36928       ['activation_162[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_155 (Batch  (None, 128, 128, 64  256        ['conv2d_163[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_163 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_155[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_50 (G  (None, 64)          0           ['activation_163[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_50 (Reshape)           (None, 1, 1, 64)     0           ['global_average_pooling2d_50[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_100 (Dense)              (None, 1, 1, 8)      512         ['reshape_50[0][0]']             \n",
            "                                                                                                  \n",
            " dense_101 (Dense)              (None, 1, 1, 64)     512         ['dense_100[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_50 (Multiply)         (None, 128, 128, 64  0           ['activation_163[0][0]',         \n",
            "                                )                                 'dense_101[0][0]']              \n",
            "                                                                                                  \n",
            " up_sampling2d_44 (UpSampling2D  (None, 256, 256, 64  0          ['multiply_50[0][0]']            \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " concatenate_46 (Concatenate)   (None, 256, 256, 12  0           ['up_sampling2d_44[0][0]',       \n",
            "                                8)                                'block1_conv2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_164 (Conv2D)            (None, 256, 256, 32  36896       ['concatenate_46[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_156 (Batch  (None, 256, 256, 32  128        ['conv2d_164[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_164 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_156[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_165 (Conv2D)            (None, 256, 256, 32  9248        ['activation_164[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_157 (Batch  (None, 256, 256, 32  128        ['conv2d_165[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_165 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_157[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_51 (G  (None, 32)          0           ['activation_165[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_51 (Reshape)           (None, 1, 1, 32)     0           ['global_average_pooling2d_51[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_102 (Dense)              (None, 1, 1, 4)      128         ['reshape_51[0][0]']             \n",
            "                                                                                                  \n",
            " dense_103 (Dense)              (None, 1, 1, 32)     128         ['dense_102[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_51 (Multiply)         (None, 256, 256, 32  0           ['activation_165[0][0]',         \n",
            "                                )                                 'dense_103[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_166 (Conv2D)            (None, 256, 256, 1)  33          ['multiply_51[0][0]']            \n",
            "                                                                                                  \n",
            " activation_166 (Activation)    (None, 256, 256, 1)  0           ['conv2d_166[0][0]']             \n",
            "                                                                                                  \n",
            " tf.math.multiply_4 (TFOpLambda  (None, 256, 256, 3)  0          ['input_5[0][0]',                \n",
            " )                                                                'activation_166[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_167 (Conv2D)            (None, 256, 256, 32  896         ['tf.math.multiply_4[0][0]']     \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_158 (Batch  (None, 256, 256, 32  128        ['conv2d_167[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_167 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_158[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_168 (Conv2D)            (None, 256, 256, 32  9248        ['activation_167[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_159 (Batch  (None, 256, 256, 32  128        ['conv2d_168[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_168 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_159[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_52 (G  (None, 32)          0           ['activation_168[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_52 (Reshape)           (None, 1, 1, 32)     0           ['global_average_pooling2d_52[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_104 (Dense)              (None, 1, 1, 4)      128         ['reshape_52[0][0]']             \n",
            "                                                                                                  \n",
            " dense_105 (Dense)              (None, 1, 1, 32)     128         ['dense_104[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_52 (Multiply)         (None, 256, 256, 32  0           ['activation_168[0][0]',         \n",
            "                                )                                 'dense_105[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_16 (MaxPooling2D  (None, 128, 128, 32  0          ['multiply_52[0][0]']            \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_169 (Conv2D)            (None, 128, 128, 64  18496       ['max_pooling2d_16[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_160 (Batch  (None, 128, 128, 64  256        ['conv2d_169[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_169 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_160[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_170 (Conv2D)            (None, 128, 128, 64  36928       ['activation_169[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_161 (Batch  (None, 128, 128, 64  256        ['conv2d_170[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_170 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_161[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_53 (G  (None, 64)          0           ['activation_170[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_53 (Reshape)           (None, 1, 1, 64)     0           ['global_average_pooling2d_53[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_106 (Dense)              (None, 1, 1, 8)      512         ['reshape_53[0][0]']             \n",
            "                                                                                                  \n",
            " dense_107 (Dense)              (None, 1, 1, 64)     512         ['dense_106[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_53 (Multiply)         (None, 128, 128, 64  0           ['activation_170[0][0]',         \n",
            "                                )                                 'dense_107[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_17 (MaxPooling2D  (None, 64, 64, 64)  0           ['multiply_53[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv2d_171 (Conv2D)            (None, 64, 64, 128)  73856       ['max_pooling2d_17[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_162 (Batch  (None, 64, 64, 128)  512        ['conv2d_171[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_171 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_162[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_172 (Conv2D)            (None, 64, 64, 128)  147584      ['activation_171[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_163 (Batch  (None, 64, 64, 128)  512        ['conv2d_172[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_172 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_163[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_54 (G  (None, 128)         0           ['activation_172[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_54 (Reshape)           (None, 1, 1, 128)    0           ['global_average_pooling2d_54[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_108 (Dense)              (None, 1, 1, 16)     2048        ['reshape_54[0][0]']             \n",
            "                                                                                                  \n",
            " dense_109 (Dense)              (None, 1, 1, 128)    2048        ['dense_108[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_54 (Multiply)         (None, 64, 64, 128)  0           ['activation_172[0][0]',         \n",
            "                                                                  'dense_109[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_18 (MaxPooling2D  (None, 32, 32, 128)  0          ['multiply_54[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv2d_173 (Conv2D)            (None, 32, 32, 256)  295168      ['max_pooling2d_18[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_164 (Batch  (None, 32, 32, 256)  1024       ['conv2d_173[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_173 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_164[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_174 (Conv2D)            (None, 32, 32, 256)  590080      ['activation_173[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_165 (Batch  (None, 32, 32, 256)  1024       ['conv2d_174[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_174 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_165[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_55 (G  (None, 256)         0           ['activation_174[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_55 (Reshape)           (None, 1, 1, 256)    0           ['global_average_pooling2d_55[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_110 (Dense)              (None, 1, 1, 32)     8192        ['reshape_55[0][0]']             \n",
            "                                                                                                  \n",
            " dense_111 (Dense)              (None, 1, 1, 256)    8192        ['dense_110[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_55 (Multiply)         (None, 32, 32, 256)  0           ['activation_174[0][0]',         \n",
            "                                                                  'dense_111[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_19 (MaxPooling2D  (None, 16, 16, 256)  0          ['multiply_55[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " average_pooling2d_9 (AveragePo  (None, 1, 1, 256)   0           ['max_pooling2d_19[0][0]']       \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_175 (Conv2D)            (None, 1, 1, 64)     16448       ['average_pooling2d_9[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_166 (Batch  (None, 1, 1, 64)    256         ['conv2d_175[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " conv2d_176 (Conv2D)            (None, 16, 16, 64)   16384       ['max_pooling2d_19[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_177 (Conv2D)            (None, 16, 16, 64)   147456      ['max_pooling2d_19[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_178 (Conv2D)            (None, 16, 16, 64)   147456      ['max_pooling2d_19[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_179 (Conv2D)            (None, 16, 16, 64)   147456      ['max_pooling2d_19[0][0]']       \n",
            "                                                                                                  \n",
            " activation_175 (Activation)    (None, 1, 1, 64)     0           ['batch_normalization_166[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_167 (Batch  (None, 16, 16, 64)  256         ['conv2d_176[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_168 (Batch  (None, 16, 16, 64)  256         ['conv2d_177[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_169 (Batch  (None, 16, 16, 64)  256         ['conv2d_178[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_170 (Batch  (None, 16, 16, 64)  256         ['conv2d_179[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " up_sampling2d_45 (UpSampling2D  (None, 16, 16, 64)  0           ['activation_175[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " activation_176 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_167[0][0]']\n",
            "                                                                                                  \n",
            " activation_177 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_168[0][0]']\n",
            "                                                                                                  \n",
            " activation_178 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_169[0][0]']\n",
            "                                                                                                  \n",
            " activation_179 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_170[0][0]']\n",
            "                                                                                                  \n",
            " concatenate_47 (Concatenate)   (None, 16, 16, 320)  0           ['up_sampling2d_45[0][0]',       \n",
            "                                                                  'activation_176[0][0]',         \n",
            "                                                                  'activation_177[0][0]',         \n",
            "                                                                  'activation_178[0][0]',         \n",
            "                                                                  'activation_179[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_180 (Conv2D)            (None, 16, 16, 64)   20480       ['concatenate_47[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_171 (Batch  (None, 16, 16, 64)  256         ['conv2d_180[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_180 (Activation)    (None, 16, 16, 64)   0           ['batch_normalization_171[0][0]']\n",
            "                                                                                                  \n",
            " up_sampling2d_46 (UpSampling2D  (None, 32, 32, 64)  0           ['activation_180[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " concatenate_48 (Concatenate)   (None, 32, 32, 832)  0           ['up_sampling2d_46[0][0]',       \n",
            "                                                                  'block4_conv4[0][0]',           \n",
            "                                                                  'multiply_55[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_181 (Conv2D)            (None, 32, 32, 256)  1917184     ['concatenate_48[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_172 (Batch  (None, 32, 32, 256)  1024       ['conv2d_181[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_181 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_172[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_182 (Conv2D)            (None, 32, 32, 256)  590080      ['activation_181[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_173 (Batch  (None, 32, 32, 256)  1024       ['conv2d_182[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_182 (Activation)    (None, 32, 32, 256)  0           ['batch_normalization_173[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_56 (G  (None, 256)         0           ['activation_182[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_56 (Reshape)           (None, 1, 1, 256)    0           ['global_average_pooling2d_56[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_112 (Dense)              (None, 1, 1, 32)     8192        ['reshape_56[0][0]']             \n",
            "                                                                                                  \n",
            " dense_113 (Dense)              (None, 1, 1, 256)    8192        ['dense_112[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_56 (Multiply)         (None, 32, 32, 256)  0           ['activation_182[0][0]',         \n",
            "                                                                  'dense_113[0][0]']              \n",
            "                                                                                                  \n",
            " up_sampling2d_47 (UpSampling2D  (None, 64, 64, 256)  0          ['multiply_56[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " concatenate_49 (Concatenate)   (None, 64, 64, 640)  0           ['up_sampling2d_47[0][0]',       \n",
            "                                                                  'block3_conv4[0][0]',           \n",
            "                                                                  'multiply_54[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_183 (Conv2D)            (None, 64, 64, 128)  737408      ['concatenate_49[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_174 (Batch  (None, 64, 64, 128)  512        ['conv2d_183[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_183 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_174[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_184 (Conv2D)            (None, 64, 64, 128)  147584      ['activation_183[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_175 (Batch  (None, 64, 64, 128)  512        ['conv2d_184[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_184 (Activation)    (None, 64, 64, 128)  0           ['batch_normalization_175[0][0]']\n",
            "                                                                                                  \n",
            " global_average_pooling2d_57 (G  (None, 128)         0           ['activation_184[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_57 (Reshape)           (None, 1, 1, 128)    0           ['global_average_pooling2d_57[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_114 (Dense)              (None, 1, 1, 16)     2048        ['reshape_57[0][0]']             \n",
            "                                                                                                  \n",
            " dense_115 (Dense)              (None, 1, 1, 128)    2048        ['dense_114[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_57 (Multiply)         (None, 64, 64, 128)  0           ['activation_184[0][0]',         \n",
            "                                                                  'dense_115[0][0]']              \n",
            "                                                                                                  \n",
            " up_sampling2d_48 (UpSampling2D  (None, 128, 128, 12  0          ['multiply_57[0][0]']            \n",
            " )                              8)                                                                \n",
            "                                                                                                  \n",
            " concatenate_50 (Concatenate)   (None, 128, 128, 32  0           ['up_sampling2d_48[0][0]',       \n",
            "                                0)                                'block2_conv2[0][0]',           \n",
            "                                                                  'multiply_53[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_185 (Conv2D)            (None, 128, 128, 64  184384      ['concatenate_50[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_176 (Batch  (None, 128, 128, 64  256        ['conv2d_185[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_185 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_176[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_186 (Conv2D)            (None, 128, 128, 64  36928       ['activation_185[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_177 (Batch  (None, 128, 128, 64  256        ['conv2d_186[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_186 (Activation)    (None, 128, 128, 64  0           ['batch_normalization_177[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_58 (G  (None, 64)          0           ['activation_186[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_58 (Reshape)           (None, 1, 1, 64)     0           ['global_average_pooling2d_58[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_116 (Dense)              (None, 1, 1, 8)      512         ['reshape_58[0][0]']             \n",
            "                                                                                                  \n",
            " dense_117 (Dense)              (None, 1, 1, 64)     512         ['dense_116[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_58 (Multiply)         (None, 128, 128, 64  0           ['activation_186[0][0]',         \n",
            "                                )                                 'dense_117[0][0]']              \n",
            "                                                                                                  \n",
            " up_sampling2d_49 (UpSampling2D  (None, 256, 256, 64  0          ['multiply_58[0][0]']            \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " concatenate_51 (Concatenate)   (None, 256, 256, 16  0           ['up_sampling2d_49[0][0]',       \n",
            "                                0)                                'block1_conv2[0][0]',           \n",
            "                                                                  'multiply_52[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_187 (Conv2D)            (None, 256, 256, 32  46112       ['concatenate_51[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_178 (Batch  (None, 256, 256, 32  128        ['conv2d_187[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_187 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_178[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_188 (Conv2D)            (None, 256, 256, 32  9248        ['activation_187[0][0]']         \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_179 (Batch  (None, 256, 256, 32  128        ['conv2d_188[0][0]']             \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " activation_188 (Activation)    (None, 256, 256, 32  0           ['batch_normalization_179[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " global_average_pooling2d_59 (G  (None, 32)          0           ['activation_188[0][0]']         \n",
            " lobalAveragePooling2D)                                                                           \n",
            "                                                                                                  \n",
            " reshape_59 (Reshape)           (None, 1, 1, 32)     0           ['global_average_pooling2d_59[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_118 (Dense)              (None, 1, 1, 4)      128         ['reshape_59[0][0]']             \n",
            "                                                                                                  \n",
            " dense_119 (Dense)              (None, 1, 1, 32)     128         ['dense_118[0][0]']              \n",
            "                                                                                                  \n",
            " multiply_59 (Multiply)         (None, 256, 256, 32  0           ['activation_188[0][0]',         \n",
            "                                )                                 'dense_119[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_189 (Conv2D)            (None, 256, 256, 1)  33          ['multiply_59[0][0]']            \n",
            "                                                                                                  \n",
            " activation_189 (Activation)    (None, 256, 256, 1)  0           ['conv2d_189[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate_52 (Concatenate)   (None, 256, 256, 2)  0           ['activation_166[0][0]',         \n",
            "                                                                  'activation_189[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 29,297,570\n",
            "Trainable params: 29,290,274\n",
            "Non-trainable params: 7,296\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "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')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "tFW4l2A1_Ksn",
        "outputId": "1513f6c9-3745-454d-f602-b80a67d73f87"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.6.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.8/dist-packages (from gdown) (2.25.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from gdown) (4.64.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from gdown) (3.9.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.8/dist-packages (from gdown) (4.6.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\n",
            "Successfully installed gdown-4.6.0\n",
            "/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",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=11hcgbRcs_9mV1QkiDq8Eb6FKENQClI2f\n",
            "To: /content/archive.zip\n",
            "100% 327M/327M [00:02<00:00, 111MB/s]\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting patool\n",
            "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 KB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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_9q52f0xo -- archive.zip\n",
            "patool: ... archive.zip extracted to `archive' (multiple files in root).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'archive'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "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",
        "\n",
        "\n",
        "\n",
        "\"\"\" Global parameters \"\"\"\n",
        "H = 256\n",
        "W = 256\n",
        "\n",
        "def create_dir(path):\n",
        "    if not os.path.exists(path):\n",
        "        os.makedirs(path)\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",
        "    return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)\n",
        "\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",
        "    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"
      ],
      "metadata": {
        "id": "eE01vv6iCSWA"
      },
      "execution_count": 8,
      "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",
        "from keras.layers import Lambda\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def iou_now(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",
        "def iou(y_true, y_pred):\n",
        "    y_pred1, y_pred2 = tf.split(y_pred, num_or_size_splits=2, axis=-1)\n",
        "    iou1 = iou_now(y_true, y_pred1)\n",
        "    iou2 = iou_now(y_true, y_pred2)\n",
        "    return (iou1 + iou2) / 2\n",
        "\n",
        "\n",
        "smooth = 1e-15\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_pred_1, y_pred_2 = tf.split(y_pred, num_or_size_splits=2, axis=-1)\n",
        "    \n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    y_pred_1 = tf.keras.layers.Flatten()(y_pred_1)\n",
        "    intersection_1 = tf.reduce_sum(y_true * y_pred_1)\n",
        "    dice_1 = (2. * intersection_1 + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred_1) + smooth)\n",
        "    \n",
        "    y_pred_2 = tf.keras.layers.Flatten()(y_pred_2)\n",
        "    intersection_2 = tf.reduce_sum(y_true * y_pred_2)\n",
        "    dice_2 = (2. * intersection_2 + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred_2) + smooth)\n",
        "    \n",
        "    return (dice_1 + dice_2) / 2\n",
        "\n",
        "def dice_loss(y_true, y_pred):\n",
        "    return 1.0 - dice_coef(y_true, y_pred)\n",
        "\n",
        "     \n",
        "\n",
        "def recall(y_true, y_pred):\n",
        "    y_pred1, y_pred2 = tf.split(y_pred, num_or_size_splits=2, axis=-1)\n",
        "    y_pred1 = tf.keras.layers.Flatten()(y_pred1)\n",
        "    y_pred2 = tf.keras.layers.Flatten()(y_pred2)\n",
        "\n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    \n",
        "    true_positives1 = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(y_true, 1), tf.equal(y_pred1, 1)), tf.float32))\n",
        "    true_positives2 = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(y_true, 1), tf.equal(y_pred2, 1)), tf.float32))\n",
        "\n",
        "    possible_positives = tf.reduce_sum(tf.cast(tf.equal(y_true, 1), tf.float32))\n",
        "\n",
        "    recall1 = true_positives1 / (possible_positives + 1e-15)\n",
        "    recall2 = true_positives2 / (possible_positives + 1e-15)\n",
        "\n",
        "    recall = (recall1 + recall2) / 2\n",
        "    return recall\n",
        "\n",
        "def precision(y_true, y_pred):\n",
        "    y_pred1, y_pred2 = tf.split(y_pred, num_or_size_splits=2, axis=-1)\n",
        "    y_pred1 = tf.keras.layers.Flatten()(y_pred1)\n",
        "    y_pred2 = tf.keras.layers.Flatten()(y_pred2)\n",
        "\n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    \n",
        "    true_positives1 = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(y_true, 1), tf.equal(y_pred1, 1)), tf.float32))\n",
        "    true_positives2 = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(y_true, 1), tf.equal(y_pred2, 1)), tf.float32))\n",
        "\n",
        "    predicted_positives1 = tf.reduce_sum(tf.cast(tf.equal(y_pred1, 1), tf.float32))\n",
        "    predicted_positives2 = tf.reduce_sum(tf.cast(tf.equal(y_pred2, 1), tf.float32))\n",
        "\n",
        "    precision1 = true_positives1 / (predicted_positives1 + 1e-15)\n",
        "    precision2 = true_positives2 / (predicted_positives2 + 1e-15)\n",
        "\n",
        "    precision = (precision1 + precision2) / 2\n",
        "    return precision\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wk3dgfF4Ceod"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "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-4\n",
        "    num_epochs = 30\n",
        "    model_path = os.path.join(\"files\", \"Double_Unet_model.h5\")\n",
        "    csv_path = os.path.join(\"files\", \"Double_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",
        "\n",
        "    \"\"\" Model \"\"\"\n",
        "    metrics = [dice_coef, iou,recall, precision]\n",
        "    model.compile(loss=dice_loss, optimizer=Adam(lr), metrics=[\"accuracy\",metrics])\n",
        "\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.fit(\n",
        "        train_dataset,\n",
        "        epochs=num_epochs,\n",
        "        validation_data=valid_dataset,\n",
        "        callbacks=callbacks\n",
        "    )\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpduyHnZDRQ2",
        "outputId": "ff33674f-577f-47e9-c329-32b2d156f9ef"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 1840 - 1840\n",
            "Valid: 612 - 612\n",
            "Test : 612 - 612\n",
            "Epoch 1/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.9149 - accuracy: 0.8335 - dice_coef: 0.0851 - iou: 0.0446 - recall: 3.6273e-05 - precision: 0.0087\n",
            "Epoch 1: val_loss improved from inf to 0.93475, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 165s 1s/step - loss: 0.9149 - accuracy: 0.8335 - dice_coef: 0.0851 - iou: 0.0446 - recall: 3.6273e-05 - precision: 0.0087 - val_loss: 0.9347 - val_accuracy: 0.4444 - val_dice_coef: 0.0650 - val_iou: 0.0337 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - lr: 1.0000e-04\n",
            "Epoch 2/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.8797 - accuracy: 0.9242 - dice_coef: 0.1203 - iou: 0.0642 - recall: 9.4587e-04 - precision: 0.0739\n",
            "Epoch 2: val_loss improved from 0.93475 to 0.92234, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.8797 - accuracy: 0.9242 - dice_coef: 0.1203 - iou: 0.0642 - recall: 9.4587e-04 - precision: 0.0739 - val_loss: 0.9223 - val_accuracy: 0.7851 - val_dice_coef: 0.0774 - val_iou: 0.0404 - val_recall: 3.5409e-05 - val_precision: 0.0128 - lr: 1.0000e-04\n",
            "Epoch 3/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.8610 - accuracy: 0.9629 - dice_coef: 0.1390 - iou: 0.0750 - recall: 0.0013 - precision: 0.0825\n",
            "Epoch 3: val_loss improved from 0.92234 to 0.90064, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.8610 - accuracy: 0.9629 - dice_coef: 0.1390 - iou: 0.0750 - recall: 0.0013 - precision: 0.0825 - val_loss: 0.9006 - val_accuracy: 0.9451 - val_dice_coef: 0.0988 - val_iou: 0.0522 - val_recall: 0.0022 - val_precision: 0.2051 - lr: 1.0000e-04\n",
            "Epoch 4/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.8428 - accuracy: 0.9668 - dice_coef: 0.1572 - iou: 0.0857 - recall: 0.0013 - precision: 0.0913\n",
            "Epoch 4: val_loss improved from 0.90064 to 0.85891, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.8428 - accuracy: 0.9668 - dice_coef: 0.1572 - iou: 0.0857 - recall: 0.0013 - precision: 0.0913 - val_loss: 0.8589 - val_accuracy: 0.9746 - val_dice_coef: 0.1404 - val_iou: 0.0757 - val_recall: 0.0176 - val_precision: 0.4231 - lr: 1.0000e-04\n",
            "Epoch 5/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.8206 - accuracy: 0.9684 - dice_coef: 0.1794 - iou: 0.0990 - recall: 0.0028 - precision: 0.1694\n",
            "Epoch 5: val_loss improved from 0.85891 to 0.84933, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.8206 - accuracy: 0.9684 - dice_coef: 0.1794 - iou: 0.0990 - recall: 0.0028 - precision: 0.1694 - val_loss: 0.8493 - val_accuracy: 0.9771 - val_dice_coef: 0.1500 - val_iou: 0.0812 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - lr: 1.0000e-04\n",
            "Epoch 6/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.7953 - accuracy: 0.9651 - dice_coef: 0.2047 - iou: 0.1146 - recall: 0.0030 - precision: 0.1696\n",
            "Epoch 6: val_loss improved from 0.84933 to 0.80989, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.7953 - accuracy: 0.9651 - dice_coef: 0.2047 - iou: 0.1146 - recall: 0.0030 - precision: 0.1696 - val_loss: 0.8099 - val_accuracy: 0.9805 - val_dice_coef: 0.1894 - val_iou: 0.1051 - val_recall: 0.0325 - val_precision: 0.4959 - lr: 1.0000e-04\n",
            "Epoch 7/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.7663 - accuracy: 0.8964 - dice_coef: 0.2337 - iou: 0.1329 - recall: 0.0053 - precision: 0.2956\n",
            "Epoch 7: val_loss improved from 0.80989 to 0.75974, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.7663 - accuracy: 0.8964 - dice_coef: 0.2337 - iou: 0.1329 - recall: 0.0053 - precision: 0.2956 - val_loss: 0.7597 - val_accuracy: 0.9705 - val_dice_coef: 0.2392 - val_iou: 0.1365 - val_recall: 0.0071 - val_precision: 0.3974 - lr: 1.0000e-04\n",
            "Epoch 8/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.7263 - accuracy: 0.6107 - dice_coef: 0.2737 - iou: 0.1593 - recall: 0.0068 - precision: 0.3427\n",
            "Epoch 8: val_loss improved from 0.75974 to 0.72838, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.7263 - accuracy: 0.6107 - dice_coef: 0.2737 - iou: 0.1593 - recall: 0.0068 - precision: 0.3427 - val_loss: 0.7284 - val_accuracy: 0.6965 - val_dice_coef: 0.2704 - val_iou: 0.1569 - val_recall: 8.1047e-04 - val_precision: 0.2436 - lr: 1.0000e-04\n",
            "Epoch 9/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.6669 - accuracy: 0.4000 - dice_coef: 0.3331 - iou: 0.2009 - recall: 0.0113 - precision: 0.4553\n",
            "Epoch 9: val_loss improved from 0.72838 to 0.67621, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.6669 - accuracy: 0.4000 - dice_coef: 0.3331 - iou: 0.2009 - recall: 0.0113 - precision: 0.4553 - val_loss: 0.6762 - val_accuracy: 0.8116 - val_dice_coef: 0.3226 - val_iou: 0.1933 - val_recall: 0.0224 - val_precision: 0.7756 - lr: 1.0000e-04\n",
            "Epoch 10/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.5907 - accuracy: 0.2514 - dice_coef: 0.4093 - iou: 0.2588 - recall: 0.0210 - precision: 0.6022\n",
            "Epoch 10: val_loss improved from 0.67621 to 0.57545, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.5907 - accuracy: 0.2514 - dice_coef: 0.4093 - iou: 0.2588 - recall: 0.0210 - precision: 0.6022 - val_loss: 0.5754 - val_accuracy: 0.7336 - val_dice_coef: 0.4230 - val_iou: 0.2695 - val_recall: 8.4371e-04 - val_precision: 0.3205 - lr: 1.0000e-04\n",
            "Epoch 11/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.2049 - dice_coef: 0.4902 - iou: 0.3266 - recall: 0.0395 - precision: 0.6756\n",
            "Epoch 11: val_loss improved from 0.57545 to 0.50530, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.5098 - accuracy: 0.2049 - dice_coef: 0.4902 - iou: 0.3266 - recall: 0.0395 - precision: 0.6756 - val_loss: 0.5053 - val_accuracy: 0.3137 - val_dice_coef: 0.4931 - val_iou: 0.3285 - val_recall: 0.1928 - val_precision: 0.9245 - lr: 1.0000e-04\n",
            "Epoch 12/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.4310 - accuracy: 0.1944 - dice_coef: 0.5690 - iou: 0.3997 - recall: 0.0542 - precision: 0.7239\n",
            "Epoch 12: val_loss improved from 0.50530 to 0.43731, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.4310 - accuracy: 0.1944 - dice_coef: 0.5690 - iou: 0.3997 - recall: 0.0542 - precision: 0.7239 - val_loss: 0.4373 - val_accuracy: 0.3078 - val_dice_coef: 0.5610 - val_iou: 0.3916 - val_recall: 0.0756 - val_precision: 0.8252 - lr: 1.0000e-04\n",
            "Epoch 13/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.3567 - accuracy: 0.2109 - dice_coef: 0.6433 - iou: 0.4762 - recall: 0.0773 - precision: 0.8253\n",
            "Epoch 13: val_loss improved from 0.43731 to 0.35895, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.3567 - accuracy: 0.2109 - dice_coef: 0.6433 - iou: 0.4762 - recall: 0.0773 - precision: 0.8253 - val_loss: 0.3590 - val_accuracy: 0.1326 - val_dice_coef: 0.6395 - val_iou: 0.4723 - val_recall: 0.1489 - val_precision: 0.7592 - lr: 1.0000e-04\n",
            "Epoch 14/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.3027 - accuracy: 0.2430 - dice_coef: 0.6973 - iou: 0.5374 - recall: 0.0913 - precision: 0.8504\n",
            "Epoch 14: val_loss improved from 0.35895 to 0.32191, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.3027 - accuracy: 0.2430 - dice_coef: 0.6973 - iou: 0.5374 - recall: 0.0913 - precision: 0.8504 - val_loss: 0.3219 - val_accuracy: 0.4603 - val_dice_coef: 0.6764 - val_iou: 0.5136 - val_recall: 0.0358 - val_precision: 0.7651 - lr: 1.0000e-04\n",
            "Epoch 15/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2528 - accuracy: 0.2907 - dice_coef: 0.7472 - iou: 0.5985 - recall: 0.1007 - precision: 0.8690\n",
            "Epoch 15: val_loss improved from 0.32191 to 0.28436, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.2528 - accuracy: 0.2907 - dice_coef: 0.7472 - iou: 0.5985 - recall: 0.1007 - precision: 0.8690 - val_loss: 0.2844 - val_accuracy: 0.5832 - val_dice_coef: 0.7140 - val_iou: 0.5574 - val_recall: 0.0104 - val_precision: 0.8182 - lr: 1.0000e-04\n",
            "Epoch 16/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.2189 - accuracy: 0.3565 - dice_coef: 0.7811 - iou: 0.6427 - recall: 0.1187 - precision: 0.9160\n",
            "Epoch 16: val_loss improved from 0.28436 to 0.25209, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 157s 1s/step - loss: 0.2189 - accuracy: 0.3565 - dice_coef: 0.7811 - iou: 0.6427 - recall: 0.1187 - precision: 0.9160 - val_loss: 0.2521 - val_accuracy: 0.6965 - val_dice_coef: 0.7460 - val_iou: 0.5979 - val_recall: 0.0514 - val_precision: 0.8658 - lr: 1.0000e-04\n",
            "Epoch 17/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1879 - accuracy: 0.4170 - dice_coef: 0.8121 - iou: 0.6850 - recall: 0.1358 - precision: 0.9238\n",
            "Epoch 17: val_loss did not improve from 0.25209\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.1879 - accuracy: 0.4170 - dice_coef: 0.8121 - iou: 0.6850 - recall: 0.1358 - precision: 0.9238 - val_loss: 0.2634 - val_accuracy: 0.3985 - val_dice_coef: 0.7348 - val_iou: 0.5845 - val_recall: 0.0456 - val_precision: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 18/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1745 - accuracy: 0.4519 - dice_coef: 0.8255 - iou: 0.7044 - recall: 0.1503 - precision: 0.9544\n",
            "Epoch 18: val_loss improved from 0.25209 to 0.24562, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.1745 - accuracy: 0.4519 - dice_coef: 0.8255 - iou: 0.7044 - recall: 0.1503 - precision: 0.9544 - val_loss: 0.2456 - val_accuracy: 0.4522 - val_dice_coef: 0.7529 - val_iou: 0.6068 - val_recall: 0.0648 - val_precision: 0.9393 - lr: 1.0000e-04\n",
            "Epoch 19/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1558 - accuracy: 0.4939 - dice_coef: 0.8442 - iou: 0.7318 - recall: 0.1625 - precision: 0.9590\n",
            "Epoch 19: val_loss improved from 0.24562 to 0.24388, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.1558 - accuracy: 0.4939 - dice_coef: 0.8442 - iou: 0.7318 - recall: 0.1625 - precision: 0.9590 - val_loss: 0.2439 - val_accuracy: 0.3970 - val_dice_coef: 0.7546 - val_iou: 0.6091 - val_recall: 0.0132 - val_precision: 0.9592 - lr: 1.0000e-04\n",
            "Epoch 20/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1387 - accuracy: 0.5352 - dice_coef: 0.8613 - iou: 0.7575 - recall: 0.1789 - precision: 0.9651\n",
            "Epoch 20: val_loss improved from 0.24388 to 0.21370, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.1387 - accuracy: 0.5352 - dice_coef: 0.8613 - iou: 0.7575 - recall: 0.1789 - precision: 0.9651 - val_loss: 0.2137 - val_accuracy: 0.6638 - val_dice_coef: 0.7846 - val_iou: 0.6492 - val_recall: 0.0968 - val_precision: 0.9597 - lr: 1.0000e-04\n",
            "Epoch 21/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1280 - accuracy: 0.5641 - dice_coef: 0.8720 - iou: 0.7740 - recall: 0.1926 - precision: 0.9727\n",
            "Epoch 21: val_loss improved from 0.21370 to 0.20929, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.1280 - accuracy: 0.5641 - dice_coef: 0.8720 - iou: 0.7740 - recall: 0.1926 - precision: 0.9727 - val_loss: 0.2093 - val_accuracy: 0.4891 - val_dice_coef: 0.7889 - val_iou: 0.6545 - val_recall: 0.0382 - val_precision: 0.8665 - lr: 1.0000e-04\n",
            "Epoch 22/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1227 - accuracy: 0.5905 - dice_coef: 0.8773 - iou: 0.7824 - recall: 0.2041 - precision: 0.9840\n",
            "Epoch 22: val_loss did not improve from 0.20929\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.1227 - accuracy: 0.5905 - dice_coef: 0.8773 - iou: 0.7824 - recall: 0.2041 - precision: 0.9840 - val_loss: 0.2252 - val_accuracy: 0.8460 - val_dice_coef: 0.7730 - val_iou: 0.6338 - val_recall: 0.0655 - val_precision: 0.9677 - lr: 1.0000e-04\n",
            "Epoch 23/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1231 - accuracy: 0.6240 - dice_coef: 0.8769 - iou: 0.7817 - recall: 0.2147 - precision: 0.9817\n",
            "Epoch 23: val_loss improved from 0.20929 to 0.20666, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.1231 - accuracy: 0.6240 - dice_coef: 0.8769 - iou: 0.7817 - recall: 0.2147 - precision: 0.9817 - val_loss: 0.2067 - val_accuracy: 0.6207 - val_dice_coef: 0.7917 - val_iou: 0.6585 - val_recall: 0.2552 - val_precision: 0.9327 - lr: 1.0000e-04\n",
            "Epoch 24/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.6432 - dice_coef: 0.8864 - iou: 0.7967 - recall: 0.2293 - precision: 0.9825\n",
            "Epoch 24: val_loss improved from 0.20666 to 0.18820, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 150s 1s/step - loss: 0.1136 - accuracy: 0.6432 - dice_coef: 0.8864 - iou: 0.7967 - recall: 0.2293 - precision: 0.9825 - val_loss: 0.1882 - val_accuracy: 0.8505 - val_dice_coef: 0.8099 - val_iou: 0.6836 - val_recall: 0.1265 - val_precision: 0.9586 - lr: 1.0000e-04\n",
            "Epoch 25/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1084 - accuracy: 0.6634 - dice_coef: 0.8916 - iou: 0.8051 - recall: 0.2404 - precision: 0.9825\n",
            "Epoch 25: val_loss did not improve from 0.18820\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.1084 - accuracy: 0.6634 - dice_coef: 0.8916 - iou: 0.8051 - recall: 0.2404 - precision: 0.9825 - val_loss: 0.1916 - val_accuracy: 0.5199 - val_dice_coef: 0.8068 - val_iou: 0.6802 - val_recall: 0.1654 - val_precision: 0.9624 - lr: 1.0000e-04\n",
            "Epoch 26/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.1040 - accuracy: 0.6618 - dice_coef: 0.8960 - iou: 0.8125 - recall: 0.2438 - precision: 0.9828\n",
            "Epoch 26: val_loss improved from 0.18820 to 0.17518, saving model to files/Double_Unet_model.h5\n",
            "115/115 [==============================] - 149s 1s/step - loss: 0.1040 - accuracy: 0.6618 - dice_coef: 0.8960 - iou: 0.8125 - recall: 0.2438 - precision: 0.9828 - val_loss: 0.1752 - val_accuracy: 0.9524 - val_dice_coef: 0.8230 - val_iou: 0.7029 - val_recall: 0.2784 - val_precision: 0.9484 - lr: 1.0000e-04\n",
            "Epoch 27/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.0933 - accuracy: 0.7068 - dice_coef: 0.9067 - iou: 0.8298 - recall: 0.2490 - precision: 0.9811\n",
            "Epoch 27: val_loss did not improve from 0.17518\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.0933 - accuracy: 0.7068 - dice_coef: 0.9067 - iou: 0.8298 - recall: 0.2490 - precision: 0.9811 - val_loss: 0.1830 - val_accuracy: 0.9142 - val_dice_coef: 0.8153 - val_iou: 0.6915 - val_recall: 0.2146 - val_precision: 0.9401 - lr: 1.0000e-04\n",
            "Epoch 28/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.0869 - accuracy: 0.7378 - dice_coef: 0.9131 - iou: 0.8406 - recall: 0.2515 - precision: 0.9810\n",
            "Epoch 28: val_loss did not improve from 0.17518\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.0869 - accuracy: 0.7378 - dice_coef: 0.9131 - iou: 0.8406 - recall: 0.2515 - precision: 0.9810 - val_loss: 0.1801 - val_accuracy: 0.5586 - val_dice_coef: 0.8180 - val_iou: 0.6953 - val_recall: 0.1636 - val_precision: 0.9427 - lr: 1.0000e-04\n",
            "Epoch 29/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.0862 - accuracy: 0.7261 - dice_coef: 0.9138 - iou: 0.8418 - recall: 0.2616 - precision: 0.9836\n",
            "Epoch 29: val_loss did not improve from 0.17518\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.0862 - accuracy: 0.7261 - dice_coef: 0.9138 - iou: 0.8418 - recall: 0.2616 - precision: 0.9836 - val_loss: 0.1767 - val_accuracy: 0.5687 - val_dice_coef: 0.8215 - val_iou: 0.7007 - val_recall: 0.2178 - val_precision: 0.9500 - lr: 1.0000e-04\n",
            "Epoch 30/30\n",
            "115/115 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.7420 - dice_coef: 0.9185 - iou: 0.8498 - recall: 0.2745 - precision: 0.9829\n",
            "Epoch 30: val_loss did not improve from 0.17518\n",
            "115/115 [==============================] - 148s 1s/step - loss: 0.0815 - accuracy: 0.7420 - dice_coef: 0.9185 - iou: 0.8498 - recall: 0.2745 - precision: 0.9829 - val_loss: 0.2047 - val_accuracy: 0.2303 - val_dice_coef: 0.7940 - val_iou: 0.6616 - val_recall: 0.1333 - val_precision: 0.9550 - lr: 1.0000e-04\n"
          ]
        }
      ]
    },
    {
      "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",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "if __name__ == \"__main__\":\n",
        "    create_dir(\"output\")\n",
        "    \"\"\" Loading model \"\"\"\n",
        "    with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss,'recall':recall,\"precision\":precision}):\n",
        "        model = tf.keras.models.load_model(\"/content/files/Double_Unet_model.h5\")\n",
        "    i=0\n",
        "    \"\"\" Predicting the mask \"\"\"\n",
        "    for x, y  in tqdm(zip(test_x,test_y), total=len(test_x)):\n",
        "        \"\"\" Extracing the image name. \"\"\"\n",
        "        image_name = x.split(\"/\")[-1]\n",
        "\n",
        "        \"\"\" Reading the image \"\"\"\n",
        "        img = cv2.imread(x, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (W, H))\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, (W, H))\n",
        "        img_y = np.expand_dims(img_y, axis=-1)  ## (512, 512, 1)\n",
        "        img_y = np.concatenate([img_y, img_y, img_y], axis=-1)  ## (512, 512, 3)\n",
        "\n",
        "        \"\"\" Predicting the mask. \"\"\"\n",
        "        y_pred = model.predict(x)[0] > 0.5\n",
        "        y_pred = y_pred.astype(np.int32)\n",
        "        y_pred_1, y_pred_2 = tf.split(y_pred, num_or_size_splits=2, axis=-1)\n",
        "        \"\"\" Saving the predicted mask along with the image and GT \"\"\"\n",
        "        y_pred_1 = np.concatenate([y_pred_1, y_pred_1, y_pred_1], axis=-1)\n",
        "        #y_pred_1 = np.where(y_pred_1>0,255,y_pred_1)\n",
        "        y_pred_2 = np.concatenate([y_pred_2, y_pred_2, y_pred_2], axis=-1)\n",
        "        #y_pred_2 = np.where(y_pred_2>0,255,y_pred_2)\n",
        "        sep_line = np.ones((H, 10, 3)) * 255\n",
        "\n",
        "        output_image = np.concatenate([img, sep_line, img_y, sep_line, y_pred_1*255,sep_line, y_pred_2*255], axis=1)\n",
        "        #cat_image = np.concatenate([sep_line,y_pred*255,sep_line], axis=1) # this line for just the output segment\n",
        "        #cat_image = np.concatenate([ori_x,sep_line,y_pred*255], axis=1) # this line for just the output segment and the test image\n",
        "        cv2.imwrite(f\"/content/output/{i}.png\", output_image)\n",
        "        i=i+1\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKCSYg6yDg5t",
        "outputId": "bfde81b4-6f2b-4027-b94b-cf291858bb06"
      },
      "execution_count": 54,
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        },
        {
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          "name": "stdout",
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        },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1, 4, 1)\n",
        "plt.imshow(img)\n",
        "plt.title(\"Input\")\n",
        "plt.subplot(1, 4, 2)\n",
        "plt.imshow(img_y)\n",
        "plt.title(\"Groung_truth\")\n",
        "plt.subplot(1,4,3)\n",
        "plt.imshow(y_pred_1*255,cmap='gray')\n",
        "plt.title(\"Output1\")\n",
        "plt.subplot(1,4,4)\n",
        "plt.imshow(y_pred_2*255,cmap='gray')\n",
        "plt.title(\"Output2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "id": "RDoGPSxPjvxa",
        "outputId": "6f56084f-3441-49a7-fd93-155d969a7f6d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Output2')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=cv2.imread(\"/content/output/0.png\")\n",
        "plt.imshow(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "UalHeIm6m6tL",
        "outputId": "a37798e2-a554-4e5d-d07b-c02825aef2ca"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0c0998f2b0>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "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/Double_UNET_data.csv\")\n",
        "epoch=x.iloc[:,0:1].values\n",
        "regular_iou=x.iloc[:,3:4].values\n",
        "val_iou=x.iloc[:,10:11].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": "GM8QK1gMnqLZ",
        "outputId": "a5f9145d-d8fc-465c-ee95-b8161019d98e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "wdBld8S0n5Ru"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}