105. Pre_training_Unet_with_resnet.ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T2IIwP7NCAeI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5df8cc8-1127-4ffd-c30d-439a3eb0351f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input, ZeroPadding2D\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications import ResNet50"
      ],
      "metadata": {
        "id": "RlYntzmzCE5a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_block(input, num_filters):\n",
        "    x = Conv2D(num_filters, 3, padding=\"same\")(input)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    x = Conv2D(num_filters, 3, padding=\"same\")(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    return x\n"
      ],
      "metadata": {
        "id": "xyguFxB7Chzs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def decoder_block(input, skip_features, num_filters):\n",
        "    x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(input)\n",
        "    x = Concatenate()([x, skip_features])\n",
        "    x = conv_block(x, num_filters)\n",
        "    return x"
      ],
      "metadata": {
        "id": "qaxXgoI_UmsI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_resnet50_unet(input_shape):\n",
        "    \"\"\" Input \"\"\"\n",
        "    inputs = Input(input_shape)\n",
        "\n",
        "    \"\"\" Pre-trained ResNet50 Model \"\"\"\n",
        "    resnet50 = ResNet50(include_top=False, weights=\"imagenet\", input_tensor=inputs)\n",
        "\n",
        "    \"\"\" Encoder \"\"\"\n",
        "    s1 = resnet50.get_layer(\"input_1\").output           ## (512 x 512)\n",
        "    s2 = resnet50.get_layer(\"conv1_relu\").output        ## (256 x 256)\n",
        "    s3 = resnet50.get_layer(\"conv2_block3_out\").output  ## (128 x 128)\n",
        "    s4 = resnet50.get_layer(\"conv3_block4_out\").output  ## (64 x 64)\n",
        "\n",
        "    \"\"\" Bridge \"\"\"\n",
        "    b1 = resnet50.get_layer(\"conv4_block6_out\").output  ## (32 x 32)\n",
        "\n",
        "    \"\"\" Decoder \"\"\"\n",
        "    d1 = decoder_block(b1, s4, 512)                     ## (64 x 64)\n",
        "    d2 = decoder_block(d1, s3, 256)                     ## (128 x 128)\n",
        "    d3 = decoder_block(d2, s2, 128)                     ## (256 x 256)\n",
        "    d4 = decoder_block(d3, s1, 64)                      ## (512 x 512)\n",
        "\n",
        "    \"\"\" Output \"\"\"\n",
        "    outputs = Conv2D(1, 1, padding=\"same\", activation=\"sigmoid\")(d4)\n",
        "\n",
        "    model = Model(inputs, outputs, name=\"ResNet50_U-Net\")\n",
        "    return model"
      ],
      "metadata": {
        "id": "SFc6n4LlDEDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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 sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "import keras\n",
        "from keras.utils import normalize\n",
        "from keras.metrics import MeanIoU\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.metrics import Recall, Precision\n",
        " \n",
        "\n",
        "\"\"\" Global parameters \"\"\"\n",
        "H = 256\n",
        "W = 256\n",
        "def create_dir(path):\n",
        "    \"\"\" Create a directory. \"\"\"\n",
        "    if not os.path.exists(path):\n",
        "        os.makedirs(path)\n",
        "\n",
        "def load_data(path, split=0.1):\n",
        "    images = sorted(glob(os.path.join(path,\"Images\", \"*.png\")))\n",
        "    masks1 = sorted(glob(os.path.join(path,\"Left\", \"*.png\")))\n",
        "    masks2 = sorted(glob(os.path.join(path,\"Right\", \"*.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_y1, valid_y1 = train_test_split(masks1, test_size=split_size, random_state=42)\n",
        "    train_y2, valid_y2 = train_test_split(masks2, 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_y1, test_y1 = train_test_split(train_y1, test_size=split_size, random_state=42)\n",
        "    train_y2, test_y2 = train_test_split(train_y2, test_size=split_size, random_state=42)\n",
        "\n",
        "    return (train_x, train_y1, train_y2), (valid_x, valid_y1, valid_y2), (test_x, test_y1, test_y2)\n",
        "\n",
        "\n",
        "def read_image(path):\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(path1, path2):\n",
        "    x1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE)\n",
        "    x2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE)\n",
        "    x = x1 + x2\n",
        "    x = cv2.resize(x, (W, H))\n",
        "    x = x/np.max(x)\n",
        "    x = x > 0.5\n",
        "    x = x.astype(np.float32)\n",
        "    x = np.expand_dims(x, axis=-1)\n",
        "    return x\n",
        "\n",
        "def tf_parse(x, y1, y2):\n",
        "    def _parse(x, y1, y2):\n",
        "        x = x.decode()\n",
        "        y1 = y1.decode()\n",
        "        y2 = y2.decode()\n",
        "\n",
        "        x = read_image(x)\n",
        "        y = read_mask(y1, y2)\n",
        "        return x, y\n",
        "\n",
        "    x, y = tf.numpy_function(_parse, [x, y1, y2], [tf.float32, tf.float32])\n",
        "    x.set_shape([H, W, 3])\n",
        "    y.set_shape([H, W, 1])\n",
        "    return x, y\n",
        "def tf_dataset(X, Y1, Y2, batch=8):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((X, Y1, Y2))\n",
        "    dataset = dataset.shuffle(buffer_size=200)\n",
        "    dataset = dataset.map(tf_parse)\n",
        "    dataset = dataset.batch(batch)\n",
        "    dataset = dataset.prefetch(4)\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "l9bkUK3VDV58"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "def iou(y_true, y_pred):\n",
        "    def f(y_true, y_pred):\n",
        "        intersection = (y_true * y_pred).sum()\n",
        "        union = y_true.sum() + y_pred.sum() - intersection\n",
        "        x = (intersection + 1e-15) / (union + 1e-15)\n",
        "        x = x.astype(np.float32)\n",
        "        return x\n",
        "    return tf.numpy_function(f, [y_true, y_pred], tf.float32)\n",
        "\n",
        "smooth = 1e-15\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    y_pred = tf.keras.layers.Flatten()(y_pred)\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "    return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)\n",
        "\n",
        "def dice_loss(y_true, y_pred):\n",
        "    return 1.0 - dice_coef(y_true, y_pred)\n",
        "     "
      ],
      "metadata": {
        "id": "skbH1FMyVQLN"
      },
      "execution_count": null,
      "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 = 8\n",
        "    lr = 1e-5\n",
        "    num_epochs = 10\n",
        "    model_path = os.path.join(\"files\", \"Final_model_unet_resnet.h5\")\n",
        "    csv_path = os.path.join(\"files\", \"Final_data_unet_resnet.csv\")\n",
        "    \n",
        "    \"\"\" Dataset \"\"\"\n",
        "    dataset_path = \"/content/drive/MyDrive/Data_segment/Train\"\n",
        "    (train_x, train_y1, train_y2), (valid_x, valid_y1, valid_y2),(test_x, test_y1, test_y2)  = load_data(dataset_path)\n",
        "\n",
        "    print(f\"Train: {len(train_x)} - {len(train_y1)} - {len(train_y2)}\")\n",
        "    print(f\"Valid: {len(valid_x)} - {len(valid_y1)} - {len(valid_y2)}\")\n",
        "    print(f\"Test: {len(test_x)} - {len(test_y1)} - {len(test_y2)}\")\n",
        "   \n",
        "\n",
        "    train_dataset = tf_dataset(train_x, train_y1, train_y2, batch=batch_size)\n",
        "    valid_dataset = tf_dataset(valid_x, valid_y1, valid_y2, batch=batch_size) \n",
        "\n",
        "\n",
        "    model = build_resnet50_unet((H,W,3))\n",
        "    #model.summary()\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",
        "    ]\n",
        "\n",
        "    model.fit(\n",
        "        train_dataset,\n",
        "        epochs=num_epochs,\n",
        "        validation_data=valid_dataset,\n",
        "        callbacks=callbacks\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9FCEQA1rVSwo",
        "outputId": "4a9482ba-27d8-4e71-9381-e3ab396e5733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 320 - 320 - 320\n",
            "Valid: 40 - 40 - 40\n",
            "Test: 40 - 40 - 40\n",
            "Epoch 1/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.9240 - accuracy: 0.4170 - dice_coef: 0.0760 - iou: 0.0396 - recall: 0.8473 - precision: 0.0461\n",
            "Epoch 1: val_loss improved from inf to 0.93850, saving model to files/Final_model_unet_resnet.h5\n",
            "40/40 [==============================] - 204s 4s/step - loss: 0.9240 - accuracy: 0.4170 - dice_coef: 0.0760 - iou: 0.0396 - recall: 0.8473 - precision: 0.0461 - val_loss: 0.9385 - val_accuracy: 0.3590 - val_dice_coef: 0.0615 - val_iou: 0.0317 - val_recall: 0.6338 - val_precision: 0.0320 - lr: 1.0000e-05\n",
            "Epoch 2/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.8857 - accuracy: 0.5563 - dice_coef: 0.1143 - iou: 0.0607 - recall: 0.9958 - precision: 0.0689\n",
            "Epoch 2: val_loss did not improve from 0.93850\n",
            "40/40 [==============================] - 20s 503ms/step - loss: 0.8857 - accuracy: 0.5563 - dice_coef: 0.1143 - iou: 0.0607 - recall: 0.9958 - precision: 0.0689 - val_loss: 0.9390 - val_accuracy: 0.3208 - val_dice_coef: 0.0610 - val_iou: 0.0314 - val_recall: 0.6625 - val_precision: 0.0315 - lr: 1.0000e-05\n",
            "Epoch 3/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.8476 - accuracy: 0.7766 - dice_coef: 0.1524 - iou: 0.0825 - recall: 0.9998 - precision: 0.1286\n",
            "Epoch 3: val_loss did not improve from 0.93850\n",
            "40/40 [==============================] - 20s 503ms/step - loss: 0.8476 - accuracy: 0.7766 - dice_coef: 0.1524 - iou: 0.0825 - recall: 0.9998 - precision: 0.1286 - val_loss: 0.9397 - val_accuracy: 0.0399 - val_dice_coef: 0.0603 - val_iou: 0.0311 - val_recall: 1.0000 - val_precision: 0.0330 - lr: 1.0000e-05\n",
            "Epoch 4/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.8023 - accuracy: 0.9226 - dice_coef: 0.1977 - iou: 0.1098 - recall: 0.9999 - precision: 0.2989\n",
            "Epoch 4: val_loss improved from 0.93850 to 0.93823, saving model to files/Final_model_unet_resnet.h5\n",
            "40/40 [==============================] - 21s 529ms/step - loss: 0.8023 - accuracy: 0.9226 - dice_coef: 0.1977 - iou: 0.1098 - recall: 0.9999 - precision: 0.2989 - val_loss: 0.9382 - val_accuracy: 0.0347 - val_dice_coef: 0.0618 - val_iou: 0.0319 - val_recall: 1.0000 - val_precision: 0.0329 - lr: 1.0000e-05\n",
            "Epoch 5/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.7548 - accuracy: 0.9603 - dice_coef: 0.2452 - iou: 0.1399 - recall: 0.9991 - precision: 0.4538\n",
            "Epoch 5: val_loss did not improve from 0.93823\n",
            "40/40 [==============================] - 21s 522ms/step - loss: 0.7548 - accuracy: 0.9603 - dice_coef: 0.2452 - iou: 0.1399 - recall: 0.9991 - precision: 0.4538 - val_loss: 0.9391 - val_accuracy: 0.0471 - val_dice_coef: 0.0609 - val_iou: 0.0314 - val_recall: 0.9998 - val_precision: 0.0333 - lr: 1.0000e-05\n",
            "Epoch 6/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.7097 - accuracy: 0.9795 - dice_coef: 0.2903 - iou: 0.1700 - recall: 0.9977 - precision: 0.6170\n",
            "Epoch 6: val_loss did not improve from 0.93823\n",
            "40/40 [==============================] - 20s 500ms/step - loss: 0.7097 - accuracy: 0.9795 - dice_coef: 0.2903 - iou: 0.1700 - recall: 0.9977 - precision: 0.6170 - val_loss: 0.9403 - val_accuracy: 0.3147 - val_dice_coef: 0.0597 - val_iou: 0.0308 - val_recall: 0.7042 - val_precision: 0.0331 - lr: 1.0000e-05\n",
            "Epoch 7/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.6755 - accuracy: 0.9876 - dice_coef: 0.3245 - iou: 0.1938 - recall: 0.9970 - precision: 0.7285\n",
            "Epoch 7: val_loss did not improve from 0.93823\n",
            "40/40 [==============================] - 21s 506ms/step - loss: 0.6755 - accuracy: 0.9876 - dice_coef: 0.3245 - iou: 0.1938 - recall: 0.9970 - precision: 0.7285 - val_loss: 0.9434 - val_accuracy: 0.7760 - val_dice_coef: 0.0566 - val_iou: 0.0291 - val_recall: 0.0585 - val_precision: 0.0098 - lr: 1.0000e-05\n",
            "Epoch 8/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.6534 - accuracy: 0.9909 - dice_coef: 0.3466 - iou: 0.2098 - recall: 0.9965 - precision: 0.7854\n",
            "Epoch 8: val_loss did not improve from 0.93823\n",
            "40/40 [==============================] - 21s 508ms/step - loss: 0.6534 - accuracy: 0.9909 - dice_coef: 0.3466 - iou: 0.2098 - recall: 0.9965 - precision: 0.7854 - val_loss: 0.9435 - val_accuracy: 0.8878 - val_dice_coef: 0.0565 - val_iou: 0.0291 - val_recall: 0.0472 - val_precision: 0.0188 - lr: 1.0000e-05\n",
            "Epoch 9/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.6391 - accuracy: 0.9924 - dice_coef: 0.3609 - iou: 0.2204 - recall: 0.9969 - precision: 0.8141\n",
            "Epoch 9: val_loss improved from 0.93823 to 0.93432, saving model to files/Final_model_unet_resnet.h5\n",
            "40/40 [==============================] - 22s 531ms/step - loss: 0.6391 - accuracy: 0.9924 - dice_coef: 0.3609 - iou: 0.2204 - recall: 0.9969 - precision: 0.8141 - val_loss: 0.9343 - val_accuracy: 0.8926 - val_dice_coef: 0.0657 - val_iou: 0.0340 - val_recall: 0.1563 - val_precision: 0.0605 - lr: 1.0000e-05\n",
            "Epoch 10/10\n",
            "40/40 [==============================] - ETA: 0s - loss: 0.6281 - accuracy: 0.9935 - dice_coef: 0.3719 - iou: 0.2286 - recall: 0.9971 - precision: 0.8368\n",
            "Epoch 10: val_loss improved from 0.93432 to 0.92459, saving model to files/Final_model_unet_resnet.h5\n",
            "40/40 [==============================] - 22s 532ms/step - loss: 0.6281 - accuracy: 0.9935 - dice_coef: 0.3719 - iou: 0.2286 - recall: 0.9971 - precision: 0.8368 - val_loss: 0.9246 - val_accuracy: 0.8986 - val_dice_coef: 0.0754 - val_iou: 0.0392 - val_recall: 0.2114 - val_precision: 0.0841 - lr: 1.0000e-05\n"
          ]
        }
      ]
    }
  ]
}