Segmentation.ipynb

{
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9YDJiZsEXo-",
        "outputId": "0ea1ad74-c778-496d-e89d-b32293897ddc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install kaggle opendatasets tensorflow tensorflow-addons keras opencv-python tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukdQbUPNEhTE",
        "outputId": "e744daac-db6b-4194-e993-4259240c4bbb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "import tensorflow_addons as tfa\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "import opendatasets as od\n",
        "import pandas as pd\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)  #Ignores all Deprecation warnings\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, AveragePooling2D, Dropout\n",
        "from tensorflow.keras.layers import Conv2DTranspose, concatenate, Activation, Flatten, Dense, BatchNormalization , UpSampling2D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cI_B3oHzEocJ",
        "outputId": "2ea967f5-5ad8-4f4d-da48-4783663c8f73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: 8bb203712d6ec3eda7463f396d1b28e3\n",
            "Your Kaggle Key: ··········\n",
            "Dataset URL: https://www.kaggle.com/datasets/rajkumarl/people-clothing-segmentation\n",
            "Downloading people-clothing-segmentation.zip to ./people-clothing-segmentation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 616M/616M [00:04<00:00, 130MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "#Downloading Dataset from Kaggle\n",
        "od.download(\"https://www.kaggle.com/datasets/rajkumarl/people-clothing-segmentation\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBf-S0YdEz5D"
      },
      "source": [
        "### **Dice Loss**\n",
        "\n",
        "Defining a fucntion to calculate the Dice Loss\n",
        "\n",
        "**Dice loss**, also known as the Sørensen-Dice coefficient or F1 score, is a loss function commonly used in image segmentation tasks to measure the dissimilarity between the predicted segmentation and the ground truth.\n",
        "\n",
        "The dice loss measures the similarity between the predicted segmentation mask and the ground truth mask.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6yomlMHUEqRK"
      },
      "outputs": [],
      "source": [
        "def dice_loss(y_true, y_pred):\n",
        "    smooth = 1e-5                                 #used to avoid division by zero\n",
        "    #Typecasting the Data to float32\n",
        "    y_true = tf.cast(y_true, tf.float32)\n",
        "    y_pred = tf.cast(y_pred, tf.float32)\n",
        "\n",
        "    #calculates the intersection between the y_true and y_pred tensors\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "\n",
        "    #calculates the union of the y_true and y_pred tensors\n",
        "    union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)\n",
        "\n",
        "    dice = (2.0 * intersection + smooth) / (union + smooth)\n",
        "    loss = 1.0 - dice\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ilc2OYVzE3nr"
      },
      "outputs": [],
      "source": [
        "dataset_dir1 = '/content/people-clothing-segmentation/png_images/IMAGES'\n",
        "dataset_dir2 = '/content/people-clothing-segmentation/png_masks/MASKS'\n",
        "\n",
        "images = []\n",
        "masks = []\n",
        "\n",
        "# Iterate through each image file\n",
        "for filename in os.listdir(dataset_dir1):\n",
        "    mask_name = \"seg_\"+filename[4:]\n",
        "\n",
        "    image = cv2.imread(os.path.join(dataset_dir1, filename))\n",
        "    mask = cv2.imread(os.path.join(dataset_dir2, mask_name))\n",
        "\n",
        "    image = cv2.resize(image, (512, 512,))\n",
        "    mask = cv2.resize(mask, (512, 512,))\n",
        "\n",
        "    images.append(image)\n",
        "    masks.append(mask)\n",
        "\n",
        "image_train = np.array(images)\n",
        "mask_train = np.array(masks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnHKVdfQFh1i",
        "outputId": "57c44a75-9fda-42f9-ac6c-0f734b8ba260"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The shape of the Masks in the training Data:(1000, 512, 512, 3))\n"
          ]
        }
      ],
      "source": [
        "print(\"The shape of the Masks in the training Data:{})\".format(mask_train.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "lxErLgR-Fj3t",
        "outputId": "e5e674a5-a57c-4b35-e43a-6adfa18af5eb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.subplot(1,2,1)\n",
        "plt.imshow(image_train[56])\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(mask_train[56][:,:,0])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5fU-N7xFol0",
        "outputId": "8802178b-6211-4aca-e8f5-78baf5647824"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image data type: uint8\n",
            "(1000, 512, 512, 3)\n",
            "Mask data type: uint8\n",
            "(1000, 512, 512, 3)\n"
          ]
        }
      ],
      "source": [
        "image_data_type = image_train.dtype\n",
        "print(\"Image data type:\", image_data_type)\n",
        "print(image_train.shape)\n",
        "\n",
        "mask_data_type = mask_train.dtype\n",
        "print(\"Mask data type:\", mask_data_type)\n",
        "print(mask_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Mm3Vu-i3FyBu"
      },
      "outputs": [],
      "source": [
        "masked_image = cv2.bitwise_and(image_train, image_train, mask=mask_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XwLHUdTLFz26",
        "outputId": "a0bff85d-597c-4c25-f7c7-59a413383769"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x1500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 15))\n",
        "plt.subplot(1, 3, 1)\n",
        "x_train1 = cv2.cvtColor(image_train[29], cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(x_train1)\n",
        "plt.title('Input')\n",
        "\n",
        "# Plot the input image\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.imshow(mask_train[29][:,:,0])\n",
        "plt.title('Input')\n",
        "\n",
        "# Plot the prediction\n",
        "plt.subplot(1, 3, 3)\n",
        "rgb_image = cv2.cvtColor(masked_image[29], cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# Display the RGB image using Matplotlib\n",
        "plt.imshow(rgb_image)\n",
        "plt.title('Prediction')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "dZSk9qxDF5Eu"
      },
      "outputs": [],
      "source": [
        "class MultiLayerPerceptron():\n",
        "\n",
        "    def __init__(self, input):\n",
        "        super().__init__()\n",
        "        self.inp = input\n",
        "\n",
        "        self.conv_relu = Conv2D(1, kernel_size=3, strides=(1, 1), padding='SAME', activation='relu')\n",
        "        self.conv = Conv2D(1, kernel_size=3, strides=(1, 1), padding='SAME')\n",
        "        self.out = self.MLP()\n",
        "\n",
        "    def MLP(self):\n",
        "        out = self.conv_relu(self.inp)\n",
        "        out = self.conv(self.inp)\n",
        "        return out\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "vTaoH7RCGHL5"
      },
      "outputs": [],
      "source": [
        "class ConvBlockAttentionModule():\n",
        "\n",
        "  def __init__(self,input) :\n",
        "    self.out = self.CBAM(input)\n",
        "\n",
        "\n",
        "  def CBAM(self,input):\n",
        "\n",
        "    max1 = tf.reduce_max(input,axis=-1,keepdims = True)\n",
        "    avg1 = tf.reduce_mean(input,axis=-1,keepdims = True)\n",
        "    max1 = MultiLayerPerceptron(max1).out\n",
        "    avg1 = MultiLayerPerceptron(avg1).out\n",
        "\n",
        "    cam = tf.add(max1,avg1)\n",
        "    cam = tf.keras.activations.sigmoid(cam)\n",
        "    out1 = tf.math.multiply(cam,input)\n",
        "\n",
        "    max2 = MaxPooling2D(pool_size = (2,2), strides = 1, padding = 'SAME')(out1)\n",
        "    # print(np.shape(max2))\n",
        "    avg2 = AveragePooling2D(pool_size = (2,2), strides = 1, padding = 'SAME')(out1)\n",
        "    # print(np.shape(avg2))\n",
        "    out2 = tf.concat([avg2,max2],axis = -1)\n",
        "    # print(np.shape(out2))\n",
        "    out2 = Conv2D(1,kernel_size=3,strides=(1,1), padding= 'same')(out2)\n",
        "    # print(np.shape(out2))\n",
        "    out2 = tf.keras.activations.sigmoid(out2)\n",
        "    # print(np.shape(out2))\n",
        "\n",
        "    out = tf.math.multiply(out1,out2)\n",
        "\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "M33s_prhGKgK"
      },
      "outputs": [],
      "source": [
        "class DownSampling():\n",
        "\n",
        "  def __init__(self,input,n_filters):\n",
        "    # super().__init__()\n",
        "    self.inp = input\n",
        "    self.nf = n_filters\n",
        "    self.skip = self.Down_skip()\n",
        "    self.output = self.Down_out()\n",
        "\n",
        "  def Down_out(self) :\n",
        "\n",
        "    out = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(self.inp)\n",
        "    out = BatchNormalization(axis=-1)(out)\n",
        "    out = tf.keras.layers.LeakyReLU(alpha = 0.2)(out)\n",
        "    out = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(out)\n",
        "    out = BatchNormalization(axis=-1)(out)\n",
        "    out = tf.keras.layers.LeakyReLU(alpha = 0.2)(out)\n",
        "    avg = AveragePooling2D(pool_size=(2,2),strides = 2, padding = 'valid')(out)\n",
        "\n",
        "    # output = ConvBlockAttentionModule(avg).out\n",
        "    output = avg\n",
        "\n",
        "    return output\n",
        "\n",
        "  def Down_skip(self) :\n",
        "\n",
        "    out = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(self.inp)\n",
        "    out = BatchNormalization(axis=-1)(out)\n",
        "    out = tf.keras.layers.LeakyReLU(alpha = 0.2)(out)\n",
        "    out = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(out)\n",
        "    out = BatchNormalization(axis=-1)(out)\n",
        "    out = tf.keras.layers.LeakyReLU(alpha = 0.2)(out)\n",
        "\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX85ciD0GOq5",
        "outputId": "256c7870-ff08-402c-ec3a-75af77f0a3de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<keras.src.layers.reshaping.up_sampling2d.UpSampling2D at 0x7c7a01eb9390>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tf.keras.layers.UpSampling2D(\n",
        "    size=(2,2), data_format=None , interpolation ='bilinear'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "2BWCB6lZGQco"
      },
      "outputs": [],
      "source": [
        "class UpSampling():\n",
        "\n",
        "  def __init__(self,input,skip,n_filters):\n",
        "    # super().__init__()\n",
        "    self.inp = input\n",
        "    self.sk = skip\n",
        "    self.nf = n_filters\n",
        "    self.out = self.UpS();\n",
        "\n",
        "  def UpS(self) :\n",
        "\n",
        "    up = UpSampling2D(size=(2, 2), interpolation=\"bilinear\")(self.inp)\n",
        "    up = Conv2D(self.nf, kernel_size=3, activation=\"relu\", padding=\"same\")(up)\n",
        "    up = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(up)\n",
        "    up = BatchNormalization(axis=-1)(up)\n",
        "    up = tf.keras.layers.LeakyReLU(alpha = 0.2)(up)\n",
        "    up = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(up)\n",
        "    up = BatchNormalization(axis=-1)(up)\n",
        "    up = tf.keras.layers.LeakyReLU(alpha = 0.2)(up)\n",
        "\n",
        "    # out = ConvBlockAttentionModule(up).out\n",
        "    out = up\n",
        "\n",
        "    out = concatenate([out,self.sk], axis = 3)\n",
        "\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IxTaYFBdGSNr"
      },
      "outputs": [],
      "source": [
        "class ResidualBlock():\n",
        "\n",
        "  def __init__(self,input,n_filters):\n",
        "    # super().__init__()\n",
        "    self.inp = input\n",
        "    self.nf = n_filters\n",
        "    self.out = self.Residual();\n",
        "\n",
        "  def Residual (self) :\n",
        "\n",
        "    res = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(self.inp)\n",
        "    res = BatchNormalization(axis=-1)(res)\n",
        "    res = tf.keras.layers.LeakyReLU()(res)\n",
        "    res = Conv2D(self.nf,kernel_size=3,strides = (1,1),padding = 'same')(res)\n",
        "    res = BatchNormalization(axis=-1)(res)\n",
        "\n",
        "    out = tf.keras.layers.Add()([res,self.inp])\n",
        "\n",
        "    out = tf.keras.layers.ReLU()(out)\n",
        "\n",
        "    # out = ConvBlockAttentionModule(out).out\n",
        "\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ec04Lh7KGTWT"
      },
      "outputs": [],
      "source": [
        "class UNet_Model():\n",
        "\n",
        "  def __init__(self,input_size,n_filters=4,n_classes = 59):\n",
        "    # super().__init__()\n",
        "    self.inps = input_size\n",
        "    self.nf = n_filters\n",
        "    self.nc = n_classes\n",
        "    self.model = self.UNet();\n",
        "\n",
        "\n",
        "  def UNet(self) :\n",
        "\n",
        "    inputs = Input(self.inps)\n",
        "\n",
        "    down1 = DownSampling(inputs,self.nf)\n",
        "    down2 = DownSampling(down1.output,self.nf*2)\n",
        "    down3 = DownSampling(down2.output,self.nf*4)\n",
        "    down4 = DownSampling(down3.output,self.nf*8)\n",
        "    down5 = DownSampling(down4.output,self.nf*16)\n",
        "\n",
        "    res1 = ResidualBlock(down5.output,self.nf*16)\n",
        "    res2 = ResidualBlock(res1.out,self.nf*16)\n",
        "    res3 = ResidualBlock(res2.out,self.nf*16)\n",
        "    res4 = ResidualBlock(res3.out,self.nf*16)\n",
        "\n",
        "    up1 = UpSampling (res4.out,down5.skip,self.nf*16)\n",
        "    up2 = UpSampling (up1.out,down4.skip,self.nf*8)\n",
        "    up3 = UpSampling (up2.out,down3.skip,self.nf*4)\n",
        "    up4 = UpSampling (up3.out,down2.skip,self.nf*2)\n",
        "    up5 = UpSampling (up4.out,down1.skip,self.nf)\n",
        "\n",
        "    final = Conv2D(self.nc,1,padding = 'same')(up5.out)\n",
        "    model = tf.keras.Model(inputs=inputs,outputs=final)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9Ze4X-IGUrd",
        "outputId": "7d01c53a-39b7-4bd0-906f-a34b0db9ed4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 512, 512, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 512, 512, 4)          112       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 512, 512, 4)          16        ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 512, 512, 4)          0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 512, 512, 4)          148       ['leaky_re_lu_2[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 512, 512, 4)          16        ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 512, 512, 4)          0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 256, 256, 4)          0         ['leaky_re_lu_3[0][0]']       \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 256, 256, 8)          296       ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 256, 256, 8)          32        ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 256, 256, 8)          0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 256, 256, 8)          584       ['leaky_re_lu_6[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 256, 256, 8)          32        ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 256, 256, 8)          0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (Avera  (None, 128, 128, 8)          0         ['leaky_re_lu_7[0][0]']       \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 128, 128, 16)         1168      ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 128, 128, 16)         64        ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_10 (LeakyReLU)  (None, 128, 128, 16)         0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 128, 128, 16)         2320      ['leaky_re_lu_10[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 128, 128, 16)         64        ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_11 (LeakyReLU)  (None, 128, 128, 16)         0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (Avera  (None, 64, 64, 16)           0         ['leaky_re_lu_11[0][0]']      \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 64, 64, 32)           4640      ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 64, 64, 32)           128       ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_14 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 64, 64, 32)           9248      ['leaky_re_lu_14[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 64, 64, 32)           128       ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_15 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (Avera  (None, 32, 32, 32)           0         ['leaky_re_lu_15[0][0]']      \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 32, 32, 64)           18496     ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 32, 32, 64)           256       ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_18 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 32, 32, 64)           36928     ['leaky_re_lu_18[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 32, 32, 64)           256       ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_19 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (Avera  (None, 16, 16, 64)           0         ['leaky_re_lu_19[0][0]']      \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 16, 16, 64)           36928     ['average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 16, 16, 64)           256       ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_20 (LeakyReLU)  (None, 16, 16, 64)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 16, 16, 64)           36928     ['leaky_re_lu_20[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 16, 16, 64)           256       ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 16, 16, 64)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ',                            \n",
            "                                                                     'average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu (ReLU)                (None, 16, 16, 64)           0         ['add[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 16, 16, 64)           36928     ['re_lu[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 16, 16, 64)           256       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_21 (LeakyReLU)  (None, 16, 16, 64)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 16, 16, 64)           36928     ['leaky_re_lu_21[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 16, 16, 64)           256       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " add_1 (Add)                 (None, 16, 16, 64)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ',                            \n",
            "                                                                     're_lu[0][0]']               \n",
            "                                                                                                  \n",
            " re_lu_1 (ReLU)              (None, 16, 16, 64)           0         ['add_1[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 16, 16, 64)           36928     ['re_lu_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 16, 16, 64)           256       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_22 (LeakyReLU)  (None, 16, 16, 64)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 16, 16, 64)           36928     ['leaky_re_lu_22[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 16, 16, 64)           256       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 16, 16, 64)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ',                            \n",
            "                                                                     're_lu_1[0][0]']             \n",
            "                                                                                                  \n",
            " re_lu_2 (ReLU)              (None, 16, 16, 64)           0         ['add_2[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 16, 16, 64)           36928     ['re_lu_2[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 16, 16, 64)           256       ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_23 (LeakyReLU)  (None, 16, 16, 64)           0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 16, 16, 64)           36928     ['leaky_re_lu_23[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 16, 16, 64)           256       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 16, 16, 64)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ',                            \n",
            "                                                                     're_lu_2[0][0]']             \n",
            "                                                                                                  \n",
            " re_lu_3 (ReLU)              (None, 16, 16, 64)           0         ['add_3[0][0]']               \n",
            "                                                                                                  \n",
            " up_sampling2d_1 (UpSamplin  (None, 32, 32, 64)           0         ['re_lu_3[0][0]']             \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 32, 32, 64)           36928     ['up_sampling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 32, 32, 64)           36928     ['conv2d_28[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 32, 32, 64)           18496     ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 32, 32, 64)           256       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 32, 32, 64)           256       ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_24 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_16 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 32, 32, 64)           36928     ['leaky_re_lu_24[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 32, 32, 64)           36928     ['leaky_re_lu_16[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 32, 32, 64)           256       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 32, 32, 64)           256       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_25 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_17 (LeakyReLU)  (None, 32, 32, 64)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 32, 32, 128)          0         ['leaky_re_lu_25[0][0]',      \n",
            "                                                                     'leaky_re_lu_17[0][0]']      \n",
            "                                                                                                  \n",
            " up_sampling2d_2 (UpSamplin  (None, 64, 64, 128)          0         ['concatenate[0][0]']         \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 64, 64, 32)           36896     ['up_sampling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 64, 64, 32)           9248      ['conv2d_31[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 64, 64, 32)           4640      ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 64, 64, 32)           128       ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 64, 64, 32)           128       ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_26 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_12 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 64, 64, 32)           9248      ['leaky_re_lu_26[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 64, 64, 32)           9248      ['leaky_re_lu_12[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 64, 64, 32)           128       ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 64, 64, 32)           128       ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_27 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_13 (LeakyReLU)  (None, 64, 64, 32)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 64, 64, 64)           0         ['leaky_re_lu_27[0][0]',      \n",
            " )                                                                   'leaky_re_lu_13[0][0]']      \n",
            "                                                                                                  \n",
            " up_sampling2d_3 (UpSamplin  (None, 128, 128, 64)         0         ['concatenate_1[0][0]']       \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 128, 128, 16)         9232      ['up_sampling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 128, 128, 16)         2320      ['conv2d_34[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 128, 128, 16)         1168      ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 128, 128, 16)         64        ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 128, 128, 16)         64        ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_28 (LeakyReLU)  (None, 128, 128, 16)         0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_8 (LeakyReLU)   (None, 128, 128, 16)         0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 128, 128, 16)         2320      ['leaky_re_lu_28[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 128, 128, 16)         2320      ['leaky_re_lu_8[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 128, 128, 16)         64        ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 128, 128, 16)         64        ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_29 (LeakyReLU)  (None, 128, 128, 16)         0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_9 (LeakyReLU)   (None, 128, 128, 16)         0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate  (None, 128, 128, 32)         0         ['leaky_re_lu_29[0][0]',      \n",
            " )                                                                   'leaky_re_lu_9[0][0]']       \n",
            "                                                                                                  \n",
            " up_sampling2d_4 (UpSamplin  (None, 256, 256, 32)         0         ['concatenate_2[0][0]']       \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 256, 256, 8)          2312      ['up_sampling2d_4[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 256, 256, 8)          584       ['conv2d_37[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 256, 256, 8)          296       ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 256, 256, 8)          32        ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 256, 256, 8)          32        ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_30 (LeakyReLU)  (None, 256, 256, 8)          0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 256, 256, 8)          0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 256, 256, 8)          584       ['leaky_re_lu_30[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 256, 256, 8)          584       ['leaky_re_lu_4[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 256, 256, 8)          32        ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 256, 256, 8)          32        ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_31 (LeakyReLU)  (None, 256, 256, 8)          0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 256, 256, 8)          0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate  (None, 256, 256, 16)         0         ['leaky_re_lu_31[0][0]',      \n",
            " )                                                                   'leaky_re_lu_5[0][0]']       \n",
            "                                                                                                  \n",
            " up_sampling2d_5 (UpSamplin  (None, 512, 512, 16)         0         ['concatenate_3[0][0]']       \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 512, 512, 4)          580       ['up_sampling2d_5[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 512, 512, 4)          148       ['conv2d_40[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 512, 512, 4)          112       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 512, 512, 4)          16        ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 512, 512, 4)          16        ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " leaky_re_lu_32 (LeakyReLU)  (None, 512, 512, 4)          0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu (LeakyReLU)     (None, 512, 512, 4)          0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 512, 512, 4)          148       ['leaky_re_lu_32[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 512, 512, 4)          148       ['leaky_re_lu[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 512, 512, 4)          16        ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 512, 512, 4)          16        ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " leaky_re_lu_33 (LeakyReLU)  (None, 512, 512, 4)          0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 512, 512, 4)          0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " concatenate_4 (Concatenate  (None, 512, 512, 8)          0         ['leaky_re_lu_33[0][0]',      \n",
            " )                                                                   'leaky_re_lu_1[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 512, 512, 59)         531       ['concatenate_4[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 633263 (2.42 MB)\n",
            "Trainable params: 630751 (2.41 MB)\n",
            "Non-trainable params: 2512 (9.81 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#from keras.optimizers import SGD\n",
        "\n",
        "#sgd = SGD(lr=0.001, momentum=0.9)\n",
        "\n",
        "u = UNet_Model((512,512,3)).model\n",
        "u.compile(optimizer='adam',loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])\n",
        "u.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LG63Al_bGXea",
        "outputId": "7c699b67-8b83-43d0-f426-8d8f6deff875"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(512, 512, 3)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.shape(mask_train[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "wIr-1gf7GaNc"
      },
      "outputs": [],
      "source": [
        "def create_mask(pred_mask):\n",
        "    pred_mask = tf.argmax(pred_mask, axis=-1)\n",
        "    pred_mask = pred_mask[..., tf.newaxis]\n",
        "    return pred_mask[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNox4P8ZGbFX",
        "outputId": "e1031552-c404-4535-a850-d70a27d4cfdd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1000, 512, 512, 1)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "masks_train = np.expand_dims(mask_train[:,:,:,0], axis =-1)\n",
        "masks_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "QoR0QlTXGwGJ"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "fYJJrp5WGb3Y"
      },
      "outputs": [],
      "source": [
        "# image_train, image_test, mask_train, mask_test = train_test_split(images, masks, test_size=0.1, random_state=42)\n",
        "\n",
        "# train_dataset = tf.data.Dataset.from_tensor_slices((image_train, mask_train)).batch(15)\n",
        "# test_dataset = tf.data.Dataset.from_tensor_slices((image_test, mask_test)).batch(15)\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((image_train[0:900], masks_train[0:900]))\n",
        "train_dataset = train_dataset.batch(15)\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((image_train[901:1000], masks_train[901:1000]))\n",
        "test_dataset = test_dataset.batch(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "QFj89ueDG2TR"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8L-yyc71Iaia"
      },
      "outputs": [],
      "source": [
        "u.load_weights(\"content/A100_Seg_model_ckpt/model.ckpt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0T1LFr7Gx2o",
        "outputId": "13a626f9-1540-4a39-f953-b2d0faf5d905"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 3.8106 - accuracy: 0.2736\n",
            "Epoch 1: val_loss improved from inf to 3.38425, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 42s 205ms/step - loss: 3.8106 - accuracy: 0.2736 - val_loss: 3.3843 - val_accuracy: 0.6505\n",
            "Epoch 2/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 3.2867 - accuracy: 0.6553\n",
            "Epoch 2: val_loss improved from 3.38425 to 2.94049, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 3.2867 - accuracy: 0.6553 - val_loss: 2.9405 - val_accuracy: 0.6821\n",
            "Epoch 3/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 2.6724 - accuracy: 0.6872\n",
            "Epoch 3: val_loss improved from 2.94049 to 2.37297, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 2.6724 - accuracy: 0.6872 - val_loss: 2.3730 - val_accuracy: 0.7357\n",
            "Epoch 4/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 2.0855 - accuracy: 0.7579\n",
            "Epoch 4: val_loss improved from 2.37297 to 2.09372, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 2.0855 - accuracy: 0.7579 - val_loss: 2.0937 - val_accuracy: 0.7482\n",
            "Epoch 5/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 1.5825 - accuracy: 0.7830\n",
            "Epoch 5: val_loss improved from 2.09372 to 1.61649, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 1.5825 - accuracy: 0.7830 - val_loss: 1.6165 - val_accuracy: 0.7573\n",
            "Epoch 6/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 1.2292 - accuracy: 0.7914\n",
            "Epoch 6: val_loss improved from 1.61649 to 1.21457, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 1.2292 - accuracy: 0.7914 - val_loss: 1.2146 - val_accuracy: 0.7777\n",
            "Epoch 7/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 1.0262 - accuracy: 0.7961\n",
            "Epoch 7: val_loss improved from 1.21457 to 1.03235, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 1.0262 - accuracy: 0.7961 - val_loss: 1.0323 - val_accuracy: 0.7836\n",
            "Epoch 8/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.9133 - accuracy: 0.8001\n",
            "Epoch 8: val_loss improved from 1.03235 to 0.92083, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.9133 - accuracy: 0.8001 - val_loss: 0.9208 - val_accuracy: 0.7923\n",
            "Epoch 9/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.8521 - accuracy: 0.8024\n",
            "Epoch 9: val_loss improved from 0.92083 to 0.90841, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.8521 - accuracy: 0.8024 - val_loss: 0.9084 - val_accuracy: 0.7901\n",
            "Epoch 10/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.8085 - accuracy: 0.8059\n",
            "Epoch 10: val_loss improved from 0.90841 to 0.84345, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.8085 - accuracy: 0.8059 - val_loss: 0.8434 - val_accuracy: 0.7997\n",
            "Epoch 11/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.7776 - accuracy: 0.8097\n",
            "Epoch 11: val_loss improved from 0.84345 to 0.82125, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.7776 - accuracy: 0.8097 - val_loss: 0.8212 - val_accuracy: 0.8071\n",
            "Epoch 12/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.7565 - accuracy: 0.8142\n",
            "Epoch 12: val_loss improved from 0.82125 to 0.80249, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.7565 - accuracy: 0.8142 - val_loss: 0.8025 - val_accuracy: 0.8069\n",
            "Epoch 13/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.7361 - accuracy: 0.8201\n",
            "Epoch 13: val_loss improved from 0.80249 to 0.78302, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.7361 - accuracy: 0.8201 - val_loss: 0.7830 - val_accuracy: 0.8098\n",
            "Epoch 14/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.7160 - accuracy: 0.8235\n",
            "Epoch 14: val_loss improved from 0.78302 to 0.76015, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.7160 - accuracy: 0.8235 - val_loss: 0.7602 - val_accuracy: 0.8116\n",
            "Epoch 15/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.7022 - accuracy: 0.8258\n",
            "Epoch 15: val_loss improved from 0.76015 to 0.74518, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.7022 - accuracy: 0.8258 - val_loss: 0.7452 - val_accuracy: 0.8135\n",
            "Epoch 16/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.6819 - accuracy: 0.8289\n",
            "Epoch 16: val_loss improved from 0.74518 to 0.73132, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.6819 - accuracy: 0.8289 - val_loss: 0.7313 - val_accuracy: 0.8155\n",
            "Epoch 17/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.6639 - accuracy: 0.8320\n",
            "Epoch 17: val_loss improved from 0.73132 to 0.70828, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.6639 - accuracy: 0.8320 - val_loss: 0.7083 - val_accuracy: 0.8202\n",
            "Epoch 18/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.6491 - accuracy: 0.8352\n",
            "Epoch 18: val_loss improved from 0.70828 to 0.70319, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 163ms/step - loss: 0.6491 - accuracy: 0.8352 - val_loss: 0.7032 - val_accuracy: 0.8226\n",
            "Epoch 19/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.6381 - accuracy: 0.8380\n",
            "Epoch 19: val_loss improved from 0.70319 to 0.68234, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 162ms/step - loss: 0.6381 - accuracy: 0.8380 - val_loss: 0.6823 - val_accuracy: 0.8261\n",
            "Epoch 20/20\n",
            "60/60 [==============================] - ETA: 0s - loss: 0.6277 - accuracy: 0.8406\n",
            "Epoch 20: val_loss improved from 0.68234 to 0.67680, saving model to content/A100_Seg_model_ckpt/model.ckpt\n",
            "60/60 [==============================] - 10s 167ms/step - loss: 0.6277 - accuracy: 0.8406 - val_loss: 0.6768 - val_accuracy: 0.8298\n"
          ]
        }
      ],
      "source": [
        "checkpoint_path = \"content/A100_Seg_model_ckpt/model.ckpt\"\n",
        "checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_loss', verbose=1, save_weights_only=True, save_best_only=True, mode='min')\n",
        "\n",
        "history = u.fit(train_dataset, epochs=20, validation_data=test_dataset, callbacks=[checkpoint])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsOkfpaUJZtX",
        "outputId": "04a3c4a8-5c74-4310-a091-ad08c531ad35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aX1_tXVOKr-A"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}