srgan.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/swagata87/school2024iop/blob/main/srgan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zGcCsOilc8b1"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Layer\n",
        "from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n",
        "                                     Flatten, InputLayer, BatchNormalization, Input,GlobalAvgPool2D,PReLU )\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPLHB2s5gBJu"
      },
      "source": [
        "# Prepare Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Plya5Y4OeOPl",
        "outputId": "c77fb941-ad78-48ce-99f1-f70b9a666a65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!pip install -q kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d badasstechie/celebahq-resized-256x256\n",
        "!unzip \"/content/celebahq-resized-256x256.zip\" -d \"/content/dataset/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ni3A9hDmgKKU"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 128\n",
        "IM_SHAPE = (64,64,3)\n",
        "B=24\n",
        "LEARNING_RATE = 1e-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9_RtczneOSH",
        "outputId": "184f151b-7616-4b71-c3d9-6ec6931f72e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 30000 files belonging to 1 classes.\n"
          ]
        }
      ],
      "source": [
        "ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"/content/dataset/celeba_hq_256\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YZjG7N8geOU_"
      },
      "outputs": [],
      "source": [
        "def preprocess(image):\n",
        "  return tf.image.resize(image,[IM_SHAPE[0]//4,IM_SHAPE[1]//4],method='bicubic')/255,tf.cast(image,tf.float32)/127.5 - 1.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2lAK49h977P",
        "outputId": "85318a28-3d60-4a1f-e54f-98947f932448"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "ds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tgl0_Xn6CmHn",
        "outputId": "603df166-1ec2-4318-b73e-6d8ee5dab0bf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "235"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_JBWJWy0e-G9"
      },
      "outputs": [],
      "source": [
        "train_dataset = (\n",
        "    ds.take(12000)\n",
        "    .map(preprocess)\n",
        "    .unbatch()\n",
        "    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n",
        "    .batch(BATCH_SIZE,drop_remainder=True)\n",
        "    .prefetch(tf.data.AUTOTUNE)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bssNU2hc0eo3",
        "outputId": "501937c2-3f2d-4d8d-f45c-e8171c92bafc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(128, 16, 16, 3) (128, 64, 64, 3)\n"
          ]
        }
      ],
      "source": [
        "for d1,d2 in train_dataset.take(1):\n",
        "  print(d1.shape,d2.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "id": "5AhVXUnre-O1",
        "outputId": "195a5e73-f899-4bbd-da08-9b013488b0a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (8,8))\n",
        "k=0\n",
        "n = 6\n",
        "for i in range(n):\n",
        "    ax = plt.subplot(3,2, k+1)\n",
        "    if i<2:\n",
        "      plt.imshow(d1[i])\n",
        "    elif i>=2 and i<4:\n",
        "      plt.imshow(cv2.resize(np.array(d1[i-2]),(64,64)))\n",
        "    else:\n",
        "      plt.imshow((d2[i-4]+1)/2)\n",
        "    plt.axis(\"off\")\n",
        "    k+=1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdtWfpUGgC4E"
      },
      "source": [
        "# MOdeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "sPzQhU4cJvmd"
      },
      "outputs": [],
      "source": [
        "class ResBlock(Layer):\n",
        "    def __init__(self,n_filters,filter_size,strides,name='res_block'):\n",
        "      super(ResBlock, self).__init__(name=name)\n",
        "      self.n_filters=n_filters\n",
        "      self.filter_size=filter_size\n",
        "      self.strides=strides\n",
        "\n",
        "    def build(self,input_shape):\n",
        "      self.conv_1=Conv2D(\n",
        "          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n",
        "      self.batch_norm_1=BatchNormalization()\n",
        "      self.prelu=PReLU()\n",
        "      self.conv_2=Conv2D(\n",
        "          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n",
        "      self.batch_norm_2=BatchNormalization()\n",
        "\n",
        "    def call(self,x_in):\n",
        "      x=self.conv_1(x_in)\n",
        "      x=self.prelu(self.batch_norm_1(x))\n",
        "      x=self.conv_2(x)\n",
        "      x=self.batch_norm_2(x)\n",
        "      return x+x_in"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "6DElSXYFLCaS"
      },
      "outputs": [],
      "source": [
        "class UpsampleBlock(Layer):\n",
        "    def __init__(self,n_filters,filter_size,strides,name='upsample_block'):\n",
        "      super(UpsampleBlock, self).__init__(name=name)\n",
        "      self.n_filters=n_filters\n",
        "      self.filter_size=filter_size\n",
        "      self.strides=strides\n",
        "\n",
        "    def build(self,input_shape):\n",
        "      self.conv=Conv2D(\n",
        "          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n",
        "      self.prelu=PReLU()\n",
        "    def call(self,x):\n",
        "      x=self.conv(x)\n",
        "      x=tf.nn.depth_to_space(x, 2)\n",
        "      x=self.prelu(x)\n",
        "      return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpxObeQNe-RF",
        "outputId": "539e94d5-27d6-4f27-d35b-5a2c622d5783"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 16, 16, 3)]          0         []                            \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 16, 16, 64)           15616     ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " p_re_lu (PReLU)             (None, 16, 16, 64)           16384     ['conv2d[0][0]']              \n",
            "                                                                                                  \n",
            " res_block_0 (ResBlock)      (None, 16, 16, 64)           90752     ['p_re_lu[0][0]']             \n",
            "                                                                                                  \n",
            " res_block_1 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_0[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_2 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_1[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_3 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_2[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_4 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_3[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_5 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_4[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_6 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_5[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_7 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_6[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_8 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_7[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_9 (ResBlock)      (None, 16, 16, 64)           90752     ['res_block_8[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_10 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_9[0][0]']         \n",
            "                                                                                                  \n",
            " res_block_11 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_10[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_12 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_11[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_13 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_12[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_14 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_13[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_15 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_14[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_16 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_15[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_17 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_16[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_18 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_17[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_19 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_18[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_20 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_19[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_21 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_20[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_22 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_21[0][0]']        \n",
            "                                                                                                  \n",
            " res_block_23 (ResBlock)     (None, 16, 16, 64)           90752     ['res_block_22[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 16, 16, 64)           331840    ['res_block_23[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 16, 16, 64)           256       ['conv2d_1[0][0]']            \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " tf.__operators__.add (TFOp  (None, 16, 16, 64)           0         ['batch_normalization[0][0]', \n",
            " Lambda)                                                             'p_re_lu[0][0]']             \n",
            "                                                                                                  \n",
            " upsample_block_1 (Upsample  (None, 32, 32, 64)           213248    ['tf.__operators__.add[0][0]']\n",
            " Block)                                                                                           \n",
            "                                                                                                  \n",
            " upsample_block_2 (Upsample  (None, 64, 64, 64)           409856    ['upsample_block_1[0][0]']    \n",
            " Block)                                                                                           \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 3)            15555     ['upsample_block_2[0][0]']    \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3180803 (12.13 MB)\n",
            "Trainable params: 3174531 (12.11 MB)\n",
            "Non-trainable params: 6272 (24.50 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "input_lr=tf.keras.layers.Input(shape=(IM_SHAPE[0]//4,IM_SHAPE[1]//4,3))\n",
        "input_conv=tf.keras.layers.Conv2D(64,9,1,padding='same')(input_lr)\n",
        "input_conv=PReLU()(input_conv)\n",
        "\n",
        "x=input_conv\n",
        "for i in range(B):\n",
        "  x=ResBlock(64,3,1,name='res_block_'+str(i))(x)\n",
        "\n",
        "x=tf.keras.layers.Conv2D(64,9,padding='same')(x)\n",
        "x=tf.keras.layers.BatchNormalization()(x)\n",
        "\n",
        "x+=input_conv\n",
        "\n",
        "x=UpsampleBlock(256,3,1,name='upsample_block_1')(x)\n",
        "x=UpsampleBlock(256,3,1,name='upsample_block_2')(x)\n",
        "\n",
        "output_sr=tf.keras.layers.Conv2D(3,9,activation='tanh',padding='same')(x)\n",
        "\n",
        "srresnet=tf.keras.models.Model(input_lr,output_sr)\n",
        "srresnet.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "0iZbRVkySUs6"
      },
      "outputs": [],
      "source": [
        "class ConvBlock(Layer):\n",
        "    def __init__(self,n_filters,filter_size,strides,name='conv_block'):\n",
        "      super(ConvBlock, self).__init__(name=name)\n",
        "      self.n_filters=n_filters\n",
        "      self.filter_size=filter_size\n",
        "      self.strides=strides\n",
        "\n",
        "    def build(self,input_shape):\n",
        "      self.conv=Conv2D(\n",
        "          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n",
        "      self.batch_norm=BatchNormalization()\n",
        "\n",
        "    def call(self,x):\n",
        "      x=self.conv(x)\n",
        "      x=LeakyReLU()(self.batch_norm(x))\n",
        "      return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIgbRN3ie-Tc",
        "outputId": "2923cff4-41bb-4842-d021-fbb537418f47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 64, 64, 64)        0         \n",
            "                                                                 \n",
            " conv_block_0 (ConvBlock)    (None, 32, 32, 64)        37184     \n",
            "                                                                 \n",
            " conv_block_1 (ConvBlock)    (None, 32, 32, 128)       74368     \n",
            "                                                                 \n",
            " conv_block_2 (ConvBlock)    (None, 16, 16, 128)       148096    \n",
            "                                                                 \n",
            " conv_block_3 (ConvBlock)    (None, 16, 16, 256)       296192    \n",
            "                                                                 \n",
            " conv_block_4 (ConvBlock)    (None, 8, 8, 256)         591104    \n",
            "                                                                 \n",
            " conv_block_5 (ConvBlock)    (None, 8, 8, 512)         1182208   \n",
            "                                                                 \n",
            " conv_block_6 (ConvBlock)    (None, 4, 4, 512)         2361856   \n",
            "                                                                 \n",
            " global_average_pooling2d (  (None, 512)               0         \n",
            " GlobalAveragePooling2D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1024)              525312    \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 1025      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5219137 (19.91 MB)\n",
            "Trainable params: 5215425 (19.90 MB)\n",
            "Non-trainable params: 3712 (14.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "input_lr=tf.keras.layers.Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3))\n",
        "input_conv=tf.keras.layers.Conv2D(64,3,padding='same')(input_lr)\n",
        "input_conv=tf.keras.layers.LeakyReLU()(input_conv)\n",
        "\n",
        "channel_nums=[64,128,128,256,256,512,512]\n",
        "stride_sizes=[2,1,2,1,2,1,2]\n",
        "\n",
        "disc=input_conv\n",
        "for i in range(7):\n",
        "  disc=ConvBlock(\n",
        "      channel_nums[i],3,stride_sizes[i],name='conv_block_'+str(i))(disc)\n",
        "disc = GlobalAvgPool2D()(disc)\n",
        "disc=tf.keras.layers.Dense(1024)(disc)\n",
        "disc=tf.keras.layers.LeakyReLU()(disc)\n",
        "\n",
        "disc_output=tf.keras.layers.Dense(1,activation='sigmoid')(disc)\n",
        "\n",
        "discriminator=tf.keras.models.Model(input_lr,disc_output)\n",
        "discriminator.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "tktFYE07vrjR"
      },
      "outputs": [],
      "source": [
        "def pixel_MSE(y_true,y_pred):\n",
        "  return tf.reduce_mean( (y_true - y_pred) ** 2 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "s9KnfnbEp0Ak",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d3a0d94-1c84-4531-c4b3-b837980151a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80134624/80134624 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "VGG19=tf.keras.applications.VGG19(weights='imagenet',include_top=False,input_shape=(256,256,3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "wa6-iNREtpAQ"
      },
      "outputs": [],
      "source": [
        "def VGG_loss(y_hr,y_sr,i_m=2,j_m=2):\n",
        "  i,j=0,0\n",
        "  accumulated_loss=0.0\n",
        "  for l in VGG19.layers:\n",
        "    cl_name=l.__class__.__name__\n",
        "    if cl_name=='Conv2D':\n",
        "      j+=1\n",
        "    if cl_name=='MaxPooling2D':\n",
        "      i+=1\n",
        "      j=0\n",
        "    if i==i_m and j==j_m:\n",
        "      break\n",
        "\n",
        "    y_hr=l(y_hr)\n",
        "    y_sr=l(y_sr)\n",
        "    if cl_name=='Conv2D':\n",
        "      mse=tf.keras.losses.MeanSquaredError(name='mean_squared_error')\n",
        "      accumulated_loss+=mse(y_hr,y_sr)*0.006\n",
        "\n",
        "  return accumulated_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "BzgVRF5lzbuK"
      },
      "outputs": [],
      "source": [
        "def content_loss(y_true,y_pred):\n",
        "  mse=tf.keras.losses.MeanSquaredError(name='mean_squared_error')\n",
        "  return mse(y_true,y_pred)+VGG_loss(y_true,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "EX8x_kl6fLWh"
      },
      "outputs": [],
      "source": [
        "class GANMonitor(tf.keras.callbacks.Callback):\n",
        "\n",
        "  def on_epoch_end(self, epoch,logs=None):\n",
        "    plt.figure(figsize = (16,16))\n",
        "    k=0\n",
        "    n = 6\n",
        "    for i in range(n):\n",
        "      ax = plt.subplot(3,2, k+1)\n",
        "      if i<2:\n",
        "        plt.imshow(d1[i])\n",
        "      elif i>=2 and i<4:\n",
        "        out=self.model.generator(tf.expand_dims(d1[i-2],axis=0))\n",
        "        plt.imshow((out[0]+1)/2)\n",
        "      else:\n",
        "        plt.imshow((d2[i-4]+1)/2)\n",
        "      plt.axis(\"off\")\n",
        "      k+=1\n",
        "\n",
        "    plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "g-FukMaRe-XN"
      },
      "outputs": [],
      "source": [
        "class SRGAN(tf.keras.Model):\n",
        "    def __init__(self, discriminator, generator):\n",
        "        super(SRGAN, self).__init__()\n",
        "        self.discriminator = discriminator\n",
        "        self.generator = generator\n",
        "\n",
        "    def compile(self, d_optimizer, g_optimizer, loss_disc,loss_gen,):\n",
        "        super(SRGAN, self).compile()\n",
        "        self.d_optimizer = d_optimizer\n",
        "        self.g_optimizer = g_optimizer\n",
        "        self.loss_disc=loss_disc\n",
        "        self.loss_gen=loss_gen\n",
        "        self.d_loss_metric = tf.keras.metrics.Mean(name=\"d_loss\")\n",
        "        self.g_loss_metric = tf.keras.metrics.Mean(name=\"g_loss\")\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.d_loss_metric, self.g_loss_metric]\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "\n",
        "      lr_images,hr_images = real_images\n",
        "\n",
        "      batch_size = tf.shape(hr_images)[0]\n",
        "\n",
        "      generated_images = self.generator(lr_images)\n",
        "\n",
        "      real_labels = tf.ones((batch_size, 1))\n",
        "      fake_labels = tf.zeros((batch_size, 1))\n",
        "\n",
        "      # Train the discriminator\n",
        "      with tf.GradientTape() as tape:\n",
        "\n",
        "          real_predictions = self.discriminator(hr_images)\n",
        "          d_loss_real = self.loss_disc(real_labels,real_predictions)\n",
        "\n",
        "          fake_predictions = self.discriminator(generated_images)\n",
        "          d_loss_fake = self.loss_disc(fake_labels,fake_predictions)\n",
        "\n",
        "          d_loss = 0.5*(d_loss_fake+d_loss_real)\n",
        "      grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n",
        "      self.d_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_weights))\n",
        "\n",
        "      misleading_labels = tf.ones((batch_size, 1))\n",
        "\n",
        "      with tf.GradientTape() as tape:\n",
        "          predictions = self.generator(lr_images)\n",
        "          g_loss = self.loss_gen(hr_images, predictions)\n",
        "          g_loss=g_loss+1e-3*self.loss_disc(misleading_labels, self.discriminator(predictions))\n",
        "\n",
        "      grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
        "      self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n",
        "\n",
        "      # Update metrics\n",
        "      self.d_loss_metric.update_state(d_loss)\n",
        "      self.g_loss_metric.update_state(g_loss)\n",
        "      return {\n",
        "          \"d_loss\": self.d_loss_metric.result(),\n",
        "          \"g_loss\": self.g_loss_metric.result(),\n",
        "      }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "J5p4XGOGfLYv"
      },
      "outputs": [],
      "source": [
        "epochs = 1\n",
        "\n",
        "gan = SRGAN(discriminator=discriminator, generator=srresnet, )\n",
        "gan.compile(\n",
        "    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
        "    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
        "    loss_disc=tf.keras.losses.BinaryCrossentropy(),\n",
        "    loss_gen=content_loss,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir generated"
      ],
      "metadata": {
        "id": "_yTHWqAraFfm"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=gan.fit(\n",
        "    train_dataset, epochs=epochs, callbacks=[GANMonitor()]\n",
        ")"
      ],
      "metadata": {
        "id": "ae7A6uuN7NUj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "175dcb19-e5e1-4ab8-9099-02253b0c8aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     22/Unknown - 1473s 65s/step - d_loss: 0.4573 - g_loss: 0.7499"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X57mSI-y7NWw"
      },
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