Pix2Pix/PyTorch/.ipynb_checkpoints/pix2pix_pytorch-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import functools\n",
    "import torchvision.models as models\n",
    "import torch.nn.functional as F\n",
    "from torchsummary import summary\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets import ImageFolder\n",
    "import torch.optim as optim\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision.utils import make_grid\n",
    "from skimage import io, transform\n",
    "from torch.autograd import Variable\n",
    "from torchvision.utils import save_image\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8.1'"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchvision.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '4,5,6,7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_WIDTH = 256\n",
    "IMG_HEIGHT = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Data Preprocessing'''\n",
    "    \n",
    "def read_image(image):\n",
    "        \n",
    "        image = np.array(image)\n",
    "        width = image.shape[1]\n",
    "        width_half = width // 2\n",
    "       \n",
    "        input_image = image[:, :width_half, :]\n",
    "        target_image = image[:, width_half:, :]\n",
    "    \n",
    "        input_image = input_image.astype(np.float32)\n",
    "        target_image = target_image.astype(np.float32)\n",
    "    \n",
    "        return input_image, target_image\n",
    "\n",
    "def random_crop(image, dim):\n",
    "    height, width, _ = dim\n",
    "    rand_crop = np.random.uniform(low=0,high=256)\n",
    "    x, y = np.random.uniform(low=0,high=int(height-256)), np.random.uniform(low=0,high=int(width-256))  \n",
    "    #return print(image.shape)\n",
    "    return image[:, int(x):int(x)+256, int(y):int(y)+256]\n",
    "\n",
    "    \n",
    "\n",
    "def random_jittering_mirroring(input_image, target_image, height=286, width=286):\n",
    "    \n",
    "    #resizing to 286x286\n",
    "    input_image = cv2.resize(input_image, (height, width) ,interpolation=cv2.INTER_NEAREST)\n",
    "    target_image = cv2.resize(target_image, (height, width),\n",
    "                               interpolation=cv2.INTER_NEAREST)\n",
    "    \n",
    "    #print(input_image.shape)\n",
    "    #cropping (random jittering) to 256x256\n",
    "    stacked_image = np.stack([input_image, target_image], axis=0)\n",
    "    cropped_image = random_crop(stacked_image, dim=[IMG_HEIGHT, IMG_WIDTH, 3])\n",
    "    \n",
    "    input_image, target_image = cropped_image[0], cropped_image[1]\n",
    "    #print(input_image.shape)\n",
    "    if torch.rand(()) > 0.5:\n",
    "     # random mirroring\n",
    "        input_image = np.fliplr(input_image)\n",
    "        target_image = np.fliplr(target_image)\n",
    "    return input_image, target_image\n",
    "        \n",
    "def normalize(inp, tar):\n",
    "    input_image = (inp / 127.5) - 1\n",
    "    target_image = (tar / 127.5) - 1\n",
    "    return input_image, target_image\n",
    "        \n",
    "class Train_Normalize(object):\n",
    "    def __call__(self, image):\n",
    "        inp, tar = read_image(image)\n",
    "        inp, tar = random_jittering_mirroring(inp, tar)\n",
    "        inp, tar = normalize(inp, tar)\n",
    "        image_a = torch.from_numpy(inp.copy().transpose((2,0,1)))\n",
    "        image_b = torch.from_numpy(tar.copy().transpose((2,0,1)))\n",
    "        return image_a, image_b    \n",
    "    \n",
    "class Val_Normalize(object):\n",
    "    def __call__(self, image):\n",
    "        inp, tar = read_image(image)\n",
    "        #inp, tar = random_jittering_mirroring(inp, tar)\n",
    "        inp, tar = normalize(inp, tar)\n",
    "        image_a = torch.from_numpy(inp.copy().transpose((2,0,1)))\n",
    "        image_b = torch.from_numpy(tar.copy().transpose((2,0,1)))\n",
    "        return image_a, image_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = 'edges2shoes/train_data/'\n",
    "n_gpus = 4\n",
    "batch_size = 64 * n_gpus\n",
    "\n",
    "train_ds = ImageFolder(DIR, transform=transforms.Compose([\n",
    "        Train_Normalize()]))\n",
    "train_dl = DataLoader(train_ds, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAL_DIR = 'edges2shoes/val_data/'\n",
    "\n",
    "batch_size = 64 * n_gpus\n",
    "\n",
    "val_ds = ImageFolder(VAL_DIR, transform=transforms.Compose([\n",
    "        Val_Normalize()]))\n",
    "val_dl = DataLoader(val_ds, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(inputs, target, figsize=(10, 5)):\n",
    "    inputs = np.uint8(inputs)\n",
    "    target = np.uint8(target)\n",
    "    tar = np.rollaxis(target[0], 0, 3)\n",
    "    inp = np.rollaxis(inputs[0], 0, 3)\n",
    "    title = ['Input Image', 'Ground Truth']\n",
    "    display_list = [inp, tar]\n",
    "    plt.figure(figsize=figsize)\n",
    "  \n",
    "    for i in range(2):\n",
    "        plt.subplot(1, 3, i+1)\n",
    "        plt.title(title[i])\n",
    "        plt.axis('off')\n",
    "        plt.imshow(display_list[i])\n",
    "    plt.axis('off')\n",
    " \n",
    "    #plt.imshow(image)    \n",
    "\n",
    "def show_batch(dl):\n",
    "    j=0\n",
    "    for (images_a, images_b), _ in dl:\n",
    "        j += 1\n",
    "        imshow(images_a, images_b)\n",
    "        if j == 3:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_batch(train_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom weights initialization called on generator and discriminator        \n",
    "def init_weights(net, init_type='normal', scaling=0.02):\n",
    "    \"\"\"Initialize network weights.\n",
    "    Parameters:\n",
    "        net (network)   -- network to be initialized\n",
    "        init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal\n",
    "        init_gain (float)    -- scaling factor for normal, xavier and orthogonal.\n",
    "    We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might\n",
    "    work better for some applications. Feel free to try yourself.\n",
    "    \"\"\"\n",
    "    def init_func(m):  # define the initialization function\n",
    "        classname = m.__class__.__name__\n",
    "        if hasattr(m, 'weight') and (classname.find('Conv')) != -1:\n",
    "            torch.nn.init.normal_(m.weight.data, 0.0, scaling)\n",
    "        elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.\n",
    "            torch.nn.init.normal_(m.weight.data, 1.0, scaling)\n",
    "            torch.nn.init.constant_(m.bias.data, 0.0)\n",
    "\n",
    "    print('initialize network with %s' % init_type)\n",
    "    net.apply(init_func)  # apply the initialization function <init_func>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_norm_layer(norm_type='instance'):\n",
    "    \"\"\"Return a normalization layer\n",
    "    Parameters:\n",
    "        norm_type (str) -- the name of the normalization layer: batch | instance | none\n",
    "    For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).\n",
    "    \"\"\"\n",
    "    if norm_type == 'batch':\n",
    "        norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)\n",
    "    return norm_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UnetGenerator(nn.Module):\n",
    "    \"\"\"Create a Unet-based generator\"\"\"\n",
    "\n",
    "    def __init__(self, input_nc, output_nc, nf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):\n",
    "        \"\"\"Construct a Unet generator\n",
    "        Parameters:\n",
    "            input_nc (int)  -- the number of channels in input images\n",
    "            output_nc (int) -- the number of channels in output images\n",
    "            num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,\n",
    "                                image of size 128x128 will become of size 1x1 # at the bottleneck\n",
    "            nf (int)       -- the number of filters in the last conv layer\n",
    "            norm_layer      -- normalization layer\n",
    "        We construct the U-Net from the innermost layer to the outermost layer.\n",
    "        It is a recursive process.\n",
    "        \"\"\"\n",
    "        super(UnetGenerator, self).__init__()\n",
    "        # construct unet structure\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 8, nf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)  # add the innermost layer\n",
    "        \n",
    "        # add intermediate layers with ngf * 8 filters\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 8, nf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 8, nf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 8, nf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)\n",
    "        \n",
    "        # gradually reduce the number of filters from nf * 8 to nf\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 4, nf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n",
    "        unet_block = UnetSkipConnectionBlock(nf * 2, nf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n",
    "        unet_block = UnetSkipConnectionBlock(nf, nf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n",
    "        self.model = UnetSkipConnectionBlock(output_nc, nf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)  # add the outermost layer\n",
    "\n",
    "    def forward(self, input):\n",
    "        \"\"\"Standard forward\"\"\"\n",
    "        return self.model(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UnetSkipConnectionBlock(nn.Module):\n",
    "    \"\"\"Defines the Unet submodule with skip connection.\n",
    "        X -------------------identity----------------------\n",
    "        |-- downsampling -- |submodule| -- upsampling --|\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, outer_nc, inner_nc, input_nc=None,\n",
    "                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):\n",
    "        \"\"\"Construct a Unet submodule with skip connections.\n",
    "        Parameters:\n",
    "            outer_nc (int) -- the number of filters in the outer conv layer\n",
    "            inner_nc (int) -- the number of filters in the inner conv layer\n",
    "            input_nc (int) -- the number of channels in input images/features\n",
    "            submodule (UnetSkipConnectionBlock) -- previously defined submodules\n",
    "            outermost (bool)    -- if this module is the outermost module\n",
    "            innermost (bool)    -- if this module is the innermost module\n",
    "            norm_layer          -- normalization layer\n",
    "            use_dropout (bool)  -- if use dropout layers.\n",
    "        \"\"\"\n",
    "        super(UnetSkipConnectionBlock, self).__init__()\n",
    "        self.outermost = outermost\n",
    "        if input_nc is None:\n",
    "            input_nc = outer_nc\n",
    "        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,\n",
    "                             stride=2, padding=1, bias=False)\n",
    "        downrelu = nn.LeakyReLU(0.2, True)\n",
    "        downnorm = norm_layer(inner_nc)\n",
    "        uprelu = nn.ReLU(True)\n",
    "        upnorm = norm_layer(outer_nc)\n",
    "\n",
    "        if outermost:\n",
    "            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,\n",
    "                                        kernel_size=4, stride=2,\n",
    "                                        padding=1)\n",
    "            down = [downconv]\n",
    "            up = [uprelu, upconv, nn.Tanh()]\n",
    "            model = down + [submodule] + up\n",
    "        elif innermost:\n",
    "            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,\n",
    "                                        kernel_size=4, stride=2,\n",
    "                                        padding=1, bias=False)\n",
    "            down = [downrelu, downconv]\n",
    "            up = [uprelu, upconv, upnorm]\n",
    "            model = down + up\n",
    "        else:\n",
    "            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,\n",
    "                                        kernel_size=4, stride=2,\n",
    "                                        padding=1, bias=False)\n",
    "            down = [downrelu, downconv, downnorm]\n",
    "            up = [uprelu, upconv, upnorm]\n",
    "\n",
    "            if use_dropout:\n",
    "                model = down + [submodule] + up + [nn.Dropout(0.5)]\n",
    "            else:\n",
    "                model = down + [submodule] + up\n",
    "\n",
    "        self.model = nn.Sequential(*model)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.outermost:\n",
    "            return self.model(x)\n",
    "        else:   # add skip connections\n",
    "            return torch.cat([x, self.model(x)], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.device_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = UnetGenerator(3, 3, 64, norm_layer=norm_layer, use_dropout=False)#.cuda().float()\n",
    "generator.apply(weights_init)\n",
    "\n",
    "generator = torch.nn.DataParallel(generator)  # multi-GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = torch.ones(1, 3,256,256)\n",
    "#gen = generator(inp)\n",
    "device = 'cuda'\n",
    "inp = inp.to(device)\n",
    "generator = generator.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 128, 128]           3,072\n",
      "         LeakyReLU-2         [-1, 64, 128, 128]               0\n",
      "            Conv2d-3          [-1, 128, 64, 64]         131,072\n",
      "       BatchNorm2d-4          [-1, 128, 64, 64]             256\n",
      "         LeakyReLU-5          [-1, 128, 64, 64]               0\n",
      "            Conv2d-6          [-1, 256, 32, 32]         524,288\n",
      "       BatchNorm2d-7          [-1, 256, 32, 32]             512\n",
      "         LeakyReLU-8          [-1, 256, 32, 32]               0\n",
      "            Conv2d-9          [-1, 512, 16, 16]       2,097,152\n",
      "      BatchNorm2d-10          [-1, 512, 16, 16]           1,024\n",
      "        LeakyReLU-11          [-1, 512, 16, 16]               0\n",
      "           Conv2d-12            [-1, 512, 8, 8]       4,194,304\n",
      "      BatchNorm2d-13            [-1, 512, 8, 8]           1,024\n",
      "        LeakyReLU-14            [-1, 512, 8, 8]               0\n",
      "           Conv2d-15            [-1, 512, 4, 4]       4,194,304\n",
      "      BatchNorm2d-16            [-1, 512, 4, 4]           1,024\n",
      "        LeakyReLU-17            [-1, 512, 4, 4]               0\n",
      "           Conv2d-18            [-1, 512, 2, 2]       4,194,304\n",
      "      BatchNorm2d-19            [-1, 512, 2, 2]           1,024\n",
      "        LeakyReLU-20            [-1, 512, 2, 2]               0\n",
      "           Conv2d-21            [-1, 512, 1, 1]       4,194,304\n",
      "             ReLU-22            [-1, 512, 1, 1]               0\n",
      "  ConvTranspose2d-23            [-1, 512, 2, 2]       4,194,304\n",
      "      BatchNorm2d-24            [-1, 512, 2, 2]           1,024\n",
      "UnetSkipConnectionBlock-25           [-1, 1024, 2, 2]               0\n",
      "             ReLU-26           [-1, 1024, 2, 2]               0\n",
      "  ConvTranspose2d-27            [-1, 512, 4, 4]       8,388,608\n",
      "      BatchNorm2d-28            [-1, 512, 4, 4]           1,024\n",
      "UnetSkipConnectionBlock-29           [-1, 1024, 4, 4]               0\n",
      "             ReLU-30           [-1, 1024, 4, 4]               0\n",
      "  ConvTranspose2d-31            [-1, 512, 8, 8]       8,388,608\n",
      "      BatchNorm2d-32            [-1, 512, 8, 8]           1,024\n",
      "UnetSkipConnectionBlock-33           [-1, 1024, 8, 8]               0\n",
      "             ReLU-34           [-1, 1024, 8, 8]               0\n",
      "  ConvTranspose2d-35          [-1, 512, 16, 16]       8,388,608\n",
      "      BatchNorm2d-36          [-1, 512, 16, 16]           1,024\n",
      "UnetSkipConnectionBlock-37         [-1, 1024, 16, 16]               0\n",
      "             ReLU-38         [-1, 1024, 16, 16]               0\n",
      "  ConvTranspose2d-39          [-1, 256, 32, 32]       4,194,304\n",
      "      BatchNorm2d-40          [-1, 256, 32, 32]             512\n",
      "UnetSkipConnectionBlock-41          [-1, 512, 32, 32]               0\n",
      "             ReLU-42          [-1, 512, 32, 32]               0\n",
      "  ConvTranspose2d-43          [-1, 128, 64, 64]       1,048,576\n",
      "      BatchNorm2d-44          [-1, 128, 64, 64]             256\n",
      "UnetSkipConnectionBlock-45          [-1, 256, 64, 64]               0\n",
      "             ReLU-46          [-1, 256, 64, 64]               0\n",
      "  ConvTranspose2d-47         [-1, 64, 128, 128]         262,144\n",
      "      BatchNorm2d-48         [-1, 64, 128, 128]             128\n",
      "UnetSkipConnectionBlock-49        [-1, 128, 128, 128]               0\n",
      "             ReLU-50        [-1, 128, 128, 128]               0\n",
      "  ConvTranspose2d-51          [-1, 3, 256, 256]           6,147\n",
      "             Tanh-52          [-1, 3, 256, 256]               0\n",
      "UnetSkipConnectionBlock-53          [-1, 3, 256, 256]               0\n",
      "    UnetGenerator-54          [-1, 3, 256, 256]               0\n",
      "================================================================\n",
      "Total params: 54,413,955\n",
      "Trainable params: 54,413,955\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.75\n",
      "Forward/backward pass size (MB): 135.96\n",
      "Params size (MB): 207.57\n",
      "Estimated Total Size (MB): 344.28\n",
      "----------------------------------------------------------------\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(summary(generator,(3,256,256)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    \"\"\"Defines a PatchGAN discriminator\"\"\"\n",
    "\n",
    "    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):\n",
    "        \"\"\"Construct a PatchGAN discriminator\n",
    "        Parameters:\n",
    "            input_nc (int)  -- the number of channels in input images\n",
    "            ndf (int)       -- the number of filters in the last conv layer\n",
    "            n_layers (int)  -- the number of conv layers in the discriminator\n",
    "            norm_layer      -- normalization layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "        kw = 4\n",
    "        padw = 1\n",
    "        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]\n",
    "        nf_mult = 1\n",
    "        nf_mult_prev = 1\n",
    "        for n in range(1, n_layers):  # gradually increase the number of filters\n",
    "            nf_mult_prev = nf_mult\n",
    "            nf_mult = min(2 ** n, 8)\n",
    "            sequence += [\n",
    "                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=False),\n",
    "                norm_layer(ndf * nf_mult),\n",
    "                nn.LeakyReLU(0.2, True)\n",
    "            ]\n",
    "\n",
    "        nf_mult_prev = nf_mult\n",
    "        nf_mult = min(2 ** n_layers, 8)\n",
    "        sequence += [\n",
    "            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=False),\n",
    "            norm_layer(ndf * nf_mult),\n",
    "            nn.LeakyReLU(0.2, True)\n",
    "        ]\n",
    "\n",
    "        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()]  # output 1 channel prediction map\n",
    "        self.model = nn.Sequential(*sequence)\n",
    "\n",
    "    def forward(self, input):\n",
    "        \"\"\"Standard forward.\"\"\"\n",
    "        return self.model(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = Discriminator(6, 64, n_layers=3, norm_layer=norm_layer)#.cuda().float()\n",
    "discriminator.apply(weights_init)\n",
    "discriminator = torch.nn.DataParallel(discriminator)  # multi-GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = torch.ones(1,6,256,256)\n",
    "disc = discriminator(inp)\n",
    "# device = 'cuda'\n",
    "# inp = inp.to(device)\n",
    "# discriminator = discriminator.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 128, 128]           6,208\n",
      "         LeakyReLU-2         [-1, 64, 128, 128]               0\n",
      "            Conv2d-3          [-1, 128, 64, 64]         131,072\n",
      "       BatchNorm2d-4          [-1, 128, 64, 64]             256\n",
      "         LeakyReLU-5          [-1, 128, 64, 64]               0\n",
      "            Conv2d-6          [-1, 256, 32, 32]         524,288\n",
      "       BatchNorm2d-7          [-1, 256, 32, 32]             512\n",
      "         LeakyReLU-8          [-1, 256, 32, 32]               0\n",
      "            Conv2d-9          [-1, 512, 31, 31]       2,097,152\n",
      "      BatchNorm2d-10          [-1, 512, 31, 31]           1,024\n",
      "        LeakyReLU-11          [-1, 512, 31, 31]               0\n",
      "           Conv2d-12            [-1, 1, 30, 30]           8,193\n",
      "          Sigmoid-13            [-1, 1, 30, 30]               0\n",
      "    Discriminator-14            [-1, 1, 30, 30]               0\n",
      "================================================================\n",
      "Total params: 2,768,705\n",
      "Trainable params: 2,768,705\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 1.50\n",
      "Forward/backward pass size (MB): 45.28\n",
      "Params size (MB): 10.56\n",
      "Estimated Total Size (MB): 57.34\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(discriminator,(6,256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "adversarial_loss = nn.BCELoss() \n",
    "l1_loss = nn.L1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_loss(generated_image, target_img, G, real_target):\n",
    "    gen_loss = adversarial_loss(G, real_target)\n",
    "    l1_l = l1_loss(generated_image, target_img)\n",
    "    gen_total_loss = gen_loss + (100 * l1_l)\n",
    "    #print(gen_loss)\n",
    "    return gen_total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_loss(output, label):\n",
    "    disc_loss = adversarial_loss(output, label)\n",
    "    return disc_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 2e-4 \n",
    "G_optimizer = optim.Adam(generator.parameters(), lr = learning_rate, betas=(0.5, 0.999))\n",
    "D_optimizer = optim.Adam(discriminator.parameters(), lr = learning_rate, betas=(0.5, 0.999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 200\n",
    "D_loss_plot, G_loss_plot = [], []\n",
    "for epoch in range(1, num_epochs+1): \n",
    "  \n",
    "\n",
    "    D_loss_list, G_loss_list = [], []\n",
    "   \n",
    "    for (input_img, target_img), _ in train_dl:\n",
    "       \n",
    "        D_optimizer.zero_grad()\n",
    "        input_img = input_img.to(device)\n",
    "        target_img = target_img.to(device)\n",
    "        \n",
    "#         print(\"Inp:\",input_img.shape)\n",
    "#         print(\"Tar:\", target_img.shape)\n",
    "        \n",
    "        generated_image = generator(input_img)\n",
    "        \n",
    "        disc_inp_fake = torch.cat((input_img, generated_image), 1)\n",
    "       \n",
    "        \n",
    "        \n",
    "        real_target = Variable(torch.ones(input_img.size(0), 1, 30, 30).to(device))\n",
    "        fake_target = Variable(torch.zeros(input_img.size(0), 1, 30, 30).to(device))\n",
    "        \n",
    "#         print(\"Fake_targ:\",fake_target.shape)\n",
    "        D_fake = discriminator(disc_inp_fake.detach())\n",
    "        \n",
    "#         print(\"D_fake:\",D_fake.shape)\n",
    "        D_fake_loss   =  discriminator_loss(D_fake, fake_target)\n",
    "        # print(discriminator(real_images))\n",
    "        #D_real_loss.backward()\n",
    "    \n",
    "        disc_inp_real = torch.cat((input_img, target_img), 1)\n",
    "        \n",
    "                                         \n",
    "        output = discriminator(disc_inp_real)\n",
    "        D_real_loss = discriminator_loss(output,  real_target)\n",
    "\n",
    "    \n",
    "        # train with fake\n",
    "        #D_fake_loss.backward()\n",
    "      \n",
    "        D_total_loss = (D_real_loss + D_fake_loss) / 2\n",
    "        D_loss_list.append(D_total_loss)\n",
    "      \n",
    "        D_total_loss.backward()\n",
    "        D_optimizer.step()\n",
    "        \n",
    "        \n",
    "        # Train generator with real labels\n",
    "        G_optimizer.zero_grad()\n",
    "        fake_gen = torch.cat((input_img, generated_image), 1)\n",
    "#         print('fake_gen:', fake_gen)\n",
    "        G = discriminator(fake_gen)\n",
    "        G_loss = generator_loss(generated_image, target_img, G, real_target)                                 \n",
    "        G_loss_list.append(G_loss)\n",
    "\n",
    "        G_loss.backward()\n",
    "        G_optimizer.step()\n",
    "        \n",
    "    print('Epoch: [%d/%d]: D_loss: %.3f, G_loss: %.3f' % (\n",
    "            (epoch), num_epochs, torch.mean(torch.FloatTensor(D_loss_list)),\\\n",
    "             torch.mean(torch.FloatTensor(G_loss_list))))\n",
    "    \n",
    "    D_loss_plot.append(torch.mean(torch.FloatTensor(D_loss_list)))\n",
    "    G_loss_plot.append(torch.mean(torch.FloatTensor(G_loss_list)))\n",
    "     \n",
    "    torch.save(generator.state_dict(), 'torch/training_weights/generator_epoch_%d.pth' % (epoch))\n",
    "    torch.save(discriminator.state_dict(), 'torch/training_weights/discriminator_epoch_%d.pth' % (epoch))\n",
    "    \n",
    "    for (inputs, targets), _ in val_dl:\n",
    "        inputs = inputs.to(device)\n",
    "        generated_output = generator(inputs)\n",
    "        save_image(generated_output.data[:10], 'torch/images/sample_%d'%epoch + '.png', nrow=5, normalize=True)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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