pix2pix/Pix2Pix_pytorch.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKuTFPaw1w9z"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aniketmaurya/pytorch-gans/blob/main/pix2pix/Pix2Pix_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hs5r4ii51w94"
      },
      "source": [
        "!pip install -U -q pytorch-lightning"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "054Huvnpsd40"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxLXDt5M1w95"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from tqdm.auto import tqdm\n",
        "from torchvision import transforms\n",
        "from torchvision.utils import make_grid\n",
        "from torch.utils.data import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "try:\n",
        "    from rich import print\n",
        "except: pass"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlPOusgo1w96",
        "scrolled": true
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "from torchvision.transforms.functional import center_crop\n",
        "import os\n",
        "from glob import glob\n",
        "from pathlib import Path\n",
        "from PIL import Image"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "el4m3uAw1w97"
      },
      "source": [
        "# New parameters\n",
        "adv_criterion = nn.BCEWithLogitsLoss() \n",
        "recon_criterion = nn.L1Loss() \n",
        "lambda_recon = 200\n",
        "\n",
        "n_epochs = 20\n",
        "\n",
        "display_step = 200\n",
        "batch_size = 4\n",
        "lr = 0.0002\n",
        "target_size = 256\n",
        "device = 'cuda'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvVVMjYz1w97"
      },
      "source": [
        "import pytorch_lightning as pl"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jnEsFUy1w97"
      },
      "source": [
        "def show_tensor_images(image_tensor,\n",
        "                       num_images=25,\n",
        "                       size=(3, 64, 64),\n",
        "                       ret=False):\n",
        "    '''\n",
        "    Function for visualizing images: Given a tensor of images, number of images, and\n",
        "    size per image, plots and prints the images in an uniform grid.\n",
        "    '''\n",
        "    image_tensor = (image_tensor + 1) / 2\n",
        "    image_unflat = image_tensor.detach().cpu()\n",
        "    image_grid = make_grid(image_unflat[:num_images], nrow=5)\n",
        "    if ret:\n",
        "        return image_grid.permute(1, 2, 0).squeeze()\n",
        "    plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
        "    plt.show()\n",
        "\n",
        "def display_progress(cond, fake, real, figsize=(10,5)):\n",
        "    cond = cond.detach().cpu().permute(1, 2, 0)\n",
        "    fake = fake.detach().cpu().permute(1, 2, 0)\n",
        "    real = real.detach().cpu().permute(1, 2, 0)\n",
        "    \n",
        "    fig, ax = plt.subplots(1, 3, figsize=figsize)\n",
        "    ax[0].imshow(cond)\n",
        "    ax[2].imshow(fake)\n",
        "    ax[1].imshow(real)\n",
        "    plt.show()"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMJWEkQM1w98"
      },
      "source": [
        "from torchvision import transforms"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM_aHTFM1w98"
      },
      "source": [
        "!wget http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz\n",
        "!tar -xvf facades.tar.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKKx1xzf1w98",
        "outputId": "1d242197-86c9-42c0-c174-c4e0c731975b"
      },
      "source": [
        "# http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz\n",
        "path = './facades/train/'\n",
        "\n",
        "class FacadesDataset(Dataset):\n",
        "    def __init__(self, path, target_size=None):\n",
        "        self.filenames = glob(str(Path(path)/'*'))\n",
        "        self.target_size = target_size\n",
        "        \n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.filenames)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        filename = self.filenames[idx]\n",
        "        image = Image.open(filename)\n",
        "        image = transforms.functional.to_tensor(image)\n",
        "        image_width = image.shape[2]        \n",
        "        \n",
        "        real = image[:, :, :image_width // 2]\n",
        "        condition = image[:, :, image_width // 2:]\n",
        "        \n",
        "        target_size = self.target_size\n",
        "        if target_size:\n",
        "            condition = nn.functional.interpolate(condition, size=target_size)\n",
        "            real = nn.functional.interpolate(real, size=target_size)\n",
        "        \n",
        "        return real, condition\n",
        "    \n",
        "    \n",
        "dataset = FacadesDataset(path, target_size=target_size)\n",
        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
        "transforms.functional.to_pil_image(dataset[0][0])\n",
        "transforms.functional.to_pil_image(dataset[0][0]).size\n",
        "dataset[0][0].shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 256, 256])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2-IhD2S1w99"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ee9ehCkvryAG"
      },
      "source": [
        "- Let Ck denote a Convolution-BatchNorm-ReLU layer with k filters.\n",
        "\n",
        "- CDk denotes a Convolution-BatchNorm- Dropout-ReLU layer with a dropout rate of 50%.\n",
        "\n",
        "- All convolutions are 4×4 spatial filters applied with stride 2.\n",
        "\n",
        "- Convolutions in the encoder, and in the discriminator, downsample by a factor of 2, whereas in the decoder they upsample by a factor of 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdaPyBpsryAH"
      },
      "source": [
        "class DownSampleConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel=4, strides=2, padding=1, activation=True, batchnorm=True):\n",
        "        \"\"\"\n",
        "        Paper details:\n",
        "        - C64-C128-C256-C512-C512-C512-C512-C512\n",
        "        - All convolutions are 4×4 spatial filters applied with stride 2\n",
        "        - Convolutions in the encoder downsample by a factor of 2\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        self.activation = activation\n",
        "        self.batchnorm = batchnorm\n",
        "        \n",
        "        self.conv = nn.Conv2d(in_channels, out_channels, kernel, strides, padding)\n",
        "        \n",
        "        if batchnorm:\n",
        "            self.bn = nn.BatchNorm2d(out_channels)\n",
        "            \n",
        "        if activation:\n",
        "            self.act = nn.LeakyReLU(0.2)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        if self.batchnorm:\n",
        "            x = self.bn(x)\n",
        "        if self.activation:\n",
        "            x = self.act(x)\n",
        "        return x\n",
        "\n",
        "encoder = DownSampleConv(3, 16)\n",
        "assert encoder(torch.randn(1, 3, 256, 256)).shape[-2:] == (128, 128)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2hFiRXYryAI"
      },
      "source": [
        "class UpSampleConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel=4, strides=2, padding=1, activation=True, batchnorm=True, dropout=False):\n",
        "        super().__init__()\n",
        "        self.activation = activation\n",
        "        self.batchnorm = batchnorm\n",
        "        self.dropout = dropout\n",
        "        \n",
        "        self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel, strides, padding)\n",
        "        \n",
        "        if batchnorm:\n",
        "            self.bn = nn.BatchNorm2d(out_channels)\n",
        "            \n",
        "        if activation:\n",
        "            self.act = nn.ReLU(True)\n",
        "        \n",
        "        if dropout:\n",
        "            self.drop = nn.Dropout2d(0.5)\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.deconv(x)\n",
        "        if self.batchnorm:\n",
        "            x = self.bn(x)\n",
        "        \n",
        "        if self.dropout:\n",
        "            x = self.drop(x)\n",
        "        return x\n",
        "    \n",
        "decoder = UpSampleConv(3, 16)\n",
        "assert decoder(torch.randn(1, 3, 256, 256)).shape[-2:] == (512, 512)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22815-fXryAI",
        "outputId": "68af8ac8-15ef-4dae-a4f4-ea9d1da0c5cd"
      },
      "source": [
        "decoder(torch.randn(2, 3, 256, 256)).shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 16, 512, 512])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYka8x9bryAI"
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, hidden_channels=64):\n",
        "        \"\"\"\n",
        "        Paper details:\n",
        "        - Encoder: C64-C128-C256-C512-C512-C512-C512-C512\n",
        "        - All convolutions are 4×4 spatial filters applied with stride 2\n",
        "        - Convolutions in the encoder downsample by a factor of 2\n",
        "        - Decoder: CD512-CD1024-CD1024-C1024-C1024-C512 -C256-C128\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        \n",
        "        # encoder/donwsample convs\n",
        "        self.encoders = [\n",
        "                DownSampleConv(in_channels, 64, batchnorm=False), # bs x 64 x 128 x 128\n",
        "                DownSampleConv(64, 128), # bs x 128 x 64 x 64\n",
        "                DownSampleConv(128, 256), # bs x 256 x 32 x 32\n",
        "                DownSampleConv(256, 512), # bs x 512 x 16 x 16\n",
        "                DownSampleConv(512, 512), # bs x 512 x 8 x 8\n",
        "                DownSampleConv(512, 512), # bs x 512 x 4 x 4\n",
        "                DownSampleConv(512, 512), # bs x 512 x 2 x 2\n",
        "                DownSampleConv(512, 512, batchnorm=False), # bs x 512 x 1 x 1\n",
        "        ]\n",
        "        \n",
        "        # decoder/upsample convs\n",
        "        self.decoders = [\n",
        "                UpSampleConv(512, 512, dropout=True), # bs x 512 x 2 x 2\n",
        "                UpSampleConv(1024, 512, dropout=True), # bs x 512 x 4 x 4\n",
        "                UpSampleConv(1024, 512, dropout=True), # bs x 512 x 8 x 8\n",
        "                UpSampleConv(1024, 512), # bs x 512 x 16 x 16\n",
        "                UpSampleConv(1024, 256), # bs x 256 x 32 x 32\n",
        "                UpSampleConv(512, 128), # bs x 128 x 64 x 64\n",
        "                UpSampleConv(256, 64), # bs x 64 x 128 x 128\n",
        "        ]\n",
        "        self.decoder_channels = [512, 512, 512, 512, 256, 128, 64]\n",
        "        self.final_conv = nn.ConvTranspose2d(64, out_channels, kernel_size=4, stride=2, padding=1)\n",
        "        self.tanh = nn.Tanh()\n",
        "        \n",
        "        self.encoders = nn.ModuleList(self.encoders)\n",
        "        self.decoders = nn.ModuleList(self.decoders)\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        skips_cons = []\n",
        "        for encoder in self.encoders:\n",
        "            x = encoder(x)\n",
        "            \n",
        "            skips_cons.append(x)\n",
        "\n",
        "        skips_cons = list(reversed(skips_cons[:-1]))\n",
        "        decoders = self.decoders[:-1]\n",
        "        \n",
        "        i = 0\n",
        "        for decoder, skip in zip(decoders, skips_cons):\n",
        "            x = decoder(x)\n",
        "            assert self.decoder_channels[i]==x.shape[1], f'{x.shape, self.decoder_channels[i]}'\n",
        "            # print(x.shape, skip.shape)\n",
        "            x = torch.cat((x, skip), axis=1)\n",
        "            i+=1\n",
        "            \n",
        "        x = self.decoders[-1](x)\n",
        "        # print(x.shape)\n",
        "        x = self.final_conv(x)\n",
        "        return self.tanh(x)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOubm8k5ryAJ"
      },
      "source": [
        "gen = Generator(3, 3)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zw9ilKByryAJ",
        "outputId": "ea0876f2-898f-4cd0-cf84-e5da43397a10"
      },
      "source": [
        "gen(torch.randn(1, 3, 256, 256)).shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 256, 256])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nx1cjV-S1w-A"
      },
      "source": [
        "gen = Generator(3, 3)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0xZH6d5ryAK",
        "outputId": "72374fbd-9e6c-4421-8e63-36a8709e2e81"
      },
      "source": [
        "gen(torch.randn(1, 3, 256, 256)).shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 256, 256])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i7_eTQfryAL"
      },
      "source": [
        "class PatchGAN(nn.Module):\n",
        "\n",
        "    def __init__(self, input_channels):\n",
        "        super().__init__()\n",
        "        self.d1 = DownSampleConv(input_channels, 64, batchnorm=False)\n",
        "        self.d2 = DownSampleConv(64, 128)\n",
        "        self.d3 = DownSampleConv(128, 256)\n",
        "        self.d4 = DownSampleConv(256, 512)\n",
        "        self.final = nn.Conv2d(512, 1, kernel_size=1)\n",
        "\n",
        "    def forward(self, x, y):\n",
        "        x = torch.cat([x, y], axis=1)\n",
        "        x0 = self.d1(x)\n",
        "        x1 = self.d2(x0)\n",
        "        x2 = self.d3(x1)\n",
        "        x3 = self.d4(x2)\n",
        "        xn = self.final(x3)\n",
        "        return xn"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDmIWi7VryAL"
      },
      "source": [
        "# C64-C128-C256-C512"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5oz9sO7ryAL"
      },
      "source": [
        "disc = PatchGAN(3*2)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdJiHQvLryAM",
        "outputId": "05c34df5-1341-469d-94a8-5f179cbe470e"
      },
      "source": [
        "disc(torch.randn(1, 3, 256, 256), torch.randn(1, 3, 256, 256)).shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 16, 16])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgysakIoryAM",
        "outputId": "d7fac8cd-21cf-4895-c655-001cc620b02a"
      },
      "source": [
        "disc(torch.randn(1, 3, 256, 256), torch.randn(1, 3, 256, 256)).shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 16, 16])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWnq9f9z1w-D"
      },
      "source": [
        "# https://stackoverflow.com/questions/49433936/how-to-initialize-weights-in-pytorch\n",
        "\n",
        "def _weights_init(m):\n",
        "    if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n",
        "        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n",
        "    if isinstance(m, nn.BatchNorm2d):\n",
        "        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n",
        "        torch.nn.init.constant_(m.bias, 0)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G82gCfZk4u-u"
      },
      "source": [
        "def show_tensor_images(image_tensor, num_images=25, size=(3, 256, 256)):\n",
        "    '''\n",
        "    Function for visualizing images: Given a tensor of images, number of images, and\n",
        "    size per image, plots and prints the images in an uniform grid.\n",
        "    '''\n",
        "    image_shifted = image_tensor\n",
        "    image_unflat = image_shifted.detach().cpu().view(-1, *size)\n",
        "    image_grid = make_grid(image_unflat[:num_images], nrow=5)\n",
        "    plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
        "    plt.show()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP5OZVZU1w-D"
      },
      "source": [
        "class Pix2Pix(pl.LightningModule):\n",
        "    def __init__(self,\n",
        "                 in_channels,\n",
        "                 out_channels,\n",
        "                 hidden_channels=32,\n",
        "                 depth=6,\n",
        "                 learning_rate=0.0002,\n",
        "                 lambda_recon=200):\n",
        "\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "\n",
        "        self.gen = Generator(in_channels, out_channels)\n",
        "        self.patch_gan = PatchGAN(in_channels + out_channels)\n",
        "\n",
        "        # intializing weights\n",
        "        self.gen = self.gen.apply(_weights_init)\n",
        "        self.patch_gan = self.patch_gan.apply(_weights_init)\n",
        "        \n",
        "        self.adversarial_criterion = nn.BCEWithLogitsLoss()\n",
        "        self.recon_criterion = nn.L1Loss()\n",
        "\n",
        "    def _gen_step(self, real_images, conditioned_images):\n",
        "        # Pix2Pix has adversarial and a reconstruction loss\n",
        "        # First calculate the adversarial loss\n",
        "        fake_images = self.gen(conditioned_images)\n",
        "        disc_logits = self.patch_gan(fake_images, conditioned_images)\n",
        "        adversarial_loss = self.adversarial_criterion(disc_logits, torch.ones_like(disc_logits))\n",
        "\n",
        "        # calculate reconstruction loss\n",
        "        recon_loss = self.recon_criterion(fake_images, real_images)\n",
        "        lambda_recon = self.hparams.lambda_recon\n",
        "\n",
        "        return adversarial_loss + lambda_recon * recon_loss\n",
        "\n",
        "    def _disc_step(self, real_images, conditioned_images):\n",
        "        fake_images = self.gen(conditioned_images).detach()\n",
        "        fake_logits = self.patch_gan(fake_images, conditioned_images)\n",
        "\n",
        "        real_logits = self.patch_gan(real_images, conditioned_images)\n",
        "\n",
        "        fake_loss = self.adversarial_criterion(fake_logits, torch.zeros_like(fake_logits))\n",
        "        real_loss = self.adversarial_criterion(real_logits, torch.ones_like(real_logits))\n",
        "        return (real_loss + fake_loss) / 2\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        lr = self.hparams.learning_rate\n",
        "        gen_opt = torch.optim.Adam(self.gen.parameters(), lr=lr)\n",
        "        disc_opt = torch.optim.Adam(self.patch_gan.parameters(), lr=lr)\n",
        "        return disc_opt, gen_opt\n",
        "\n",
        "    def training_step(self, batch, batch_idx, optimizer_idx):\n",
        "        real, condition = batch\n",
        "\n",
        "        loss = None\n",
        "        if optimizer_idx == 0:\n",
        "            loss = self._disc_step(real, condition)\n",
        "            self.log('PatchGAN Loss', loss)\n",
        "        elif optimizer_idx == 1:\n",
        "            loss = self._gen_step(real, condition)\n",
        "            self.log('Generator Loss', loss)\n",
        "        \n",
        "        if self.current_epoch%25==0 and batch_idx==0 and optimizer_idx==1:\n",
        "            fake = self.gen(condition).detach()\n",
        "            display_progress(condition[0], fake[0], real[0])\n",
        "        return loss"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlZOuMvP1w-E"
      },
      "source": [
        "pix2pix = Pix2Pix(3, 3)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6z_hPpv1w-E",
        "outputId": "69a4a6f9-1e1c-4464-c6be-1d6e343c5dce"
      },
      "source": [
        "trainer = pl.Trainer(max_epochs=1000, gpus=1)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: None, using: 0 TPU cores\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBYbnUsvXNs1"
      },
      "source": [
        "# trainer.save_checkpoint(\"epoch-59.ckpt\")\n",
        "# pix2pix = Pix2Pix.load_from_checkpoint(\"epoch-59.ckpt\")"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707,
          "referenced_widgets": [
            "a8bd334a162f4c31b05a3ed3dbaeb0ac",
            "d127e67b4ad54b95bdb9d83abfae026f",
            "795198cea9fe42edb7d38e8dd127f5bb",
            "7d9acb456bc9458897cec9fe8ea56e2e",
            "e411db2666e247559566c25ca3161c1c",
            "eb44c0cfad4f44bbbbfae5b64adf3186",
            "dd139afae4f044559960df251bb83a55",
            "b170956290f8420a9cbb289bcb1f7b9b"
          ]
        },
        "id": "kJsjXk1C1w-E",
        "outputId": "411dbf6d-4e86-43b5-aa88-033f6a3f747b"
      },
      "source": [
        "trainer.fit(pix2pix, dataloader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  | Name                  | Type              | Params\n",
            "------------------------------------------------------------\n",
            "0 | gen                   | Generator         | 54.4 M\n",
            "1 | patch_gan             | PatchGAN          | 2.8 M \n",
            "2 | adversarial_criterion | BCEWithLogitsLoss | 0     \n",
            "3 | recon_criterion       | L1Loss            | 0     \n",
            "------------------------------------------------------------\n",
            "57.2 M    Trainable params\n",
            "0         Non-trainable params\n",
            "57.2 M    Total params\n",
            "228.713   Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8bd334a162f4c31b05a3ed3dbaeb0ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpx5O2sXWgGi"
      },
      "source": [
        ""
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrzeNY90XAHG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}