gans_colorising(1).ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0bHgBaGWJSi"
      },
      "source": [
        "import torch \n",
        "import torchvision\n",
        "import torchvision.transforms as T\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.nn as nn\n",
        "from skimage.color import rgb2lab, lab2rgb, rgb2gray, gray2rgb\n",
        "from skimage.transform import resize\n",
        "from skimage.io import imsave\n",
        "import numpy as np\n",
        "import random\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwpCnr-MWyQ3"
      },
      "source": [
        "data_dir = \"/content/drive/MyDrive/dataset.zip\"\n",
        "\n",
        "!unzip /content/drive/MyDrive/dataset.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUGqoOEEXL4H",
        "outputId": "6ad00b40-2dd7-4369-cca4-0e8e07eef93a"
      },
      "source": [
        "test_data = \"/content/dataset/Test\"\n",
        "train_data = \"/content/dataset/Train\"\n",
        "\n",
        "print(len(os.listdir(test_data)))\n",
        "print(len(os.listdir(train_data)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "494\n",
            "9294\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShPBmb1ZXpTa"
      },
      "source": [
        "!mkdir images"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ge3NNSgpYKtG"
      },
      "source": [
        "!mv /content/dataset/Test /content/images"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymuREhPJYdi-",
        "outputId": "203c17be-7a47-4ed9-92eb-ace2d38a3ffb"
      },
      "source": [
        "data_dir = \"/content/dataset\"\n",
        "os.listdir(data_dir +\"/Train\")\n",
        "print(data_dir +\"/Train\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/dataset/Train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dc8nnMkeYpVi"
      },
      "source": [
        "!rm /content/dataset/.DS_Store"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCauyr0wY_Z4"
      },
      "source": [
        "image_size = 128\n",
        "batch_size = 128\n",
        "stats = (0.5,0.5,0.5),(0.5,0.5,0.5)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFyd7tqQZNQH"
      },
      "source": [
        "disc_train_ds = ImageFolder(data_dir , transform=T.Compose([\n",
        "                                                            T.Resize(image_size),\n",
        "                                                            T.ToTensor()\n",
        "]))\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klZWWqOjlAz8"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "3SKsxRUjciX8",
        "outputId": "a95c0a72-f00e-4f7a-e168-503915ad130e"
      },
      "source": [
        "img , l = disc_train_ds[7]\n",
        "plt.imshow(img.permute(1,2,0))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fad2f844320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7E10AJFcwvw"
      },
      "source": [
        "disc_train_dl = DataLoader(disc_train_ds , batch_size ,num_workers=3, pin_memory=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jz3O6PIbdSr6"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "        \n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl: \n",
        "            yield to_device(b, self.device)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AiSMl-RdeBf"
      },
      "source": [
        "disc_train_dl = DeviceDataLoader(disc_train_dl,device)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QthcIKrdxzN"
      },
      "source": [
        "def to_cpu(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOYhNVxAd0Cx"
      },
      "source": [
        "# Data Loader for generator "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmOSjRbjd5P_"
      },
      "source": [
        "gen_train_ds = ImageFolder(data_dir , transform=T.Compose([\n",
        "                                                            T.Grayscale(1),\n",
        "                                                            T.Resize(image_size),\n",
        "                                                            T.ToTensor()\n",
        "]))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6RdFr_CeTIb",
        "outputId": "aad7e667-7bab-4ef8-cb36-8ab2c7552156"
      },
      "source": [
        "img , l = gen_train_ds[7]\n",
        "img.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 128, 128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgdrQHJreX9_"
      },
      "source": [
        "gen_train_dl = DataLoader(gen_train_ds , batch_size ,num_workers=3, pin_memory=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5uZn7WNfena"
      },
      "source": [
        "gen_train_dl = DeviceDataLoader(gen_train_dl,device)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGxVVlasfw5Z"
      },
      "source": [
        "discriminator = nn.Sequential(\n",
        "    # in: 3 x 128 x 128\n",
        "\n",
        "    nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(64),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 64 x 64 x 64\n",
        "\n",
        "    nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 128 x 32 x 32\n",
        "\n",
        "    nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(256),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 256 x 16 x 16\n",
        "\n",
        "    nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(512),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 512 x 8 x 8\n",
        "\n",
        "    nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(512),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 512 x 4 x 4\n",
        "\n",
        "    nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=0, bias=False),\n",
        "    # out: 1 x 1 x 1\n",
        "\n",
        "    nn.Flatten(),\n",
        "    nn.Sigmoid())"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLpWGGTghAn5"
      },
      "source": [
        "latent_size = 128"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADUyFr7rgbqH"
      },
      "source": [
        "generator = nn.Sequential(\n",
        "    # in: batch_size x 1 x 128 x 128\n",
        "\n",
        "    nn.Conv2d(1, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(64),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 64 x 64 x 64\n",
        "\n",
        "    nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 128 x 32 x 32\n",
        "\n",
        "    nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(256),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 256 x 16 x 16\n",
        "\n",
        "    nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(512),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 512 x 8 x 8\n",
        "\n",
        "    nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(512),\n",
        "    nn.LeakyReLU(0.2, inplace=True),\n",
        "    # out: 512 x 4 x 4\n",
        "\n",
        "    nn.Conv2d(512, 256, kernel_size=4, stride=1, padding=0, bias=False),\n",
        "    # out: 256 x 1 x 1\n",
        "    \n",
        "    \n",
        "    \n",
        "    \n",
        "    # in: 256 x 1 x 1\n",
        "\n",
        "    nn.ConvTranspose2d(256, 512, kernel_size=4, stride=1, padding=0, bias=False),\n",
        "    nn.BatchNorm2d(512),\n",
        "    nn.ReLU(True),\n",
        "    # out: 512 x 4 x 4\n",
        "\n",
        "    nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(256),\n",
        "    nn.ReLU(True),\n",
        "    # out: 256 x 8 x 8\n",
        "\n",
        "    nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.ReLU(True),\n",
        "    # out: 128 x 16 x 16\n",
        "\n",
        "    nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(64),\n",
        "    nn.ReLU(True),\n",
        "    # out: 64 x 32 x 32\n",
        "\n",
        "    nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.BatchNorm2d(64),\n",
        "    nn.ReLU(True),\n",
        "    # out: 64 x 64 x 64\n",
        "\n",
        "    nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "    nn.ReLU()   #change this if i apply normalization\n",
        "    # out: 3 x 128 x 128\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPYFkZTgibr6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3a5cfa8-2e95-410e-c51e-6d0c1707341a"
      },
      "source": [
        "xb , l = disc_train_ds[0]\n",
        "xb = xb.unsqueeze(0)\n",
        "lab_batch = rgb2lab(xb.permute(1,2,3,0))\n",
        "gray_images = lab_batch[:,:,:,0]\n",
        "gray_images = torch.from_numpy(gray_images)\n",
        "#gray_images = gray_images.permute(3,0,1,2)\n",
        "print(gray_images.shape)\n",
        "#gray_images = gray_images.reshape(gray_images.shape+(1,))\n",
        "gray_images = gray_images.unsqueeze(0)\n",
        "print(gray_images.shape)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 128, 128])\n",
            "torch.Size([1, 1, 128, 128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "daihHzSlT2Ww",
        "outputId": "f2a911dd-bfe8-4807-db23-f9257952df7d"
      },
      "source": [
        "##### WORKDED ###############\n",
        "xb , l = disc_train_ds[0]\n",
        "\n",
        "lab_batch = rgb2lab(xb.permute(1,2,0))\n",
        "lab_batch = torch.from_numpy(lab_batch )\n",
        "lab_batch = torch.tensor(lab_batch, dtype=torch.float32)\n",
        "lab_batch = lab_batch.permute(2,0,1)\n",
        "gray_image = lab_batch[0,:,:]\n",
        "gray_image.shape\n",
        "gray_image = gray_image.unsqueeze(0)\n",
        "gray_image = gray_image.unsqueeze(0)\n",
        "gray_image.shape\n"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 128, 128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKnHefpxXYGx",
        "outputId": "61c89014-722a-419a-d100-f0d082fe7bd6"
      },
      "source": [
        "###### real_images batch t0 gray images batch\n",
        "#def convert_gray(real_images):\n",
        "for xb , l in disc_train_dl:\n",
        "  print(xb.shape)\n",
        "  xb = to_cpu(xb , 'cpu')\n",
        "  lab_batch = rgb2lab(xb.permute(0,2,3,1))\n",
        "  lab_batch = torch.from_numpy(lab_batch  )\n",
        "  lab_batch = torch.tensor(lab_batch, dtype=torch.float32)\n",
        "  lab_batch = lab_batch.permute(0,3,1,2)\n",
        "  gray_image = lab_batch[:,0,:,:]\n",
        "  print(gray_image.shape)\n",
        "  gray_image = gray_image.unsqueeze(1)\n",
        "  print(gray_image.shape)\n",
        "  break;\n",
        "\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([128, 3, 128, 128])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([128, 128, 128])\n",
            "torch.Size([128, 1, 128, 128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUlc2hUNbXsN"
      },
      "source": [
        "**Coverting rgb tensor to grayscale tensor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6lQrIEibEVz"
      },
      "source": [
        "def convert_gray(real_images):\n",
        "  xb = to_cpu(real_images , 'cpu')\n",
        "  lab_batch = rgb2lab(xb.permute(0,2,3,1))\n",
        "  lab_batch = torch.from_numpy(lab_batch)\n",
        "  lab_batch = torch.tensor(lab_batch, dtype=torch.float32)\n",
        "  lab_batch = lab_batch.permute(0,3,1,2)\n",
        "  gray_image = lab_batch[:,0,:,:]\n",
        "  gray_image = gray_image.unsqueeze(1)\n",
        "  return gray_image\n",
        "  "
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFKg5WgghzKZ",
        "outputId": "da365626-ac4f-4c94-c749-a4f6dc74ca02"
      },
      "source": [
        "\n",
        "fake_images = generator(gray_image)\n",
        "print(fake_images.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 3, 128, 128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAaY-Ii_iF0g",
        "outputId": "b4c79ee8-3233-4d2b-bf9d-0ab93b3d0d04"
      },
      "source": [
        "discriminator(fake_images)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4494]], grad_fn=<SigmoidBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyGtRcwckD6a"
      },
      "source": [
        "generator = to_device(generator, device)\n",
        "discriminator = to_device(discriminator, device)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTGMTHG3lnmT"
      },
      "source": [
        ""
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCKfIwUolrUE"
      },
      "source": [
        "#Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLZoNuF-ltWD"
      },
      "source": [
        "def train_discriminator(real_images ,opt_d):\n",
        "    # Clear discriminator gradients\n",
        "    opt_d.zero_grad()\n",
        "\n",
        "    # Pass real images through discriminator\n",
        "    real_preds = discriminator(real_images)\n",
        "    real_targets = torch.ones(real_images.size(0), 1, device=device)\n",
        "    real_loss = F.binary_cross_entropy(real_preds, real_targets)\n",
        "    real_score = torch.mean(real_preds).item()\n",
        "    \n",
        "    # Generate fake images\n",
        "    gray_images = convert_gray(real_images)\n",
        "    gray_images = to_device(gray_images , device)\n",
        "    fake_images = generator(gray_images)\n",
        "\n",
        "    # Pass fake images through discriminator\n",
        "    fake_targets = torch.zeros(fake_images.size(0), 1, device=device)\n",
        "    fake_preds = discriminator(fake_images)\n",
        "    fake_loss = F.binary_cross_entropy(fake_preds, fake_targets)\n",
        "    fake_score = torch.mean(fake_preds).item()\n",
        "\n",
        "    # Update discriminator weights\n",
        "    loss = real_loss + fake_loss\n",
        "    loss.backward()\n",
        "    opt_d.step()\n",
        "    return loss.item(), real_score, fake_score"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taOQfN8FmycG"
      },
      "source": [
        "def train_generator(real_images , opt_g):\n",
        "    # Clear generator gradients\n",
        "    opt_g.zero_grad()\n",
        "    \n",
        "    # Generate fake images\n",
        "    gray_images = convert_gray(real_images)\n",
        "    gray_images = to_device(gray_images , device)\n",
        "    fake_images = generator(gray_images)\n",
        "    \n",
        "    # Try to fool the discriminator\n",
        "    preds = discriminator(fake_images)\n",
        "    targets = torch.ones(fake_images.size(0), 1, device=device)\n",
        "    loss = F.binary_cross_entropy(preds, targets)\n",
        "    \n",
        "    # Update generator weights\n",
        "    loss.backward()\n",
        "    opt_g.step()\n",
        "    \n",
        "    return loss.item()"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwVZQEDVnHFs"
      },
      "source": [
        "from torchvision.utils import save_image\n",
        "\n",
        "sample_dir = 'generated'\n",
        "os.makedirs(sample_dir, exist_ok=True)\n",
        "\n",
        "def save_samples(index, latent_tensors, show=True):\n",
        "    fake_images = generator(latent_tensors)\n",
        "    fake_fname = 'generated-images-{0:0=4d}.png'.format(index)\n",
        "    save_image(denorm(fake_images), os.path.join(sample_dir, fake_fname), nrow=8)\n",
        "    print('Saving', fake_fname)\n",
        "    if show:\n",
        "        fig, ax = plt.subplots(figsize=(8, 8))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        ax.imshow(make_grid(fake_images.cpu().detach(), nrow=8).permute(1, 2, 0))"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EynqcwHJnNih"
      },
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WvWEN-ZZnT9U"
      },
      "source": [
        "def fit(epochs, lr, start_idx=1):\n",
        "    torch.cuda.empty_cache()\n",
        "    \n",
        "    # Losses & scores\n",
        "    losses_g = []\n",
        "    losses_d = []\n",
        "    real_scores = []\n",
        "    fake_scores = []\n",
        "    \n",
        "    # Create optimizers\n",
        "    opt_d = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999))\n",
        "    opt_g = torch.optim.Adam(generator.parameters(), lr=lr, betas=(0.5, 0.999))\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        for real_images, _  in tqdm(disc_train_dl):\n",
        "            # Train discriminator\n",
        "            loss_d, real_score, fake_score = train_discriminator(real_images, opt_d)\n",
        "            # Train generator\n",
        "            loss_g = train_generator(real_images,opt_g)\n",
        "            \n",
        "        # Record losses & scores\n",
        "        losses_g.append(loss_g)\n",
        "        losses_d.append(loss_d)\n",
        "        real_scores.append(real_score)\n",
        "        fake_scores.append(fake_score)\n",
        "        \n",
        "        # Log losses & scores (last batch)\n",
        "        print(\"Epoch [{}/{}], loss_g: {:.4f}, loss_d: {:.4f}, real_score: {:.4f}, fake_score: {:.4f}\".format(\n",
        "            epoch+1, epochs, loss_g, loss_d, real_score, fake_score))\n",
        "    \n",
        "        # Save generated images\n",
        "        #save_samples(epoch+start_idx, fixed_latent, show=False)\n",
        "    \n",
        "    return losses_g, losses_d, real_scores, fake_scores"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZPnDXkKpbkF"
      },
      "source": [
        "lr = 0.0002\n",
        "epochs = 1"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRxrCF8b5RLy",
        "outputId": "4d73e969-4266-4808-b7a3-9399872a95f0"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Dec  3 09:19:02 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   75C    P0    35W /  70W |   6157MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362,
          "referenced_widgets": [
            "da47c55f75b6494a9b0b7388249155da",
            "31842f8eae874b759d4a777c1cbd1476",
            "4bb06428c9e5404db2997934a2adadf3",
            "1875ab7a6f744b9487765a253eb7645e",
            "e7e65de1ca834cd09e207e60b1fc5e47",
            "7e7c50baf49c479db242c1804da6ad5c",
            "fca693e9cfba43699ca43111815c7915",
            "a34c4fbfd6444fd3a645e44377ce51ac"
          ]
        },
        "id": "ojcVZ5cMpc5o",
        "outputId": "6092d4d9-6629-4db5-a5b7-6817abd3bbf3"
      },
      "source": [
        "history = fit(epochs,lr)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da47c55f75b6494a9b0b7388249155da",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=73.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-93-2f3170a493d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-66-6c34fcb0ffa0>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, lr, start_idx)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mreal_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m  \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisc_train_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0;31m# Train discriminator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0mloss_d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfake_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_discriminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m             \u001b[0;31m# Train generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mloss_g\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopt_g\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-58-1e778fbf45ba>\u001b[0m in \u001b[0;36mtrain_discriminator\u001b[0;34m(real_images, opt_d)\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mopt_d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfake_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aj5PsP42smQr"
      },
      "source": [
        "saving model weights "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnJeNsS7pe9y"
      },
      "source": [
        "torch.save(generator.state_dict() , 'G.ckpt')\n",
        "torch.save(discriminator.state_dict() , 'D.ckpt')"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtDmI_PCyTTx",
        "outputId": "197a64b6-a257-4c28-9c1d-4859e3ba1a9c"
      },
      "source": [
        "xb , l = disc_train_ds[6]\n",
        "\n",
        "lab_batch = rgb2lab(xb.permute(1,2,0))\n",
        "lab_batch = torch.from_numpy(lab_batch )\n",
        "lab_batch = torch.tensor(lab_batch, dtype=torch.float32)\n",
        "lab_batch = lab_batch.permute(2,0,1)\n",
        "gray_image = lab_batch[0,:,:]\n",
        "gray_image.shape\n",
        "gray_image = gray_image.unsqueeze(0)\n",
        "gray_image = gray_image.unsqueeze(0)\n",
        "gray_image.shape\n",
        "gray_image = to_device(gray_image,device)\n",
        "\n",
        "fake_img = generator(gray_image)\n",
        "yb = to_cpu(fake_img , 'cpu')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIzo1XKnzRQ4",
        "outputId": "0f27c341-e64d-4497-e27b-807ded3636e2"
      },
      "source": [
        "yb.shape ,xb.shape"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 3, 128, 128]), torch.Size([3, 128, 128]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "VXYyERgDyzeM",
        "outputId": "9808cba2-3c45-4bfa-afd8-2527d087a7ee"
      },
      "source": [
        "plt.imshow(xb.permute(1,2,0))\n",
        "plt.imshow(yb[0].detach().permute(1,2,0))"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "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": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fad0cdd94e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mWtu1yCy2fa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}