W_GAN.ipynb

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            "Fri Jul 22 13:11:34 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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   32C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\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"
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          "text": [
            "-1\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to dataset/MNIST/raw/train-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting dataset/MNIST/raw/train-images-idx3-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to dataset/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting dataset/MNIST/raw/train-labels-idx1-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to dataset/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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          "output_type": "stream",
          "text": [
            "Extracting dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to dataset/MNIST/raw\n",
            "\n"
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      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision.datasets import MNIST\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.utils import save_image, make_grid\n",
        "device= 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, noise_dim, z_dim, img_channels):\n",
        "        super(Generator, self).__init__()\n",
        "        self.network = nn.Sequential(\n",
        "            nn.ConvTranspose2d(in_channels=noise_dim, out_channels=z_dim*8, kernel_size=(4, 4), stride=(1, 1), padding=(0, 0), bias=False),\n",
        "            nn.BatchNorm2d(z_dim*8),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.ConvTranspose2d(in_channels=z_dim*8, out_channels=z_dim * 4, kernel_size=(4, 4), stride=(2, 2),\n",
        "                               padding=(1, 1), bias=False),\n",
        "            nn.BatchNorm2d(z_dim * 4),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.ConvTranspose2d(in_channels=z_dim * 4, out_channels=z_dim * 2, kernel_size=(4, 4), stride=(2, 2),\n",
        "                               padding=(1, 1), bias=False),\n",
        "            nn.BatchNorm2d(z_dim * 2),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.ConvTranspose2d(in_channels=z_dim * 2, out_channels=z_dim, kernel_size=(4, 4), stride=(2, 2),\n",
        "                               padding=(1, 1), bias=False),\n",
        "            nn.BatchNorm2d(z_dim),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.ConvTranspose2d(in_channels=z_dim, out_channels=img_channels, kernel_size=(4, 4), stride=(2, 2),\n",
        "                               padding=(1, 1), bias=False),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.network(x)\n",
        "\n",
        "\n",
        "class Critic(nn.Module):\n",
        "    def __init__(self, img_channels, z_dim):\n",
        "        super(Critic, self).__init__()\n",
        "        self.network = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=img_channels, out_channels=z_dim, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1),\n",
        "                      bias=False),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(in_channels=z_dim, out_channels=z_dim * 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1),\n",
        "                      bias=False),\n",
        "            nn.BatchNorm2d(z_dim * 2),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(in_channels=z_dim * 2, out_channels=z_dim * 4, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1),\n",
        "                      bias=False),\n",
        "            nn.BatchNorm2d(z_dim * 4),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(in_channels=z_dim * 4, out_channels=z_dim * 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1),\n",
        "                      bias=False),\n",
        "            nn.BatchNorm2d(z_dim * 8),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(in_channels=z_dim * 8, out_channels=1, kernel_size=(4, 4), stride=(1, 1), padding=(0, 0),\n",
        "                      bias=False),\n",
        "\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.network(x)\n",
        "\n",
        "\n",
        "gen = Generator(noise_dim=100, z_dim=128, img_channels=3).to(device)\n",
        "critic = Critic(img_channels=3, z_dim=128).to(device)\n",
        "\n",
        "print(gen.__class__.__name__.find('Conv'))\n",
        "\n",
        "\n",
        "def weight_init(m):\n",
        "    class_name = m.__class__.__name__\n",
        "    if class_name.find('Conv') != -1:\n",
        "        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n",
        "    if class_name.find('BatchNorm') != -1:\n",
        "        torch.nn.init.normal_(m.weight, 1.0, 0.02)\n",
        "\n",
        "\n",
        "gen.apply(weight_init)\n",
        "critic.apply(weight_init)\n",
        "\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Resize([64, 64]),\n",
        "                                transforms.Lambda(lambda x: x.repeat(3, 1, 1)),\n",
        "                                transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False),\n",
        "                                transforms.ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)])\n",
        "batch_size = 64\n",
        "num_epochs = 10\n",
        "lr = 3e-4\n",
        "opt_gen = optim.RMSprop(gen.parameters(), lr=lr)\n",
        "opt_critic = optim.RMSprop(critic.parameters(), lr=lr)\n",
        "data_set = MNIST(root='dataset/', transform=transform, train=True, download=True)\n",
        "\n",
        "data_load = DataLoader(dataset=data_set, batch_size=batch_size, shuffle=True)\n",
        "\n"
      ]
    },
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          "background_save": true,
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        },
        "id": "xBSTIcRfnzl9",
        "outputId": "1be59250-286c-43f9-e9e3-3fc39e746c82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "938\n",
            "Epoch: 0\n",
            "300/938\n",
            "600/938\n",
            "900/938\n",
            "torch.Size([64, 3, 64, 64])\n",
            "torch.Size([3, 530, 530])\n",
            "Epoch: 1\n",
            "300/938\n",
            "600/938\n",
            "900/938\n",
            "torch.Size([64, 3, 64, 64])\n",
            "torch.Size([3, 530, 530])\n",
            "Epoch: 2\n",
            "300/938\n",
            "600/938\n"
          ]
        }
      ],
      "source": [
        "# print(gen(torch.rand(batch_size, 100, 1, 1)).shape)\n",
        "print(len(data_load))\n",
        "for epoch in range(num_epochs):\n",
        "    print(f'Epoch: {epoch}')\n",
        "    for i, (data, _) in enumerate(data_load):\n",
        "        data = data.to(device)\n",
        "        # rand = torch.rand(batch_size, 100, 1, 1).to(device)\n",
        "        # fake = gen(rand).detach()\n",
        "\n",
        "        if i >= 300 and i % 300 == 0:\n",
        "          print(f\"{i}/{len(data_load)}\")\n",
        "\n",
        "        for _ in range(5):\n",
        "            noise = torch.randn(batch_size, 100, 1, 1).to(device)\n",
        "            fake = gen(noise).to(device)\n",
        "            critic.zero_grad()\n",
        "            critic_fake = critic(fake)\n",
        "            critic_real = critic(data)\n",
        "            critic_real = critic_real.reshape(-1)\n",
        "            critic_fake = critic_fake.reshape(-1)\n",
        "            critic_loss = -(torch.mean(critic_real) - torch.mean(critic_fake))\n",
        "            critic_loss.backward(retain_graph=True)\n",
        "            opt_critic.step()\n",
        "\n",
        "            for p in critic.parameters():\n",
        "                p.data.clamp_(-0.01, 0.01)\n",
        "\n",
        "        # opt_gen.zero_grad()\n",
        "        output_gen = critic(fake).reshape(-1)\n",
        "        gen_loss = -torch.mean(output_gen)\n",
        "        gen.zero_grad()\n",
        "        gen_loss.backward()\n",
        "        opt_gen.step()\n",
        "    random_img = torch.randn((batch_size, 100, 1, 1)).to(device)\n",
        "    img = gen(random_img)\n",
        "    print(img.shape)\n",
        "    grid = make_grid(img)\n",
        "    print(grid.shape)\n",
        "    name = str(epoch//4)+\"Wgan.jpg\"\n",
        "    save_image(grid, name)"
      ]
    },
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          "text": [
            "torch.Size([64, 1, 64, 64])\n",
            "torch.Size([3, 530, 530])\n"
          ]
        }
      ],
      "source": [
        "random_img = torch.randn((batch_size, 100, 1, 1)).to(device)\n",
        "img = gen(random_img)\n",
        "print(img.shape)\n",
        "grid = make_grid(img)\n",
        "print(grid.shape)\n",
        "save_image(grid, \"/content/drive/MyDrive/Colab Notebooks/WGAN.jpg\")"
      ]
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