DCGAN.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TensorFlowing/Deep-Learning-Experiments/blob/master/DCGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oA_5EY__DAeC"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from tqdm.auto import tqdm\n",
        "from torchvision import transforms\n",
        "from torchvision.datasets import MNIST\n",
        "from torchvision.utils import make_grid\n",
        "from torch.utils.data import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "torch.manual_seed(0) # Set for testing purposes, please do not change!\n",
        "\n",
        "\n",
        "def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):\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",
        "    plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
        "    plt.show()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2qgvIODC7aH"
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    '''\n",
        "    Generator Class\n",
        "    Values:\n",
        "        z_dim: the dimension of the noise vector, a scalar\n",
        "        im_chan: the number of channels in the images, fitted for the dataset used, a scalar\n",
        "              (MNIST is black-and-white, so 1 channel is your default)\n",
        "        hidden_dim: the inner dimension, a scalar\n",
        "    '''\n",
        "    def __init__(self, z_dim=10, im_chan=1, hidden_dim=64):\n",
        "        super(Generator, self).__init__()\n",
        "        self.z_dim = z_dim\n",
        "        # Build the neural network\n",
        "        self.gen = nn.Sequential(\n",
        "            self.make_gen_block(z_dim, hidden_dim * 4),\n",
        "            self.make_gen_block(hidden_dim * 4, hidden_dim * 2, kernel_size=4, stride=1),\n",
        "            self.make_gen_block(hidden_dim * 2, hidden_dim),\n",
        "            self.make_gen_block(hidden_dim, im_chan, kernel_size=4, final_layer=True),\n",
        "        )\n",
        "\n",
        "    def make_gen_block(self, input_channels, output_channels, kernel_size=3, stride=2, final_layer=False):\n",
        "        '''\n",
        "        Function to return a sequence of operations corresponding to a generator block of DCGAN, \n",
        "        corresponding to a transposed convolution, a batchnorm (except for in the last layer), and an activation.\n",
        "        Parameters:\n",
        "            input_channels: how many channels the input feature representation has\n",
        "            output_channels: how many channels the output feature representation should have\n",
        "            kernel_size: the size of each convolutional filter, equivalent to (kernel_size, kernel_size)\n",
        "            stride: the stride of the convolution\n",
        "            final_layer: a boolean, true if it is the final layer and false otherwise \n",
        "                      (affects activation and batchnorm)\n",
        "        '''\n",
        "        #     Steps:\n",
        "        #       1) Do a transposed convolution using the given parameters.\n",
        "        #       2) Do a batchnorm, except for the last layer.\n",
        "        #       3) Follow each batchnorm with a ReLU activation.\n",
        "        #       4) If its the final layer, use a Tanh activation after the deconvolution.\n",
        "        # Build the neural block\n",
        "        if not final_layer:\n",
        "            return nn.Sequential(\n",
        "                #### START CODE HERE ####\n",
        "                nn.ConvTranspose2d(input_channels, output_channels, kernel_size, stride),\n",
        "                nn.BatchNorm2d(num_features=output_channels),\n",
        "                nn.ReLU()\n",
        "                #### END CODE HERE ####\n",
        "            )\n",
        "        else: # Final Layer\n",
        "            return nn.Sequential(\n",
        "                #### START CODE HERE ####\n",
        "                nn.ConvTranspose2d(input_channels, output_channels, kernel_size, stride),\n",
        "                nn.Tanh()\n",
        "                #### END CODE HERE ####\n",
        "            )\n",
        "\n",
        "    def unsqueeze_noise(self, noise):\n",
        "        '''\n",
        "        Function for completing a forward pass of the generator: Given a noise tensor, \n",
        "        returns a copy of that noise with width and height = 1 and channels = z_dim.\n",
        "        Parameters:\n",
        "            noise: a noise tensor with dimensions (n_samples, z_dim)\n",
        "        '''\n",
        "        return noise.view(len(noise), self.z_dim, 1, 1)\n",
        "\n",
        "    def forward(self, noise):\n",
        "        '''\n",
        "        Function for completing a forward pass of the generator: Given a noise tensor, \n",
        "        returns generated images.\n",
        "        Parameters:\n",
        "            noise: a noise tensor with dimensions (n_samples, z_dim)\n",
        "        '''\n",
        "        x = self.unsqueeze_noise(noise)\n",
        "        return self.gen(x)\n",
        "\n",
        "def get_noise(n_samples, z_dim, device='cpu'):\n",
        "    '''\n",
        "    Function for creating noise vectors: Given the dimensions (n_samples, z_dim)\n",
        "    creates a tensor of that shape filled with random numbers from the normal distribution.\n",
        "    Parameters:\n",
        "        n_samples: the number of samples to generate, a scalar\n",
        "        z_dim: the dimension of the noise vector, a scalar\n",
        "        device: the device type\n",
        "    '''\n",
        "    return torch.randn(n_samples, z_dim, device=device)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMj1W6ZyDOZ-",
        "outputId": "c0a6944c-4f53-43de-d1a3-ede5aff2d6cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "'''\n",
        "Test your make_gen_block() function\n",
        "'''\n",
        "gen = Generator()\n",
        "num_test = 100\n",
        "\n",
        "# Test the hidden block\n",
        "test_hidden_noise = get_noise(num_test, gen.z_dim)\n",
        "test_hidden_block = gen.make_gen_block(10, 20, kernel_size=4, stride=1)\n",
        "test_uns_noise = gen.unsqueeze_noise(test_hidden_noise)\n",
        "hidden_output = test_hidden_block(test_uns_noise)\n",
        "\n",
        "# Check that it works with other strides\n",
        "test_hidden_block_stride = gen.make_gen_block(20, 20, kernel_size=4, stride=2)\n",
        "\n",
        "test_final_noise = get_noise(num_test, gen.z_dim) * 20\n",
        "test_final_block = gen.make_gen_block(10, 20, final_layer=True)\n",
        "test_final_uns_noise = gen.unsqueeze_noise(test_final_noise)\n",
        "final_output = test_final_block(test_final_uns_noise)\n",
        "\n",
        "# Test the whole thing:\n",
        "test_gen_noise = get_noise(num_test, gen.z_dim)\n",
        "test_uns_gen_noise = gen.unsqueeze_noise(test_gen_noise)\n",
        "gen_output = gen(test_uns_gen_noise)\n",
        "\n",
        "# UNIT TESTS\n",
        "assert tuple(hidden_output.shape) == (num_test, 20, 4, 4)\n",
        "assert hidden_output.max() > 1\n",
        "assert hidden_output.min() == 0\n",
        "assert hidden_output.std() > 0.2\n",
        "assert hidden_output.std() < 1\n",
        "assert hidden_output.std() > 0.5\n",
        "\n",
        "assert tuple(test_hidden_block_stride(hidden_output).shape) == (num_test, 20, 10, 10)\n",
        "\n",
        "assert final_output.max().item() == 1\n",
        "assert final_output.min().item() == -1\n",
        "\n",
        "assert tuple(gen_output.shape) == (num_test, 1, 28, 28)\n",
        "assert gen_output.std() > 0.5\n",
        "assert gen_output.std() < 0.8\n",
        "print(\"Success!\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Success!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToCqKbnmDWKz"
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "    '''\n",
        "    Discriminator Class\n",
        "    Values:\n",
        "        im_chan: the number of channels in the images, fitted for the dataset used, a scalar\n",
        "              (MNIST is black-and-white, so 1 channel is your default)\n",
        "    hidden_dim: the inner dimension, a scalar\n",
        "    '''\n",
        "    def __init__(self, im_chan=1, hidden_dim=16):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.disc = nn.Sequential(\n",
        "            self.make_disc_block(im_chan, hidden_dim),\n",
        "            self.make_disc_block(hidden_dim, hidden_dim * 2),\n",
        "            self.make_disc_block(hidden_dim * 2, 1, final_layer=True),\n",
        "        )\n",
        "\n",
        "    def make_disc_block(self, input_channels, output_channels, kernel_size=4, stride=2, final_layer=False):\n",
        "        '''\n",
        "        Function to return a sequence of operations corresponding to a discriminator block of DCGAN, \n",
        "        corresponding to a convolution, a batchnorm (except for in the last layer), and an activation.\n",
        "        Parameters:\n",
        "            input_channels: how many channels the input feature representation has\n",
        "            output_channels: how many channels the output feature representation should have\n",
        "            kernel_size: the size of each convolutional filter, equivalent to (kernel_size, kernel_size)\n",
        "            stride: the stride of the convolution\n",
        "            final_layer: a boolean, true if it is the final layer and false otherwise \n",
        "                      (affects activation and batchnorm)\n",
        "        '''\n",
        "        #     Steps:\n",
        "        #       1) Add a convolutional layer using the given parameters.\n",
        "        #       2) Do a batchnorm, except for the last layer.\n",
        "        #       3) Follow each batchnorm with a LeakyReLU activation with slope 0.2.\n",
        "        \n",
        "        # Build the neural block\n",
        "        if not final_layer:\n",
        "            return nn.Sequential(\n",
        "                #### START CODE HERE #### #\n",
        "                nn.Conv2d(input_channels, output_channels, kernel_size, stride),\n",
        "                nn.BatchNorm2d(output_channels),\n",
        "                nn.LeakyReLU(negative_slope=0.2)\n",
        "                #### END CODE HERE ####\n",
        "            )\n",
        "        else: # Final Layer\n",
        "            return nn.Sequential(\n",
        "                #### START CODE HERE #### #\n",
        "                nn.Conv2d(input_channels, output_channels, kernel_size, stride),\n",
        "#                 nn.LeakyReLU(negative_slope=0.2)\n",
        "                #### END CODE HERE ####\n",
        "            )\n",
        "\n",
        "    def forward(self, image):\n",
        "        '''\n",
        "        Function for completing a forward pass of the discriminator: Given an image tensor, \n",
        "        returns a 1-dimension tensor representing fake/real.\n",
        "        Parameters:\n",
        "            image: a flattened image tensor with dimension (im_dim)\n",
        "        '''\n",
        "        disc_pred = self.disc(image)\n",
        "        return disc_pred.view(len(disc_pred), -1)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4b4C183DaFJ",
        "outputId": "ed3e93e0-35a8-492d-8f01-74630e78500d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "'''\n",
        "Test your make_disc_block() function\n",
        "'''\n",
        "num_test = 100\n",
        "\n",
        "gen = Generator()\n",
        "disc = Discriminator()\n",
        "test_images = gen(get_noise(num_test, gen.z_dim))\n",
        "\n",
        "# Test the hidden block\n",
        "test_hidden_block = disc.make_disc_block(1, 5, kernel_size=6, stride=3)\n",
        "hidden_output = test_hidden_block(test_images)\n",
        "\n",
        "# Test the final block\n",
        "test_final_block = disc.make_disc_block(1, 10, kernel_size=2, stride=5, final_layer=True)\n",
        "final_output = test_final_block(test_images)\n",
        "\n",
        "# Test the whole thing:\n",
        "disc_output = disc(test_images)\n",
        "\n",
        "# Test the hidden block\n",
        "assert tuple(hidden_output.shape) == (num_test, 5, 8, 8)\n",
        "# Because of the LeakyReLU slope\n",
        "assert -hidden_output.min() / hidden_output.max() > 0.15\n",
        "assert -hidden_output.min() / hidden_output.max() < 0.25\n",
        "assert hidden_output.std() > 0.5\n",
        "assert hidden_output.std() < 1\n",
        "\n",
        "# Test the final block\n",
        "\n",
        "assert tuple(final_output.shape) == (num_test, 10, 6, 6)\n",
        "assert final_output.max() > 1.0\n",
        "assert final_output.min() < -1.0\n",
        "assert final_output.std() > 0.3\n",
        "assert final_output.std() < 0.6\n",
        "\n",
        "# Test the whole thing:\n",
        "\n",
        "assert tuple(disc_output.shape) == (num_test, 1)\n",
        "assert disc_output.std() > 0.25\n",
        "assert disc_output.std() < 0.5\n",
        "print(\"Success!\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Success!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUkjSrYlD5uL",
        "outputId": "59bc0402-55df-4cc3-e930-bd94ff8e0b5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
          "referenced_widgets": [
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            "6530370714c440b2a129bebdcc3495e3",
            "9039f2266faa43baa1433aad507363ef"
          ]
        }
      },
      "source": [
        "criterion = nn.BCEWithLogitsLoss()\n",
        "z_dim = 64\n",
        "display_step = 500\n",
        "batch_size = 128\n",
        "# A learning rate of 0.0002 works well on DCGAN\n",
        "lr = 0.0002\n",
        "\n",
        "# These parameters control the optimizer's momentum, which you can read more about here:\n",
        "# https://distill.pub/2017/momentum/ but you don’t need to worry about it for this course!\n",
        "beta_1 = 0.5 \n",
        "beta_2 = 0.999\n",
        "device = 'cuda'\n",
        "\n",
        "# You can tranform the image values to be between -1 and 1 (the range of the tanh activation)\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,)),\n",
        "])\n",
        "\n",
        "dataloader = DataLoader(\n",
        "    MNIST('.', download=True, transform=transform),\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6341e411a0cb4689bc28d935f7202d35",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a8900a5c5044554b10c29818ef72986",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef445773054b4efcb6f585420f318996",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6a5edb4c90244c2ae25dd95cd1cd836",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afI72gycD_E-"
      },
      "source": [
        "gen = Generator(z_dim).to(device)\n",
        "gen_opt = torch.optim.Adam(gen.parameters(), lr=lr, betas=(beta_1, beta_2))\n",
        "disc = Discriminator().to(device) \n",
        "disc_opt = torch.optim.Adam(disc.parameters(), lr=lr, betas=(beta_1, beta_2))\n",
        "\n",
        "# You initialize the weights to the normal distribution\n",
        "# with mean 0 and standard deviation 0.02\n",
        "def weights_init(m):\n",
        "    if isinstance(m, nn.Conv2d) or isinstance(m, 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)\n",
        "gen = gen.apply(weights_init)\n",
        "disc = disc.apply(weights_init)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYFNaP65DqVO"
      },
      "source": [
        "n_epochs = 50\n",
        "cur_step = 0\n",
        "mean_generator_loss = 0\n",
        "mean_discriminator_loss = 0\n",
        "for epoch in range(n_epochs):\n",
        "    # Dataloader returns the batches\n",
        "    for real, _ in tqdm(dataloader):\n",
        "        cur_batch_size = len(real)\n",
        "        real = real.to(device)\n",
        "\n",
        "        ## Update discriminator ##\n",
        "        disc_opt.zero_grad()\n",
        "        fake_noise = get_noise(cur_batch_size, z_dim, device=device)\n",
        "        fake = gen(fake_noise)\n",
        "        disc_fake_pred = disc(fake.detach())\n",
        "        disc_fake_loss = criterion(disc_fake_pred, torch.zeros_like(disc_fake_pred))\n",
        "        disc_real_pred = disc(real)\n",
        "        disc_real_loss = criterion(disc_real_pred, torch.ones_like(disc_real_pred))\n",
        "        disc_loss = (disc_fake_loss + disc_real_loss) / 2\n",
        "\n",
        "        # Keep track of the average discriminator loss\n",
        "        mean_discriminator_loss += disc_loss.item() / display_step\n",
        "        # Update gradients\n",
        "        disc_loss.backward(retain_graph=True)\n",
        "        # Update optimizer\n",
        "        disc_opt.step()\n",
        "\n",
        "        ## Update generator ##\n",
        "        gen_opt.zero_grad()\n",
        "        fake_noise_2 = get_noise(cur_batch_size, z_dim, device=device)\n",
        "        fake_2 = gen(fake_noise_2)\n",
        "        disc_fake_pred = disc(fake_2)\n",
        "        gen_loss = criterion(disc_fake_pred, torch.ones_like(disc_fake_pred))\n",
        "        gen_loss.backward()\n",
        "        gen_opt.step()\n",
        "\n",
        "        # Keep track of the average generator loss\n",
        "        mean_generator_loss += gen_loss.item() / display_step\n",
        "\n",
        "        ## Visualization code ##\n",
        "        if cur_step % display_step == 0 and cur_step > 0:\n",
        "            print(f\"Step {cur_step}: Generator loss: {mean_generator_loss}, discriminator loss: {mean_discriminator_loss}\")\n",
        "            show_tensor_images(fake)\n",
        "            show_tensor_images(real)\n",
        "            mean_generator_loss = 0\n",
        "            mean_discriminator_loss = 0\n",
        "        cur_step += 1\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}