C3W2_Pix2PixHD_(Optional).ipynb

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        "<a href=\"https://colab.research.google.com/github/https-deeplearning-ai/GANs-Public/blob/master/C3W2_Pix2PixHD_(Optional).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "1czVdIlqnImH"
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      "source": [
        "# Pix2PixHD\n",
        "\n",
        "*Please note that this is an optional notebook, meant to introduce more advanced concepts if you're up for a challenge, so don't worry if you don't completely follow!*\n",
        "\n",
        "It is recommended that you should already be familiar with:\n",
        " - Residual blocks, from [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) (He et al. 2015)\n",
        " - Perceptual loss, from [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155) (Johnson et al. 2016)\n",
        " - VGG architecture, from [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) (Simonyan et al. 2015)\n",
        " - Instance normalization (which you should know from StyleGAN), from [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022) (Ulyanov et al. 2017)\n",
        " - Reflection padding, which Pytorch has implemented in [torch.nn.ReflectionPad2d](https://pytorch.org/docs/stable/generated/torch.nn.ReflectionPad2d.html)\n",
        "\n",
        "**Goals**\n",
        "\n",
        "In this notebook, you will learn about Pix2PixHD, which synthesizes high-resolution images from semantic label maps. Proposed in [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://arxiv.org/abs/1711.11585) (Wang et al. 2018), Pix2PixHD improves upon Pix2Pix via multiscale architecture, improved adversarial loss, and instance maps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fB1Vq8ps7Bfd"
      },
      "source": [
        "## Residual Blocks\n",
        "\n",
        "The residual block, which is relevant in many state-of-the-art computer vision models, is used in all parts of Pix2PixHD. If you're not familiar with residual blocks, please take a look [here](https://paperswithcode.com/method/residual-block). Now, you'll start by first implementing a basic residual block."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHD_wif07f4b"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class ResidualBlock(nn.Module):\n",
        "    '''\n",
        "    ResidualBlock Class\n",
        "    Values\n",
        "        channels: the number of channels throughout the residual block, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, channels):\n",
        "        super().__init__()\n",
        "\n",
        "        self.layers = nn.Sequential(\n",
        "            nn.ReflectionPad2d(1),\n",
        "            nn.Conv2d(channels, channels, kernel_size=3, padding=0),\n",
        "            nn.InstanceNorm2d(channels, affine=False),\n",
        "\n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            nn.ReflectionPad2d(1),\n",
        "            nn.Conv2d(channels, channels, kernel_size=3, padding=0),\n",
        "            nn.InstanceNorm2d(channels, affine=False),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return x + self.layers(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sqet5X_Sf-KZ"
      },
      "source": [
        "## Multiscale Generator: Generating at multiple scales (resolutions)\n",
        "\n",
        "The Pix2PixHD generator is comprised of two separate subcomponent generators: $G_1$ is called the global generator and operates at low resolution (1024 x 512) to transfer styles. $G_2$ is the local enhancer and operates at high resolution (2048 x 1024) to deal with higher resolution.\n",
        "\n",
        "The architecture for each network is adapted from [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155) (Johnson et al. 2016) and is comprised of\n",
        "\n",
        "\\begin{align*}\n",
        "    G = \\left[G^{(F)}, G^{(R)}, G^{(B)}\\right],\n",
        "\\end{align*}\n",
        "\n",
        "where $G^{(F)}$ is a frontend of convolutional blocks (downsampling), $G^{(R)}$ is a set of residual blocks, and $G^{(B)}$ is a backend of transposed convolutional blocks (upsampling). This is just a type of encoder-decoder generator that you learned about with Pix2Pix!\n",
        "\n",
        "$G_1$ is trained first on low-resolution images. Then, $G_2$ is added to the pre-trained $G_1$ and both are trained jointly on high-resolution images. Specifically, $G_2^{(F)}$ encodes a high-resolution image, $G_1$ encodes a downsampled, low-resolution image, and the outputs from both are summed and passed sequentially to $G_2^{(R)}$ and $G_2^{(B)}$. This pre-training and fine-tuning scheme works well because the model is able to learn accurate coarser representations before using them to touch up its refined representations, since learning high-fidelity representations is generally a pretty hard task.\n",
        "\n",
        "> ![Pix2PixHD Generator](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/Pix2PixHD-Generator.png?raw=true)\n",
        "*Pix2PixHD Generator, taken from Figure 3 of [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://arxiv.org/abs/1711.11585) (Wang et al. 2018). Following our notation, $G = \\left[G_2^{(F)}, G_1^{(F)}, G_1^{(R)}, G_1^{(B)}, G_2^{(R)}, G_2^{(B)}\\right]$ from left to right.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QzAlLDpuhZ5"
      },
      "source": [
        "### Global Subgenerator ($G_1$)\n",
        "\n",
        "Let's first start by building the global generator ($G_1$). Even though the global generator is nested inside the local enhancer, you'll still need a separate module for training $G_1$ on its own first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rarPcc8xutN5"
      },
      "source": [
        "class GlobalGenerator(nn.Module):\n",
        "    '''\n",
        "    GlobalGenerator Class:\n",
        "    Implements the global subgenerator (G1) for transferring styles at lower resolutions.\n",
        "    Values:\n",
        "        in_channels: the number of input channels, a scalar\n",
        "        out_channels: the number of output channels, a scalar\n",
        "        base_channels: the number of channels in first convolutional layer, a scalar\n",
        "        fb_blocks: the number of frontend / backend blocks, a scalar\n",
        "        res_blocks: the number of residual blocks, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, out_channels,\n",
        "                 base_channels=64, fb_blocks=3, res_blocks=9):\n",
        "        super().__init__()\n",
        "\n",
        "        # Initial convolutional layer\n",
        "        g1 = [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.Conv2d(in_channels, base_channels, kernel_size=7, padding=0),\n",
        "            nn.InstanceNorm2d(base_channels, affine=False),\n",
        "            nn.ReLU(inplace=True),\n",
        "        ]\n",
        "\n",
        "        channels = base_channels\n",
        "        # Frontend blocks\n",
        "        for _ in range(fb_blocks):\n",
        "            g1 += [\n",
        "                nn.Conv2d(channels, 2 * channels, kernel_size=3, stride=2, padding=1),\n",
        "                nn.InstanceNorm2d(2 * channels, affine=False),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels *= 2\n",
        "\n",
        "        # Residual blocks\n",
        "        for _ in range(res_blocks):\n",
        "            g1 += [ResidualBlock(channels)]\n",
        "\n",
        "        # Backend blocks\n",
        "        for _ in range(fb_blocks):\n",
        "            g1 += [\n",
        "                nn.ConvTranspose2d(channels, channels // 2, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
        "                nn.InstanceNorm2d(channels // 2, affine=False),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels //= 2\n",
        "\n",
        "        # Output convolutional layer as its own nn.Sequential since it will be omitted in second training phase\n",
        "        self.out_layers = nn.Sequential(\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.Conv2d(base_channels, out_channels, kernel_size=7, padding=0),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "        self.g1 = nn.Sequential(*g1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.g1(x)\n",
        "        x = self.out_layers(x)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esIlNlgE7s3h"
      },
      "source": [
        "### Local Enhancer Subgenerator ($G_2$)\n",
        "\n",
        "And now onto the local enhancer ($G_2$)! Recall that the local enhancer uses (a pretrained) $G_1$ as part of its architecture. Following our earlier notation, recall that the residual connections from the last layers of $G_2^{(F)}$ and $G_1^{(B)}$ are added together and passed through $G_2^{(R)}$ and $G_2^{(B)}$ to synthesize a high-resolution image. Because of this, you should reuse the $G_1$ implementation so that the weights are consistent for the second training phase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLkFsvt28T9Z"
      },
      "source": [
        "class LocalEnhancer(nn.Module):\n",
        "    '''\n",
        "    LocalEnhancer Class:  \n",
        "    Implements the local enhancer subgenerator (G2) for handling larger scale images.\n",
        "    Values:\n",
        "        in_channels: the number of input channels, a scalar\n",
        "        out_channels: the number of output channels, a scalar\n",
        "        base_channels: the number of channels in first convolutional layer, a scalar\n",
        "        global_fb_blocks: the number of global generator frontend / backend blocks, a scalar\n",
        "        global_res_blocks: the number of global generator residual blocks, a scalar\n",
        "        local_res_blocks: the number of local enhancer residual blocks, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, out_channels, base_channels=32, global_fb_blocks=3, global_res_blocks=9, local_res_blocks=3):\n",
        "        super().__init__()\n",
        "\n",
        "        global_base_channels = 2 * base_channels\n",
        "\n",
        "        # Downsampling layer for high-res -> low-res input to g1\n",
        "        self.downsample = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)\n",
        "\n",
        "        # Initialize global generator without its output layers\n",
        "        self.g1 = GlobalGenerator(\n",
        "            in_channels, out_channels, base_channels=global_base_channels, fb_blocks=global_fb_blocks, res_blocks=global_res_blocks,\n",
        "        ).g1\n",
        "\n",
        "        self.g2 = nn.ModuleList()\n",
        "\n",
        "        # Initialize local frontend block\n",
        "        self.g2.append(\n",
        "            nn.Sequential(\n",
        "                # Initial convolutional layer\n",
        "                nn.ReflectionPad2d(3),\n",
        "                nn.Conv2d(in_channels, base_channels, kernel_size=7, padding=0), \n",
        "                nn.InstanceNorm2d(base_channels, affine=False),\n",
        "                nn.ReLU(inplace=True),\n",
        "\n",
        "                # Frontend block\n",
        "                nn.Conv2d(base_channels, 2 * base_channels, kernel_size=3, stride=2, padding=1), \n",
        "                nn.InstanceNorm2d(2 * base_channels, affine=False),\n",
        "                nn.ReLU(inplace=True),\n",
        "            )\n",
        "        )\n",
        "\n",
        "        # Initialize local residual and backend blocks\n",
        "        self.g2.append(\n",
        "            nn.Sequential(\n",
        "                # Residual blocks\n",
        "                *[ResidualBlock(2 * base_channels) for _ in range(local_res_blocks)],\n",
        "\n",
        "                # Backend blocks\n",
        "                nn.ConvTranspose2d(2 * base_channels, base_channels, kernel_size=3, stride=2, padding=1, output_padding=1), \n",
        "                nn.InstanceNorm2d(base_channels, affine=False),\n",
        "                nn.ReLU(inplace=True),\n",
        "\n",
        "                # Output convolutional layer\n",
        "                nn.ReflectionPad2d(3),\n",
        "                nn.Conv2d(base_channels, out_channels, kernel_size=7, padding=0),\n",
        "                nn.Tanh(),\n",
        "            )\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Get output from g1_B\n",
        "        x_g1 = self.downsample(x)\n",
        "        x_g1 = self.g1(x_g1)\n",
        "\n",
        "        # Get output from g2_F\n",
        "        x_g2 = self.g2[0](x)\n",
        "\n",
        "        # Get final output from g2_B\n",
        "        return self.g2[1](x_g1 + x_g2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6g5OLPHB-e0"
      },
      "source": [
        "And voilà! You now have modules for both the global subgenerator and local enhancer subgenerator!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HoyIbXajoMC"
      },
      "source": [
        "## Multiscale Discriminator: Discriminating at different scales too!\n",
        "\n",
        "Pix2PixHD uses 3 separate subcomponents (subdiscriminators $D_1$, $D_2$, and $D_3$) to generate predictions. They all have the same architectures but $D_2$ and $D_3$ operate on inputs downsampled by 2x and 4x, respectively. The GAN objective is now modified as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\min_G \\max_{D_1,D_2,D_3}\\sum_{k=1,2,3}\\mathcal{L}_{\\text{GAN}}(G, D_k)\n",
        "\\end{align*}\n",
        "\n",
        "Each subdiscriminator is a PatchGAN, which you should be familiar with from Pix2Pix!\n",
        "\n",
        "Let's first implement a single PatchGAN - this implementation will be slightly different than the one you saw in Pix2Pix since the intermediate feature maps will be needed for computing loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYgX2B_hDxkA"
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "    '''\n",
        "    Discriminator Class\n",
        "    Implements the discriminator class for a subdiscriminator, \n",
        "    which can be used for all the different scales, just with different argument values.\n",
        "    Values:\n",
        "        in_channels: the number of channels in input, a scalar\n",
        "        base_channels: the number of channels in first convolutional layer, a scalar\n",
        "        n_layers: the number of convolutional layers, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, base_channels=64, n_layers=3):\n",
        "        super().__init__()\n",
        "\n",
        "        # Use nn.ModuleList so we can output intermediate values for loss.\n",
        "        self.layers = nn.ModuleList()\n",
        "\n",
        "        # Initial convolutional layer\n",
        "        self.layers.append(\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(in_channels, base_channels, kernel_size=4, stride=2, padding=2),\n",
        "                nn.LeakyReLU(0.2, inplace=True),\n",
        "            )\n",
        "        )\n",
        "\n",
        "        # Downsampling convolutional layers\n",
        "        channels = base_channels\n",
        "        for _ in range(1, n_layers):\n",
        "            prev_channels = channels\n",
        "            channels = min(2 * channels, 512)\n",
        "            self.layers.append(\n",
        "                nn.Sequential(\n",
        "                    nn.Conv2d(prev_channels, channels, kernel_size=4, stride=2, padding=2),\n",
        "                    nn.InstanceNorm2d(channels, affine=False),\n",
        "                    nn.LeakyReLU(0.2, inplace=True),\n",
        "                )\n",
        "            )\n",
        "\n",
        "        # Output convolutional layer\n",
        "        prev_channels = channels\n",
        "        channels = min(2 * channels, 512)\n",
        "        self.layers.append(\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(prev_channels, channels, kernel_size=4, stride=1, padding=2),\n",
        "                nn.InstanceNorm2d(channels, affine=False),\n",
        "                nn.LeakyReLU(0.2, inplace=True),\n",
        "                nn.Conv2d(channels, 1, kernel_size=4, stride=1, padding=2),\n",
        "            )\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = [] # for feature matching loss\n",
        "        for layer in self.layers:\n",
        "            x = layer(x)\n",
        "            outputs.append(x)\n",
        "\n",
        "        return outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZchSdgY1Jrd5"
      },
      "source": [
        "Now you're ready to implement the multiscale discriminator in full! This puts together the different subdiscriminator scales."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_kqcsh4Jwjz"
      },
      "source": [
        "class MultiscaleDiscriminator(nn.Module):\n",
        "    '''\n",
        "    MultiscaleDiscriminator Class\n",
        "    Values:\n",
        "        in_channels: number of input channels to each discriminator, a scalar\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_layers: number of downsampling layers in each discriminator, a scalar\n",
        "        n_discriminators: number of discriminators at different scales, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, base_channels=64, n_layers=3, n_discriminators=3):\n",
        "        super().__init__()\n",
        "\n",
        "        # Initialize all discriminators\n",
        "        self.discriminators = nn.ModuleList()\n",
        "        for _ in range(n_discriminators):\n",
        "            self.discriminators.append(\n",
        "                Discriminator(in_channels, base_channels=base_channels, n_layers=n_layers)\n",
        "            )\n",
        "\n",
        "        # Downsampling layer to pass inputs between discriminators at different scales\n",
        "        self.downsample = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = []\n",
        "\n",
        "        for i, discriminator in enumerate(self.discriminators):\n",
        "            # Downsample input for subsequent discriminators\n",
        "            if i != 0:\n",
        "                x = self.downsample(x)\n",
        "\n",
        "            outputs.append(discriminator(x))\n",
        "\n",
        "        # Return list of multiscale discriminator outputs\n",
        "        return outputs\n",
        "\n",
        "    @property\n",
        "    def n_discriminators(self):\n",
        "        return len(self.discriminators)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSFpoKLlLdpb"
      },
      "source": [
        "## Instance Boundary Map: Learning boundaries between instances\n",
        "\n",
        "Here's a new method that adds additional information as conditional input!\n",
        "\n",
        "The authors observed that previous approaches have typically taken in a label map (aka. segmentation map) that labels all the pixels to be of a certain class (i.e. car) but doesn't differentiate between two instances of the same class (i.e. two cars in the image). This is the difference between *semantic label maps*, which have class labels but not instance labels, and *instance label maps*, which represent unique instances with unique numbers.\n",
        "\n",
        "The authors found that the most important information in the instance lelab map is actually the boundaries between instances (i.e. the outline of each car). You can create boundary maps by mapping each pixel maps to a 1 if it's a different instance from its 4 neighbors, and 0 otherwise.\n",
        "\n",
        "To include this information, the authors concatenate the boundary map with the semantic label map as input. From the figure below, you can see that including both as input results in much sharper generated images (right) than only inputting the semantic label map (left).\n",
        "\n",
        "> ![Semantic label map input vs instance boundary map input](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/Pix2PixHD-Instance-Map.png?raw=true)\n",
        "![Semantic label map vs instance boundary map](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/Pix2PixHD-Instance-Map-2.png?raw=true)\n",
        "*Semantic label map input (top left) and its blurry output between instances (bottom left) vs. instance boundary map (top right) and the much clearer output between instances from inputting both the semantic label map and the instance boundary map (bottom right). Taken from Figures 4 and 5 of [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://arxiv.org/abs/1711.11585) (Wang et al. 2018).*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzRC73GwEjY-"
      },
      "source": [
        "## Instance-level Feature Encoder: Adding controllable diversity\n",
        "\n",
        "As you already know, the task of generation has more than one possible realistic output. For example, an object of class `road` could be concrete, cobblestone, dirt, etc. To learn this diversity, the authors introduce an encoder $E$, which takes the original image as input and outputs a feature map (like the feature extractor from Course 2, Week 1). They apply *instance-wise averaging*, averaging the feature vectors across all occurrences of each instance  (so that every pixel corresponding to the same instance has the same feature vector). They then concatenate this instance-level feature embedding with the semantic label and instance boundary maps as input to the generator.\n",
        "\n",
        "What's cool is that the encoder $E$ is trained jointly with $G_1$. One huge backprop! When training $G_2$, $E$ is fed a downsampled image and the corresponding output is upsampled to pass into $G_2$.\n",
        "\n",
        "To allow for control over different features (e.g. concrete, cobblestone, and dirt) for inference, the authors first use K-means clustering to cluster all the feature vectors for each object class in the training set. You can think of this as a dictionary, mapping each class label to a set of feature vectors (so $K$ centroids, each representing different clusters of features). Now during inference, you can perform a random lookup from this dictionary for each class (e.g. road) in the semantic label map to generate one type of feature (e.g. dirt). To provide greater control, you can select among different feature types for each class to generate diverse feature types and, as a result, multi-modal outputs from the same input. \n",
        "\n",
        "Higher values of $K$ increase diversity and potentially decrease fidelity. You've seen this tradeoff between diversity and fidelity before with the truncation trick, and this is just another way to trade-off between them.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtxfYI8zR5MF"
      },
      "source": [
        "class Encoder(nn.Module):\n",
        "    '''\n",
        "    Encoder Class\n",
        "    Values:\n",
        "        in_channels: number of input channels to each discriminator, a scalar\n",
        "        out_channels: number of channels in output feature map, a scalar\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_layers: number of downsampling layers, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, out_channels, base_channels=16, n_layers=4):\n",
        "        super().__init__()\n",
        "\n",
        "        self.out_channels = out_channels\n",
        "        channels = base_channels\n",
        "\n",
        "        layers = [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.Conv2d(in_channels, base_channels, kernel_size=7, padding=0), \n",
        "            nn.InstanceNorm2d(base_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "        ]\n",
        "\n",
        "        # Downsampling layers\n",
        "        for i in range(n_layers):\n",
        "            layers += [\n",
        "                nn.Conv2d(channels, 2 * channels, kernel_size=3, stride=2, padding=1),\n",
        "                nn.InstanceNorm2d(2 * channels),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels *= 2\n",
        "    \n",
        "        # Upsampling layers\n",
        "        for i in range(n_layers):\n",
        "            layers += [\n",
        "                nn.ConvTranspose2d(channels, channels // 2, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
        "                nn.InstanceNorm2d(channels // 2),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels //= 2\n",
        "\n",
        "        layers += [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.Conv2d(base_channels, out_channels, kernel_size=7, padding=0),\n",
        "            nn.Tanh(),\n",
        "        ]\n",
        "\n",
        "        self.layers = nn.Sequential(*layers)\n",
        "\n",
        "    def instancewise_average_pooling(self, x, inst):\n",
        "        '''\n",
        "        Applies instance-wise average pooling.\n",
        "\n",
        "        Given a feature map of size (b, c, h, w), the mean is computed for each b, c\n",
        "        across all h, w of the same instance\n",
        "        '''\n",
        "        x_mean = torch.zeros_like(x)\n",
        "        classes = torch.unique(inst, return_inverse=False, return_counts=False) # gather all unique classes present\n",
        "\n",
        "        for i in classes:\n",
        "            for b in range(x.size(0)):\n",
        "                indices = torch.nonzero(inst[b:b+1] == i, as_tuple=False) # get indices of all positions equal to class i\n",
        "                for j in range(self.out_channels):\n",
        "                    x_ins = x[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3]]\n",
        "                    mean_feat = torch.mean(x_ins).expand_as(x_ins)\n",
        "                    x_mean[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3]] = mean_feat\n",
        "\n",
        "        return x_mean\n",
        "\n",
        "    def forward(self, x, inst):\n",
        "        x = self.layers(x)\n",
        "        x = self.instancewise_average_pooling(x, inst)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQHD_S0pYx7Z"
      },
      "source": [
        "## Additional Loss Functions\n",
        "\n",
        "In addition to the architectural and feature-map enhancements, the authors also incorporate a feature matching loss based on the discriminator. Essentially, they output intermediate feature maps at different resolutions from the discriminator and try to minimize the difference between the real and fake image features.\n",
        "\n",
        "The authors found this to stabilize training. In this case, this forces the generator to produce natural statistics at multiple scales. This feature-matching loss is similar to StyleGAN's perceptual loss. For some semantic label map $s$ and corresponding image $x$,\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{FM}} = \\mathbb{E}_{s,x}\\left[\\sum_{i=1}^T\\dfrac{1}{N_i}\\left|\\left|D^{(i)}_k(s, x) - D^{(i)}_k(s, G(s))\\right|\\right|_1\\right]\n",
        "\\end{align*}\n",
        "\n",
        "where $T$ is the total number of layers, $N_i$ is the number of elements at layer $i$, and $D^{(i)}_k$ denotes the $i$th layer in discriminator $k$.\n",
        "\n",
        "The authors also report minor improvements in performance when adding perceptual loss, formulated as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{VGG}} = \\mathbb{E}_{s,x}\\left[\\sum_{i=1}^N\\dfrac{1}{M_i}\\left|\\left|F^i(x) - F^i(G(s))\\right|\\right|_1\\right]\n",
        "\\end{align*}\n",
        "\n",
        "where $F^i$ denotes the $i$th layer with $M_i$ elements of the VGG19 network. `torchvision` provides a pretrained VGG19 network, so you'll just need a simple wrapper for it to get the intermediate outputs.\n",
        "\n",
        "The overall loss looks like this:\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L} = \\mathcal{L}_{\\text{GAN}} + \\lambda_1\\mathcal{L}_{\\text{FM}} + \\lambda_2\\mathcal{L}_{\\text{VGG}}\n",
        "\\end{align*}\n",
        "\n",
        "where $\\lambda_1 = \\lambda_2 = 10$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCXhrrsudbB2"
      },
      "source": [
        "import torchvision.models as models\n",
        "\n",
        "class VGG19(nn.Module):\n",
        "    '''\n",
        "    VGG19 Class\n",
        "    Wrapper for pretrained torchvision.models.vgg19 to output intermediate feature maps\n",
        "    '''\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        vgg_features = models.vgg19(pretrained=True).features\n",
        "\n",
        "        self.f1 = nn.Sequential(*[vgg_features[x] for x in range(2)])\n",
        "        self.f2 = nn.Sequential(*[vgg_features[x] for x in range(2, 7)])\n",
        "        self.f3 = nn.Sequential(*[vgg_features[x] for x in range(7, 12)])\n",
        "        self.f4 = nn.Sequential(*[vgg_features[x] for x in range(12, 21)])\n",
        "        self.f5 = nn.Sequential(*[vgg_features[x] for x in range(21, 30)])\n",
        "\n",
        "        for param in self.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "    def forward(self, x):\n",
        "        h1 = self.f1(x)\n",
        "        h2 = self.f2(h1)\n",
        "        h3 = self.f3(h2)\n",
        "        h4 = self.f4(h3)\n",
        "        h5 = self.f5(h4)\n",
        "        return [h1, h2, h3, h4, h5]\n",
        "\n",
        "class Loss(nn.Module):\n",
        "    '''\n",
        "    Loss Class\n",
        "    Implements composite loss for GauGAN\n",
        "    Values:\n",
        "        lambda1: weight for feature matching loss, a float\n",
        "        lambda2: weight for vgg perceptual loss, a float\n",
        "        device: 'cuda' or 'cpu' for hardware to use\n",
        "        norm_weight_to_one: whether to normalize weights to (0, 1], a bool\n",
        "    '''\n",
        "\n",
        "    def __init__(self, lambda1=10., lambda2=10., device='cuda', norm_weight_to_one=True):\n",
        "        super().__init__()\n",
        "        self.vgg = VGG19().to(device)\n",
        "        self.vgg_weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]\n",
        "\n",
        "        lambda0 = 1.0\n",
        "        # Keep ratio of composite loss, but scale down max to 1.0\n",
        "        scale = max(lambda0, lambda1, lambda2) if norm_weight_to_one else 1.0\n",
        "\n",
        "        self.lambda0 = lambda0 / scale\n",
        "        self.lambda1 = lambda1 / scale\n",
        "        self.lambda2 = lambda2 / scale\n",
        "\n",
        "    def adv_loss(self, discriminator_preds, is_real):\n",
        "        '''\n",
        "        Computes adversarial loss from nested list of fakes outputs from discriminator.\n",
        "        '''\n",
        "        target = torch.ones_like if is_real else torch.zeros_like\n",
        "\n",
        "        adv_loss = 0.0\n",
        "        for preds in discriminator_preds:\n",
        "            pred = preds[-1]\n",
        "            adv_loss += F.mse_loss(pred, target(pred))\n",
        "        return adv_loss\n",
        "\n",
        "    def fm_loss(self, real_preds, fake_preds):\n",
        "        '''\n",
        "        Computes feature matching loss from nested lists of fake and real outputs from discriminator.\n",
        "        '''\n",
        "        fm_loss = 0.0\n",
        "        for real_features, fake_features in zip(real_preds, fake_preds):\n",
        "            for real_feature, fake_feature in zip(real_features, fake_features):\n",
        "                fm_loss += F.l1_loss(real_feature.detach(), fake_feature)\n",
        "        return fm_loss\n",
        "\n",
        "    def vgg_loss(self, x_real, x_fake):\n",
        "        '''\n",
        "        Computes perceptual loss with VGG network from real and fake images.\n",
        "        '''\n",
        "        vgg_real = self.vgg(x_real)\n",
        "        vgg_fake = self.vgg(x_fake)\n",
        "\n",
        "        vgg_loss = 0.0\n",
        "        for real, fake, weight in zip(vgg_real, vgg_fake, self.vgg_weights):\n",
        "            vgg_loss += weight * F.l1_loss(real.detach(), fake)\n",
        "        return vgg_loss\n",
        "\n",
        "    def forward(self, x_real, label_map, instance_map, boundary_map, encoder, generator, discriminator):\n",
        "        '''\n",
        "        Function that computes the forward pass and total loss for generator and discriminator.\n",
        "        '''\n",
        "        feature_map = encoder(x_real, instance_map)\n",
        "        x_fake = generator(torch.cat((label_map, boundary_map, feature_map), dim=1))\n",
        "\n",
        "        # Get necessary outputs for loss/backprop for both generator and discriminator\n",
        "        fake_preds_for_g = discriminator(torch.cat((label_map, boundary_map, x_fake), dim=1))\n",
        "        fake_preds_for_d = discriminator(torch.cat((label_map, boundary_map, x_fake.detach()), dim=1))\n",
        "        real_preds_for_d = discriminator(torch.cat((label_map, boundary_map, x_real.detach()), dim=1))\n",
        "\n",
        "        g_loss = (\n",
        "            self.lambda0 * self.adv_loss(fake_preds_for_g, True) + \\\n",
        "            self.lambda1 * self.fm_loss(real_preds_for_d, fake_preds_for_g) / discriminator.n_discriminators + \\\n",
        "            self.lambda2 * self.vgg_loss(x_fake, x_real)\n",
        "        )\n",
        "        d_loss = 0.5 * (\n",
        "            self.adv_loss(real_preds_for_d, True) + \\\n",
        "            self.adv_loss(fake_preds_for_d, False)\n",
        "        )\n",
        "\n",
        "        return g_loss, d_loss, x_fake.detach()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcyavR5oLmau"
      },
      "source": [
        "## Training Pix2PixHD\n",
        "\n",
        "You now have the Pix2PixHD model coded up! All you have to do now is prepare your dataset. Pix2PixHD is trained on the Cityscapes dataset, which unfortunately requires registration. You'll have to download the dataset and put it in your `data` folder to initialize the dataset code below.\n",
        "\n",
        "Specifically, you should download the `gtFine_trainvaltest` and `leftImg8bit_trainvaltest` and specify the corresponding data splits into the dataloader."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mII5L2cZLlpO"
      },
      "source": [
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import torchvision.transforms as transforms\n",
        "from PIL import Image\n",
        "\n",
        "def scale_width(img, target_width, method):\n",
        "    '''\n",
        "    Function that scales an image to target_width while retaining aspect ratio.\n",
        "    '''\n",
        "    w, h = img.size\n",
        "    if w == target_width: return img\n",
        "    target_height = target_width * h // w\n",
        "    return img.resize((target_width, target_height), method)\n",
        "\n",
        "class CityscapesDataset(torch.utils.data.Dataset):\n",
        "    '''\n",
        "    CityscapesDataset Class\n",
        "    Values:\n",
        "        paths: (a list of) paths to load examples from, a list or string\n",
        "        target_width: the size of image widths for resizing, a scalar\n",
        "        n_classes: the number of object classes, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, paths, target_width=1024, n_classes=35):\n",
        "        super().__init__()\n",
        "\n",
        "        self.n_classes = n_classes\n",
        "\n",
        "        # Collect list of examples\n",
        "        self.examples = {}\n",
        "        if type(paths) == str:\n",
        "            self.load_examples_from_dir(paths)\n",
        "        elif type(paths) == list:\n",
        "            for path in paths:\n",
        "                self.load_examples_from_dir(path)\n",
        "        else:\n",
        "            raise ValueError('`paths` should be a single path or list of paths')\n",
        "\n",
        "        self.examples = list(self.examples.values())\n",
        "        assert all(len(example) == 3 for example in self.examples)\n",
        "\n",
        "        # Initialize transforms for the real color image\n",
        "        self.img_transforms = transforms.Compose([\n",
        "            transforms.Lambda(lambda img: scale_width(img, target_width, Image.BICUBIC)),\n",
        "            transforms.Lambda(lambda img: np.array(img)),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "        ])\n",
        "\n",
        "        # Initialize transforms for semantic label and instance maps\n",
        "        self.map_transforms = transforms.Compose([\n",
        "            transforms.Lambda(lambda img: scale_width(img, target_width, Image.NEAREST)),\n",
        "            transforms.Lambda(lambda img: np.array(img)),\n",
        "            transforms.ToTensor(),\n",
        "        ])\n",
        "\n",
        "    def load_examples_from_dir(self, abs_path):\n",
        "        '''\n",
        "        Given a folder of examples, this function returns a list of paired examples.\n",
        "        '''\n",
        "        assert os.path.isdir(abs_path)\n",
        "\n",
        "        img_suffix = '_leftImg8bit.png'\n",
        "        label_suffix = '_gtFine_labelIds.png'\n",
        "        inst_suffix = '_gtFine_instanceIds.png'\n",
        "\n",
        "        for root, _, files in os.walk(abs_path):\n",
        "            for f in files:\n",
        "                if f.endswith(img_suffix):\n",
        "                    prefix = f[:-len(img_suffix)]\n",
        "                    attr = 'orig_img'\n",
        "                elif f.endswith(label_suffix):\n",
        "                    prefix = f[:-len(label_suffix)]\n",
        "                    attr = 'label_map'\n",
        "                elif f.endswith(inst_suffix):\n",
        "                    prefix = f[:-len(inst_suffix)]\n",
        "                    attr = 'inst_map'\n",
        "                else:\n",
        "                    continue\n",
        "\n",
        "                if prefix not in self.examples.keys():\n",
        "                    self.examples[prefix] = {}\n",
        "                self.examples[prefix][attr] = root + '/' + f\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        example = self.examples[idx]\n",
        "\n",
        "        # Load image and maps\n",
        "        img = Image.open(example['orig_img']).convert('RGB') # color image: (3, 512, 1024)\n",
        "        inst = Image.open(example['inst_map'])               # instance map: (512, 1024)\n",
        "        label = Image.open(example['label_map'])             # semantic label map: (512, 1024)\n",
        "\n",
        "        # Apply corresponding transforms\n",
        "        img = self.img_transforms(img)\n",
        "        inst = self.map_transforms(inst)\n",
        "        label = self.map_transforms(label).long() * 255\n",
        "\n",
        "        # Convert labels to one-hot vectors\n",
        "        label = torch.zeros(self.n_classes, img.shape[1], img.shape[2]).scatter_(0, label, 1.0).to(img.dtype)\n",
        "\n",
        "        # Convert instance map to instance boundary map\n",
        "        bound = torch.ByteTensor(inst.shape).zero_()\n",
        "        bound[:, :, 1:] = bound[:, :, 1:] | (inst[:, :, 1:] != inst[:, :, :-1])\n",
        "        bound[:, :, :-1] = bound[:, :, :-1] | (inst[:, :, 1:] != inst[:, :, :-1])\n",
        "        bound[:, 1:, :] = bound[:, 1:, :] | (inst[:, 1:, :] != inst[:, :-1, :])\n",
        "        bound[:, :-1, :] = bound[:, :-1, :] | (inst[:, 1:, :] != inst[:, :-1, :])\n",
        "        bound = bound.to(img.dtype)\n",
        "\n",
        "        return (img, label, inst, bound)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.examples)\n",
        "\n",
        "    @staticmethod\n",
        "    def collate_fn(batch):\n",
        "        imgs, labels, insts, bounds = [], [], [], []\n",
        "        for (x, l, i, b) in batch:\n",
        "            imgs.append(x)\n",
        "            labels.append(l)\n",
        "            insts.append(i)\n",
        "            bounds.append(b)\n",
        "        return (\n",
        "            torch.stack(imgs, dim=0),\n",
        "            torch.stack(labels, dim=0),\n",
        "            torch.stack(insts, dim=0),\n",
        "            torch.stack(bounds, dim=0),\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51StN80TCpgE"
      },
      "source": [
        "Now initialize everything you'll need for training. Don't be worried if there looks like a lot of random code, it's all stuff you've seen before!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KG1ZIAI8DgJX"
      },
      "source": [
        "from tqdm import tqdm\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "n_classes = 35                  # total number of object classes\n",
        "rgb_channels = n_features = 3\n",
        "device = 'cuda'\n",
        "train_dir = ['data']\n",
        "epochs = 200                    # total number of train epochs\n",
        "decay_after = 100               # number of epochs with constant lr\n",
        "lr = 0.0002\n",
        "betas = (0.5, 0.999)\n",
        "\n",
        "def lr_lambda(epoch):\n",
        "    ''' Function for scheduling learning '''\n",
        "    return 1. if epoch < decay_after else 1 - float(epoch - decay_after) / (epochs - decay_after)\n",
        "\n",
        "def weights_init(m):\n",
        "    ''' Function for initializing all model weights '''\n",
        "    if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n",
        "        nn.init.normal_(m.weight, 0., 0.02)\n",
        "\n",
        "loss_fn = Loss(device=device)\n",
        "\n",
        "## Phase 1: Low Resolution (1024 x 512)\n",
        "dataloader1 = DataLoader(\n",
        "    CityscapesDataset(train_dir, target_width=1024, n_classes=n_classes),\n",
        "    collate_fn=CityscapesDataset.collate_fn, batch_size=1, shuffle=True, drop_last=False, pin_memory=True,\n",
        ")\n",
        "encoder = Encoder(rgb_channels, n_features).to(device).apply(weights_init)\n",
        "generator1 = GlobalGenerator(n_classes + n_features + 1, rgb_channels).to(device).apply(weights_init)\n",
        "discriminator1 = MultiscaleDiscriminator(n_classes + 1 + rgb_channels, n_discriminators=2).to(device).apply(weights_init)\n",
        "\n",
        "g1_optimizer = torch.optim.Adam(list(generator1.parameters()) + list(encoder.parameters()), lr=lr, betas=betas)\n",
        "d1_optimizer = torch.optim.Adam(list(discriminator1.parameters()), lr=lr, betas=betas)\n",
        "g1_scheduler = torch.optim.lr_scheduler.LambdaLR(g1_optimizer, lr_lambda)\n",
        "d1_scheduler = torch.optim.lr_scheduler.LambdaLR(d1_optimizer, lr_lambda)\n",
        "\n",
        "\n",
        "## Phase 2: High Resolution (2048 x 1024)\n",
        "dataloader2 = DataLoader(\n",
        "    CityscapesDataset(train_dir, target_width=2048, n_classes=n_classes),\n",
        "    collate_fn=CityscapesDataset.collate_fn, batch_size=1, shuffle=True, drop_last=False, pin_memory=True,\n",
        ")\n",
        "generator2 = LocalEnhancer(n_classes + n_features + 1, rgb_channels).to(device).apply(weights_init)\n",
        "discriminator2 = MultiscaleDiscriminator(n_classes + 1 + rgb_channels).to(device).apply(weights_init)\n",
        "\n",
        "g2_optimizer = torch.optim.Adam(list(generator2.parameters()) + list(encoder.parameters()), lr=lr, betas=betas)\n",
        "d2_optimizer = torch.optim.Adam(list(discriminator2.parameters()), lr=lr, betas=betas)\n",
        "g2_scheduler = torch.optim.lr_scheduler.LambdaLR(g2_optimizer, lr_lambda)\n",
        "d2_scheduler = torch.optim.lr_scheduler.LambdaLR(d2_optimizer, lr_lambda)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwyO_padKxW5"
      },
      "source": [
        "And now the training loop, which is pretty much the same between the two phases:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-2lU4TnK5ik"
      },
      "source": [
        "from torchvision.utils import make_grid\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Parse torch version for autocast\n",
        "# ######################################################\n",
        "version = torch.__version__\n",
        "version = tuple(int(n) for n in version.split('.')[:-1])\n",
        "has_autocast = version >= (1, 6)\n",
        "# ######################################################\n",
        "\n",
        "def show_tensor_images(image_tensor):\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[:1], nrow=1)\n",
        "    plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
        "    plt.show()\n",
        "\n",
        "def train(dataloader, models, optimizers, schedulers, device):\n",
        "    encoder, generator, discriminator = models\n",
        "    g_optimizer, d_optimizer = optimizers\n",
        "    g_scheduler, d_scheduler = schedulers\n",
        "\n",
        "    cur_step = 0\n",
        "    display_step = 100\n",
        "\n",
        "    mean_g_loss = 0.0\n",
        "    mean_d_loss = 0.0\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        # Training epoch\n",
        "        for (x_real, labels, insts, bounds) in tqdm(dataloader, position=0):\n",
        "            x_real = x_real.to(device)\n",
        "            labels = labels.to(device)\n",
        "            insts = insts.to(device)\n",
        "            bounds = bounds.to(device)\n",
        "\n",
        "            # Enable autocast to FP16 tensors (new feature since torch==1.6.0)\n",
        "            # If you're running older versions of torch, comment this out\n",
        "            # and use NVIDIA apex for mixed/half precision training\n",
        "            if has_autocast:\n",
        "                with torch.cuda.amp.autocast(enabled=(device=='cuda')):\n",
        "                    g_loss, d_loss, x_fake = loss_fn(\n",
        "                        x_real, labels, insts, bounds, encoder, generator, discriminator\n",
        "                    )\n",
        "            else:\n",
        "                g_loss, d_loss, x_fake = loss_fn(\n",
        "                    x_real, labels, insts, bounds, encoder, generator, discriminator\n",
        "                )\n",
        "\n",
        "            g_optimizer.zero_grad()\n",
        "            g_loss.backward()\n",
        "            g_optimizer.step()\n",
        "\n",
        "            d_optimizer.zero_grad()\n",
        "            d_loss.backward()\n",
        "            d_optimizer.step()\n",
        "\n",
        "            mean_g_loss += g_loss.item() / display_step\n",
        "            mean_d_loss += d_loss.item() / display_step\n",
        "\n",
        "            if cur_step % display_step == 0 and cur_step > 0:\n",
        "                print('Step {}: Generator loss: {:.5f}, Discriminator loss: {:.5f}'\n",
        "                      .format(cur_step, mean_g_loss, mean_d_loss))\n",
        "                show_tensor_images(x_fake.to(x_real.dtype))\n",
        "                show_tensor_images(x_real)\n",
        "                mean_g_loss = 0.0\n",
        "                mean_d_loss = 0.0\n",
        "            cur_step += 1\n",
        "\n",
        "        g_scheduler.step()\n",
        "        d_scheduler.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCcV0ttk6_Mv"
      },
      "source": [
        "And now you can train your models! Remember to set the local enhancer subgenerator to the global subgenerator that you train in the first phase.\n",
        "\n",
        "In their official repository, the authors don't continue to train the encoder. Instead, they precompute all feature maps upsample them, and concatenate this to the input to the local enhancer subgenerator. (They also leave a re-train option for it). For simplicity, the script below will just downsample and upsample high-resolution inputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-41crv17Pcn"
      },
      "source": [
        "# Phase 1: Low Resolution\n",
        "#######################################################################\n",
        "train(\n",
        "    dataloader1,\n",
        "    [encoder, generator1, discriminator1],\n",
        "    [g1_optimizer, d1_optimizer],\n",
        "    [g1_scheduler, d1_scheduler],\n",
        "    device,\n",
        ")\n",
        "\n",
        "\n",
        "# Phase 2: High Resolution\n",
        "#######################################################################\n",
        "# Update global generator in local enhancer with trained\n",
        "generator2.g1 = generator1.g1\n",
        "\n",
        "# Freeze encoder and wrap to support high-resolution inputs/outputs\n",
        "def freeze(encoder):\n",
        "    encoder.eval()\n",
        "    for p in encoder.parameters():\n",
        "        p.requires_grad = False\n",
        "\n",
        "    @torch.jit.script\n",
        "    def forward(x, inst):\n",
        "        x = F.interpolate(x, scale_factor=0.5, recompute_scale_factor=True)\n",
        "        inst = F.interpolate(inst.float(), scale_factor=0.5, recompute_scale_factor=True)\n",
        "        feat = encoder(x, inst.int())\n",
        "        return F.interpolate(feat, scale_factor=2.0, recompute_scale_factor=True)\n",
        "    return forward\n",
        "\n",
        "train(\n",
        "    dataloader2,\n",
        "    [freeze(encoder), generator2, discriminator2],\n",
        "    [g2_optimizer, d2_optimizer],\n",
        "    [g2_scheduler, d2_scheduler],\n",
        "    device,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37egKCs2YqEO"
      },
      "source": [
        "## Inference with Pix2PixHD\n",
        "\n",
        "Recall that in inference time, the encoder feature maps from training are saved and clustered with K-means by object class. Again, you'll have to download the Cityscapes dataset into your `data` folder and then run these functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69wfl6vre_cM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "cb08aea5-a28a-421b-e5dd-6441d8ee04c7"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Encode features by class label\n",
        "features = {}\n",
        "for (x, _, inst, _) in tqdm(dataloader2):\n",
        "    x = x.to(device)\n",
        "    inst = inst.to(device)\n",
        "    area = inst.size(2) * inst.size(3)\n",
        "\n",
        "    # Get pooled feature map\n",
        "    with torch.no_grad():\n",
        "        feature_map = encoder(x, inst)\n",
        "\n",
        "    for i in torch.unique(inst):\n",
        "        label = i if i < 1000 else i // 1000\n",
        "        label = int(label.flatten(0).item())\n",
        "\n",
        "        # All indices should have same feature per class from pooling\n",
        "        idx = torch.nonzero(inst == i, as_tuple=False)\n",
        "        n_inst = idx.size(0)\n",
        "        idx = idx[0, :]\n",
        "\n",
        "        # Retrieve corresponding encoded feature\n",
        "        feature = feature_map[idx[0], :, idx[2], idx[3]].unsqueeze(0)\n",
        "\n",
        "        # Compute rate of feature appearance (in official code, they compute per block)\n",
        "        block_size = 32\n",
        "        rate_per_block = 32 * n_inst / area\n",
        "        rate = torch.ones((1, 1), device=device).to(feature.dtype) * rate_per_block\n",
        "\n",
        "        feature = torch.cat((feature, rate), dim=1)\n",
        "        if label in features.keys():\n",
        "            features[label] = torch.cat((features[label], feature), dim=0)\n",
        "        else:\n",
        "            features[label] = feature\n",
        "\n",
        "\n",
        "# Cluster features by class label\n",
        "k = 10\n",
        "centroids = {}\n",
        "for label in range(n_classes):\n",
        "    if label not in features.keys():\n",
        "        continue\n",
        "    feature = features[label]\n",
        "\n",
        "    # Thresholding by 0.5 isn't mentioned in the paper, but is present in the\n",
        "    # official code repository, probably so that only frequent features are clustered\n",
        "    feature = feature[feature[:, -1] > 0.5, :-1].cpu().numpy()\n",
        "\n",
        "    if feature.shape[0]:\n",
        "        n_clusters = min(feature.shape[0], k)\n",
        "        kmeans = KMeans(n_clusters=n_clusters).fit(feature)\n",
        "        centroids[label] = kmeans.cluster_centers_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 174/174 [02:07<00:00,  1.36it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-27-59535d645100>\"\u001b[0;36m, line \u001b[0;32m54\u001b[0m\n\u001b[0;31m    return centroids\u001b[0m\n\u001b[0m                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1LVqMkTjn06"
      },
      "source": [
        "After getting the encoded feature centroids per class, you can now run inference! Remember that the generator is trained to take in a concatenation of the semantic label map, instance boundary map, and encoded feature map.\n",
        "\n",
        "Congrats on making it to the end of this complex notebook! Have fun with this powerful model and be responsible of course ;)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BpqE3ngk_3s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "939172eb-c90f-4050-90d4-22175fa5073a"
      },
      "source": [
        "def infer(label_map, instance_map, boundary_map):\n",
        "    # Sample feature vector centroids\n",
        "    b, _, h, w = label_map.shape\n",
        "    feature_map = torch.zeros((b, n_features, h, w), device=device).to(label_map.dtype)\n",
        "\n",
        "    for i in torch.unique(instance_map):\n",
        "        label = i if i < 1000 else i // 1000\n",
        "        label = int(label.flatten(0).item())\n",
        "\n",
        "        if label in centroids.keys():\n",
        "            centroid_idx = random.randint(0, centroids[label].shape[0] - 1)\n",
        "            idx = torch.nonzero(instance_map == int(i), as_tuple=False)\n",
        "\n",
        "            feature = torch.from_numpy(centroids[label][centroid_idx, :]).to(device)\n",
        "            feature_map[idx[:, 0], :, idx[:, 2], idx[:, 3]] = feature\n",
        "\n",
        "    with torch.no_grad():\n",
        "        x_fake = generator2(torch.cat((label_map, boundary_map, feature_map), dim=1))\n",
        "    return x_fake\n",
        "\n",
        "for x, labels, insts, bounds in dataloader2:\n",
        "    x_fake = infer(labels.to(device), insts.to(device), bounds.to(device))\n",
        "    show_tensor_images(x_fake.to(x.dtype))\n",
        "    show_tensor_images(x)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          }
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}