RealNVP.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_j4rUySxiTSc"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 5, 5\n",
        "rcParams['figure.dpi'] = 100\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import distributions\n",
        "from torch.nn.parameter import Parameter\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torchvision import datasets, transforms\n",
        "from tqdm import tqdm\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "HUVjrGWbUel6"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "class RealNVPBlock(nn.Module):\n",
        "    def __init__(self, in_features, hidden_features):\n",
        "        super(RealNVPBlock, self).__init__()\n",
        "        self.net_s = nn.Sequential(\n",
        "            nn.Linear(in_features // 2, hidden_features),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features, hidden_features*2),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features*2, hidden_features*2),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features*2, hidden_features),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features, in_features // 2),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        self.net_t = nn.Sequential(\n",
        "            nn.Linear(in_features // 2, hidden_features),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features, hidden_features*2),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features*2, hidden_features*2),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features*2, hidden_features),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_features, in_features // 2),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "    def backward(self, y):\n",
        "        y_a, y_b = y.chunk(2, dim=1)\n",
        "        x_a = y_a\n",
        "        s = self.net_s(y_a)\n",
        "        t = self.net_t(y_a)\n",
        "        x_b = (y_b - t)*torch.exp(-s)\n",
        "        x = torch.cat([x_a, x_b], dim=1)\n",
        "        return x\n",
        "    def forward(self,x):\n",
        "        x_a, x_b = x.chunk(2,dim=1)\n",
        "        y_a = x_a\n",
        "        s = self.net_s(x_a)\n",
        "        t = self.net_t(x_a)\n",
        "        y_b = x_b * torch.exp(s) + t\n",
        "        log_det_Jac = -s.sum(dim=1)\n",
        "        y = torch.cat([y_a, y_b], dim=1)\n",
        "        return y,log_det_Jac\n",
        "class RealNVP(nn.Module):\n",
        "    def __init__(self, num_blocks, in_features, hidden_features):\n",
        "        super(RealNVP, self).__init__()\n",
        "        self.in_features=  in_features\n",
        "        self.blocks = nn.ModuleList([RealNVPBlock(self.in_features, hidden_features) for _ in range(num_blocks)])\n",
        "        self.blocks = self.blocks.to(device)\n",
        "        self.prior =  distributions.MultivariateNormal(torch.zeros(self.in_features), torch.eye(self.in_features))\n",
        "    def forward(self, x):\n",
        "        # pdb.set_trace()\n",
        "        sum_log_det_Jac= x.new_zeros(x.shape[0]).to(device)\n",
        "        for block in self.blocks:\n",
        "            x,log_det_Jac = block.forward(x)\n",
        "            sum_log_det_Jac +=log_det_Jac\n",
        "            x = torch.flip(x,dims=(1,))\n",
        "        y = x\n",
        "        return y,sum_log_det_Jac\n",
        "    def backward(self,y):\n",
        "        for block in reversed(self.blocks):\n",
        "            y = block.backward(y)\n",
        "            y = torch.flip(y,dims=(1,))\n",
        "        x = y\n",
        "        return x\n",
        "    def log_prob(self,x):\n",
        "        y, logp = self.forward(x)\n",
        "        # pdb.set_trace()\n",
        "        y = y.to('cpu')\n",
        "        p_z = self.prior.log_prob(y)\n",
        "        p_z = p_z.to(device)\n",
        "        return (p_z+logp)\n",
        "    def sample(self, batchSize):\n",
        "        y = self.prior.sample((batchSize,1))\n",
        "        if y.dim()>2:\n",
        "          y = torch.squeeze(y)\n",
        "        y=y.to('cpu')\n",
        "        logp = self.prior.log_prob(y)\n",
        "        y = y.to(device)\n",
        "        x = self.backward(y)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "Pkp_I7hAfx4F"
      },
      "outputs": [],
      "source": [
        "num_blocks = 6\n",
        "in_features = 28 * 28\n",
        "hidden_features = 512\n",
        "batch_size = 512\n",
        "learning_rate = 0.001\n",
        "epochs = 100\n",
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((-.5,), (-.5,))])\n",
        "\n",
        "# Download and load the training data\n",
        "train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "# Create a DataLoader to batch and shuffle the data\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
        "test_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "lXodjuBgn6Si"
      },
      "outputs": [],
      "source": [
        "import pdb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "real_nvp_model = RealNVP(num_blocks, in_features, hidden_features).to(device)\n",
        "train_loss =[]\n",
        "test_loss = []\n",
        "result_images=[]"
      ],
      "metadata": {
        "id": "25UGBICo7gM9"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcgH0vk0TTYX",
        "outputId": "b16afe98-3923-443f-86a9-81b16bf88eab"
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            "iter 0: loss = 1457550.909 validation loss = 777.408\n"
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            "\n"
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      ],
      "source": [
        "optimizer = torch.optim.Adam(real_nvp_model.parameters(),lr=learning_rate)\n",
        "for t in tqdm(range(epochs)):\n",
        "  tr_loss = 0\n",
        "  for batch in train_loader:\n",
        "    # pdb.set_trace()\n",
        "    images,_ = batch\n",
        "    images = images.view(-1, in_features).to(device)\n",
        "    loss = -(real_nvp_model.log_prob(images)).mean()\n",
        "    tr_loss +=loss.item()\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward(retain_graph=True)\n",
        "    optimizer.step()\n",
        "  train_loss.append(tr_loss/len(train_loader))\n",
        "  with torch.no_grad():\n",
        "    te_loss = 0\n",
        "    for batch in test_loader:\n",
        "      images,_=batch\n",
        "      images.view(-1,in_features)\n",
        "      images = images.view(-1, in_features).to(device)\n",
        "      te_loss +=-(real_nvp_model.log_prob(images)).mean().item()\n",
        "    test_loss.append(te_loss/len(test_loader))\n",
        "    x = real_nvp_model.sample(16).cpu().detach().view(-1,28,28).numpy()\n",
        "    result_images.append(x)\n",
        "  print('iter %s:' % t, 'loss = %.3f' % (tr_loss/len(train_loader)), 'validation loss = %.3f' % (te_loss/len(test_loader)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes = plt.subplots(4,4,figsize=(8,8))\n",
        "for j,img in enumerate(result_images):\n",
        "  for i,ax in enumerate(axes.flatten()):\n",
        "    ax.imshow(img[i],cmap='Greys')\n",
        "\n",
        "  fig.savefig(f'/content/data/png/{j}.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "1UMRlyeskKME",
        "outputId": "c54c6579-e212-4931-b2e2-4a79326a7482"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import imageio\n",
        "\n",
        "png_dir = '/content/data/png'\n",
        "images = []\n",
        "files = [f'/content/data/png/{i}.png' for i in range(epochs)]\n",
        "for file_name in files:\n",
        "    if file_name.endswith('.png'):\n",
        "        file_path = os.path.join(png_dir, file_name)\n",
        "        images.append(imageio.imread(file_path))\n",
        "\n",
        "# Make it pause at the end so that the viewers can ponder\n",
        "for _ in range(10):\n",
        "    images.append(imageio.imread(file_path))\n",
        "\n",
        "imageio.mimsave('/content/data/png/movie.gif', images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZP7dCPXqeIG",
        "outputId": "bee473c9-f734-4a56-ba0e-14eb8e4c4293"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-96-b39287929b8e>:10: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
            "  images.append(imageio.imread(file_path))\n",
            "<ipython-input-96-b39287929b8e>:14: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
            "  images.append(imageio.imread(file_path))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "XHBAcdO0En_g",
        "outputId": "7d10bf6e-9bf6-4dce-8eac-424405b644ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(np.arange(1,100),train_loss[5:],label='Train')\n",
        "plt.plot(np.arange(1,100),test_loss[5:],label='Test')\n",
        "plt.title('Error per Iteration')\n",
        "plt.ylabel('Error')\n",
        "plt.xlabel('Iteration')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}