VAE_Tutorial_01_Basic.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/Steve-YJ/deep-learning-from-scratch-studying/blob/master/VAE_Tutorial_01_Basic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU0S97gFWFG6",
        "colab_type": "text"
      },
      "source": [
        "# VAE Tutorial\n",
        "* Reference: https://github.com/Jackson-Kang/Pytorch-VAE-tutorial/blob/master/Variational_AutoEncoder.ipynb\n",
        "* Goal - Implement it and understand it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G60w5k3zQpoL",
        "colab_type": "text"
      },
      "source": [
        "### 🔖 Review\n",
        "\n",
        "* 2nd. After learning theory, Review it.\n",
        "* <a href = 'deepinsight.tistory.com/121'>Lecture Note</a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_HRP2dnWP_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import library\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tqdm import tqdm  # what is tqdm??\n",
        "                       # tqdm: tqdm은 즉석에서 progress bar를 생성해주고,\n",
        "                       # 함수나 반복문의 TTC(Time To Completion)을 예측하는 파이썬 패키지를 말한다.\n",
        "from torchvision.utils import save_image"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSod235tRgCA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1685867d-1375-4a8d-e383-cdefb185df28"
      },
      "source": [
        "torch.cuda.is_available()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edVnMl9MRsos",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6a135c75-4c09-4e04-b785-982d95f50979"
      },
      "source": [
        "torch.cuda.get_device_name()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tesla K80'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eqQ4HbLRvyP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "811f5b11-cec2-418b-9d51-81b6f26954ac"
      },
      "source": [
        "torch.cuda.get_device_capability()  ## get the cuda capability of device"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YigpjK0WQZD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model Hyperparameters\n",
        "# Set Hyperparameters\n",
        "\n",
        "dataset_path = '~/datasets'\n",
        "\n",
        "cuda = True\n",
        "device = torch.device(\"cuda\" if cuda else \"cpu\")\n",
        "# another method: torch.device('cuda')\n",
        "# cuda = torch.device('cuda)\n",
        "\n",
        "batch_size = 50 \n",
        "\n",
        "x_dim = 784\n",
        "hidden_dim = 400  # 은닉항의 노드는 400\n",
        "latent_dim = 20   # latent vector의 차원은 20차원\n",
        "\n",
        "lr = 1e-3\n",
        "epochs = 10"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbbQ5aeRW4yE",
        "colab_type": "text"
      },
      "source": [
        "## Step1. Load(or download) Dataset"
      ]
    },
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        "outputId": "c30f57ad-a3fb-4268-c833-f27845edf489"
      },
      "source": [
        "from torchvision.datasets import MNIST\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "mnist_transform = transforms.Compose([\n",
        "        transforms.ToTensor()\n",
        "])\n",
        "\n",
        "kwargs = {'num_workers': 1, 'pin_memory': True}\n",
        "\n",
        "train_dataset = MNIST(dataset_path, transform=mnist_transform, train=True, download=True)\n",
        "test_dataset = MNIST(dataset_path, transform=mnist_transform, train=False, download=True)\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, **kwargs) \n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True, **kwargs) "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /root/datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "Extracting /root/datasets/MNIST/raw/train-images-idx3-ubyte.gz to /root/datasets/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /root/datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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            "Extracting /root/datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /root/datasets/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /root/datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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            "Extracting /root/datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /root/datasets/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /root/datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /root/datasets/MNIST/raw\n",
            "Processing...\n",
            "\n",
            "\n",
            "\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: 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.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrIl5CKmo8ox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a3b4c085-2291-4d17-a4be-7d278f715f7a"
      },
      "source": [
        "train_dataset[0][0].shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ubK7kwDW_Mi",
        "colab_type": "text"
      },
      "source": [
        "## Step 2. Define our model: Variational AutoEncoder(VAE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pg-DM0x6WQpP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "    A simple implementation of Gaussian MLP Encoder and Decoder\n",
        "\n",
        "'''\n",
        "## 간단한 Gaussian MLP(Multi Layer Perceptron) Encoder & Decoder !!\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    ## input -> hidden -> latent\n",
        "    def __init__(self, input_dim, hidden_dim, latent_dim):\n",
        "        super(Encoder, self).__init__()\n",
        "\n",
        "        self.FC_input = nn.Linear(input_dim, hidden_dim)\n",
        "        self.FC_mean = nn.Linear(hidden_dim, latent_dim)\n",
        "        self.FC_var = nn.Linear(hidden_dim, latent_dim)\n",
        "        self.training=True\n",
        "\n",
        "    def forward(self, x):\n",
        "        h_ = torch.relu(self.FC_input(x))\n",
        "        mean = self.FC_mean(h_)\n",
        "        log_var = self.FC_var(h_)   # encoder produces mean and log_var\n",
        "                                    # e.g_ parameters of simple tractable normal distribution \"g\"\n",
        "        var = torch.exp(0.5 * log_var)\n",
        "        z = self.reparameterization(mean, var)\n",
        "        \n",
        "        return z, mean, log_var\n",
        "\n",
        "    def reparameterization(self, mean, var,):\n",
        "        epsilon = torch.rand_like(var).to(device)        # sampling epsilon\n",
        "        \n",
        "        z = mean + var*epsilon                          # reparameterization trick\n",
        "        \n",
        "        return z"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ6iQ2jAWQrn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Decoder(nn.Module):\n",
        "    def __init__(self, latent_dim, hidden_dim, output_dim):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.FC_hidden = nn.Linear(latent_dim, hidden_dim)\n",
        "        self.FC_output = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h = torch.relu(self.FC_hidden(x))\n",
        "        x_hat = torch.sigmoid(self.FC_output(h))\n",
        "        return x_hat"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r89muxWbWQt8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Model(nn.Module):\n",
        "    def __init__(self, Encoder, Decoder):\n",
        "        super(Model, self).__init__()\n",
        "        self.Encoder = Encoder\n",
        "        self.Decoder = Decoder\n",
        "\n",
        "    def forward(self, x):\n",
        "        z, mean, log_var = self.Encoder(x)\n",
        "        x_hat = self.Decoder(z)\n",
        "        return x_hat, mean, log_var"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BzWsL1dWQv8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = Encoder(input_dim=x_dim, hidden_dim=hidden_dim, latent_dim=latent_dim)\n",
        "decoder = Decoder(latent_dim=latent_dim, hidden_dim=hidden_dim, output_dim=x_dim)\n",
        "\n",
        "model = Model(Encoder=encoder, Decoder=decoder).to(device)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "On4-JvFnXLKa",
        "colab_type": "text"
      },
      "source": [
        "## Step 3. Define Loss function (reprod, loss) and optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPzfSoWlXEnQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define Loss Function and Optimizer\n",
        "\n",
        "from torch.optim import Adam\n",
        "BCE_loss = nn.BCELoss()\n",
        "\n",
        "def loss_function(x, x_hat, mean, log_var):\n",
        "    reproduction_loss = nn.functional.binary_cross_entropy(x_hat, x, reduction='sum')\n",
        "    KLD = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())\n",
        "    return reproduction_loss + KLD\n",
        "\n",
        "optimizer = Adam(model.parameters(), lr = lr)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yq6gDQKXMQJ",
        "colab_type": "text"
      },
      "source": [
        "## Step 4. Train Variational AutoEncoder(VAE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQeUB75fWRJ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "22e47417-6bac-4bf2-c7c9-f45674a0eb4e"
      },
      "source": [
        "# train AE\n",
        "print(\"Start training VAE...\")\n",
        "\n",
        "model.train()\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    overall_loss = 0\n",
        "    for batch_idx, (x, _) in enumerate(train_loader):\n",
        "        x = x.view(batch_size, x_dim)\n",
        "        x = x.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        x_hat, mean, log_var = model(x)\n",
        "        loss = loss_function(x, x_hat, mean, log_var)\n",
        "        \n",
        "        overall_loss += loss.item()\n",
        "        \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "    print(\"\\tEpoch\", epoch + 1, \"complete!\", \"\\tAverage Loss: \", overall_loss / (batch_idx*batch_size))\n",
        "    \n",
        "print(\"Finish!!\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Start training VAE...\n",
            "\tEpoch 1 complete! \tAverage Loss:  121.67967263572508\n",
            "\tEpoch 2 complete! \tAverage Loss:  95.62829336132975\n",
            "\tEpoch 3 complete! \tAverage Loss:  91.84380072993771\n",
            "\tEpoch 4 complete! \tAverage Loss:  89.8773356744227\n",
            "\tEpoch 5 complete! \tAverage Loss:  88.69825079656223\n",
            "\tEpoch 6 complete! \tAverage Loss:  87.8496799253284\n",
            "\tEpoch 7 complete! \tAverage Loss:  87.2937277687461\n",
            "\tEpoch 8 complete! \tAverage Loss:  86.83682787821232\n",
            "\tEpoch 9 complete! \tAverage Loss:  86.44482322101099\n",
            "\tEpoch 10 complete! \tAverage Loss:  86.1606694380734\n",
            "Finish!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0etHKHHXQxB",
        "colab_type": "text"
      },
      "source": [
        "## Step 5. Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kg5vNnRuXSiR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R3D-EzqXTtx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b945e5ee-e82c-4472-9131-da652c8ca328"
      },
      "source": [
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch_idx, (x, _) in enumerate(tqdm(test_loader)):\n",
        "        x = x.view(batch_size, x_dim)\n",
        "        x = x.to(device)\n",
        "\n",
        "        x_hat, _, _ = model(x)\n",
        "\n",
        "        break"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 0/200 [00:00<?, ?it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7CCx2xCXTwJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_image(x, idx):\n",
        "    x = x.view(batch_size, 28, 28)\n",
        "\n",
        "    fig = plt.figure()\n",
        "    plt.imshow(x[idx].cpu().numpy())"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDbGMgXGXTyv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6ab6782f-7d32-43c9-9d91-553ab6d09c65"
      },
      "source": [
        "show_image(x, idx=2)"
      ],
      "execution_count": 25,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uK5z5gA5XT02",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "38c71539-d10e-4a83-e9f0-cc925dc8b24e"
      },
      "source": [
        "show_image(x_hat, idx=2)"
      ],
      "execution_count": 26,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzDBuE1PXUfa",
        "colab_type": "text"
      },
      "source": [
        "## Step 6. Generate image from noise vector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqtnBt8fXYVV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with torch.no_grad():\n",
        "    noise = torch.randn(batch_size, latent_dim).to(device)\n",
        "    generated_images = decoder(noise)\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q129DYe0iO2V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_image(generated_images.view(batch_size, 1, 28, 28), 'generated_sample.png')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIQrAxHdiVxC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2bc7080e-c2ee-4ba2-a9fd-a41c6684d6c0"
      },
      "source": [
        "show_image(generated_images, idx=1)"
      ],
      "execution_count": 29,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRbRu9lxiY35",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "13482e8e-eccf-4834-cb6c-d5c57f7adc11"
      },
      "source": [
        "show_image(generated_images, idx=2)"
      ],
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "426Qi8gvicwV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}