gan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gan.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "qae_ZzPlYXrd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "BdkG5WVeYcEH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Imports**"
      ]
    },
    {
      "metadata": {
        "id": "UbRGJ84wgTDb",
        "colab_type": "code",
        "outputId": "4929ab04-ffd9-45e0-8edb-38194248034c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "cell_type": "code",
      "source": [
        "%load_ext autoreload\n",
        "%matplotlib inline\n",
        "%autoreload 2\n",
        "%load utils\n",
        "\n",
        "!pip install TensorboardX\n",
        "from utils import Logger\n",
        "\n",
        "\n",
        "from IPython import display\n",
        "\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "import torch\n",
        "from torch.optim import Adam\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torch.nn as nn\n",
        "from torchvision import transforms, datasets\n",
        "import pandas as pd\n",
        "from skimage import io, transform\n",
        "import tensorflow as tf\n",
        "import logging\n",
        "from tensorflow import layers\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n",
            "Collecting TensorboardX\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/76/89dd44458eb976347e5a6e75eb79fecf8facd46c1ce259bad54e0044ea35/tensorboardX-1.6-py2.py3-none-any.whl (129kB)\n",
            "\u001b[K    100% |████████████████████████████████| 133kB 8.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from TensorboardX) (1.14.6)\n",
            "Requirement already satisfied: protobuf>=3.2.0 in /usr/local/lib/python3.6/dist-packages (from TensorboardX) (3.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from TensorboardX) (1.11.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.2.0->TensorboardX) (40.9.0)\n",
            "Installing collected packages: TensorboardX\n",
            "Successfully installed TensorboardX-1.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ForgqLgEYiC9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Upload dataset and unzip**"
      ]
    },
    {
      "metadata": {
        "id": "AsBQ-zGtKUeN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !unzip blues.zip \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zm5KfeJSqM9m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input_dim = 558 * 544\n",
        "output_dim = 1\n",
        "batch_size = 100\n",
        "DATA_FOLDER = 'blues'\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Yq6flYK2UK_h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset=datasets.ImageFolder(DATA_FOLDER)   # Set dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6XKEkY8tYtVu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Define model**"
      ]
    },
    {
      "metadata": {
        "id": "XfXlGWLXJseq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class model(nn.Module):\n",
        "\n",
        "  def __init__(self,input_dim, output_dim):\n",
        "    super(model, self).__init__()\n",
        "    self.linear = nn.Linear(input_dim, output_dim)\n",
        "    \n",
        "#   def length(self,x):\n",
        "#     return len(x)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    out = self.linear(x)\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZUONr5RBKYnA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "gan_model =model(input_dim, output_dim)\n",
        "batch_size = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zQvKnSl3YxDL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Make dataset iterable**"
      ]
    },
    {
      "metadata": {
        "id": "ra32ImPAJsSL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=10, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n3zYriduY5TQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Visualize dataset**"
      ]
    },
    {
      "metadata": {
        "id": "EYncq2i3TfQ-",
        "colab_type": "code",
        "outputId": "68dcbb8b-b670-45b6-abd3-adc2936a8de0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "# dataset[0][0].numpy().shape\n",
        "# show_img = dataset[0][0].numpy().reshape(28,28)\n",
        "plt.imshow(dataset[0][0], cmap = 'gray')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f63332daf60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WHdv+M08gixSZgU9Lz04CnfcMnUsOM8flOGFmA4WFgFT0w9TyaSkH+oPmFBAJ8EoRY5Qk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M0se2+dgpJ3mCFhXr9C/I1t7Augt1KGfrZP+FvrJE88LJ/6cJG0kZK9sAOVLOpbV4jfGaAn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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "c2hbnl-QTe_x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ewWVQ1ZzQn8k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**NETWORKS**"
      ]
    },
    {
      "metadata": {
        "id": "uaRPDWRVibwW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class DiscriminativeNet(torch.nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        super(DiscriminativeNet, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels=3, out_channels=128, kernel_size=4, \n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        self.conv2 = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels=128, out_channels=256, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        self.conv3 = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels=256, out_channels=512, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(512),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        self.conv4 = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels=512, out_channels=1024, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(1024),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        self.out = nn.Sequential(\n",
        "            nn.Linear(1024*4*4, 1),\n",
        "            nn.Sigmoid(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Convolutional layers\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.conv4(x)\n",
        "        # Flatten and apply sigmoid\n",
        "        x = x.view(-1, 1024*4*4)\n",
        "        x = self.out(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RWhljMDsX4Ej",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g7ud0LGVCIaP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class GenerativeNet(torch.nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        super(GenerativeNet, self).__init__()\n",
        "        \n",
        "        self.linear = torch.nn.Linear(100, 1024*4*4)\n",
        "        \n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(\n",
        "                in_channels=1024, out_channels=512, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(512),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "        self.conv2 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(\n",
        "                in_channels=512, out_channels=256, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "        self.conv3 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(\n",
        "                in_channels=256, out_channels=128, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            ),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "        self.conv4 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(\n",
        "                in_channels=128, out_channels=3, kernel_size=4,\n",
        "                stride=2, padding=1, bias=False\n",
        "            )\n",
        "        )\n",
        "        self.out = torch.nn.Tanh()\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Project and reshape\n",
        "        x = self.linear(x)\n",
        "        x = x.view(x.shape[0], 1024, 4, 4)\n",
        "        # Convolutional layers\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.conv4(x)\n",
        "        # Apply Tanh\n",
        "        return self.out(x)\n",
        "    \n",
        "# Noise\n",
        "def noise(size):\n",
        "    n = Variable(torch.randn(size, 100))\n",
        "    if torch.cuda.is_available(): return n.cuda()\n",
        "    return n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3xEzgkzoDOxC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def init_weights(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1 or classname.find('BatchNorm') != -1:\n",
        "        m.weight.data.normal_(0.00, 0.02)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xb5CZHMlDU5E",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create Network instances and init weights\n",
        "generator = GenerativeNet()\n",
        "generator.apply(init_weights)\n",
        "\n",
        "discriminator = DiscriminativeNet()\n",
        "discriminator.apply(init_weights)\n",
        "\n",
        "# Enable cuda if available\n",
        "if torch.cuda.is_available():\n",
        "    generator.cuda()\n",
        "    discriminator.cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8MHQZB4tQz3u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Optimization**"
      ]
    },
    {
      "metadata": {
        "id": "y-Ut2_hTQ11i",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimizers\n",
        "d_optimizer = Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
        "g_optimizer = Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
        "\n",
        "# Loss function\n",
        "loss = nn.BCELoss()\n",
        "\n",
        "# Number of epochs\n",
        "num_epochs = 200"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UiM8aB40Q4Wo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Training**"
      ]
    },
    {
      "metadata": {
        "id": "WvSKojH4DYXf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def real_data_target(size):\n",
        "    '''\n",
        "    Tensor containing ones, with shape = size\n",
        "    '''\n",
        "    data = Variable(torch.ones(size, 1))\n",
        "    if torch.cuda.is_available(): return data.cuda()\n",
        "    return data\n",
        "\n",
        "def fake_data_target(size):\n",
        "    '''\n",
        "    Tensor containing zeros, with shape = size\n",
        "    '''\n",
        "    data = Variable(torch.zeros(size, 1))\n",
        "    if torch.cuda.is_available(): return data.cuda()\n",
        "    return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NSyM6UiYDcEq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_discriminator(optimizer, real_data, fake_data):\n",
        "    # Reset gradients\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    # 1.1 Train on Real Data\n",
        "    prediction_real = discriminator(real_data)\n",
        "    # Calculate error and backpropagate\n",
        "    error_real = loss(prediction_real, real_data_target(real_data.size(0)))\n",
        "    error_real.backward()\n",
        "\n",
        "    # 1.2 Train on Fake Data\n",
        "    prediction_fake = discriminator(fake_data)\n",
        "    # Calculate error and backpropagate\n",
        "    error_fake = loss(prediction_fake, fake_data_target(real_data.size(0)))\n",
        "    error_fake.backward()\n",
        "    \n",
        "    # 1.3 Update weights with gradients\n",
        "    optimizer.step()\n",
        "    \n",
        "    # Return error\n",
        "    return error_real + error_fake, prediction_real, prediction_fake\n",
        "\n",
        "def train_generator(optimizer, fake_data):\n",
        "    # 2. Train Generator\n",
        "    # Reset gradients\n",
        "    optimizer.zero_grad()\n",
        "    # Sample noise and generate fake data\n",
        "    prediction = discriminator(fake_data)\n",
        "    # Calculate error and backpropagate\n",
        "    error = loss(prediction, real_data_target(prediction.size(0)))\n",
        "    error.backward()\n",
        "    # Update weights with gradients\n",
        "    optimizer.step()\n",
        "    # Return error\n",
        "    return error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X8BMp7WTRDPM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Generate Samples For Testing**"
      ]
    },
    {
      "metadata": {
        "id": "JnPPWlyKDcI6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_test_samples = 16\n",
        "test_noise = noise(num_test_samples)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "koQ6VP1JRJVw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##**Start training**"
      ]
    },
    {
      "metadata": {
        "id": "NXD2vs8RgliC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "614c2f21-c37b-4cbf-b53b-8f79d9137163"
      },
      "cell_type": "code",
      "source": [
        "logger = Logger(model_name='DCGAN', data_name='CIFAR10')\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for n_batch, (real_batch,_) in enumerate(data_loader):\n",
        "        \n",
        "        # 1. Train Discriminator\n",
        "        real_data = Variable(real_batch)\n",
        "        if torch.cuda.is_available(): real_data = real_data.cuda()\n",
        "        # Generate fake data\n",
        "        fake_data = generator(noise(real_data.size(0))).detach()\n",
        "        # Train D\n",
        "        d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, \n",
        "                                                                real_data, fake_data)\n",
        "\n",
        "        # 2. Train Generator\n",
        "        # Generate fake data\n",
        "        fake_data = generator(noise(real_batch.size(0)))\n",
        "        # Train G\n",
        "        g_error = train_generator(g_optimizer, fake_data)\n",
        "        # Log error\n",
        "        logger.log(d_error, g_error, epoch, n_batch, num_batches)\n",
        "        \n",
        "        # Display Progress\n",
        "        if (n_batch) % 100 == 0:\n",
        "            display.clear_output(True)\n",
        "            # Display Images\n",
        "            test_images = generator(test_noise).data.cpu()\n",
        "            logger.log_images(test_images, num_test_samples, epoch, n_batch, num_batches);\n",
        "            # Display status Logs\n",
        "            logger.display_status(\n",
        "                epoch, num_epochs, n_batch, num_batches,\n",
        "                d_error, g_error, d_pred_real, d_pred_fake\n",
        "            )\n",
        "        # Model Checkpoints\n",
        "        logger.save_models(generator, discriminator, epoch)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-0dcb06eda781>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mn_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreal_batch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;31m# 1. Train Discriminator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    613\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    616\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpin_memory_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: batch must contain tensors, numbers, dicts or lists; found <class 'PIL.Image.Image'>"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "7YKvLtzSDcMC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}