TB_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TB_project.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HariPrasathHari/Airline-reservation-django/blob/master/TB_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "Pr9gx95KzKY_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!wget http://openi.nlm.nih.gov/imgs/collections/ChinaSet_AllFiles.zip\n",
        "!unzip ChinaSet_AllFiles.zip\n",
        "!pip install Pillow==4.0.0\n",
        "!pip install PIL\n",
        "!pip install image\n",
        "# http://pytorch.org/\n",
        "from os.path import exists\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
        "accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision\n",
        "import torch\n",
        "import numpy as np\n",
        "import numpy\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data.sampler import SubsetRandomSampler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8CTi1Q9NDnek",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# number of subprocesses to use for data loading\n",
        "num_workers = 0\n",
        "# how many samples per batch to load\n",
        "batch_size = 20\n",
        "# percentage of training set to use as validation\n",
        "valid_size = 0.2\n",
        "n_epochs =30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kDVtZiiSDtaR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "import torch.nn\n",
        "from torch.nn import functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rYFEJ34xE87G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir tb \n",
        "!mkdir tb/normal\n",
        "!mkdir tb/positive\n",
        "!cp ChinaSet_AllFiles/CXR_png/CHNCXR*[1].png tb/positive/\n",
        "!cp ChinaSet_AllFiles/CXR_png/CHNCXR*[0].png tb/normal/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ELs0XhJXcWcN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!ls tb/normal/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3crPosIXGNBZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transform =  transforms.Compose([\n",
        "    transforms.Resize(255),\n",
        "    transforms.CenterCrop(224), \n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])\n",
        "]\n",
        ")\n",
        "dataset =  datasets.ImageFolder(\"tb\",transform)\n",
        "dataloader = torch.utils.data.DataLoader(dataset,batch_size = 20 ,shuffle = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lTOUL1Lnbtaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "d72b9d04-db45-4c82-a1e6-4a4388e8eca3"
      },
      "cell_type": "code",
      "source": [
        "print(dataset)\n",
        "print(len(dataloader))\n",
        "print(len(train_loader))\n",
        "print(len(valid_loader))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset ImageFolder\n",
            "    Number of datapoints: 662\n",
            "    Root Location: tb\n",
            "    Transforms (if any): Compose(\n",
            "                             Resize(size=255, interpolation=PIL.Image.BILINEAR)\n",
            "                             CenterCrop(size=(224, 224))\n",
            "                             ToTensor()\n",
            "                             Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
            "                         )\n",
            "    Target Transforms (if any): None\n",
            "34\n",
            "2\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bWy_sBOGgFon",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# workaround \n",
        "from PIL import Image\n",
        "def register_extension(id, extension): Image.EXTENSION[extension.lower()] = id.upper()\n",
        "Image.register_extension = register_extension\n",
        "def register_extensions(id, extensions): \n",
        "  for extension in extensions: register_extension(id, extension)\n",
        "Image.register_extensions = register_extensions"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p6iVTU2YgpjK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "dcc3d48d-c597-46af-f82d-d3bb1d239ba4"
      },
      "cell_type": "code",
      "source": [
        "!pip install Pillow==4.1.1"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: Pillow==4.1.1 in /usr/local/lib/python3.6/dist-packages (4.1.1)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow==4.1.1) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qdlHo8-gHmcT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_iter = iter(dataloader)\n",
        "images, labels = next(data_iter)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rdRerNOJHuDI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def helper_imshow(image, ax=None, title=None, normalize=True):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "    image = image.numpy().transpose((1, 2, 0))\n",
        "\n",
        "    if normalize:\n",
        "        mean = np.array([0.5, 0.5, 0.5])\n",
        "        std = np.array([0.5, 0.5, 0.5])\n",
        "        image = std * image + mean\n",
        "        image = np.clip(image, 0, 1)\n",
        "    ax.imshow(image)\n",
        "    ax.spines['top'].set_visible(False)\n",
        "    ax.spines['right'].set_visible(False)\n",
        "    ax.spines['left'].set_visible(False)\n",
        "    ax.spines['bottom'].set_visible(False)\n",
        "    ax.tick_params(axis='both', length=0)\n",
        "    ax.set_xticklabels('')\n",
        "    ax.set_yticklabels('')\n",
        "    return ax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ORV2eR6CIyRr",
        "colab_type": "code",
        "outputId": "dfae3f2a-a0cc-46eb-bbd0-716a5ee21378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "helper_imshow(images[0],normalize = True)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9ff1a3080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A7+K+GQueY0fVckns69nZ2SSP0+U5k/1xPumVVj/hmFgs2MCSd7Fgefw+i1yObfNakAVk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\nRqPRaGzifwHYK2KA71vfsAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa9ff1c50f0>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 231,
              "height": 231
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nSfW6vvhMBzu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_train = len(dataloader)\n",
        "num_train = 662\n",
        "indices = list(range(num_train))\n",
        "np.random.shuffle(indices)\n",
        "split = int(np.floor(valid_size * num_train))\n",
        "train_idx, valid_idx = indices[split:], indices[:split]\n",
        "\n",
        "# define samplers for obtaining training and validation batches\n",
        "train_sampler = SubsetRandomSampler(train_idx)\n",
        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
        "\n",
        "# prepare data loaders (combine dataset and sampler)\n",
        "train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "    sampler=train_sampler, num_workers=num_workers)\n",
        "valid_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, \n",
        "    sampler=valid_sampler, num_workers=num_workers)\n",
        "\n",
        "classes = ['postive','normal']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "v7S-uPhgbF6_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "783b92d1-1ddd-4ccc-9db2-2f93eff6f27b"
      },
      "cell_type": "code",
      "source": [
        "print(num_train)\n"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Sdz9uv2uRzxC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# helper function to un-normalize and display an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5  # unnormalize\n",
        "    plt.imshow(np.transpose(img, (1, 2, 0)))  # convert from Tensor image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZJzWwrtDe35y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# obtain one batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy() # convert images to numpy for display\n",
        "\n",
        "# plot the images in the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "# display 20 images\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    imshow(images[idx])\n",
        "    ax.set_title(classes[labels[idx]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WjVtc1AEfAsE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "317cd78d-d52d-4d42-8782-75680b728b0d"
      },
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# define the CNN architecture\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        # convolutional layer (sees 32x32x3 image tensor)\n",
        "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
        "        # convolutional layer (sees 16x16x16 tensor)\n",
        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
        "        # convolutional layer (sees 8x8x32 tensor)\n",
        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "        # max pooling layer\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        # linear layer (64 * 4 * 4 -> 500)\n",
        "        self.fc1 = nn.Linear(50176, 1024)\n",
        "        # linear layer (500 -> 10)\n",
        "        self.fc2 = nn.Linear(1024, 2)\n",
        "        # dropout layer (p=0.25)\n",
        "        self.dropout = nn.Dropout(0.25)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # add sequence of convolutional and max pooling layers\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = self.pool(F.relu(self.conv3(x)))\n",
        "        # flatten image input\n",
        "        x = x.view(x.size(0), -1)\n",
        "        # add dropout layer\n",
        "        x = self.dropout(x)\n",
        "        # add 1st hidden layer, with relu activation function\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # add dropout layer\n",
        "        x = self.dropout(x)\n",
        "        # add 2nd hidden layer, with relu activation function\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# create a complete CNN\n",
        "model = Net()\n",
        "print(model)\n",
        "\n",
        "# move tensors to GPU if CUDA is available\n",
        "if train_on_gpu:\n",
        "    model.cuda()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc1): Linear(in_features=50176, out_features=1024, bias=True)\n",
            "  (fc2): Linear(in_features=1024, out_features=2, bias=True)\n",
            "  (dropout): Dropout(p=0.25)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RQ6lfrPljF2Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "# specify loss function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# specify optimizer\n",
        "optimizer = optim.Adam(model.parameters(),lr=0.002)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KYiH6I6ejHzn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "outputId": "549ddb4e-8db7-49ae-a0dd-ed612d53c47e"
      },
      "cell_type": "code",
      "source": [
        "n_epochs = 30 # you may increase this number to train a final model\n",
        "\n",
        "valid_loss_min = np.Inf \n",
        "train_on_gpu = True\n",
        "for epoch in range(1, n_epochs+1):\n",
        "\n",
        "    train_loss = 0.0\n",
        "    valid_loss = 0.0\n",
        "    \n",
        "    ###################\n",
        "    # train the model #\n",
        "    ###################\n",
        "    model.train()\n",
        "    i =0 \n",
        "    for data, target in train_loader:\n",
        "        print(i)\n",
        "        i = i+1\n",
        "        # move tensors to GPU if CUDA is available\n",
        "        if train_on_gpu:\n",
        "            data, target = data.cuda(), target.cuda()\n",
        "        # clear the gradients of all optimized variables\n",
        "        optimizer.zero_grad()\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        output = model(data)\n",
        "        # calculate the batch loss\n",
        "        loss = criterion(output, target)\n",
        "        # backward pass: compute gradient of the loss with respect to model parameters\n",
        "        loss.backward()\n",
        "        # perform a single optimization step (parameter update)\n",
        "        optimizer.step()\n",
        "        # update training loss\n",
        "        train_loss += loss.item()*data.size(0)\n",
        "        \n",
        "    ######################    \n",
        "    # validate the model #\n",
        "    ######################\n",
        "    model.eval()\n",
        "    for data, target in valid_loader:\n",
        "        # move tensors to GPU if CUDA is available\n",
        "        if train_on_gpu:\n",
        "            data, target = data.cuda(), target.cuda()\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        output = model(data)\n",
        "        # calculate the batch loss\n",
        "        loss = criterion(output, target)\n",
        "        # update average validation loss \n",
        "        valid_loss += loss.item()*data.size(0)\n",
        "    \n",
        "    # calculate average losses\n",
        "    train_loss = train_loss/len(train_loader.dataset)\n",
        "    valid_loss = valid_loss/len(valid_loader.dataset)\n",
        "        \n",
        "    # print training/validation statistics \n",
        "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
        "        epoch, train_loss, valid_loss))\n",
        "    \n",
        "    # save model if validation loss has decreased\n",
        "    if valid_loss <= valid_loss_min:\n",
        "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "        valid_loss_min,valid_loss))\n",
        "        torch.save(model.state_dict(), 'model_tb.pt')\n",
        "        valid_loss_min = valid_loss"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n",
            "23\n",
            "24\n",
            "25\n",
            "26\n",
            "Epoch: 1 \tTraining Loss: 0.410202 \tValidation Loss: 0.105834\n",
            "Validation loss decreased (inf --> 0.105834).  Saving model ...\n",
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Norhcte3jP6V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# track test loss\n",
        "test_loss = 0.0\n",
        "class_correct = list(0. for i in range(2))\n",
        "class_total = list(0. for i in range(2))\n",
        "\n",
        "model.eval()\n",
        "# iterate over test data\n",
        "for data, target in valid_loader:\n",
        "    # move tensors to GPU if CUDA is available\n",
        "    if train_on_gpu:\n",
        "        data, target = data.cuda(), target.cuda()\n",
        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
        "    output = model(data)\n",
        "    # calculate the batch loss\n",
        "    loss = criterion(output, target)\n",
        "    # update test loss \n",
        "    test_loss += loss.item()*data.size(0)\n",
        "    # convert output probabilities to predicted class\n",
        "    _, pred = torch.max(output, 1)    \n",
        "    # compare predictions to true label\n",
        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
        "    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n",
        "    # calculate test accuracy for each object class\n",
        "    for i in range(data.size(0)):\n",
        "        label = target.data[i]\n",
        "        class_correct[label] += correct[i].item()\n",
        "        class_total[label] += 1\n",
        "\n",
        "# average test loss\n",
        "test_loss = test_loss/len(valid_loader.dataset)\n",
        "print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
        "\n",
        "for i in range(2):\n",
        "    if class_total[i] > 0:\n",
        "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n",
        "            classes[i], 100 * class_correct[i] / class_total[i],\n",
        "            np.sum(class_correct[i]), np.sum(class_total[i])))\n",
        "    else:\n",
        "        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
        "\n",
        "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n",
        "    100. * np.sum(class_correct) / np.sum(class_total),\n",
        "    np.sum(class_correct), np.sum(class_total)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GRFLZg8CeSKj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}