3a_ALL-IDB classification using pre-trained networks-Part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ALL-IDB classification using AlexNet\n",
    "### Dataset used:- [ALL-IDB:Acute Lymphoblastic Leukemia Image Database for Image Processing](https://homes.di.unimi.it/scotti/all/)\n",
    "Follow the instructions provided in the linked website to download the dataset. After downloading, extract the files to the current directory (same folder as your codes). Note that ALL_IDB2 is used in this  tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import struct\n",
    "import torch\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import TensorDataset,DataLoader\n",
    "from torchvision import models\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import copy\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Datapath = 'ALL_IDB2/img/'\n",
    "listing = os.listdir(Datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ALL_IDB2 dataset has 260 images in total\n",
    "TrainImages = torch.FloatTensor(200,3,224,224)\n",
    "TrainLabels = torch.LongTensor(200)\n",
    "TestImages = torch.FloatTensor(60,3,224,224)\n",
    "TestLabels = torch.LongTensor(60)\n",
    "\n",
    "# First 200 images are used for training and the remaining 60 for testing\n",
    "img_no = 0\n",
    "for file in listing:\n",
    "    im=Image.open(Datapath + file)\n",
    "    im = im.resize((224,224))\n",
    "    im = np.array(im)   \n",
    "    if img_no < 200:\n",
    "        TrainImages[img_no] = torch.from_numpy(im).transpose(0,2).unsqueeze(0)\n",
    "        TrainLabels[img_no] = int(listing[img_no][6:7])\n",
    "    else:\n",
    "        TestImages[img_no - 200] = torch.from_numpy(im).transpose(0,2).unsqueeze(0)\n",
    "        TestLabels[img_no - 200] = int(listing[img_no][6:7])\n",
    "    img_no = img_no + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([200, 3, 224, 224])\n",
      "torch.Size([200])\n",
      "torch.Size([60, 3, 224, 224])\n",
      "torch.Size([60])\n"
     ]
    }
   ],
   "source": [
    "print(TrainImages.size())\n",
    "print(TrainLabels.size())\n",
    "print(TestImages.size())\n",
    "print(TestLabels.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating pytorch dataset\n",
    "trainDataset = TensorDataset(TrainImages, TrainLabels)\n",
    "testDataset = TensorDataset(TestImages, TestLabels)\n",
    "# Creating dataloader\n",
    "BatchSize = 32\n",
    "trainLoader = DataLoader(trainDataset, batch_size=BatchSize, shuffle=True,num_workers=4, pin_memory=True)\n",
    "testLoader = DataLoader(testDataset, batch_size=BatchSize, shuffle=True,num_workers=4, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU is available!\n"
     ]
    }
   ],
   "source": [
    "# Check availability of GPU\n",
    "use_gpu = torch.cuda.is_available()\n",
    "if use_gpu:\n",
    "    print('GPU is available!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet (\n",
      "  (features): Sequential (\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU (inplace)\n",
      "    (2): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1))\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU (inplace)\n",
      "    (5): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1))\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU (inplace)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU (inplace)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU (inplace)\n",
      "    (12): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1))\n",
      "  )\n",
      "  (classifier): Sequential (\n",
      "    (0): Dropout (p = 0.5)\n",
      "    (1): Linear (9216 -> 4096)\n",
      "    (2): ReLU (inplace)\n",
      "    (3): Dropout (p = 0.5)\n",
      "    (4): Linear (4096 -> 4096)\n",
      "    (5): ReLU (inplace)\n",
      "    (fc): Linear (4096 -> 2)\n",
      "    (softmax): LogSoftmax ()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# AlexNet\n",
    "net = models.alexnet(pretrained=True)\n",
    "new_classifier = nn.Sequential(*list(net.classifier.children())[:-1])\n",
    "new_classifier.add_module('fc',nn.Linear(4096,2))\n",
    "new_classifier.add_module('softmax',nn.LogSoftmax())\n",
    "net.classifier = new_classifier\n",
    "print(net)\n",
    "if use_gpu:\n",
    "    net = net.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define loss function and optimizer: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss() # Negative Log-Likelihood\n",
    "optimizer = optim.SGD(net.parameters(), lr=1e-4, momentum=0.9) # Stochastic gradient descent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At Iteration: 1 /20  ;  Training Loss: 0.026363 ; Testing Acc: 50.000 ; Time consumed: 0m 1s \n",
      "At Iteration: 2 /20  ;  Training Loss: 0.024245 ; Testing Acc: 65.000 ; Time consumed: 0m 0s \n",
      "At Iteration: 3 /20  ;  Training Loss: 0.020147 ; Testing Acc: 80.000 ; Time consumed: 0m 0s \n",
      "At Iteration: 4 /20  ;  Training Loss: 0.018053 ; Testing Acc: 91.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 5 /20  ;  Training Loss: 0.014854 ; Testing Acc: 73.333 ; Time consumed: 0m 0s \n",
      "At Iteration: 6 /20  ;  Training Loss: 0.012897 ; Testing Acc: 91.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 7 /20  ;  Training Loss: 0.011462 ; Testing Acc: 80.000 ; Time consumed: 0m 0s \n",
      "At Iteration: 8 /20  ;  Training Loss: 0.011243 ; Testing Acc: 93.333 ; Time consumed: 0m 0s \n",
      "At Iteration: 9 /20  ;  Training Loss: 0.009161 ; Testing Acc: 91.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 10 /20  ;  Training Loss: 0.010263 ; Testing Acc: 88.333 ; Time consumed: 0m 0s \n",
      "At Iteration: 11 /20  ;  Training Loss: 0.010276 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 12 /20  ;  Training Loss: 0.008153 ; Testing Acc: 90.000 ; Time consumed: 0m 0s \n",
      "At Iteration: 13 /20  ;  Training Loss: 0.006886 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 14 /20  ;  Training Loss: 0.006116 ; Testing Acc: 91.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 15 /20  ;  Training Loss: 0.005336 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 16 /20  ;  Training Loss: 0.005496 ; Testing Acc: 95.000 ; Time consumed: 0m 0s \n",
      "At Iteration: 17 /20  ;  Training Loss: 0.005559 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 18 /20  ;  Training Loss: 0.004889 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 19 /20  ;  Training Loss: 0.004555 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "At Iteration: 20 /20  ;  Training Loss: 0.004617 ; Testing Acc: 96.667 ; Time consumed: 0m 0s \n",
      "Training completed in 0m 10s\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEPCAYAAACHuClZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f809820acd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8134e15290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iterations = 20\n",
    "trainLoss = []\n",
    "testAcc = []\n",
    "\n",
    "start = time.time()\n",
    "for epoch in range(iterations):\n",
    "    epochStart = time.time()\n",
    "    runningLoss = 0    \n",
    "    net.train(True) # For training\n",
    "    for data in trainLoader:\n",
    "        inputs,labels = data\n",
    "        # Wrap them in Variable\n",
    "        if use_gpu:\n",
    "            inputs, labels = Variable(inputs.float().cuda()), \\\n",
    "                Variable(labels.long().cuda())\n",
    "        else:\n",
    "            inputs, labels = Variable(inputs), Variable(labelslong()) \n",
    "        inputs = inputs/255.0\n",
    "        # Initialize gradients to zero\n",
    "        optimizer.zero_grad()\n",
    "        # Feed-forward input data through the network\n",
    "        outputs = net(inputs)\n",
    "        # Compute loss/error\n",
    "        loss = criterion(outputs, labels)        \n",
    "        # Backpropagate loss and compute gradients\n",
    "        loss.backward()\n",
    "        # Update the network parameters\n",
    "        optimizer.step()\n",
    "        # Accumulate loss per batch\n",
    "        runningLoss += loss.data[0]    \n",
    "    avgTrainLoss = runningLoss/200\n",
    "    trainLoss.append(avgTrainLoss)\n",
    "    # Evaluating performance on test set for each epoch\n",
    "    net.train(False) # For testing\n",
    "    inputs = TestImages/255\n",
    "    if use_gpu:\n",
    "        inputs = Variable(inputs.cuda())\n",
    "        outputs = net(inputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        predicted = predicted.cpu()\n",
    "    else:\n",
    "        inputs = Variable(inputs)\n",
    "        outputs = net(inputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    total += TestLabels.size(0)\n",
    "    correct += (predicted == TestLabels).sum()\n",
    "    avgTestAcc = correct/60.0\n",
    "    testAcc.append(avgTestAcc)\n",
    "        \n",
    "    # Plotting Loss vs Epochs\n",
    "    fig1 = plt.figure(1)        \n",
    "    plt.plot(range(epoch+1),trainLoss,'r--',label='train')        \n",
    "    if epoch==0:\n",
    "        plt.legend(loc='upper left')\n",
    "        plt.xlabel('Epochs')\n",
    "        plt.ylabel('Loss')    \n",
    "    # Plotting testing accuracy vs Epochs\n",
    "    fig2 = plt.figure(2)        \n",
    "    plt.plot(range(epoch+1),testAcc,'g-',label='test')        \n",
    "    if epoch==0:\n",
    "        plt.legend(loc='upper left')\n",
    "        plt.xlabel('Epochs')\n",
    "        plt.ylabel('Testing accuracy')    \n",
    "    epochEnd = time.time()-epochStart\n",
    "    print('At Iteration: {:.0f} /{:.0f}  ;  Training Loss: {:.6f} ; Testing Acc: {:.3f} ; Time consumed: {:.0f}m {:.0f}s '\\\n",
    "          .format(epoch + 1,iterations,avgTrainLoss,avgTestAcc*100,epochEnd//60,epochEnd%60))\n",
    "end = time.time()-start\n",
    "print('Training completed in {:.0f}m {:.0f}s'.format(end//60,end%60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}