06_image_classification_cnn_resnet_optional.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyTorch Tutorial\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying MNIST & CIFAR-10 with Convnets & ResNets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.init as init\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import torchvision.transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define image transformations &  Initialize datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "mnist_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n",
    "mnist_train = torchvision.datasets.MNIST(root='./data', train=True, transform=mnist_transforms, download=True)\n",
    "mnist_test = torchvision.datasets.MNIST(root='./data', train=False, transform=mnist_transforms, download=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create multi-threaded DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader = torch.utils.data.DataLoader(mnist_train, batch_size=64, shuffle=True, num_workers=2)\n",
    "testloader = torch.utils.data.DataLoader(mnist_test, batch_size=64, shuffle=True, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main classifier that subclasses nn.Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    \"\"\"Convnet Classifier\"\"\"\n",
    "    def __init__(self):\n",
    "        super(Classifier, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            # Layer 1\n",
    "            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(3, 3), padding=1),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2, 2), stride=2),\n",
    "            \n",
    "            # Layer 2\n",
    "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), padding=1),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2, 2), stride=2),\n",
    "            \n",
    "            # Layer 3\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), padding=1),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2, 2), stride=2),\n",
    "            \n",
    "            # Layer 4\n",
    "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=1),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2, 2), stride=2)\n",
    "        )\n",
    "        # Logistic Regression\n",
    "        self.clf = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.clf(self.conv(x).squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cuda_available = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = Classifier()\n",
    "if cuda_available:\n",
    "    clf = clf.cuda()\n",
    "optimizer = torch.optim.Adam(clf.parameters(), lr=1e-4)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abursuc/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 Loss : 1.202 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abursuc/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:26: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 Test Acc : 90.000\n",
      "--------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-5:\n",
      "Process Process-6:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 52, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 52, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 343, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 343, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"/Users/abursuc/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/Users/abursuc/anaconda3/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m     91\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         \"\"\"\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/abursuc/anaconda3/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     87\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     88\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(2):\n",
    "    losses = []\n",
    "    # Train\n",
    "    for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
    "        if cuda_available:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        inputs, targets = Variable(inputs), Variable(targets)\n",
    "        outputs = clf(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.data[0])\n",
    "\n",
    "    print('Epoch : %d Loss : %.3f ' % (epoch, np.mean(losses)))\n",
    "    \n",
    "    # Evaluate\n",
    "    clf.eval()\n",
    "    total = 0\n",
    "    correct = 0\n",
    "    for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "        if cuda_available:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "\n",
    "        inputs, targets = Variable(inputs, volatile=True), Variable(targets, volatile=True)\n",
    "        outputs = clf(inputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += targets.size(0)\n",
    "        correct += predicted.eq(targets.data).cpu().sum()\n",
    "\n",
    "    print('Epoch : %d Test Acc : %.3f' % (epoch, 100.*correct/total))\n",
    "    print('--------------------------------------------------------------')\n",
    "    clf.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "cifar_train_transform = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.RandomCrop(32, padding=4),\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "cifar_test_transform = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=cifar_train_transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=cifar_test_transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a single Residual Block\n",
    "\n",
    "Adapted from https://github.com/kuangliu/pytorch-cifar and https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, in_channels, out_channels, stride=1):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        \n",
    "        # Conv Layer 1\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channels=in_channels, out_channels=out_channels,\n",
    "            kernel_size=(3, 3), stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        \n",
    "        # Conv Layer 2\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            in_channels=out_channels, out_channels=out_channels,\n",
    "            kernel_size=(3, 3), stride=1, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "    \n",
    "        # Shortcut connection to downsample residual\n",
    "        self.shortcut = nn.Sequential()\n",
    "        if stride != 1 or in_channels != out_channels:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    in_channels=in_channels, out_channels=out_channels,\n",
    "                    kernel_size=(1, 1), stride=stride, bias=False\n",
    "                ),\n",
    "                nn.BatchNorm2d(out_channels)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        out += self.shortcut(x)\n",
    "        out = F.relu(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CIFARResNet18(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(CIFARResNet18, self).__init__()\n",
    "        \n",
    "        # Initial input conv\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channels=3, out_channels=64, kernel_size=(3, 3),\n",
    "            stride=1, padding=1, bias=False\n",
    "        )\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        \n",
    "        # Create stages 1-4\n",
    "        self.stage1 = self._create_stage(64, 64, stride=1)\n",
    "        self.stage2 = self._create_stage(64, 128, stride=2)\n",
    "        self.stage3 = self._create_stage(128, 256, stride=2)\n",
    "        self.stage4 = self._create_stage(256, 512, stride=2)\n",
    "        self.linear = nn.Linear(512, num_classes)\n",
    "    \n",
    "    # A stage is just two residual blocks for ResNet18\n",
    "    def _create_stage(self, in_channels, out_channels, stride):\n",
    "        return nn.Sequential(\n",
    "            ResidualBlock(in_channels, out_channels, stride),\n",
    "            ResidualBlock(out_channels, out_channels, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.stage1(out)\n",
    "        out = self.stage2(out)\n",
    "        out = self.stage3(out)\n",
    "        out = self.stage4(out)\n",
    "        out = F.avg_pool2d(out, 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = CIFARResNet18()\n",
    "if cuda_available:\n",
    "    clf = clf.cuda()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(clf.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[150, 200], gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 Loss : 1.631 Time : 35.428 seconds \n",
      "Epoch : 0 Test Acc : 46.180\n",
      "--------------------------------------------------------------\n",
      "Epoch : 1 Loss : 1.120 Time : 36.086 seconds \n",
      "Epoch : 1 Test Acc : 57.270\n",
      "--------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-40:\n",
      "KeyboardInterrupt\n",
      "Process Process-39:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-410d974e8f8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\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[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "    self.run()\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 35, in _worker_loop\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/site-packages/torch/utils/data/dataloader.py\", line 35, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "    r = index_queue.get()\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 376, in get\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/multiprocessing/queues.py\", line 378, in get\n",
      "    racquire()\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/sandeep/anaconda2/lib/python2.7/site-packages/torch/multiprocessing/queue.py\", line 21, in recv\n",
      "    buf = self.recv_bytes()\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1):\n",
    "    losses = []\n",
    "    scheduler.step()\n",
    "    # Train\n",
    "    start = time.time()\n",
    "    for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
    "        if cuda_available:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        inputs, targets = Variable(inputs), Variable(targets)\n",
    "        outputs = clf(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.data[0])\n",
    "    end = time.time()\n",
    "\n",
    "    print('Epoch : %d Loss : %.3f Time : %.3f seconds ' % (epoch, np.mean(losses), end - start))\n",
    "    # Evaluate\n",
    "    clf.eval()\n",
    "    total = 0\n",
    "    correct = 0\n",
    "    for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "        if cuda_available:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "\n",
    "        inputs, targets = Variable(inputs, volatile=True), Variable(targets, volatile=True)\n",
    "        outputs = clf(inputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += targets.size(0)\n",
    "        correct += predicted.eq(targets.data).cpu().sum()\n",
    "\n",
    "    print('Epoch : %d Test Acc : %.3f' % (epoch, 100.*correct/total))\n",
    "    print('--------------------------------------------------------------')\n",
    "    clf.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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