lab-10_5_2_Aadvance-CNN(VGG_cifar10).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10-5 VGG for cifar10\n",
    "\n",
    "## original code is =>\n",
    "https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import visdom\n",
    "\n",
    "vis = visdom.Visdom()\n",
    "vis.close(env=\"main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define loss tracker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_tracker(loss_plot, loss_value, num):\n",
    "    '''num, loss_value, are Tensor'''\n",
    "    vis.line(X=num,\n",
    "             Y=loss_value,\n",
    "             win = loss_plot,\n",
    "             update='append'\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "torch.manual_seed(777)\n",
    "if device =='cuda':\n",
    "    torch.cuda.manual_seed_all(777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./cifar10', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=512,\n",
    "                                          shuffle=True, num_workers=0)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./cifar10', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=0)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "vis.images(images/2 + 0.5)\n",
    "\n",
    "# show images\n",
    "#imshow(torchvision.utils.make_grid(images))\n",
    "\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make VGG16 using vgg.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vgg\n",
    "#import torchvision.models.vgg as vgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = [32,32,'M', 64,64,128,128,128,'M',256,256,256,512,512,512,'M'] #13 + 3 =vgg16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGG(nn.Module):\n",
    "\n",
    "    def __init__(self, features, num_classes=1000, init_weights=True):\n",
    "        super(VGG, self).__init__()\n",
    "        self.features = features\n",
    "        #self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(512 * 4 * 4, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(4096, num_classes),\n",
    "        )\n",
    "        if init_weights:\n",
    "            self._initialize_weights()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        #x = self.avgpool(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                nn.init.constant_(m.weight, 1)\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                nn.init.normal_(m.weight, 0, 0.01)\n",
    "                nn.init.constant_(m.bias, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16= VGG(vgg.make_layers(cfg),10,True).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=torch.Tensor(1,3,32,32).to(device)\n",
    "out = vgg16(a)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.SGD(vgg16.parameters(), lr = 0.005,momentum=0.9)\n",
    "\n",
    "lr_sche = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_plt = vis.line(Y=torch.Tensor(1).zero_(),opts=dict(title='loss_tracker', legend=['loss'], showlegend=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(len(trainloader))\n",
    "epochs = 50\n",
    "\n",
    "for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "    running_loss = 0.0\n",
    "    lr_sche.step()\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs\n",
    "        inputs, labels = data\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = vgg16(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 30 == 29:    # print every 30 mini-batches\n",
    "            loss_tracker(loss_plt, torch.Tensor([running_loss/30]), torch.Tensor([i + epoch*len(trainloader) ]))\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 30))\n",
    "            running_loss = 0.0\n",
    "        \n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = vgg16(images.to(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, predicted = torch.max(outputs, 1)\n",
    "\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = vgg16(images)\n",
    "        \n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        \n",
    "        total += labels.size(0)\n",
    "        \n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  }
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