upload cifar10

Author: L1aoXingyu

chapter4_CNN/cifar10/main.py

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+import torch
+import torchvision.datasets as dsets
+import torchvision.transforms as transforms
+from torch import nn, optim
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+
+# Image Preprocessing
+train_transform = transforms.Compose([
+    transforms.Scale(40),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(32),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+
+test_transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+# CIFAR-10 Dataset
+train_dataset = dsets.CIFAR10(
+    root='./data', train=True, transform=train_transform, download=True)
+
+test_dataset = dsets.CIFAR10(
+    root='./data', train=False, transform=test_transform)
+
+# Data Loader (Input Pipeline)
+train_loader = DataLoader(dataset=train_dataset, batch_size=128, shuffle=True)
+
+test_loader = DataLoader(dataset=test_dataset, batch_size=128, shuffle=False)
+
+
+# 3x3 Convolution
+def conv3x3(in_channels, out_channels, stride=1):
+    return nn.Conv2d(
+        in_channels,
+        out_channels,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False)
+
+
+# Residual Block
+class ResidualBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = conv3x3(in_channels, out_channels, stride)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(out_channels, out_channels)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.downsample = downsample
+
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.downsample:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+
+
+# ResNet Module
+class ResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_channels = 16
+        self.conv = conv3x3(3, 16)
+        self.bn = nn.BatchNorm2d(16)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self.make_layer(block, 16, layers[0])
+        self.layer2 = self.make_layer(block, 32, layers[0], 2)
+        self.layer3 = self.make_layer(block, 64, layers[1], 2)
+        self.avg_pool = nn.AvgPool2d(8)
+        self.fc = nn.Linear(64, num_classes)
+
+    def make_layer(self, block, out_channels, blocks, stride=1):
+        downsample = None
+        if (stride != 1) or (self.in_channels != out_channels):
+            downsample = nn.Sequential(
+                conv3x3(self.in_channels, out_channels, stride=stride),
+                nn.BatchNorm2d(out_channels))
+        layers = []
+        layers.append(
+            block(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels
+        for i in range(1, blocks):
+            layers.append(block(out_channels, out_channels))
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.bn(out)
+        out = self.relu(out)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.avg_pool(out)
+        out = out.view(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+
+resnet = ResNet(ResidualBlock, [3, 3, 3])
+resnet.cuda()
+
+# Loss and Optimizer
+criterion = nn.CrossEntropyLoss()
+lr = 0.001
+optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
+
+# Training
+total_epoch = 50
+for epoch in range(total_epoch):
+    running_loss = 0
+    running_acc = 0
+    running_num = 0
+    for i, (images, labels) in enumerate(train_loader):
+        if torch.cuda.is_available():
+            images = Variable(images.cuda())
+            labels = Variable(labels.cuda())
+        else:
+            images = Variable(images)
+            labels = Variable(labels)
+        # Forward + Backward + Optimize
+        optimizer.zero_grad()
+        outputs = resnet(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        # =====================log=====================
+        running_num += labels.size(0)
+        running_loss += loss.data[0] * labels.size(0)
+        _, correct_label = torch.max(outputs, 1)
+        correct_num = (correct_label == labels).sum()
+        running_acc += correct_num.data[0]
+        if (i + 1) % 100 == 0:
+            print_loss = running_loss / running_num
+            print_acc = running_acc / running_num
+            print("Epoch [{}/{}], Iter [{}/{}] Loss: {:.6f} Acc: {:.6f}".
+                  format(epoch + 1, total_epoch, i + 1,
+                         len(train_loader), print_loss, print_acc))
+
+    # Decaying Learning Rate
+    if (epoch + 1) % 20 == 0:
+        lr /= 3
+        optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
+
+# Test
+correct = 0
+total = 0
+for images, labels in test_loader:
+    if torch.cuda.is_available:
+        images = Variable(images.cuda())
+    else:
+        images = Variable(images)
+    outputs = resnet(images)
+    _, predicted = torch.max(outputs.data, 1)
+    total += labels.size(0)
+    correct += (predicted.cpu() == labels).sum()
+
+print('Accuracy of the model on the test images: {:.2f} %%'.format(
+    100 * correct / total))
+
+# Save the Model
+torch.save(resnet.state_dict(), 'resnet.pth')