Pytorch入门实战-----ResNet识别CIFAR-10数据集
跑了一个epoch，正确率为：
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
#Image Preprocessing
transform = transforms.Compose([
transforms.Scale(40),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32),
transforms.ToTensor()
])
#CIFAR-10 Dataset
train_dataset = dsets.CIFAR10(
root='./data',
train=True,
transform = transform,
download=True
)
test_dataset = dsets.CIFAR10(
root='./data',
train=False,
transform = transform,
download=True
)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=50,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=50,
shuffle=False)
#3*3 Convolution
def con3x3(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 = con3x3(in_channels,out_channels,stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
#inplace=True计算结果不会有影响，利用inplace计算可以节省内存，
#同时还可以省去反复申请和释放内存的时间；但是会对原变量进行覆盖。
self.conv2 = con3x3(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:#计算残差
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
#ResNet Model
class ResNet(nn.Module):
def __init__(self,block,layers,num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 16
self.conv = con3x3(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(
con3x3(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, [2, 2, 2, 2])
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
lr = 0.001
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
# Training
for epoch in range(1):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images)
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = resnet(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1)%1 == 0:
print("Epoch [%d/%d], Iter [%d/%d] Loss: %.4f" % (epoch+1,1,i+1,len(train_loader),loss.item()))
#Decaying Learning Rate
if (epoch + 1) % 2 == 0:
lr /= 3
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
# Test
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images)
outputs = resnet(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))
# Save the Model
torch.save(resnet.state_dict(), 'resnet.pkl')