import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
num_epochs=3
batch_size=20
learning_rate=0.001
transform=transforms.Compose([transforms.Pad(4),transforms.RandomHorizontalFlip(),transforms.RandomCrop(32),transforms.ToTensor()])
train_dataset=torchvision.datasets.CIFAR10(root='../../data/',train=True,transform=transform,download=True)
test_dataset=torchvision.datasets.CIFAR10(root='../../data/',train=False,transform=transforms.ToTensor())
train_loader=torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
test_loader=torch.utils.data.DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=False)
def conv3x3(in_channels,out_channels,stride=1):
return nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=stride,padding=1,bias=False)
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:#残差线是否需要下采样
residual=self.downsample(x)
out+=residual
out=self.relu(out)
return out
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[1],2)
self.layer3=self.make_layer(block,64,layers[2],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
model=ResNet(ResidualBlock,[2,2,2]).to(device)
criterion=nn.CrossEntropyLoss()
optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate)
def update_lr(optimizer,lr):
for param_group in optimizer.param_groups:
param_grop['lr']=lr
total_step=len(train_loader)
curr_lr = learning_rate
for epoch in range(num_epochs):
for i,(images,labels) in enumerate(train_loader):
images=images.to(device)
labels=labels.to(device)
outputs=model(images)
loss=criterion(outputs,labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1)%100 ==0:
print('epoch [{}/{}],step[{}/{}],loss:{:.4f}'.format(epoch+1,num_epochs,i+1,total_step,loss.item()))
if (epoch+1)%1==0:
curr_lr/=3
update_lr(optimizer,curr_lr)
model.eval()
with torch.no_grad():
correct=0
total=0
for image,labels in test_loader:
images=images.to(device)
labels=labels.to(device)
outputs=model(images)
_,predicted=torch.max(outputs.data,1)
total+=labels.size(0)
correct+=(predicted==labels).sum().item()
print('accuracy of the model on the test images:{}%'.format(100*correct/total))
