pytorch中的model.eval()和BN层的使用

class ConvNet(nn.module):
 def __init__(self, num_class=10):
  super(ConvNet, self).__init__()
  self.layer1 = nn.Sequential(nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
  nn.BatchNorm2d(16),
  nn.ReLU(),
  nn.MaxPool2d(kernel_size=2, stride=2))
  self.layer2 = nn.Sequential(nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
  nn.BatchNorm2d(32),
  nn.ReLU(),
  nn.MaxPool2d(kernel_size=2, stride=2))
  self.fc = nn.Linear(7*7*32, num_classes)

 def forward(self, x):
  out = self.layer1(x)
  out = self.layer2(out)
  print(out.size())
  out = out.reshape(out.size(0), -1)
  out = self.fc(out)
  return out

# Test the model
model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
 correct = 0
 total = 0
 for images, 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()

def freeze_bn(m):
 if isinstance(m, nn.BatchNorm2d):
  m.eval()
model.apply(freeze_bn)