设置.train()，self.training=True

设置.eval()，self.training=False

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias = False),
            nn.BatchNorm2d(32, affine=False),
        )

        self.classifier = nn.Linear(512, 20, bias=False)
        
    def forward(self, input):
        x_features = self.features(input)
        x = x_features.view(x_features.size(0), -1)
        
        if self.training is False:
            return x
        
        x = self.classifier(x)
        return x

m = MyNet()
m.train()
print(m.training)
m.eval()
print(m.training)

True
False

input = torch.randn(1, 1, 4, 4)
m.train()
output = m(input)
print(output.size())
m.eval()
output1 = m(input)
print(output1.size())

torch.Size([1, 20])
torch.Size([1, 512])