pytorch怎么使用model.eval()和BN层

使用代码如下:

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():  # 使梯度不进行反向传播
    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()
  • 如果网络模型model中含有BN层,则在预测时应当将模式切换为评估模式,即model.eval()。

  • 评估模拟下BN层的均值和方差应该是整个训练集的均值和方差,即 moving mean/variance。

  • 训练模式下BN层的均值和方差为mini-batch的均值和方差,因此应当特别注意。

  • 补充:Pytorch 模型训练模式和eval模型下差别巨大(Pytorch train and eval)附解决方案

  • 当pytorch模型写明是eval()时有时表现的结果相对于train(True)差别非常巨大,这种差别经过逐层查看,主要来源于使用了BN,在eval下,使用的BN是一个固定的running rate,而在train下这个running rate会根据输入发生改变。

解决方案是冻住bn

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

这样可以获得稳定输出的结果。

参考于:https://m.w3cschool.cn/article/50427707.html

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