pytorch中的model.eval()和BN层
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()
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。
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