PyTorch之前向传播函数forward用法解读

weight = weight - learning_rate * gradient

class FeatureL2Norm(torch.nn.Module):
    def __init__(self):
        super(FeatureL2Norm, self).__init__()
    def forward(self, feature):
        epsilon = 1e-6
#        print(feature.size())
#        print(torch.pow(torch.sum(torch.pow(feature,2),1)+epsilon,0.5).size())
        norm = torch.pow(torch.sum(torch.pow(feature,2),1)+epsilon,0.5).unsqueeze(1).expand_as(feature)
        return torch.div(feature,norm)

class FeatureRegression(nn.Module):
    def __init__(self, output_dim=6, use_cuda=True):
        super(FeatureRegression, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(225, 128, kernel_size=7, padding=0),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 64, kernel_size=5, padding=0),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )
        self.linear = nn.Linear(64 * 5 * 5, output_dim)
        if use_cuda:
            self.conv.cuda()
            self.linear.cuda()
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

def __call__(self, *input, **kwargs):
   result = self.forward(*input, **kwargs)
   for hook in self._forward_hooks.values():
       #将注册的hook拿出来用
       hook_result = hook(self, input, result)
   ...
   return result

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
	layer1 = nn.Sequential()
	layer1.add_module('conv1', nn.Conv(1, 6, 3, padding=1))
	layer1.add_moudle('pool1', nn.MaxPool2d(2, 2))
	self.layer1 = layer1
	layer2 = nn.Sequential()
	layer2.add_module('conv2', nn.Conv(6, 16, 5))
	layer2.add_moudle('pool2', nn.MaxPool2d(2, 2))
	self.layer2 = layer2
	layer3 = nn.Sequential()
	layer3.add_module('fc1', nn.Linear(400, 120))
	layer3.add_moudle('fc2', nn.Linear(120, 84))
	layer3.add_moudle('fc3', nn.Linear(84, 10))
	self.layer3 = layer3
    def forward(self, x):
	x = self.layer1(x)
	x = self.layer2(x)
	x = x.view(x.size(0), -1)
	x = self.layer3(x)
	return x