pytorch：建模时Lambda表达式的使用，使建模更加一般化

最新推荐文章于 2024-02-04 22:32:31 发布

小贤2016

最新推荐文章于 2024-02-04 22:32:31 发布

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分类专栏：计算机视觉 pytorch python 文章标签： python pytorch

本文链接：https://blog.youkuaiyun.com/u013362955/article/details/83189340

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以VGG模型为例：

class LambdaBase(nn.Sequential):   #lambda表达式
    def __init__(self, fn, *args):
        super(LambdaBase, self).__init__(*args)

        self.lambda_func = fn

    def forward_prepare(self, input):
        output = []
        for module in self._modules.values():
            output.append(module(input))
        return output if output else input

class Lambda(LambdaBase):
    def forward(self, input):
        return self.lambda_func(self.forward_prepare(input))

class LambdaMap(LambdaBase): #map
    def forward(self, input):
        return list(map(self.lambda_func,self.forward_prepare(input)))

class LambdaReduce(LambdaBase):#reduce
    def forward(self, input):
        return reduce(self.lambda_func,self.forward_prepare(input))

使用：

VGG_Face_torch = nn.Sequential( # Sequential,
	nn.Conv2d(in_channels=3,out_channels=64,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
	nn.ReLU(),
	nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
	nn.Conv2d(64,128,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
	nn.Conv2d(128,256,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((2, 2), (2, 2), (0, 0),ceil_mode=True),
        #ceil_mode - 如果等于True，计算输出信号大小的时候，会使用向上取整，代替默认的向下取整的操作
	nn.Conv2d(256,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
	nn.ReLU(),
	nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
	Lambda(lambda x: x.view(x.size(0),-1)), # (batchsize,channels,x,y),View相当于reshape，即变成batchsize*y
	#如果batchsize为1，则直接x.view(1,-1)
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(25088,4096)), # Linear,
	nn.ReLU(),
	nn.Dropout(0.5),
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(4096,4096)), # Linear,
	nn.ReLU(),
	nn.Dropout(0.5),
	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(4096,2622)), # Linear,
)