Pytorch中 nn.ModuleList 與nn.Sequential 的區別

nn.ModuleList

• Class torch.nn.ModuleList(modules=None)
  簡單的說,就是把子模塊存儲在list中.它類似於list, 既可以 append 操作,也可以做 insert 操作,也可以 extend 操作. 但是由於把layers存入Modulelist中後只是完成了存儲作用,所以不能直接在forward中直接運行,需要通過索引調出相應的submodule.

  class MyModule(nn.Module):
      def __init__(self):
          super(MyModule, self).__init__()
          self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
          # extend操作
  		self.linears.extend([nn.Linear(10, 10),nn.Linear(10, 10) ])
  		# append操作
      	self.linears.append(nn.Linear(10, 10))
      def forward(self, x):
          # ModuleList can act as an iterable, or be indexed using ints
          for i, l in enumerate(self.linears):
              x = self.linears[i // 2](x) + l(x)
          return x
  

nn.Sequential

• Class torch.nn.Sequential(*args)
  順序容器.模塊將按照順序存進sequential中,相當於一個包裝起來的子模塊集,可以在forward中直接運行.

  class MyModule(nn.Module):
      def __init__(self):
          super(MyModule, self).__init__()
          self.layers = nn.Sequential(
            nn.Conv2d(1,20,5),
            nn.ReLU(),
            nn.Conv2d(20,64,5),
            nn.ReLU()
          )
      def forward(self, x):
          # ModuleList can act as an iterable, or be indexed using ints
          x = self.layers
          return x