AttributeError: 'Sequential' object has no attribute 'weight'

6

You don’t understand the key points.

  • Use number indexing for Sequential and ModuleList
  • Use key for ModuleDict
  • Use .module_name otherwise

For example, if I have a network like

import torch.nn as nn

class SubNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(2, 2, 2)
        self.relu = nn.ReLU(inplace=True)
    def forward(self, x):
        return x

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.module = nn.Sequential(
            nn.Sequential(
                nn.Conv2d(1, 1, 1),
                SubNet(),
            ),
            nn.Conv2d(3, 3, 3)
        )
        self.conv = nn.Conv2d(4, 4, 4)
    def forward(self, x):
        return x

net = Net()
print(net)

The output is

Net(
  (module): Sequential(
    (0): Sequential(
      (0): Conv2d(1, 1, kernel_size=(1, 1), stride=(1, 1))
      (1): SubNet(
        (conv): Conv2d(2, 2, kernel_size=(2, 2), stride=(1, 1))
        (relu): ReLU(inplace=True)
      )
    )
    (1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  )
  (conv): Conv2d(4, 4, kernel_size=(4, 4), stride=(1, 1))
)

Then we have
net.module[0][0].weight → Conv2d(1, 1, 1)'s weight
net.module[0][1].conv.weight → Conv2d(2, 2, 2)'s weight
net.module[1].weight → Conv2d(3, 3, 3)'s weight
net.conv.weight → Conv2d(4, 4, 4)'s weight