| import torch.nn as nn | |
| class IntermediateSequential(nn.Sequential): | |
| def __init__(self, *args, return_intermediate=True): | |
| super().__init__(*args) | |
| self.return_intermediate = return_intermediate | |
| def forward(self, input): | |
| if not self.return_intermediate: | |
| return super().forward(input) | |
| intermediate_outputs = {} | |
| output = input | |
| for name, module in self.named_children(): | |
| output = intermediate_outputs[name] = module(output) | |
| return output, intermediate_outputs | |