Hello - I am new to pytorch & these forums, apologies if I haven’t directed this question to the right place.  Before I begin, I am using Python 3.7 and torch==1.5.0 incase either of these matter.
I am struggling to take inputs forward through an arbitrary pre-trained CNN, one layer/module at a time. I understand that forward hooks can be used to capture intermediate features. What I am hoping to find is a generalized way to replay intermediate features through subsequent layers, without requiring a full forward pass.  Is there a best practice for this in PyTorch?
[edit] perhaps simplest form of my question is how to call
x = net.features(x)
y = net.classifier(x)

When I call all net.children() in sequence I either get an output shape that does not match what’s produced by full forward pass, or else execution errors passing intermediate features to the next layer.  Calling net.features() followed by net.classifier() also produces same/similar size mismatch errors described above.
I am not looking for fix specific to densenet161, but use it here as an example:
import torch
import torchvision.models as tmodels
net = tmodels.densenet161(pretrained=True)
net.eval()

x = torch.randn(1, 3, 227, 227)
y = net(x).data.numpy()
print('y full:', y.shape)

for child in net.children():
  x = child(x)
y = x.data.numpy()
print('y part:', y.shape)

With this I get size mismatch errors in the net.children() loop:
$ python ./test.py 
y full: (1, 1000)
Traceback (most recent call last):
  File "./test.py", line 11, in <module>
    x = child(x)
  File "/Users/mn/miniconda3/envs/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/mn/miniconda3/envs/env/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 87, in forward
    return F.linear(input, self.weight, self.bias)
  File "/Users/mn/miniconda3/envs/env/lib/python3.7/site-packages/torch/nn/functional.py", line 1612, in linear
    output = input.matmul(weight.t())
RuntimeError: size mismatch, m1: [15456 x 7], m2: [2208 x 1000] at ../aten/src/TH/generic/THTensorMath.cpp:41

Insights/help much appreciated.  Thanks!