How to get the output from a specific layer from a PyTorch model?

Question

How to extract the features from a specific layer from a pre-trained PyTorch model (such as ResNet or VGG), without doing a forward pass again?

Answer 1

New answer

Edit: there's a new feature in torchvision v0.11.0 that allows extracting features.

For example, if you wanna extract features from the layer layer4.2.relu_2, you can do like:

import torch
from torchvision.models import resnet50
from torchvision.models.feature_extraction import create_feature_extractor

x = torch.rand(1, 3, 224, 224)

model = resnet50()

return_nodes = {
    "layer4.2.relu_2": "layer4"
}
model2 = create_feature_extractor(model, return_nodes=return_nodes)
intermediate_outputs = model2(x)

Old answer

You can register a forward hook on the specific layer you want. Something like:

def some_specific_layer_hook(module, input_, output):
    pass  # the value is in 'output'

model.some_specific_layer.register_forward_hook(some_specific_layer_hook)
    
model(some_input)

For example, to obtain the res5c output in ResNet, you may want to use a nonlocal variable (or global in Python 2):

res5c_output = None

def res5c_hook(module, input_, output):
    nonlocal res5c_output
    res5c_output = output

resnet.layer4.register_forward_hook(res5c_hook)

resnet(some_input)
    
# Then, use `res5c_output`.