Project-MONAI / Project-MONAI/MONAI
How to obtain the intermediate features (bottleneck features) in the downsampling stage of U-Net?
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- Dominant language
- Python
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Description
intermediate_outputs = {}
def forward_hook(layer_name):
def hook(module, input, output):
intermediate_outputs[layer_name] = output
return hook
input = torch.randn(4,1,64,64,64)
net.model[?].register_forward_hook(forward_hook("XXX"))
How should this be set?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue mentions a U-Net instance through net.model[?] and a forward hook that stores intermediate_outputs. Start by inspecting the model structure and its downsampling modules to determine which entry point corresponds to the requested bottleneck features. Done means providing a confirmed module path or documented way to obtain those outputs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100