🧠Simple utility functions and wrappers for hooking onto layers within PyTorch models for feature extraction.
Tip
This library is intended to be a simplified and well-documented implementation for extracting a PyTorch model's intermediate layer output(s). For a more sophisticated and complete implementation, either consider using torchvision.models.feature_extraction, or check the official torch.fx.
import torch
from torchvision.models import vgg11
from torch_featurelayer import FeatureLayer
# Load a pretrained VGG-11 model
model = vgg11(weights='DEFAULT').eval()
# Hook onto layer `features.15` of the model
layer_path = 'features.15'
hooked_model = FeatureLayer(model, layer_path)
# Forward pass an input tensor through the model
x = torch.randn(1, 3, 224, 224)
feature_output, output = hooked_model(x)
# Print the output shape
print(f'Feature layer output shape: {feature_output.shape}') # [1, 512, 14, 14]
print(f'Model output shape: {output.shape}') # [1, 1000]Check the examples directory for more.
torch_featurelayer.FeatureLayer(model: torch.nn.Module, feature_layer_path: str)
torch_featurelayer.FeatureLayers(model: torch.nn.Module, feature_layer_paths: list[str])
torch_featurelayer.get_layer_candidates(module: nn.Module, max_depth: int = 1)