Forward hook

A diagnostic tool for intercepting and modifying a neural network layer's output during the forward pass.

Forward hooks allow developers to extract intermediate activations or modify layer outputs without altering a model's underlying source code. By registering a function via PyTorch's register_forward_hook, you gain direct access to the input and output tensors of a specific nn.Module during execution. This is essential for debugging deep architectures (like ResNet-50), visualizing feature maps, or implementing Grad-CAM for model interpretability. It keeps the production codebase clean while providing a surgical entry point for real-time data inspection.

https://pytorch.org/docs/stable/generated/torch.nn.modules.module.register_forward_hook.html