CNN Receptive Fields

Receptive field computation for any CNN in PyTorch.

The receptive field of a neuron is defined as the region of the input that can change the output of such a neuron.

The contribution to the value of neuron n of pixel (i, j) in an input image x is computed by

• setting all the weights in the network to constant values
• replacing Max-Pooling by Average Pooling
• switching off batch norm
• switching on pixels in the input one at a time to check responding neurons.
• contributions are then rescaled to be in [0, 1]

The resulting receptive fields are a set of images R^l = [R_n^l | n is a neuron in layer l] that have the same size of the input.

The value of R^l_n[i, j] \in [0, 1] corresponds to how much pixel (i, j), in an input image - i.e. x[:, i, j] - would contribute to the output of neuron n, which lives in layer l, if the network had constant weights.

Install

(this is for the receptivefield functions only)

python -m pip install git+https://github.com/leonardopetrini/cnn-receptive-fields.git

Usage

Examples of how to use the code are reported examples.ipynb.

Dependencies

• torch
• torch.fx

For the example

• torchvision
• matplotlib

cf. requirements.txt for versions.