mkurman/captum

Overview

Captum (Comprehension in PyTorch) provides model interpretability for PyTorch models. Implements Integrated Gradients, Gradient SHAP, DeepLIFT, Occlusion, Feature Ablation, and Layer Conductance. Supports computer vision, NLP, and tabular models.

Installation

bash
uv pip install captum

Integrated Gradients

python
import torch
import torch.nn as nn
from captum.attr import IntegratedGradients

model = nn.Linear(10, 2)
input = torch.randn(1, 10)
baseline = torch.zeros(1, 10)

ig = IntegratedGradients(model)
attrs = ig.attribute(input, baseline, target=0)
print(f"Feature attributions: {attrs}")

Occlusion

python
from captum.attr import Occlusion

occ = Occlusion(model)
attrs = occ.attribute(input, target=0, sliding_window_shapes=(1,))  # 1D
print(attrs)

Visualization

python
from captum.attr import visualization as viz

_ = viz.visualize_image_attr(
    attrs.squeeze().numpy(),
    original_image=input.squeeze().numpy(),
    method="heat_map",
    sign="absolute_value",
    show_colorbar=True,
)

References

• Captum docs
• Captum GitHub