Grad-CAM with PyTorch

PyTorch implementation of Grad-CAM (Gradient-weighted Class Activation Mapping) [1]. Grad-CAM localizes and highlights discriminative regions that a convolutional neural network-based model activates to predict visual concepts. This repository only supports image classification models.

Dependencies

• Python 2.7+/3.6+
• PyTorch 0.4.1+
• torchvision 0.2.1+
• click
• opencv

Usage

python demo.py --help

• -i, --image-path: a path to an image (required)
• -a, --arch: a model name from torchvision.models, e.g. "resnet152" (required)
• -t, --target-layer: a layer to be visualized, e.g. "layer4.2" (required)
• -k, --topk: the number of classes to generate (default: 3)
• --cuda/--no-cuda: GPU or CPU

The command above generates, for top k classes:

• Gradients by vanilla backpropagation
• Gradients by guided backpropagation [2]
• Gradients by deconvnet [2]
• Grad-CAM [1]
• Guided Grad-CAM [1]

The guided-* do not support F.relu but only nn.ReLU in this codes. For instance, off-the-shelf inception_v3 cannot cut off negative gradients during backward operation (#2).

Examples

Demo

python demo.py -a resnet152 \
               -t layer4 \
               -i samples/cat_dog.png

Method	bull mastiff	tiger cat	boxer
Probability	0.54285	0.19302	0.10428
Grad-CAM [1]
Vanilla backpropagation
"Deconvnet" [2]
Guided backpropagation [2]
Guided Grad-CAM [1]

Grad-CAM with different models for "bull mastiff" class

python demo.py -a <model name> \
               -t <layer name> \
               -i samples/cat_dog.png

Model	resnet152	vgg19	vgg19_bn	densenet201	squeezenet1_1
Layer*	layer4	features	features	features	features
Grad-CAM [1]

* PyTorch module name

Grad-CAM at different layers of resnet152 for "bull mastiff" class

python demo2.py --image-path samples/cat_dog.png

Layer*	layer1	layer2	layer3	layer4
Grad-CAM [1]

References

1. R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization". arXiv, 2016
2. J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. "Striving for Simplicity: The All Convolutional Net". arXiv, 2014