PyTorch implementation of Grad-CAM (Gradient-weighted Class Activation Mapping) [1] in image classification. This repository also contains implementations of vanilla backpropagation, guided backpropagation [2], deconvnet [2], and guided Grad-CAM [1], occlusion sensitivity maps [3].

Python 2.7 / 3.+

$ pip install click opencv-python matplotlib tqdm numpy
$ pip install "torch>=0.4.1" torchvision

python main.py [DEMO_ID] [OPTIONS]

Demo ID:

• demo1
• demo2
• demo3

Options:

• -i, --image-paths: image path, which can be provided multiple times (required)
• -a, --arch: a model name from torchvision.models, e.g. "resnet152" (required)
• -t, --target-layer: a module name to be visualized, e.g. "layer4.2" (required)
• -k, --topk: the number of classes to generate (default: 3)
• -o, --output-dir: a directory to store results (default: ./results)
• --cuda/--cpu: 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 (issue #2).

Generate all kinds of visualization maps given a torchvision model, a target layer, and images.

python main.py demo1 -a resnet152 -t layer4 \
                     -i samples/cat_dog.png -i samples/vegetables.jpg # You can add more images

Predicted class	#1 boxer	#2 bull mastiff	#3 tiger cat
Grad-CAM [1]
Vanilla backpropagation
"Deconvnet" [2]
Guided backpropagation [2]
Guided Grad-CAM [1]

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

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

Generate Grad-CAM maps for "bull mastiff" class, at different layers of ResNet-152 (hardcoded).

python main.py demo2 -i samples/cat_dog.png

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

Generate the occlusion sensitivity map [1, 3] based on logit scores. The red and blue regions indicate a relative increase and decrease from non-occluded scores respectively: the blue regions are critical!

python main.py demo3 -a resnet152 -i samples/cat_dog.png

Patch size	10x10	15x15	25x25	35x35	45x45	90x90
"boxer" sensitivity
"bull mastiff" sensitivity
"tiger cat" sensitivity

This demo takes much time to compute per-pixel logits. You can control the resolution by changing sampling stride (--stride), or increasing batch size as to fit on your GPUs (--n-batches). The model is wrapped with torch.nn.DataParallel so that runs on multiple GPUs by default.

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. In ICCV, 2017
2. J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. Striving for Simplicity: The All Convolutional Net. arXiv, 2014
3. M. D. Zeiler, R. Fergus. Visualizing and Understanding Convolutional Networks. In ECCV, 2013