/ Machine Learning

PyTorch implementation of Grad-CAM

PyTorch implementation of Grad-CAM

Grad-CAM with PyTorch

PyTorch implementation of Grad-CAM (Gradient-weighted Class Activation Mapping). 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

Basic usage

python main.py demo1 --help
  • -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 layer to be visualized, e.g. "layer4.2" (required)
  • -k, --topk: the number of classes to generate (default: 3)
  • --cuda/--cpu: GPU or CPU

The command above generates, for top k classes:

  • Gradients by vanilla backpropagation
  • Gradients by guided backpropagation
  • Gradients by deconvnet
  • Grad-CAM
  • Guided Grad-CAM

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

cat_dog

Demo 1

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

You can specify multiple images like:

python main.py demo1 -a resnet152 \
                     -t layer4 \
                     -i samples/cat_dog.png \
                     -i samples/vegetables.jpg
Predicted class #1 bull mastiff #2 tiger cat #3 boxer
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]

* PyTorch module name

Demo 2

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

python main.py demo2 -i samples/cat_dog.png
Layer* layer1 layer2 layer3 layer4
Grad-CAM [1]

Demo 3

Generate Grad-CAM with the original models. Here we use Xception v1 from my other repo and visualize at the last convolution layer (see demo3() for more details).

python main.py demo3 -i samples/cat_dog.png
Predicted class #1 bull mastiff #2 tiger cat #3 boxer
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

GitHub