Skip to content

kazuto1011/grad-cam-pytorch

master
Switch branches/tags
Code

Latest commit

Update requirements: tqdm and numpy
fd10ff7

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Jan 21, 2020
May 21, 2017
Jan 21, 2020
Jan 21, 2020

Grad-CAM with PyTorch

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].

Requirements

Python 2.7 / 3.+

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

Basic usage

python main.py [DEMO_ID] [OPTIONS]

Demo ID:

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).

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 -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]

Demo 2

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]

Demo 3

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.

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. 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

About

PyTorch implementation of Grad-CAM, vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages