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https://github.com/kazuto1011/grad-cam-pytorch
PyTorch re-implementation of Grad-CAM (+ vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps)
https://github.com/kazuto1011/grad-cam-pytorch
deconvnet grad-cam guided-backpropagation occlusion-sensitivity pytorch visualization
Last synced: about 1 month ago
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PyTorch re-implementation of Grad-CAM (+ vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps)
- Host: GitHub
- URL: https://github.com/kazuto1011/grad-cam-pytorch
- Owner: kazuto1011
- License: mit
- Created: 2017-05-18T03:45:02.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-01-21T21:11:20.000Z (almost 5 years ago)
- Last Synced: 2024-08-04T03:12:48.298Z (5 months ago)
- Topics: deconvnet, grad-cam, guided-backpropagation, occlusion-sensitivity, pytorch, visualization
- Language: Python
- Homepage:
- Size: 42.6 MB
- Stars: 784
- Watchers: 7
- Forks: 174
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Grad-CAM with PyTorch
PyTorch implementation of Grad-CAM (Gradient-weighted Class Activation Mapping) [[1](##references)] in image classification. This repository also contains implementations of vanilla backpropagation, guided backpropagation [[2](##references)], deconvnet [[2](##references)], and guided Grad-CAM [[1](##references)], occlusion sensitivity maps [[3](##references)].
## Requirements
Python 2.7 / 3.+
```bash
$ pip install click opencv-python matplotlib tqdm numpy
$ pip install "torch>=0.4.1" torchvision
```
## Basic usage
```sh
python main.py [DEMO_ID] [OPTIONS]
```
Demo ID:
* [`demo1`](#demo-1)
* [`demo2`](#demo-2)
* [`demo3`](#demo-3)
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](##references)]
* Gradients by deconvnet [[2](##references)]
* Grad-CAM [[1](##references)]
* Guided Grad-CAM [[1](##references)]
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.
```bash
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](##references)] |  |  |  |
| Vanilla backpropagation |  |  |  |
| "Deconvnet" [[2](##references)] |  |  |  |
| Guided backpropagation [[2](##references)] |  |  |  |
| Guided Grad-CAM [[1](##references)] |  |  |  |
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](##references)] |  |  |  |  |  |
## Demo 2
Generate Grad-CAM maps for "bull mastiff" class, at different layers of ResNet-152 (hardcoded).
```bash
python main.py demo2 -i samples/cat_dog.png
```
| Layer | ```relu``` | ```layer1``` | ```layer2``` | ```layer3``` | ```layer4``` |
| :--------------------------: | :-------------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: |
| Grad-CAM [[1](##references)] |  |  |  |  |  |
## Demo 3
Generate the occlusion sensitivity map [[1](##references), [3](##references)] 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!
```bash
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