(ECCV2020) Official PyTorch implementation of A Generic Visualization Approach for Convolutional Neural Networks
Paper | 1 Min Video | 10 Mins Video
The goal of this PyTorch implementation is to provide a simple, readable, and fast implementation for L2-CAF. If you have an idea to make this simpler, please let me know!
L2-CAF does not require any finetuning or specific layers. It is easy to integrate L2-CAF in various network architectures.
L2-CAF cls-specific using ResNet50
 |
 |
|---|---|
 |
 |
- Python 3+ [Tested on 3.7]
- PyTorch 1.X [Tested on torch 1.6.0 and torchvision 0.6.0]
The class_oblivious.py presents the class oblivious variant of L2-CAF, while class_specific.py presents the class specific variant of L2-CAF.
Both python class_oblivious.py or python class_specific.py will use the default hard-coded hyperparameters.
You can override the default hyperparameter from the cmd line, e.g., python class_specific.py --input_img='dog_butterfly.jpg' --cls_logits='207,323'. Check config.base_config.Config to know more about L2-CAF parameters.
- The L2-CAF is originally implemented in TensorFlow. I hope this PyTorch implementation raises awareness of L2-CAF, this cool visualization tool :)
- I noticed that visualization heatmaps are consistent across different TensorFlow models. In contrast, the output heatmaps from different PyTorch models seems inconsistent. This means the cls-oblivious heatmaps generated from ResNet50 and GoogleNet are significantly different. It is not required that heatmaps be consistent between different architecttures. Yet, I am surprised that there is a difference in behaviour between Tensorflow and PyTorch architectures.
- This PyTorch implementation visualizes attention of the last conv layer only. While L2-CAF can visualize attention in intermediate layers, I did not support this feature to keep the code simple. Yet, please note that the TensorFlow implementation supports intermediate layer visualization.
- Support more architectures
- 1.0.0
- First commit Vanilla L2-CAF on DenseNet169, GoogleNet, and ResNet50 on 16 May 2021
@inproceedings{taha2020generic,
title={A Generic Visualization Approach for Convolutional Neural Networks},
author={Taha, Ahmed and Yang, Xitong and Shrivastava, Abhinav and Davis, Larry},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2020}
}