Visual-Reasoning-eXplanation

[CVPR 2021 A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts]

Project Page | Video | Paper

Figure: An example result with the proposed VRX. To explain the prediction (i.e., fire engine and not alternatives like ambulance), VRX provides both visual and structural clues.

A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, Ziyan Wu
IEEE/ CVF International Conference on Computer Vision and Pattern Recognition (CVPR), 2021

We considered the challenging problem of interpreting the reasoning logic of a neural network decision. We propose a novel framework to interpret neural networks which extracts relevant class-specific visual concepts and organizes them using structural concepts graphs based on pairwise concept relationships. By means of knowledge distillation, we show VRX can take a step towards mimicking the reasoning process of NNs and provide logical, concept-level explanations for final model decisions. With extensive experiments, we empirically show VRX can meaningfully answer “why” and “why not” questions about the prediction, providing easy-to-understand insights about the reasoning process. We also show that these insights can potentially provide guidance on improving NN’s performance.

Figure: Examples of representing images as structural concept graph.

Figure: Pipeline for Visual Reasoning Explanation framework.

Thanks for a re-implementation from sssufmug, we added more features and finish the whole pipeline.

Getting Started

Installation

• Clone this repo:

git clone https://github.com/gyhandy/Visual-Reasoning-eXplanation.git
cd Visual-Reasoning-eXplanation

• Dependencies

pip install -r requirements.txt

Datasets

• We use a subset of ImageNet as our source data. There are intrested classes which want to do reasoning, such as fire angine, ambulance and school bus, and also other random images for discovering concepts. You can download the source data that we used in our paper here: source [http://ilab.usc.edu/andy/dataset/source.zip]

• Input files for training GNN and doing reasoning. You can get these data by doing discover concepts and match concepts yourself, but we also provide those files to help you doing inference directly. You can download the result data here: result[http://ilab.usc.edu/andy/dataset/result.zip]

Datasets Preprocess

Unzip source.zip as well as result.zip, and then place them in ./source and ./result. If you only want to do inference, you can skip discover concept, match concept and training Structural Concept Graph (SCG).

Discover concept

For more information about discover concept, you can refer to ACE: Towards Automatic Concept Based Explanations. We use the pretrained model provided by tensorflow to discover cencept. With default setting you can simply run

python3 discover_concept.py

If you want to do this step with a custom model, you should write a wrapper for it containing the following methods:

run_examples(images, BOTTLENECK_LAYER): which basically returens the activations of the images in the BOTTLENECK_LAYER. 'images' are original images without preprocessing (float between 0 and 1)
get_image_shape(): returns the shape of the model's input
label_to_id(CLASS_NAME): returns the id of the given class name.
get_gradient(activations, CLASS_ID, BOTTLENECK_LAYER): computes the gradient of the CLASS_ID logit in the logit layer with respect to activations in the BOTTLENECK_LAYER.

If you want to discover concept with GradCam, please also implement a 'gradcam.py' for your model and place it into ./src. Then run:

python3 discover_concept.py --model_to_run YOUR_LOCAL_PRETRAINED_MODEL_NAME --model_path YOUR_LOCAL_PATH_OF_PRETRAINED_MODEL --labels_path LABEL_PATH_OF_YOUR_MODEL_LABEL --use_gradcam TRUE/FALSE

Match concept

This step will use the concepts you discovered in last step to match new images. If you want to match your own images, please put them into ./source and create a new folder named IMAGE_CLASS_NAME. Then run:

python3 macth_concept.py --model_to_run YOUR_LOCAL_PRETRAINED_MODEL_NAME --model_path YOUR_LOCAL_PATH_OF_PRETRAINED_MODEL --labels_path LABEL_PATH_OF_YOUR_MODEL_LABEL --use_gradcam TRUE/FALSE

Training Structural Concept Graph (SCG)

python3 VR_training_XAI.py

Then you can find the checkpoints of model in ./result/model.

Reasoning a image

For images you want to do reasoning, you should first doing match concept to extract concept knowledge. Once extracted graph knowledge for SCG, you can do the inference. For example, if you want to inference ./source/fire_engine/n03345487_19835.JPEG, the "img_class" is "ambulance" and "img_idx" is 10367, then run:

python3 Xception_WhyNot.py --img_class fire_engine --img_idx 19835

Some visualize results

Contact / Cite

Got Questions? We would love to answer them! Please reach out by email! You may cite us in your research as:

@inproceedings{ge2021peek,
  title={A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts},
  author={Ge, Yunhao and Xiao, Yao and Xu, Zhi and Zheng, Meng and Karanam, Srikrishna and Chen, Terrence and Itti, Laurent and Wu, Ziyan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2195--2204},
  year={2021}
}

We will post other relevant resources, implementations, applications and extensions of this work here. Please stay tuned