guojiajeremy / ReContrast

ReContrast

Official PyTorch Implementation of "ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction".

NeurIPS 2023. paper proceddings

@inproceedings{guo2024recontrast,
 author = {Guo, Jia and lu, shuai and Jia, Lize and Zhang, Weihang and Li, Huiqi},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {10721--10740},
 title = {ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction},
 volume = {36},
 year = {2023}
}

1. Environments

Create a new conda environment and install required packages.

conda create -n my_env python=3.8.12
conda activate my_env
pip install -r requirements.txt

Experiments are conducted on NVIDIA GeForce RTX 3090 (24GB). Same GPU and package version are recommended.

2. Prepare Datasets

Noted that ../ is the upper directory of ReContrastAD. It is where we keep all the datasets by default. You can also alter it according to your need, just remember to modify the train_path and test_path in the code.

MVTec AD

Download the MVTec-AD dataset from URL. Unzip the file to ../mvtec_anomaly_detection/.

|-- mvtec_anomaly_detection
    |-- bottle
    |-- cable
    |-- capsule
    |-- ....

VisA

Download the VisA dataset from URL. Unzip the file to ../VisA/. Preprocess the dataset to ../VisA_pytorch/ in 1-class mode by their official splitting code.

You can also run the following command for preprocess, which is the same to their official code.

python ./prepare_data/prepare_visa.py --split-type 1cls --data-folder ../VisA --save-folder ../VisA_pytorch --split-file ./prepare_data/split_csv/1cls.csv

../VisA_pytorch/ will be like:

|-- VisA_pytorch
    |-- 1cls
        |-- candle
            |-- ground_truth
            |-- test
                    |-- good
                    |-- bad
            |-- train
                    |-- good
        |-- capsules
        |-- ....

OCT2017

Creat a new directory ../OCT2017. Download ZhangLabData form URL. Unzip the file, and move everything in ZhangLabData/CellData/OCT to ../OCT2017/. The directory should be like:

|-- OCT2017
    |-- test
        |-- CNV
        |-- DME
        |-- DRUSEN
        |-- NORMAL
    |-- train
        |-- CNV
        |-- DME
        |-- DRUSEN
        |-- NORMAL

APTOS

Creat a new directory ../APTOS. Download APTOS 2019 form URL. Unzip the file to ../APTOS/original/. Now, the directory would be like:

|-- APTOS
    |-- original
        |-- test_images
        |-- train_images
        |-- test.csv
        |-- train.csv

Run the following command to preprocess the data to ../APTOS/.

python ./prepare_data/prepare_aptos.py --data-folder ../APTOS/original --save-folder ../APTOS

The directory would be like:

|-- APTOS
    |-- test
        |-- NORMAL
        |-- ABNORMAL
    |-- train
        |-- NORMAL
    |-- original

You can delete original if you want.

ISIC2018

Creat a new directory ../ISIC2018. Go to the ISIC 2018 official website. Download "Training Data","Training Ground Truth", "Validation Data", and "Validation Ground Truth" of Task 3. Unzip them to ../ISIC2018/original/. Now, the directory would be like:

|-- ISIC2018
    |-- original
        |-- ISIC2018_Task3_Training_GroundTruth
        |-- ISIC2018_Task3_Training_Input
        |-- ISIC2018_Task3_Validation_GroundTruth
        |-- ISIC2018_Task3_Validation_Input

Run the following command to preprocess the data to ../ISIC2018/.

python ./prepare_data/prepare_isic2018.py --data-folder ../ISIC2018/original --save-folder ../ISIC2018

The directory would be like:

|-- ISIC2018
    |-- test
        |-- NORMAL
        |-- ABNORMAL
    |-- train
        |-- NORMAL
    |-- original

You can delete original if you want.

3. Run Experiments

MVTec AD

python recontrast_mvtec.py

If you want to specify a GPU

python recontrast_mvtec.py --gpu 0

VisA

python recontrast_visa.py

APTOS

python recontrast_aptos.py

OCT2017

python recontrast_oct.py

ISIC2018

python recontrast_isic.py

Model-Unifed Multi-Class Setting

Following the setting proposed by UniAD, we train an unifed model for all classes of each dataset (15 classes for MVTec AD, 12 classes for VIsA).

python recontrast_mvtec_multiclass.py

python recontrast_visa_multiclass.py

Stable Training

Our method (as well as many other UAD methods) suffers from some extent of training instability due to optimizer and batchnorm (BN) related issue, as discussed in Appendix E. By default, the BN layers of encoder are set to train mode during training. Because training instability and performance drop are observed for some categories, the BN of encoder is set to eval mode for such categories. (the choice of encoder BN mode and training loss spikes can be easily addressed by a validation set, which however is not allowed in UAD).

We explore some tricks that enable training with more stability when set encoder BN to train mode for all categories, which produces comparably good performances.

1. In encoder, we use pre-trained running_var if the batch variance of a BN channel is lower than min(5e-4, running_var)
2. We reset the decoder Adam optimizer every 500 iterations to clear historical first-order and second-order gradient.

python recontrast_mvtec_stable.py

python recontrast_visa_stable.py

Acknowledgement

Many thanks to RD4AD, for their easy-to-read code base.