TW-autoencode - Tied-weights AutoEncoder

This repository contains the source code of unrolled LRP model and baselines from the paper, Model Guidance via Explanations Turns Image Classifiers into Segmentation Models.

Updates

Apr. 2024 -- Start to clean the code.

July. 2024 -- Publish the first version of code.

Datasets

For Pascal VOC, please download the original training images from the official PASCAL site: VOCtrainval_11-May-2012.tar. Extract the folder JPEGImages and SegmentationClassAug into the corresponding dataset/pascal folder.
The data folder structure should look like the following, and the data_path argument should be --data_path /path/to/data_parent_folder.

data_parent_folder
└───VOC2012
    │   train.txt
    │   val.txt
    └───Annotations
    └───ImageSets
    └───JPEGImages
    └───SegmentationObject

Training Supervised and Semi-supervised Models

Supervised training for UNets in 4 different cases (20, 100, 500, 1464 pixel-labeled data)

python train.py --model std_unet --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 15000 --num_labels 20 --uniform_masks 1.0 --save_folder std_unet_resnet50_lab20_lr1e-5_s42

python train.py --model std_unet --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 3000 --num_labels 100 --uniform_masks 1.0 --save_folder std_unet_resnet50_lab100_lr1e-5_s42

python train.py --model std_unet --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 600 --num_labels 500 --uniform_masks 0.5 --save_folder std_unet_resnet50_lab500_lr1e-5_s42

python train.py --model std_unet --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 200 --save_folder std_unet_resnet50_lab1464_lr1e-5_s42

Semi-supervised training for multi-task UNets and Unrolled LRP models in 4 different cases (20, 100, 500, 1464 pixel-labeled data). For multi-task UNets, change --model unrolled_lrp to --model mt_unet.

python train.py --model unrolled_lrp --semisup_dataset --add_classification --iterative_gradients --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 100 --num_labels 20 --uniform_masks 1.0 --save_folder lrp0_resnet50_lab20_lr1e-5_s42

python train.py --model unrolled_lrp --semisup_dataset --add_classification --iterative_gradients --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 100 --num_labels 100 --uniform_masks 1.0 --save_folder lrp0_resnet50_lab100_lr1e-5_s42

python train.py --model unrolled_lrp --semisup_dataset --add_classification --iterative_gradients --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 100 --num_labels 500 --uniform_masks 0.5 --save_folder lrp0_resnet50_lab500_lr1e-5_s42

python train.py --model unrolled_lrp --semisup_dataset --add_classification --iterative_gradients --batch_size 10 --pretrain_weight_name ./snapshot/resnet50_10_pre_train_21 --encoder resnet50 --seed 42 --epochs 100 --save_folder lrp0_resnet50_lab1464_lr1e-5_s42

Note: The way for counting epochs differs between the semisupervised and supervised datasets. Therefore, you need to set a higher value for the epochs argument when training the UNet.

Citation

Yu, Xiaoyan, et al. "Model Guidance via Explanations Turns Image Classifiers into Segmentation Models." World Conference on Explainable Artificial Intelligence. Cham: Springer Nature Switzerland, 2024.

Contact

If you have any questions, please contact xiaoyany1101@gmail.com.

Kainmueller-Lab/TW-autoencoder

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