allenwu97 / EDAM

The code and pretrain models of EDAM-WSSS(Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation)

Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation(EDAM-WSSS) CVPR2021

The code and pretrain models of EDAM(Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation) Our papaer link

Env

We train our model with Python 3.5, PyTorch 1.1.0 and 4 Tesla V100 GPUs with 16 GB memory. Other Python modules can be installed by running

conda install --yes --file requirements.txt

You can also download our env directly [BaiduYun] tlu7

After unzipping, running

source py3.5/bin/activate

Dataset

• The PASCAL VOC 2012 development kit: You also need to specify the path ('voc12_root') of your downloaded dev kit.
• Saliency Maps [GoogleDrive][BaiduYun] (fjst): Class-agnostic saliency maps generated by PoolNet as background cues.

Pretrain Models

We provide three pre-trained models, including initialization parameters, EDAM parameters and Deeplabv2 parameters [Google Drive][BaiduYun] u7q7

Train

Training EDAM from scratch.(Noting: We have pre-processed the dataset, and the amount of data in each epoch is equivalent to the original 25 epochs)

python3 train_EDAM_cls.py --lr 0.001 --batch_size 4 --max_epoches 1 --crop_size 368 --network network.resnet38_EDAM_cls --voc12_root [Root of VOC12] --weights [Root of initialization parameters] --wt_dec 5e-4 --session_name resnet38_EDAM_cls

To monitor loss and lr, run the following command in a separate terminal.

tensorboard --logdir runs

Test

Generate pseudo labels.

python3 infer_cls.py --infer_list voc12/train_aug.txt --voc12_root /workdir/VOCdevkit/VOC2012 --network network.resnet38_EDAM_cls --weights [Path of EDAM Parameters]  --out_crf_pred [Output Path] --theta 0.2  --alpha 0.000001  --beta 0.99999 --sal_path [Path of Saliency Map]

Vis pseudo labels

python3 colorful.py --img_path [Path of Pseudo Labels] —out_path [Path of Colorful Pseudo Labels]

Segmentation Network

We use our pseudo labels fully-supervised train a Deeplab-v2 Network
We also provide the final pseudo labels for segmentation network training. [Google Drive][BaiduYun] 9aij

Results

Model	Train set	Val set	Crf?	Saliency?	Mean IoU
EDAM	trainaug	train	-	-	52.83
			✓	-	58.61
			✓	✓	68.11
DeepLab-v2	trainaug	val	-	-	69.66
			✓	-	70.96

Citation

If this code is useful to you, consider using the following citation:

@InProceedings{Wu_2021_CVPR,
    author    = {Wu, Tong and Huang, Junshi and Gao, Guangyu and Wei, Xiaoming and Wei, Xiaolin and Luo, Xuan and Liu, Chi Harold},
    title     = {Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16765-16774}
}