The main code for:

Causal Intervention for Weakly Supervised Semantic Segmentation. Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. NeurIPS, 2020. [CONTA]

• PyTorch 1.2.0, torchvision 0.4.0, and more in requirements.txt
• PASCAL VOC 2012 devkit and COCO 2014
• 8 NVIDIA GPUs with more than 1024MB of memory

pip install -r requirements.txt

• PASCAL VOC 2012 in https://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit
• COCO 2014 in https://cocodataset.org/

For pseudo-mask generaction, we follow the method IRNet without the instance-wise CAM generation step.

cd pseudo_mask & python run_sample.py

• You can either mannually edit the file, or specify commandline arguments.
• Replace the ground_truth annotation in PASCAL VOC 2012 with the generated pseudo_mask.

cd segmentation & python main.py train --config-path configs/voc12.yaml

python main.py test --config-path configs/voc12.yaml \
    --model-path data/models/voc12/deeplabv2_resnet101_msc/train_aug/final_model.pth

python main.py crf --config-path configs/voc12.yaml

• Model: DeepLab v2 with ResNet-101 backbone. Dilated rates of ASPP are (6, 12, 18, 24). Output stride is 8 times.
• GPU: All the GPUs visible to the process are used. Please specify the scope with CUDA_VISIBLE_DEVICES=0,1,2,3.
• Multi-scale loss: Loss is defined as a sum of responses from multi-scale inputs (1x, 0.75x, 0.5x) and element-wise max across the scales. The unlabeled class is ignored in the loss computation.
• Learning rate: Stochastic gradient descent (SGD) is used with momentum of 0.9 and initial learning rate of 2.5e-4. Polynomial learning rate decay is employed; the learning rate is multiplied by (1-iter/iter_max)**power at every 10 iterations.
• Monitoring: Moving average loss (average_loss in Caffe) can be monitored in TensorBoard.
• Preprocessing: Input images are randomly re-scaled by factors ranging from 0.5 to 1.5, padded if needed, and randomly cropped to 321x321.
• You can find more useful tools in /tools/xxx.

This codebase only supports DeepLab v2 training which freezes batch normalization layers, although v3/v3+ protocols require training them. If training their parameters on multiple GPUs as well in your projects, please install the extra library below.

pip install torch-encoding

Batch normalization layers in a model are automatically switched in libs/models/resnet.py.

try:
    from encoding.nn import SyncBatchNorm
    _BATCH_NORM = SyncBatchNorm
except:
    _BATCH_NORM = nn.BatchNorm2d

To process a single image:

python tools/demo.py single \
    --config-path configs/voc12.yaml \
    --model-path model.pth \
    --image-path image.jpg

To run on a webcam:

python tools/demo.py live \
    --config-path configs/voc12.yaml \
    --model-path model.pth

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@InProceedings{dong_2020_conta,
author = {Dong, Zhang and Hanwang, Zhang and Jinhui, Tang and Xiansheng, Hua and Qianru, Sun},
title = {Causal Intervention for Weakly Supervised Semantic Segmentation},
booktitle = {NeurIPS},
year = 2020
}

1. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE TPAMI, 2018.
   Project / Code / Paper

2. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman. The PASCAL Visual Object Classes (VOC) Challenge. IJCV, 2010.
   Project / Paper

3. Ahn, Jiwoon and Cho, Sunghyun and Kwak, Suha. Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations. CVPR, 2019.
   Project / Paper

• Training code for MS-COCO
• Code refactoring
• Release the checkpoint

Please contact '[email protected]'