zhenzi0322/hyperseg

HyperSeg - Official PyTorch Implementation

HyperSeg - Official PyTorch Implementation

Example segmentations on the PASCAL VOC dataset.

This repository contains the source code for the real-time semantic segmentation method described in the paper:

HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Yuval Nirkin, Lior Wolf, Tal Hassner
Paper

Abstract: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features, a multi-headed weight generating module which generates the weights of each block in the decoder immediately before they are consumed, for efficient memory utilization, and a primary network that is composed of novel dynamic patch-wise convolutions. Despite the usage of less-conventional blocks, our architecture obtains real-time performance. In terms of the runtime vs. accuracy trade-off, we surpass state of the art (SotA) results on popular semantic segmentation benchmarks: PASCAL VOC 2012 (val. set) and real-time semantic segmentation on Cityscapes, and CamVid.

Installation

Install the following packages:

conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c conda-forge
pip install opencv-python ffmpeg-python

Add the parent directory of the repository to PYTHONPATH.

Models

Template	Dataset	Resolution	mIoU (%)	FPS	Link
HyperSeg-L	PASCAL VOC	512x512	80.6 (val)	-	download
HyperSeg-M	CityScapes	1024x512	76.2 (val)	36.9	download
HyperSeg-S	CityScapes	1536x768	78.2 (val)	16.1	download
HyperSeg-S	CamVid	768x576	78.4 (test)	38.0	download
HyperSeg-L	CamVid	1024x768	79.1 (test)	16.6	-

The models FPS was measured on an NVIDIA GeForce GTX 1080TI GPU.

Either download the models under <project root>/weights or adjust the model variable in the test configuration files.

Datasets

Dataset	# Images	Classes	Resolution	Link
PASCAL VOC	10,582	21	up to 500x500	auto downloaded
CityScapes	5,000	19	2048x1024	download
CamVid	701	12	960x720	download

Either download the datasets under <project root>/data or adjust the data_dir variable in the configuration files.

Training

To train the HyperSeg-M model on Cityscapes, set the exp_dir and data_dir paths in cityscapes_efficientnet_b1_hyperseg-m.py and run:

python configs/train/cityscapes_efficientnet_b1_hyperseg-m.py

Testing

Testing a model after training

For example testing the HyperSeg-M model on Cityscapes validation set:

python test.py 'checkpoints/cityscapes/cityscapes_efficientnet_b1_hyperseg-m' \
-td "hyperseg.datasets.cityscapes.CityscapesDataset('data/cityscapes',split='val',mode='fine')" \
-it "seg_transforms.LargerEdgeResize([512,1024])"

Testing a pretrained model

For example testing the PASCAL VOC HyperSeg-L model using the available test configuration:

python configs/test/vocsbd_efficientnet_b3_hyperseg-l.py

Citation

@inproceedings{nirkin2021hyperseg,
  title={{HyperSeg}: Patch-wise Hypernetwork for Real-time Semantic Segmentation},
  author={Nirkin, Yuval and Wolf, Lior and Hassner, Tal},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month={June},
  year={2021}
}