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Tianlong Ai 432628b735
[Fix] Rename and Fix bug of projects HieraSeg (old PR #2444) (#2565) 
## Motivation
Supplementary PR #2444 
Fix tiny bug and add loss_by_feat() to compute loss to train.
The inference process have verified to be accurate.
## Modification
- modify `sep_aspp_contrast_head.py` , add `loss_by_feat()` function to
train(training still has bug, will fix in future😫)
- fix testing commands path error `bash tools/dist_test.sh
projects/HieraSeg_project/` to `bash tools/dist_test.sh
projects/HieraSeg/` at README.md
 		2023-02-06 18:55:22 +08:00
..
configs [Fix] Rename and Fix bug of projects HieraSeg (old PR #2444) (#2565) 2023-02-06 18:55:22 +08:00
decode_head [Fix] Rename and Fix bug of projects HieraSeg (old PR #2444) (#2565) 2023-02-06 18:55:22 +08:00
losses [Fix] Rename and Fix bug of projects HieraSeg (old PR #2444) (#2565) 2023-02-06 18:55:22 +08:00
README.md [Fix] Rename and Fix bug of projects HieraSeg (old PR #2444) (#2565) 2023-02-06 18:55:22 +08:00

README.md

HSSN

Description

Author: AI-Tianlong

This project implements Deep Hierarchical Semantic Segmentation inference on cityscapes dataset

Usage

Prerequisites

  • Python 3.8
  • PyTorch 1.6 or higher
  • MMSegmentation v1.0.0rc5
  • mmcv v2.0.0rc4
  • mmengine >=0.4.0

Dataset preparing

Preparing cityscapes dataset following this Dataset Preparing Guide

Testing commands

Please put hieraseg_deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024_20230112_125023-bc59a3d1.pth to mmsegmentation/checkpoints

Multi-GPUs Test

# --tta optional, multi-scale test, need mmengine >=0.4.0
bash tools/dist_test.sh [configs] [model weights] [number of gpu]  --tta

Example

bash tools/dist_test.sh projects/hssn/configs/hssn/hieraseg_deeplabv3plus_r101-d8_4xb2-80l_cityscapes-512x1024.py checkpoints/hieraseg_deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024_20230112_125023-bc59a3d1.pth 2 --tta

Results

Cityscapes

Method Backbone Crop Size mIoU mIoU (ms+flip) config model
DeeplabV3+ R-101-D8 512x1024 81.61 82.71 config model

Citation

This project is modified from qhanghu/HSSN_pytorch

@article{li2022deep,
  title={Deep Hierarchical Semantic Segmentation},
  author={Li, Liulei and Zhou, Tianfei and Wang, Wenguan and Li, Jianwu and Yang, Yi},
  journal={CVPR},
  year={2022}
}

Checklist

  • Milestone 1: PR-ready, and acceptable to be one of the projects/.

    • Finish the code

    • Basic docstrings & proper citation

    • Test-time correctness

    • A full README

  • Milestone 2: Indicates a successful model implementation.

    • Training-time correctness

  • Milestone 3: Good to be a part of our core package!

    • Type hints and docstrings

    • Unit tests

    • Code polishing

    • Metafile.yml

  • Move your modules into the core package following the codebase's file hierarchy structure.

  • Refactor your modules into the core package following the codebase's file hierarchy structure.