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semantic_segmentation

README.md

ADE20K Semantic segmentation with UniFormer

We currenent release the code and models for:

  • Semantic FPN

  • UperNet

Updates

01/18/2022

[Initial commits]:

  1. Models with Semantic FPN.

  2. Models with UperNet.

Model Zoo

The followed models and logs can be downloaded on Google Drive: total_models, total_logs.

We also release the models on Baidu Cloud: total_models (v5v2), total_logs (mhc6).

Note

Semantic FPN

We follow PVT to train our UniFormer with semantic FPN, please refer to fpn_seg.

Backbone Lr Schd mIoU #params FLOPs Model Log Shell
UniFormer-Sh14 80K 46.3 25M 172G - google run.sh/config
UniFormer-Bh14 80K 47.0 54M 328G google google run.sh/config

For the models with the window size of 32, we train one model but test with different test_config.

Backbone Lr Schd mIoU #params FLOPs Model Log Shell
UniFormer-Sw32 80K 45.6 25M 183G google google run.sh/config
UniFormer-Sh32 80K 46.2 25M 199G google google run.sh/config
UniFormer-S 80K 46.6 25M 247G google google run.sh/config
UniFormer-Bw32 80K 47.0 54M 310G google google run.sh/config
UniFormer-Bh32 80K 47.7 54M 350G google google run.sh/config
UniFormer-B 80K 48.0 54M 471G google google run.sh/config

UperNet

We follow Swin Transformer to train our UniFormer with UperNet.

Backbone Lr Schd mIoU MS mIoU #params FLOPs Model Log Shell
UniFormer-Sh14 160K 46.9 48.0 52M 947G - google run.sh/config
UniFormer-Bh14 160K 48.9 50.0 80M 1085G - google run.sh/config

For the models with the window size of 32, we train one model but test with different test_config.

Backbone Lr Schd mIoU MS mIoU #params FLOPs Model Log Shell
UniFormer-Sw32 160K 46.6 48.4 52M 939G google google run.sh/config
UniFormer-Sh32 160K 47.0 48.5 52M 955G google google run.sh/config
UniFormer-S 160K 47.6 48.5 52M 1004G google google run.sh/config
UniFormer-Bw32 160K 49.1 50.6 80M 1066G google google run.sh/config
UniFormer-Bh32 160K 49.5 50.7 80M 1106G google google run.sh/config
UniFormer-B 160K 50.0 50.8 80M 1227G google google run.sh/config

Usage

Installation

Please refer to get_started for installation and dataset preparation.

Training

  1. Download the pretrained models in our repository.

  2. Simply run the training scripts in exp as followed:

    bash ./exp/upernet_global_small/run.sh

    Or you can train other models as follower:

    # single-gpu training
python tools/train.py <CONFIG_FILE> --options model.backbone.pretrained_path=<PRETRAIN_MODEL> [other optional arguments]

# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --options model.backbone.pretrained_path=<PRETRAIN_MODEL> [other optional arguments]

[Note]:

  • We use hybrid MHRA during testing and set the corresponding hyperparameters in the config.py:

    window: False, # whether use window MHRA
hybrid: True, # whether use hybrid MHRA
window_size: 32, # size of window (>=14)

  • To avoid out of memory, we use torch.utils.checkpoint in the config.py:

    use_checkpoint=True, # whether use checkpoint
checkpoint_num=[0, 0, 2, 0], # index for using checkpoint in every stage

Testing

# single-gpu testing
python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval mIoU

# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval mIoU

Acknowledgement

This repository is built based on mmsegmentation, Swin Transformer and PVT repository.