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A novel Bilateral Segmentation Network (BiSeNet). First design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently.
git clone -b release/2.7 https://github.com/PaddlePaddle/PaddleSeg.git
cd PaddleSeg
pip3 install -r requirements.txt
pip3 install protobuf==3.20.3
pip3 install urllib3==1.26.6
yum install mesa-libGL
python3 setup.py install
Go to visit Cityscapes official website, then choose 'Download' to download the Cityscapes dataset.
Specify /path/to/cityscapes to your Cityscapes path in later training process, the unzipped dataset path structure sholud look like:
cityscapes/
├── gtFine
│ ├── test
│ ├── train
│ │ ├── aachen
│ │ └── bochum
│ └── val
│ ├── frankfurt
│ ├── lindau
│ └── munster
└── leftImg8bit
├── train
│ ├── aachen
│ └── bochum
└── val
├── frankfurt
├── lindau
└── munster
# Datasets preprocessing
pip3 install cityscapesscripts
python3 tools/data/convert_cityscapes.py --cityscapes_path /path/to/cityscapes --num_workers 8
python3 tools/data/create_dataset_list.py /path/to/cityscapes --type cityscapes --separator ","
# CityScapes PATH as follow:
ls -al /path/to/cityscapes
total 11567948
drwxr-xr-x 4 root root 227 Jul 18 03:32 .
drwxr-xr-x 6 root root 179 Jul 18 06:48 ..
-rw-r--r-- 1 root root 298 Feb 20 2016 README
drwxr-xr-x 5 root root 58 Jul 18 03:30 gtFine
-rw-r--r-- 1 root root 252567705 Jul 18 03:22 gtFine_trainvaltest.zip
drwxr-xr-x 5 root root 58 Jul 18 03:30 leftImg8bit
-rw-r--r-- 1 root root 11592327197 Jul 18 03:27 leftImg8bit_trainvaltest.zip
-rw-r--r-- 1 root root 1646 Feb 17 2016 license.txt
-rw-r--r-- 1 root root 193690 Jul 18 03:32 test.txt
-rw-r--r-- 1 root root 398780 Jul 18 03:32 train.txt
-rw-r--r-- 1 root root 65900 Jul 18 03:32 val.txt
# Change '/path/to/cityscapes' as your local Cityscapes dataset path
data_dir=/path/to/cityscapes
sed -i "s#: data/cityscapes#: ${data_dir}#g" configs/_base_/cityscapes.yml
export FLAGS_cudnn_exhaustive_search=True
export FLAGS_cudnn_batchnorm_spatial_persistent=True
# One GPU
export CUDA_VISIBLE_DEVICES=0
python3 tools/train.py --config configs/bisenet/bisenet_cityscapes_1024x1024_160k.yml --do_eval --use_vdl --save_interval 500 --save_dir output
# Four GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -u -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py \
--config configs/bisenet/bisenet_cityscapes_1024x1024_160k.yml \
--do_eval \
--use_vdl
| GPU | FP32 |
|---|---|
| 8 cards | mIoU=73.45% |
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