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A network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization.
git clone https://github.com/PaddlePaddle/PaddleSeg.git
cd PaddleSeg
pip3 install -r requirements.txt
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/convert_cityscapes.py --cityscapes_path /path/to/cityscapes --num_workers 8
python3 tools/create_dataset_list.py /path/to/cityscapes --type cityscapes --separator ","
then the cityscapes path as follows:
root@5574247e63f8:~# 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
Notice: modify configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml file, modify the datasets path as yours. The training is use AMP model.
cd PaddleSeg
export FLAGS_cudnn_exhaustive_search=True
export FLAGS_cudnn_batchnorm_spatial_persistent=True
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -u -m paddle.distributed.launch --gpus 0,1,2,3 train.py \
--config configs/unet/unet_cityscapes_1024x512_160k.yml \
--do_eval \
--use_vdl \
--save_dir output_unet_amp \
--precision fp16 \
--amp_level O1
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