A Global Context Network, or GCNet, utilises global context blocks to model long-range dependencies in images.
It is based on the Non-Local Network, but it modifies the architecture so less computation is required.
Global context blocks are applied to multiple layers in a backbone network to construct the GCNet.
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
mkdir data/
ln -s /path/to/cityscapes data/cityscapes
python3 tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8
data/
├── cityscapes
│ ├── gtFine
│ │ ├── test
│ │ ├── train
│ │ └── val
│ └── leftImg8bit
│ │ ├── test
│ │ ├── train
│ │ └── val
├── test.lst
├── trainval.lst
└── val.lst
MMCV_WITH_OPS=1 python3 setup.py build && cp build/lib.linux*/mmcv/_ext.cpython* mmcv
pip3 install -r requirements.txt
bash run_train.sh
bash dist_train.sh configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py 4
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