74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
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# Segmentation
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- [Segmentation](#segmentation)
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- [Train](#train)
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- [Test](#test)
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For semantic segmentation task, we use MMSegmentation. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.
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```shell
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pip install openmim
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```
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It is very easy to install the package.
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Besides, please refer to MMSegmentation for [installation](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/get_started.md) and [data preparation](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/user_guides/2_dataset_prepare.md).
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## Train
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After installation, you can run MMSeg with simple command.
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```shell
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# distributed version
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bash tools/benchmarks/mmsegmentation/mim_dist_train.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmsegmentation/mim_slurm_train.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
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```
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Remarks:
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- `CONFIG`: Use config files under `configs/benchmarks/mmsegmentation/`. Since repositories of OpenMMLab have support referring config files across different
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repositories, we can easily leverage the configs from MMSegmentation like:
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```shell
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_base_ = 'mmseg::fcn/fcn_r50-d8_769x769_40k_cityscapes.py'
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```
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Writing your config files from scratch is also supported.
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- `PRETRAIN`: the pre-trained model file.
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- `GPUS`: The number of GPUs that you want to use to train. We adopt 4 GPUs for segmentation tasks by default.
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Example:
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```shell
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bash ./tools/benchmarks/mmsegmentation/mim_dist_train.sh \
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configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py \
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work_dir/byol_resnet50_8xb32-accum16-coslr-200e_in1k_20220225-5c8b2c2e.pth 4
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```
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## Test
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After training, you can also run the command below to test your model.
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```shell
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# distributed version
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bash tools/benchmarks/mmsegmentation/mim_dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmsegmentation/mim_slurm_test.sh ${PARTITION} ${CONFIG} ${CHECKPOINT}
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```
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Remarks:
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- `CHECKPOINT`: The well-trained segmentation model that you want to test.
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Example:
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```shell
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bash ./tools/benchmarks/mmsegmentation/mim_dist_test.sh \
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configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py \
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work_dir/iter_20000.pth 4
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```
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