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* [Docs] Update user guides docs and tools for MMPretrain. * Fix UT * Fix Chinese docs. * Improve according to comments. * Fix windows CI.
129 lines
5.2 KiB
Markdown
129 lines
5.2 KiB
Markdown
# Downstream tasks
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## Detection
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For detection tasks, please use MMDetection. 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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mim install 'mmdet>=3.0.0rc0'
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```
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Besides, please refer to MMDet for [installation](https://mmdetection.readthedocs.io/en/dev-3.x/get_started.html) and [data preparation](https://mmdetection.readthedocs.io/en/dev-3.x/user_guides/dataset_prepare.html)
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### Train
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After installation, you can run MMDetection with simple command.
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```shell
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# distributed version
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bash tools/benchmarks/mmdetection/mim_dist_train_c4.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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bash tools/benchmarks/mmdetection/mim_dist_train_fpn.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmdetection/mim_slurm_train_c4.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
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bash tools/benchmarks/mmdetection/mim_slurm_train_fpn.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
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```
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- `${CONFIG}`: Use config file path in MMDetection directly. And for some algorithms, we also have some
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modified config files which can be found in the `benchmarks` folder under the correspondding algorithm
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folder. You can also writing your config file from scratch.
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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 8 GPUs for detection tasks by default.
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Example:
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```shell
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bash ./tools/benchmarks/mmdetection/mim_dist_train_c4.sh \
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configs/byol/benchmarks/mask-rcnn_r50-c4_ms-1x_coco.py \
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https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 8
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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/mmdetection/mim_dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmdetection/mim_slurm_test.sh ${PARTITION} ${CONFIG} ${CHECKPOINT}
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```
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- `${CONFIG}`: Use config file name in MMDetection directly. And for some algorithms, we also have some
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modified config files which can be found in the `benchmarks` folder under the correspondding algorithm
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folder. You can also writing your config file from scratch.
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- `${CHECKPOINT}`: The fine-tuned detection model that you want to test.
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- `${GPUS}`: The number of GPUs that you want to use to test. We adopt 8 GPUs for detection tasks by default.
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Example:
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```shell
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bash ./tools/benchmarks/mmdetection/mim_dist_test.sh \
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configs/byol/benchmarks/mask-rcnn_r50_fpn_ms-1x_coco.py \
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https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 8
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```
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## Segmentation
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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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mim install 'mmsegmentation>=1.0.0rc0'
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```
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Besides, please refer to MMSegmentation for [installation](https://mmsegmentation.readthedocs.io/en/dev-1.x/get_started.html) and [data preparation](https://mmsegmentation.readthedocs.io/en/dev-1.x/user_guides/2_dataset_prepare.html).
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### Train
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After installation, you can run MMSegmentation 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} ${PRETRAIN}
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```
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- `${CONFIG}`: Use config file path in MMSegmentation directly. And for some algorithms, we also have some
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modified config files which can be found in the `benchmarks` folder under the correspondding algorithm
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folder. You can also writing your config file from scratch.
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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_4xb4-20k_voc12aug-512x512.py \
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https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.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} ${CHECKPOINT}
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```
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- `${CONFIG}`: Use config file name in MMSegmentation directly. And for some algorithms, we also have some
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modified config files which can be found in the `benchmarks` folder under the correspondding algorithm
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folder. You can also writing your config file from scratch.
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- `${CHECKPOINT}`: The fine-tuned segmentation model that you want to test.
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- `${GPUS}`: The number of GPUs that you want to use to test. 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_test.sh fcn_r50-d8_4xb4-20k_voc12aug-512x512.py \
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https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 4
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```
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