[Fix] Fix dependency (#3136)
## Motivation Change the dependency `mmcls` to `mmpretrain` ## Modification - modify `mmcls` to `mmpretrain` - modify CI requirements ## BC-breaking (Optional) If users have installed mmcls but not install mmpretrain, it might raise some error.pull/3138/head
谢昕辰
GitHub
parent
d3f2922ff5
commit
ffb7e2e239
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@ -61,9 +61,9 @@ jobs:
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command: |
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pip install git+https://github.com/open-mmlab/mmengine.git@main
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pip install -U openmim
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mim install 'mmcv>=2.0.0rc4'
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pip install mmcls==1.0.0rc6
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pip install git+https://github.com/open-mmlab/mmdetection.git@main
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mim install mmcv>=2.0.0
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pip install mmpretrain>=1.0.0rc7
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pip install mmdet>=3.0.0
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pip install -r requirements/tests.txt -r requirements/optional.txt
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python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
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- run:
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@ -101,15 +101,15 @@ jobs:
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name: Build Docker image
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command: |
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docker build .circleci/docker -t mmseg:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
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docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmclassification:/mmclassification -v /home/circleci/mmdetection:/mmdetection -w /mmseg --name mmseg mmseg:gpu
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docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmpretrain:/mmpretrain -v /home/circleci/mmdetection:/mmdetection -w /mmseg --name mmseg mmseg:gpu
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- run:
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name: Install mmseg dependencies
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command: |
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docker exec mmseg pip install -e /mmengine
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docker exec mmseg pip install -U openmim
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docker exec mmseg mim install 'mmcv>=2.0.0rc4'
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docker exec mmseg pip install mmcls==1.0.0rc6
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docker exec mmseg mim install mmdet==3.0.0
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docker exec mmseg mim install mmcv>=2.0.0
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docker exec mmseg pip install mmpretrain>=1.0.0rc7
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docker exec mmseg mim install mmdet>=3.0.0
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docker exec mmseg pip install -r requirements/tests.txt -r requirements/optional.txt
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docker exec mmseg python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
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- run:
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@ -145,8 +145,8 @@ workflows:
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- main
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- build_cpu:
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name: minimum_version_cpu
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torch: 1.6.0
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torchvision: 0.7.0
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torch: 1.8.1
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torchvision: 0.9.1
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python: "3.7"
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requires:
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- lint
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@ -177,7 +177,7 @@ workflows:
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jobs:
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- build_cuda:
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name: minimum_version_gpu
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torch: 1.6.0
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torch: 1.8.1
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# Use double quotation mark to explicitly specify its type
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# as string instead of number
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cuda: "10.1"
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@ -4,7 +4,8 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer
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# TODO: delete custom_imports after mmcls supports auto import
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# please install mmcls>=1.0
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# import mmcls.models to trigger register_module in mmcls
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custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
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custom_imports = dict(
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imports=['mmpretrain.models'], allow_failed_imports=False)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53],
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@ -16,7 +17,7 @@ model = dict(
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type='EncoderDecoder',
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.PoolFormer',
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type='mmpretrain.PoolFormer',
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arch='s12',
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init_cfg=dict(
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type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.'),
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@ -1,5 +1,5 @@
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
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custom_imports = dict(imports='mmpretrain.models', allow_failed_imports=False)
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checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' # noqa
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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@ -13,7 +13,7 @@ model = dict(
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data_preprocessor=data_preprocessor,
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pretrained=None,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='base',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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@ -9,7 +9,7 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/d
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='base',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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@ -9,7 +9,7 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/d
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='large',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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@ -8,7 +8,7 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/d
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='small',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.3,
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@ -8,7 +8,7 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/d
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='tiny',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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@ -9,7 +9,7 @@ checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/d
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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type='mmpretrain.ConvNeXt',
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arch='xlarge',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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@ -39,7 +39,7 @@ Transformers have shown great potential in computer vision tasks. A common belie
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- PoolFormer backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
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```shell
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pip install "mmcls>=1.0.0rc0"
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pip install "mmpretrain>=1.0.0rc7"
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```
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- The pretrained models could also be downloaded from [PoolFormer config of MMClassification](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer).
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@ -8,7 +8,7 @@ The compatible MMSegmentation, MMCV and MMEngine versions are as below. Please i
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| MMSegmentation version | MMCV version | MMEngine version | MMClassification (optional) version | MMDetection (optional) version |
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| :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
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| dev-1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| dev-1.x branch | mmcv >= 2.0.0 | MMEngine >= 0.7.4 | mmpretrain>=1.0.0rc7 | mmdet >= 3.0.0 |
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| main branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| 1.0.0 | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| 1.0.0rc6 | mmcv >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
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@ -8,7 +8,7 @@
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| MMSegmentation version | MMCV version | MMEngine version | MMClassification (optional) version | MMDetection (optional) version |
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| :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
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| dev-1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| dev-1.x branch | mmcv >= 2.0.0 | MMEngine >= 0.7.4 | mmpretrain>=1.0.0rc7 | mmdet >= 3.0.0 |
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| main branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| 1.0.0 | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
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| 1.0.0rc6 | mmcv >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
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