[Fix] Fix dependency (open-mmlab#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.

Author: xiexinch

.circleci/test.yml

@@ -61,9 +61,9 @@ jobs:
           command: |
             pip install git+https://github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
-            mim install 'mmcv>=2.0.0rc4'
-            pip install mmcls==1.0.0rc6
-            pip install git+https://github.com/open-mmlab/mmdetection.git@main
+            mim install mmcv>=2.0.0
+            pip install mmpretrain>=1.0.0rc7
+            pip install mmdet>=3.0.0
             pip install -r requirements/tests.txt -r requirements/optional.txt
             python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - run:
@@ -101,15 +101,15 @@ jobs:
           name: Build Docker image
           command: |
             docker build .circleci/docker -t mmseg:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
-            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
+            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
       - run:
           name: Install mmseg dependencies
           command: |
             docker exec mmseg pip install -e /mmengine
             docker exec mmseg pip install -U openmim
-            docker exec mmseg mim install 'mmcv>=2.0.0rc4'
-            docker exec mmseg pip install mmcls==1.0.0rc6
-            docker exec mmseg mim install mmdet==3.0.0
+            docker exec mmseg mim install mmcv>=2.0.0
+            docker exec mmseg pip install mmpretrain>=1.0.0rc7
+            docker exec mmseg mim install mmdet>=3.0.0
             docker exec mmseg pip install -r requirements/tests.txt -r requirements/optional.txt
             docker exec mmseg python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - run:
@@ -145,8 +145,8 @@ workflows:
                 - main
       - build_cpu:
           name: minimum_version_cpu
-          torch: 1.6.0
-          torchvision: 0.7.0
+          torch: 1.8.1
+          torchvision: 0.9.1
           python: "3.7"
           requires:
             - lint
@@ -177,7 +177,7 @@ workflows:
     jobs:
       - build_cuda:
           name: minimum_version_gpu
-          torch: 1.6.0
+          torch: 1.8.1
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
           cuda: "10.1"

configs/_base_/models/fpn_poolformer_s12.py

@@ -4,7 +4,8 @@
 # TODO: delete custom_imports after mmcls supports auto import
 # please install mmcls>=1.0
 # import mmcls.models to trigger register_module in mmcls
-custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 data_preprocessor = dict(
     type='SegDataPreProcessor',
     mean=[123.675, 116.28, 103.53],
@@ -16,7 +17,7 @@
     type='EncoderDecoder',
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.PoolFormer',
+        type='mmpretrain.PoolFormer',
         arch='s12',
         init_cfg=dict(
             type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.'),

configs/_base_/models/upernet_convnext.py

@@ -1,5 +1,5 @@
 norm_cfg = dict(type='SyncBN', requires_grad=True)
-custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
+custom_imports = dict(imports='mmpretrain.models', allow_failed_imports=False)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth'  # noqa
 data_preprocessor = dict(
     type='SegDataPreProcessor',
@@ -13,7 +13,7 @@
     data_preprocessor=data_preprocessor,
     pretrained=None,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='base',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py

@@ -9,7 +9,7 @@
 model = dict(
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='base',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py

@@ -9,7 +9,7 @@
 model = dict(
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='large',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py

@@ -8,7 +8,7 @@
 model = dict(
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='small',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.3,

configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py

@@ -8,7 +8,7 @@
 model = dict(
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='tiny',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py

@@ -9,7 +9,7 @@
 model = dict(
     data_preprocessor=data_preprocessor,
     backbone=dict(
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='xlarge',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/poolformer/README.md

@@ -39,7 +39,7 @@ Transformers have shown great potential in computer vision tasks. A common belie
 - PoolFormer backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
 
 ```shell
-pip install "mmcls>=1.0.0rc0"
+pip install "mmpretrain>=1.0.0rc7"
 ```
 
 - The pretrained models could also be downloaded from [PoolFormer config of MMClassification](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer).

docs/en/notes/faq.md

@@ -8,7 +8,7 @@ The compatible MMSegmentation, MMCV and MMEngine versions are as below. Please i
 
 | MMSegmentation version |          MMCV version          | MMEngine version  | MMClassification (optional) version | MMDetection (optional) version |
 | :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
-|     dev-1.x branch     |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
+|     dev-1.x branch     |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
 |      main branch       |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
 |         1.0.0          |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
 |        1.0.0rc6        |        mmcv >= 2.0.0rc4        | MMEngine >= 0.5.0 |           mmcls>=1.0.0rc0           |       mmdet >= 3.0.0rc6        |