replace mmcls with mmpretrain (open-mmlab#10545)

.circleci/test.yml

@@ -74,6 +74,7 @@ jobs:
             pip install -r requirements/tests.txt -r requirements/optional.txt
             pip install --force-reinstall pycocotools
             pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+            pip install -r requirements/tracking.txt
             pip install git+https://github.com/cocodataset/panopticapi.git
             pip install git+https://github.com/JonathonLuiten/TrackEval.git
       - run:
@@ -93,10 +94,10 @@ jobs:
         type: string
       cuda:
         type: enum
-        enum: ["10.1", "10.2", "11.1", "11.7"]
+        enum: ["11.1", "11.7"]
       cudnn:
         type: integer
-        default: 7
+        default: 8
     machine:
       image: ubuntu-2004-cuda-11.4:202110-01
       # docker_layer_caching: true
@@ -123,6 +124,7 @@ jobs:
             docker exec mmdetection pip install -r requirements/tests.txt -r requirements/optional.txt
             docker exec mmdetection pip install pycocotools
             docker exec mmdetection pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+            docker exec mmdetection pip install -r requirements/tracking.txt
             docker exec mmdetection pip install git+https://github.com/cocodataset/panopticapi.git
             docker exec mmdetection pip install git+https://github.com/JonathonLuiten/TrackEval.git
             docker exec mmdetection python -c 'import mmcv; print(mmcv.__version__)'
@@ -157,9 +159,9 @@ workflows:
                 - dev-3.x
       - build_cpu:
           name: minimum_version_cpu
-          torch: 1.7.1
-          torchvision: 0.8.2
-          python: 3.7.4 # The lowest python 3.7.x version available on CircleCI images
+          torch: 1.8.0
+          torchvision: 0.9.0
+          python: 3.7.16
           requires:
             - lint
       - build_cpu:
@@ -178,7 +180,7 @@ workflows:
           torch: 1.8.1
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          cuda: "10.2"
+          cuda: "11.1"
           requires:
             - hold
       - build_cuda:
@@ -194,8 +196,8 @@ workflows:
     jobs:
       - build_cuda:
           name: minimum_version_gpu
-          torch: 1.6.0
-          cuda: "10.1"
+          torch: 1.8.0
+          cuda: "11.1"
           filters:
             branches:
               only:

.dev_scripts/benchmark_options.py

@@ -5,7 +5,7 @@
     'pip install instaboostfast',
     'pip install git+https://github.com/cocodataset/panopticapi.git',
     'pip install timm',
-    'pip install mmcls>=1.0.0rc0',
+    'pip install mmpretrain',
     'pip install git+https://github.com/lvis-dataset/lvis-api.git',
     'pip install -r ../requirements/multimodal.txt',
     'pip install -r ../requirements/tracking.txt',

.github/pull_request_template.md

@@ -21,5 +21,5 @@ If this PR introduces a new feature, it is better to list some use cases here, a
 
 1. Pre-commit or other linting tools are used to fix the potential lint issues.
 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
-3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls.
+3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMPreTrain.
 4. The documentation has been modified accordingly, like docstring or example tutorials.

configs/convnext/README.md

@@ -20,10 +20,10 @@ The "Roaring 20s" of visual recognition began with the introduction of Vision Tr
 
 **Note**:
 
-- ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
+- ConvNeXt backbone needs to install [MMPreTrain](https://github.com/open-mmlab/mmpretrain) first, which has abundant backbones for downstream tasks.
 
 ```shell
-pip install mmcls>=1.0
+pip install mmpretrain
 ```
 
 - The performance is unstable. `Cascade Mask R-CNN` may fluctuate about 0.2 mAP.

configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py

@@ -1,15 +1,15 @@
 _base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py'  # noqa
 
-# 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)
+# please install mmpretrain
+# import mmpretrain.models to trigger register_module in mmpretrain
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth'  # noqa
 
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='small',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.6,

configs/convnext/cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py

@@ -4,16 +4,16 @@
     '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
 ]
 
-# 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)
+# please install mmpretrain
+# import mmpretrain.models to trigger register_module in mmpretrain
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa
 
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='tiny',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py

@@ -4,16 +4,16 @@
     '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
 ]
 
-# 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)
+# please install mmpretrain
+# import mmpretrain.models to trigger register_module in mmpretrain
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa
 
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='mmcls.ConvNeXt',
+        type='mmpretrain.ConvNeXt',
         arch='tiny',
         out_indices=[0, 1, 2, 3],
         drop_path_rate=0.4,

configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py

@@ -40,9 +40,9 @@
     detector=detector,
     reid=dict(
         type='BaseReID',
-        data_preprocessor=None,
+        data_preprocessor=dict(type='mmpretrain.ClsDataPreprocessor'),
         backbone=dict(
-            type='mmcls.ResNet',
+            type='mmpretrain.ResNet',
             depth=50,
             num_stages=4,
             out_indices=(3, ),
@@ -55,7 +55,7 @@
             fc_channels=1024,
             out_channels=128,
             num_classes=380,
-            loss_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
+            loss_cls=dict(type='mmpretrain.CrossEntropyLoss', loss_weight=1.0),
             loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),
             norm_cfg=dict(type='BN1d'),
             act_cfg=dict(type='ReLU')),

configs/reid/README.md

@@ -1,6 +1,6 @@
 # Training a ReID Model
 
-You may want to train a ReID model for multiple object tracking or other applications. We support ReID model training in MMDetection, which is built upon [MMClassification](https://github.com/open-mmlab/mmclassification).
+You may want to train a ReID model for multiple object tracking or other applications. We support ReID model training in MMDetection, which is built upon [MMPretrain](https://github.com/open-mmlab/mmpretrain).
 
 ### 1. Development Environment Setup
 

configs/reid/reid_r50_8xb32-6e_mot17train80_test-mot17val20.py

@@ -9,7 +9,7 @@
         std=[58.395, 57.12, 57.375],
         to_rgb=True),
     backbone=dict(
-        type='mmcls.ResNet',
+        type='mmpretrain.ResNet',
         depth=50,
         num_stages=4,
         out_indices=(3, ),
@@ -22,7 +22,7 @@
         fc_channels=1024,
         out_channels=128,
         num_classes=380,
-        loss_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
+        loss_cls=dict(type='mmpretrain.CrossEntropyLoss', loss_weight=1.0),
         loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),
         norm_cfg=dict(type='BN1d'),
         act_cfg=dict(type='ReLU')),

configs/rtmdet/classification/README.md

@@ -4,23 +4,23 @@ In this folder, we provide the imagenet pre-training config of RTMDet's backbone
 
 ## Requirements
 
-To train with these configs, please install [MMClassification 1.x](https://github.com/open-mmlab/mmclassification/tree/1.x) first.
+To train with these configs, please install [MMPreTrain](https://github.com/open-mmlab/mmpretrain) first.
 
 Install by MIM:
 
 ```shell
-mim install mmcls>=1.0.0rc0
+mim install mmpretrain
 ```
 
 or install by pip:
 
 ```shell
-pip install mmcls>=1.0.0rc0
+pip install mmpretrain
 ```
 
 ## Prepare Dataset
 
-To pre-train on ImageNet, you need to prepare the dataset first. Please refer to the [guide](https://mmclassification.readthedocs.io/en/1.x/user_guides/dataset_prepare.html#imagenet).
+To pre-train on ImageNet, you need to prepare the dataset first. Please refer to the [guide](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#imagenet).
 
 ## How to Train
 

configs/rtmdet/classification/cspnext-s_8xb256-rsb-a1-600e_in1k.py

@@ -1,7 +1,7 @@
 _base_ = [
-    'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
-    'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
-    'mmcls::_base_/default_runtime.py'
+    'mmpretrain::_base_/datasets/imagenet_bs256_rsb_a12.py',
+    'mmpretrain::_base_/schedules/imagenet_bs2048_rsb.py',
+    'mmpretrain::_base_/default_runtime.py'
 ]
 
 model = dict(

configs/strongsort/strongsort_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py

@@ -22,9 +22,9 @@
     detector=detector,
     reid=dict(
         type='BaseReID',
-        data_preprocessor=None,
+        data_preprocessor=dict(type='mmpretrain.ClsDataPreprocessor'),
         backbone=dict(
-            type='mmcls.ResNet',
+            type='mmpretrain.ResNet',
             depth=50,
             num_stages=4,
             out_indices=(3, ),
@@ -37,7 +37,7 @@
             fc_channels=1024,
             out_channels=128,
             num_classes=380,
-            loss_cls=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
+            loss_cls=dict(type='mmpretrain.CrossEntropyLoss', loss_weight=1.0),
             loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),
             norm_cfg=dict(type='BN1d'),
             act_cfg=dict(type='ReLU'))),

configs/timm_example/README.md

@@ -27,22 +27,22 @@ Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layer
 
 ### Install additional requirements
 
-MMDetection supports timm backbones via `TIMMBackbone`, a wrapper class in MMClassification.
-Thus, you need to install `mmcls` in addition to timm.
+MMDetection supports timm backbones via `TIMMBackbone`, a wrapper class in MMPretrain.
+Thus, you need to install `mmpretrain` in addition to timm.
 If you have already installed requirements for mmdet, run
 
 ```shell
 pip install 'dataclasses; python_version<"3.7"'
 pip install timm
-pip install 'mmcls>=0.20.0'
+pip install mmpretrain
 ```
 
-See [this document](https://mmclassification.readthedocs.io/en/latest/install.html) for the details of MMClassification installation.
+See [this document](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) for the details of MMPretrain installation.
 
 ### Edit config
 
 - See example configs for basic usage.
-- See the documents of [timm feature extraction](https://rwightman.github.io/pytorch-image-models/feature_extraction/#multi-scale-feature-maps-feature-pyramid) and [TIMMBackbone](https://mmclassification.readthedocs.io/en/latest/api.html#mmcls.models.backbones.TIMMBackbone) for details.
+- See the documents of [timm feature extraction](https://rwightman.github.io/pytorch-image-models/feature_extraction/#multi-scale-feature-maps-feature-pyramid) and [TIMMBackbone](https://mmpretrain.readthedocs.io/en/latest/api/generated/mmpretrain.models.backbones.TIMMBackbone.html#mmpretrain.models.backbones.TIMMBackbone) for details.
 - Which feature map is output depends on the backbone.
   Please check `backbone out_channels` and `backbone out_strides` in your log, and modify `model.neck.in_channels` and `model.backbone.out_indices` if necessary.
 - If you use Vision Transformer models that do not support `features_only=True`, add `custom_hooks = []` to your config to disable `NumClassCheckHook`.

configs/timm_example/retinanet_timm-efficientnet-b1_fpn_1x_coco.py

@@ -4,15 +4,15 @@
     '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
 ]
 
-# 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)
+# please install mmpretrain
+# import mmpretrain.models to trigger register_module in mmpretrain
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='mmcls.TIMMBackbone',
+        type='mmpretrain.TIMMBackbone',
         model_name='efficientnet_b1',
         features_only=True,
         pretrained=True,

configs/timm_example/retinanet_timm-tv-resnet50_fpn_1x_coco.py

@@ -4,15 +4,15 @@
     '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
 ]
 
-# 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)
+# please install mmpretrain
+# import mmpretrain.models to trigger register_module in mmpretrain
+custom_imports = dict(
+    imports=['mmpretrain.models'], allow_failed_imports=False)
 
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='mmcls.TIMMBackbone',
+        type='mmpretrain.TIMMBackbone',
         model_name='tv_resnet50',  # ResNet-50 with torchvision weights
         features_only=True,
         pretrained=True,