Merge branch 'dev-1.x' into 1.x

.circleci/test.yml

@@ -59,8 +59,8 @@ jobs:
       - run:
           name: Install MMSelfSup dependencies
           command: |
-            pip install git+https://github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
+            mim install mmengine
             mim install 'mmcv>=2.0.0rc1'
             mim install 'mmdet>=3.0.0rc0'
             mim install 'mmsegmentation>=1.0.0rc0'
@@ -142,20 +142,13 @@ workflows:
             branches:
               ignore:
                 - dev-1.x
-      - build_cpu:
-          name: minimum_version_cpu
-          torch: 1.6.0
-          torchvision: 0.7.0
-          python: 3.7.4
-          requires:
-            - lint
       - build_cpu:
           name: maximum_version_cpu
           torch: 1.9.0
           torchvision: 0.10.0
           python: 3.9.0
           requires:
-            - minimum_version_cpu
+            - lint
       - hold:
           type: approval
           requires:

.github/workflows/merge_stage_test.yml

@@ -37,11 +37,10 @@ jobs:
         run: pip install pip --upgrade
       - name: Install PyTorch
         run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
-      - name: Install MMEngine
-        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
       - name: Install MMSelfSup dependencies
         run: |
           pip install -U openmim
+          mim install mmengine
           mim install 'mmcv>=2.0.0rc1'
           mim install 'mmdet>=3.0.0rc0'
           mim install 'mmsegmentation>=1.0.0rc0'

.github/workflows/pr_stage_test.yml

@@ -34,11 +34,10 @@ jobs:
         run: pip install pip --upgrade
       - name: Install PyTorch
         run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
-      - name: Install MMEngine
-        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
       - name: Install MMSelfSup dependencies
         run: |
           pip install -U openmim
+          mim install mmengine
           mim install 'mmcv>=2.0.0rc1'
           mim install 'mmdet>=3.0.0rc0'
           mim install 'mmsegmentation>=1.0.0rc0'
@@ -88,8 +87,8 @@ jobs:
           apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libxrender-dev
       - name: Install MMSelfSup dependencies
         run: |
-          pip install git+https://github.com/open-mmlab/mmengine.git@main
           pip install -U openmim
+          mim install mmengine
           mim install 'mmcv>=2.0.0rc1'
           mim install 'mmdet>=3.0.0rc0'
           mim install 'mmsegmentation>=1.0.0rc0'
@@ -172,8 +171,8 @@ jobs:
         run: pip install torch==1.8.1+${{matrix.platform}} torchvision==0.9.1+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
       - name: Install MMSelfSup dependencies
         run: |
-          pip install git+https://github.com/open-mmlab/mmengine.git@main
           pip install -U openmim
+          mim install mmengine
           mim install 'mmcv>=2.0.0rc1'
           mim install 'mmdet>=3.0.0rc0'
           mim install 'mmsegmentation>=1.0.0rc0'

README.md

@@ -31,6 +31,10 @@
 [🆕Update News](https://mmselfsup.readthedocs.io/en/dev-1.x/notes/changelog.html) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmselfsup/issues/new/choose)
 
+🌟 MMSelfSup will be integrated into MMPreTrain， which is a newly upgraded open-source framework for visual pre-training. It has set out to provide multiple powerful pre-trained backbones and support different pre-training strategies.
+
+:point_right: **MMPreTrain 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/pretrain) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:
+
 </div>
 
 <div align="center">
@@ -39,6 +43,26 @@ English | [简体中文](README_zh-CN.md)
 
 </div>
 
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
 ## Introduction
 
 MMSelfSup is an open source self-supervised representation learning toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
@@ -127,8 +151,8 @@ Supported algorithms:
 - [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/odc)
 - [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov1)
 - [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simclr)
-- [x] [MoCo v2 (arXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/byol)
-- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov2)
+- [x] [MoCo v2 (arXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov2)
+- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/byol)
 - [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/swav)
 - [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/densecl)
 - [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simsiam)
@@ -143,6 +167,7 @@ Supported algorithms:
 - [x] [BEiT v2 (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/beitv2)
 - [x] [EVA (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/eva)
 - [x] [MixMIM (ArXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mixmim)
+- [x] [PixMIM (ArXiv'2023)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim)
 
 More algorithms are in our plan.
 

README_zh-CN.md

@@ -31,6 +31,12 @@
 [🆕更新日志](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html) |
 [🤔报告问题](https://github.com/open-mmlab/mmselfsup/issues/new/choose)
 
+🌟 MMSelfSup 将整合进 MMPreTrain，一个全新升级的预训练开源算法框架。
+
+MMPreTrain 旨在提供各种强大的预训练主干网络，并支持了不同的预训练策略。
+
+:point_right: **MMPreTrain 1.0 版本即将正式发布，欢迎大家 [试用](https://github.com/open-mmlab/mmclassification/tree/pretrain) 并 [参与讨论](https://github.com/open-mmlab/mmclassification/discussions)！** :point_left:
+
 </div>
 
 <div align="center">
@@ -39,6 +45,28 @@
 
 </div>
 
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
 ## 介绍
 
 MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目成员之一。
@@ -127,8 +155,8 @@ Useful Tools
 - [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/odc)
 - [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov1)
 - [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simclr)
-- [x] [MoCo v2 (arXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/byol)
-- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov2)
+- [x] [MoCo v2 (arXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov2)
+- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/byol)
 - [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/swav)
 - [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/densecl)
 - [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simsiam)
@@ -143,6 +171,7 @@ Useful Tools
 - [x] [BEiT v2 (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/beitv2)
 - [x] [EVA (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/eva)
 - [x] [MixMIM (ArXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mixmim)
+- [x] [PixMIM (ArXiv'2023)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim)
 
 更多的算法实现已经在我们的计划中。
 

configs/benchmarks/classification/_base_/datasets/imagenet.py

@@ -10,15 +10,14 @@
     to_rgb=True,
 )
 
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='RandomResizedCrop', scale=224, backend='pillow'),
     dict(type='RandomFlip', prob=0.5, direction='horizontal'),
     dict(type='PackClsInputs'),
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
     dict(type='CenterCrop', crop_size=224),
     dict(type='PackClsInputs'),

configs/benchmarks/classification/_base_/datasets/imagenet_swin_192.py

@@ -10,9 +10,8 @@
     to_rgb=True,
 )
 
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=192,
@@ -39,7 +38,7 @@
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='ResizeEdge',
         scale=219,

configs/benchmarks/classification/_base_/datasets/places205.py

@@ -10,17 +10,16 @@
     to_rgb=True,
 )
 
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='Resize', scale=256),
     dict(type='CenterCrop', crop_size=256),
     dict(type='RandomCrop', size=224),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackSelfSupInputs')
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='Resize', scale=256),
     dict(type='CenterCrop', crop_size=224),
     dict(type='PackSelfSupInputs')

configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k-224.py

@@ -7,9 +7,8 @@
         img_size=224, stage_cfgs=dict(block_cfgs=dict(window_size=7))))
 
 # train pipeline
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=224,
@@ -37,7 +36,7 @@
 
 # test pipeline
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='mmcls.ResizeEdge',
         scale=256,

configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k.py

@@ -16,10 +16,8 @@
 )
 bgr_mean = data_preprocessor['mean'][::-1]
 bgr_std = data_preprocessor['std'][::-1]
-
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=224,
@@ -47,7 +45,7 @@
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='ResizeEdge',
         scale=256,

configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py

@@ -32,10 +32,8 @@
         dict(type='CutMix', alpha=1.0)
     ]))
 
-file_client_args = dict(backend='disk')
-
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=224,
@@ -61,7 +59,7 @@
     dict(type='PackClsInputs')
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='ResizeEdge',
         scale=256,

configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb256-coslr-100e_in1k.py

@@ -9,9 +9,8 @@
     ]))
 
 # dataset
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=224,
@@ -38,7 +37,7 @@
     dict(type='PackClsInputs'),
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
     dict(type='CenterCrop', crop_size=224),
     dict(type='PackClsInputs'),

configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb2048-coslr-90e_in1k.py

@@ -33,15 +33,14 @@
     ))
 
 # dataset settings
-file_client_args = dict(backend='disk')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='RandomResizedCrop', scale=224, backend='pillow'),
     dict(type='RandomFlip', prob=0.5, direction='horizontal'),
     dict(type='PackClsInputs'),
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
     dict(type='CenterCrop', crop_size=224),
     dict(type='PackClsInputs'),

configs/benchmarks/classification/imagenet/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448.py

@@ -9,10 +9,8 @@
     head=dict(in_channels=1280))
 
 # dataset settings
-file_client_args = dict(backend='disk')
-
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='RandomResizedCrop',
         scale=448,
@@ -39,7 +37,7 @@
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(
         type='ResizeEdge',
         scale=512,

configs/benchmarks/classification/knn_imagenet.py

@@ -1,9 +1,7 @@
 dataset_type = 'mmcls.ImageNet'
 data_root = 'data/imagenet'
-file_client_args = dict(backend='disk')
-
 extract_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='mmcls.ResizeEdge', scale=256, edge='short'),
     dict(type='CenterCrop', crop_size=224),
     dict(type='PackSelfSupInputs'),

configs/benchmarks/classification/svm_voc07.py

@@ -1,12 +1,10 @@
 dataset_type = 'ImageList'
 data_root = 'data/VOCdevkit/VOC2007/'
-file_client_args = dict(backend='disk')
-
 split_at = [5011]
 split_name = ['voc07_trainval', 'voc07_test']
 
 extract_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='mmcls.ResizeEdge', scale=256),
     dict(type='Resize', scale=224),
     dict(type='PackSelfSupInputs', meta_keys=['img_path'])