Bump version to v0.7.0

.github/workflows/build.yml

@@ -99,3 +99,40 @@ jobs:
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  test_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.8]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade --user
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install OpenCV
+        run: |
+          pip install opencv-python>=3
+      - name: Install MMCV
+        run: |
+          pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+      - name: Install unittest dependencies
+        run: |
+          pip install -r requirements.txt
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests
+        run: |
+          coverage run --branch --source mmselfsup -m pytest tests/
+      - name: Generate coverage report
+        run: |
+          coverage xml
+          coverage report -m --omit="mmselfsup/apis/*"

.pre-commit-config.yaml

@@ -40,3 +40,9 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmselfsup", "tools", "tests"]

README.md

@@ -66,7 +66,13 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## ChangeLog
 
-MMSelfSup **v0.6.0** was released in 02/02/2022.
+MMSelfSup **v0.7.0** was released in 03/03/2022.
+
+Highlights of the new version:
+
+* Support **MAE**
+* Add **Places205** benchmarks
+* Add test Windows in workflows
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -92,6 +98,7 @@ Supported algorithms:
 - [x] [DenseCL (CVPR'2021)](https://arxiv.org/abs/2011.09157)
 - [x] [SimSiam (CVPR'2021)](https://arxiv.org/abs/2011.10566)
 - [x] [MoCo v3 (ICCV'2021)](https://arxiv.org/abs/2104.02057)
+- [x] [MAE](https://arxiv.org/abs/2111.06377)
 
 More algorithms are in our plan.
 
@@ -158,20 +165,21 @@ Remarks:
 ## Projects in OpenMMLab
 
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
-- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab toolbox for text detection, recognition and understanding.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMlab toolkit for generative models.
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab few shot learning toolbox and benchmark.
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
 - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

README_zh-CN.md

@@ -64,7 +64,13 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
 
 ## 更新日志
 
-最新的 **v0.6.0** 版本已经在 2022.02.02 发布。
+最新的 **v0.7.0** 版本已经在 2022.03.03 发布。
+
+新版本亮点：
+
+* 支持 **MAE**
+* 增加 **Places205** 下游基准测试
+* 增加 Windows 测试
 
 请参考 [更新日志](docs/zh_cn/changelog.md) 获取更多细节和历史版本信息。
 
@@ -91,6 +97,7 @@ MMSelfSup 和 OpenSelfSup 的不同点写在 [对比文档](docs/en/compatibilit
 - [x] [DenseCL (CVPR'2021)](https://arxiv.org/abs/2011.09157)
 - [x] [SimSiam (CVPR'2021)](https://arxiv.org/abs/2011.10566)
 - [x] [MoCo v3 (ICCV'2021)](https://arxiv.org/abs/2104.02057)
+- [x] [MAE](https://arxiv.org/abs/2111.06377)
 
 更多的算法实现已经在我们的计划中。
 
@@ -152,22 +159,23 @@ MMSelfSup 是一款由不同学校和公司共同贡献的开源项目，我们
 ## OpenMMLab 的其他项目
 
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
-- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMLab 项目、算法、模型的统一入口
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱与测试基准
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 检测工具箱与测试基准
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱与测试基准
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱与测试基准
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 新一代生成模型工具箱
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
 - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
 
 ## 欢迎加入 OpenMMLab 社区

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

@@ -31,17 +31,21 @@
         type=dataset_type,
         data_source=dict(
             type=data_source,
-            data_prefix='data/Places205/train',
-            ann_file='data/Places205/meta/train.txt',
+            data_prefix=  # noqa: E251
+            'data/Places205/data/vision/torralba/deeplearning/images256/',
+            ann_file=  # noqa: E251
+            'data/Places205/trainvalsplit_places205/train_places205.csv',
         ),
         pipeline=train_pipeline,
         prefetch=prefetch),
     val=dict(
         type=dataset_type,
         data_source=dict(
             type=data_source,
-            data_prefix='data/Places205/val',
-            ann_file='data/Places205/meta/val.txt',
+            data_prefix=  # noqa: E251
+            'data/Places205/data/vision/torralba/deeplearning/images256/',
+            ann_file=  # noqa: E251
+            'data/Places205/trainvalsplit_places205/val_places205.csv',
         ),
         pipeline=test_pipeline,
         prefetch=prefetch))

configs/benchmarks/classification/_base_/models/vit-base-p16_ft.py

@@ -0,0 +1,17 @@
+model = dict(
+    type='Classification',
+    backbone=dict(
+        type='MIMVisionTransformer',
+        arch='b',
+        patch_size=16,
+        drop_path_rate=0.1,
+        final_norm=False),
+    head=dict(
+        type='MAEFinetuneHead',
+        num_classes=1000,
+        embed_dim=768,
+        label_smooth_val=0.1),
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/benchmarks/classification/_base_/models/vit-base-p16_linprobe.py

@@ -0,0 +1,9 @@
+model = dict(
+    type='Classification',
+    backbone=dict(
+        type='MIMVisionTransformer',
+        arch='b',
+        patch_size=16,
+        final_norm=True,
+        finetune=False),
+    head=dict(type='MAELinprobeHead', num_classes=1000, embed_dim=768))

configs/benchmarks/classification/_base_/schedules/adamw_coslr-100e_in1k.py

@@ -0,0 +1,14 @@
+# optimizer
+optimizer = dict(type='AdamW', lr=1e-3, betas=(0.9, 0.999), weight_decay=0.05)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0.,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-4,  # cannot be 0
+    warmup_by_epoch=True)
+
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=100)

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

@@ -0,0 +1,67 @@
+_base_ = [
+    '../_base_/models/vit-base-p16_ft.py',
+    '../_base_/datasets/imagenet.py',
+    '../_base_/schedules/adamw_coslr-100e_in1k.py',
+    '../_base_/default_runtime.py',
+]
+
+# dataset
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+train_pipeline = [
+    dict(
+        type='RandomAug',
+        input_size=224,
+        color_jitter=None,
+        auto_augment='rand-m9-mstd0.5-inc1',
+        interpolation='bicubic',
+        re_prob=0.25,
+        re_mode='pixel',
+        re_count=1,
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225))
+]
+test_pipeline = [
+    dict(type='Resize', size=256, interpolation=3),
+    dict(type='CenterCrop', size=224),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg)
+]
+data = dict(
+    samples_per_gpu=128,
+    drop_last=False,
+    workers_per_gpu=32,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline))
+
+# model
+model = dict(backbone=dict(init_cfg=dict()))
+
+# optimizer
+optimizer = dict(
+    lr=1e-3 * 1024 / 256,
+    paramwise_options={
+        'norm': dict(weight_decay=0.),
+        'bias': dict(weight_decay=0.),
+        'pos_embed': dict(weight_decay=0.),
+        'cls_token': dict(weight_decay=0.)
+    },
+    constructor='MAEFtOptimizerConstructor',
+    layer_decay=0.65)
+
+# learning policy
+lr_config = dict(
+    policy='StepFixCosineAnnealing',
+    min_lr=1e-6,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-4,
+    warmup_by_epoch=True,
+    by_epoch=False)
+
+# runtime
+checkpoint_config = dict(interval=1, max_keep_ckpts=3, out_dir='')
+persistent_workers = True
+log_config = dict(
+    interval=100, hooks=[
+        dict(type='TextLoggerHook'),
+    ])

configs/selfsup/_base_/datasets/imagenet_mae.py

@@ -0,0 +1,30 @@
+# dataset settings
+data_source = 'ImageNet'
+dataset_type = 'SingleViewDataset'
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+train_pipeline = [
+    dict(
+        type='RandomResizedCrop', size=224, scale=(0.2, 1.0), interpolation=3),
+    dict(type='RandomHorizontalFlip')
+]
+
+# prefetch
+prefetch = False
+if not prefetch:
+    train_pipeline.extend(
+        [dict(type='ToTensor'),
+         dict(type='Normalize', **img_norm_cfg)])
+
+# dataset summary
+data = dict(
+    imgs_per_gpu=128,
+    workers_per_gpu=8,
+    train=dict(
+        type=dataset_type,
+        data_source=dict(
+            type=data_source,
+            data_prefix='data/imagenet/train',
+            ann_file='data/imagenet/meta/train.txt',
+        ),
+        pipeline=train_pipeline,
+        prefetch=prefetch))

configs/selfsup/_base_/models/mae_vit-base-p16.py

@@ -0,0 +1,15 @@
+# model settings
+model = dict(
+    type='MAE',
+    backbone=dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75),
+    neck=dict(
+        type='MAEPretrainDecoder',
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        decoder_embed_dim=512,
+        decoder_depth=8,
+        decoder_num_heads=16,
+        mlp_ratio=4.,
+    ),
+    head=dict(type='MAEPretrainHead', norm_pix=True, patch_size=16))

configs/selfsup/_base_/schedules/adamw_coslr-200e_in1k.py

@@ -0,0 +1,15 @@
+# optimizer
+optimizer = dict(type='AdamW', lr=1.5e-4, betas=(0.9, 0.95), weight_decay=0.05)
+optimizer_config = dict()  # grad_clip, coalesce, bucket_size_mb
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0.,
+    warmup='linear',
+    warmup_iters=40,
+    warmup_ratio=1e-4,  # cannot be 0
+    warmup_by_epoch=True)
+
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=300)