Bump version to v1.0.0rc3. (#1211)

* Bump version to v1.0.0rc3

* Update pre-commit hook

.github/ISSUE_TEMPLATE/----.md

@@ -15,19 +15,19 @@ assignees: ''
 
 ### 描述你遇到的问题
 
-\[填写这里\]
+[填写这里]
 
 ### 相关信息
 
 1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出
-   \[填写这里\]
+   [填写这里]
 2. 如果你修改了，或者使用了新的配置文件，请在这里写明
 
 ```python
 [填写这里]
 ```
 
 3. 如果你是在训练过程中遇到的问题，请填写完整的训练日志和报错信息
-   \[填写这里\]
+   [填写这里]
 4. 如果你对 `mmcls` 文件夹下的代码做了其他相关的修改，请在这里写明
-   \[填写这里\]
+   [填写这里]

.github/ISSUE_TEMPLATE/---.md

@@ -10,25 +10,25 @@ assignees: ''
 
 ### 描述这个功能
 
-\[填写这里\]
+[填写这里]
 
 ### 动机
 
 请简要说明以下为什么需要添加这个新功能
 例 1. 现在进行 xxx 的时候不方便
 例 2. 最近的论文中提出了有一个很有帮助的 xx
 
-\[填写这里\]
+[填写这里]
 
 ### 相关资源
 
 是否有相关的官方实现或者第三方实现？这些会很有参考意义。
 
-\[填写这里\]
+[填写这里]
 
 ### 其他相关信息
 
 其他和这个功能相关的信息或者截图，请放在这里。
 另外如果你愿意参与实现这个功能并提交 PR，请在这里说明，我们将非常欢迎。
 
-\[填写这里\]
+[填写这里]

.github/ISSUE_TEMPLATE/---bug.md

@@ -12,7 +12,7 @@ assignees: ''
 
 简单地描述一下遇到了什么 bug
 
-\[填写这里\]
+[填写这里]
 
 ### 复现流程
 
@@ -25,20 +25,20 @@ assignees: ''
 ### 相关信息
 
 1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出
-   \[填写这里\]
+   [填写这里]
 2. 如果你修改了，或者使用了新的配置文件，请在这里写明
 
 ```python
 [填写这里]
 ```
 
 3. 如果你是在训练过程中遇到的问题，请填写完整的训练日志和报错信息
-   \[填写这里\]
+   [填写这里]
 4. 如果你对 `mmcls` 文件夹下的代码做了其他相关的修改，请在这里写明
-   \[填写这里\]
+   [填写这里]
 
 ### 附加内容
 
 任何其他有关该 bug 的信息、截图等
 
-\[填写这里\]
+[填写这里]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,7 +10,7 @@ assignees: ''
 
 A clear and concise description of what the bug is.
 
-\[here\]
+[here]
 
 ### To Reproduce
 
@@ -23,20 +23,20 @@ The command you executed.
 ### Post related information
 
 1. The output of `pip list | grep "mmcv\|mmcls\|^torch"`
-   \[here\]
+   [here]
 2. Your config file if you modified it or created a new one.
 
 ```python
 [here]
 ```
 
 3. Your train log file if you meet the problem during training.
-   \[here\]
+   [here]
 4. Other code you modified in the `mmcls` folder.
-   \[here\]
+   [here]
 
 ### Additional context
 
 Add any other context about the problem here.
 
-\[here\]
+[here]

.github/ISSUE_TEMPLATE/feature_request.md

@@ -8,25 +8,25 @@ assignees: ''
 
 ### Describe the feature
 
-\[here\]
+[here]
 
 ### Motivation
 
 A clear and concise description of the motivation of the feature.
-Ex1. It is inconvenient when \[....\].
-Ex2. There is a recent paper \[....\], which is very helpful for \[....\].
+Ex1. It is inconvenient when [....].
+Ex2. There is a recent paper [....], which is very helpful for [....].
 
-\[here\]
+[here]
 
 ### Related resources
 
 If there is an official code release or third-party implementation, please also provide the information here, which would be very helpful.
 
-\[here\]
+[here]
 
 ### Additional context
 
 Add any other context or screenshots about the feature request here.
 If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated.
 
-\[here\]
+[here]

.github/ISSUE_TEMPLATE/general-questions.md

@@ -13,19 +13,19 @@ assignees: ''
 
 ### Describe the question you meet
 
-\[here\]
+[here]
 
 ### Post related information
 
 1. The output of `pip list | grep "mmcv\|mmcls\|^torch"`
-   \[here\]
+   [here]
 2. Your config file if you modified it or created a new one.
 
 ```python
 [here]
 ```
 
 3. Your train log file if you meet the problem during training.
-   \[here\]
+   [here]
 4. Other code you modified in the `mmcls` folder.
-   \[here\]
+   [here]

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
     rev: 0.7.9
     hooks:
       - id: mdformat
-        args: ["--number", "--table-width", "200", '--disable-escape', 'backslash']
+        args: ["--number", "--table-width", "200", '--disable-escape', 'backslash', '--disable-escape', 'link-enclosure']
         additional_dependencies:
           - "mdformat-openmmlab>=0.0.4"
           - mdformat_frontmatter

README.md

@@ -58,18 +58,18 @@ The `1.x` branch works with **PyTorch 1.6+**.
 
 ## What's new
 
+v1.0.0rc3 was released in 21/11/2022.
+
+- Add **Switch Recipe** Hook, Now we can modify training pipeline, mixup and loss settings during training, see [#1101](https://github.com/open-mmlab/mmclassification/pull/1101).
+- Add **TIMM and HuggingFace** wrappers. Now you can train/use models in TIMM/HuggingFace directly, see [#1102](https://github.com/open-mmlab/mmclassification/pull/1102).
+- Support **retrieval tasks**, see [#1055](https://github.com/open-mmlab/mmclassification/pull/1055).
+- Reproduce **mobileone** training accuracy. See [#1191](https://github.com/open-mmlab/mmclassification/pull/1191)
+
 v1.0.0rc2 was released in 12/10/2022.
 
 - Support Deit-3 backbone.
 - Fix MMEngine version requirements.
 
-v1.0.0rc1 was released in 30/9/2022.
-
-- Support MViT, EdgeNeXt, Swin-Transformer V2, EfficientFormer and MobileOne.
-- Support BEiT type transformer layer.
-
-v1.0.0rc0 was released in 31/8/2022.
-
 This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
 to try according to [the documentation](https://mmclassification.readthedocs.io/en/1.x/).
 

README_zh-CN.md

@@ -57,6 +57,13 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
 
 ## 更新日志
 
+2022/11/21 发布了 v1.0.0rc3 版本
+
+- 添加了 **Switch Recipe Hook**，现在我们可以在训练过程中修改数据增强、Mixup设置、loss设置等
+- 添加了 **TIMM 和 HuggingFace** 包装器，现在我们可以直接训练、使用 TIMM 和 HuggingFace 中的模型
+- 支持了检索任务
+- 复现了 **MobileOne** 训练精度
+
 2022/10/12 发布了 v1.0.0rc2 版本
 
 - 支持了 Deit-3 主干网络

configs/_base_/datasets/cifar100_bs16.py

@@ -27,7 +27,6 @@
         test_mode=False,
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -39,7 +38,6 @@
         test_mode=True,
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, ))
 

configs/_base_/datasets/cifar10_bs16.py

@@ -27,7 +27,6 @@
         test_mode=False,
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -39,7 +38,6 @@
         test_mode=True,
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, ))
 

configs/_base_/datasets/cub_bs8_384.py

@@ -33,7 +33,6 @@
         test_mode=False,
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -45,7 +44,6 @@
         test_mode=True,
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, ))
 

configs/_base_/datasets/cub_bs8_448.py

@@ -32,7 +32,6 @@
         test_mode=False,
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -44,7 +43,6 @@
         test_mode=True,
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, ))
 

configs/_base_/datasets/imagenet21k_bs128.py

@@ -33,7 +33,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -46,7 +45,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 

configs/_base_/datasets/imagenet_bs128_mbv3.py

@@ -48,7 +48,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -61,7 +60,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 

configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py

@@ -62,7 +62,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -75,7 +74,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 

configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py

@@ -62,7 +62,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -75,7 +74,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 

configs/_base_/datasets/imagenet_bs16_pil_bicubic_384.py

@@ -35,7 +35,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -48,7 +47,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 

configs/_base_/datasets/imagenet_bs256_davit_224.py

@@ -62,7 +62,6 @@
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True,
 )
 
 val_dataloader = dict(
@@ -75,7 +74,6 @@
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
 )
 val_evaluator = dict(type='Accuracy', topk=(1, 5))