bump version to 0.3.0 (#751)

Author: geniuspatrick

README.md

@@ -295,22 +295,38 @@ Please see [configs](./configs) for the details about model performance and pret
 
 ## What is New
 
-- 2023/6/16
-1. New version `0.2.2` is released! We upgrade to support `MindSpore` v2.0 while maintaining compatibility of v1.8
-2. New models:
-   - [ConvNextV2](configs/convnextv2)
-   - mini of [CoAT](configs/coat)
-   - 1.3 of [MnasNet](configs/mnasnet)
-   - AMP(O3) version of [ShuffleNetV2](configs/shufflenetv2)
-3. New features:
-   - Gradient Accumulation
-   - DynamicLossScale for customized [TrainStep](mindcv/utils/train_step.py)
-   - OneCycleLR and CyclicLR learning rate scheduler
-   - Refactored Logging
-   - Pyramid Feature Extraction
-4. Bug fixes:
-   - Serving Deployment Tutorial(mobilenet_v3 doesn't work on ms1.8 when using Ascend backend)
-   - Some broken links on our documentation website.
+- 2024/1/17
+
+Release `0.3.0` is published. We will drop MindSpore 1.x in the future release.
+
+1. New models:
+   - Y-16GF of [RegNet](configs/regnet)
+   - [SwinTransformerV2](configs/swintransformerv2)
+   - [VOLO](configs/volo)
+   - [CMT](configs/cmt)
+   - [HaloNet](configs/halonet)
+   - [SSD](examples/det/ssd)
+   - [DeepLabV3](examples/seg/deeplabv3)
+   - [CLIP](examples/clip) & [OpenCLIP](examples/open_clip)
+2. Features:
+   - AsymmetricLoss & JSDCrossEntropy
+   - Augmentations Split
+   - Customized AMP
+3. Bug fixes:
+   - Since the classifier weights are not fully deleted, you may encounter an error passing in the `num_classes` when creating a pre-trained model.
+4. Refactoring:
+   - The names of many models have been refactored for better understanding.
+   - [Script](mindcv/models/vit.py) of `VisionTransformer`.
+   - [Script](train_with_func.py) of Mixed(PyNative+jit) mode training.
+5. Documentation:
+   - A guide of how to extract multiscale features from backbone.
+   - A guide of how to finetune the pre-trained model on a custom dataset.
+6. BREAKING CHANGES:
+   - We are going to drop support of MindSpore 1.x for it's EOL.
+   - Configuration `filter_bias_and_bn` will be removed and renamed as `weight_decay_filter`,
+   due to a prolonged misunderstanding of the MindSpore optimizer.
+   We will migrate the existing training recipes, but the signature change of function `create_optimizer` will be incompatible
+   and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
 
 See [RELEASE](RELEASE.md) for detailed history.
 

README_CN.md

@@ -298,6 +298,37 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 ## 更新
 
+- 2024/1/17
+
+新版本`0.3.0`发布。我们将在未来的发布中丢弃对MindSpore1.x版本的支持
+
+1. 新模型:
+   - [RegNet](configs/regnet)的Y-16GF规格
+   - [SwinTransformerV2](configs/swintransformerv2)
+   - [VOLO](configs/volo)
+   - [CMT](configs/cmt)
+   - [HaloNet](configs/halonet)
+   - [SSD](examples/det/ssd)
+   - [DeepLabV3](examples/seg/deeplabv3)
+   - [CLIP](examples/clip) & [OpenCLIP](examples/open_clip)
+2. 特性:
+   - 损失函数AsymmetricLoss及JSDCrossEntropy
+   - 数据增强分离(Augmentations Split)
+   - 自定义混合精度策略
+3. 错误修复:
+   - 由于分类器参数未完全弹出，您在新建预训练模型时传入参数`num_classes`可能会导致错误。
+4. 重构:
+   - 许多模型的名字发生了变更，以便更好的理解。
+   - `VisionTransformer`的模型定义[脚本](mindcv/models/vit.py)。
+   - 混合模式(PyNative+jit)的训练[脚本](train_with_func.py)。
+5. 文档:
+   - 如何提取多尺度特征的教程指引。
+   - 如何在自定义数据集上微调预训练模型的教程指引。
+6. BREAKING CHANGES:
+   - 我们将在此小版本的未来发布中丢弃对MindSpore1.x的支持。
+   - 配置项`filter_bias_and_bn`将被移除并更名为`weight_decay_filter`。
+   我们会对已有训练策略进行迁移，但函数`create_optimizer`的签名变更将是不兼容的，且未迁移旧版本的训练策略也将变得不兼容。详见[PR/752](https://github.com/mindspore-lab/mindcv/pull/752)。
+
 - 2023/6/16
 1. 新版本 `0.2.2` 发布啦！我们将`MindSpore`升级到了2.0版本，同时保持了对1.8版本的兼容
 2. 新模型:

RELEASE.md

@@ -1,5 +1,38 @@
 # Release Note
 
+## 0.3.0 (2024/1/17)
+
+Release `0.3.0` is published. We will drop MindSpore 1.x in the future release.
+
+1. New models:
+   - Y-16GF of [RegNet](configs/regnet)
+   - [SwinTransformerV2](configs/swintransformerv2)
+   - [VOLO](configs/volo)
+   - [CMT](configs/cmt)
+   - [HaloNet](configs/halonet)
+   - [SSD](examples/det/ssd)
+   - [DeepLabV3](examples/seg/deeplabv3)
+   - [CLIP](examples/clip) & [OpenCLIP](examples/open_clip)
+2. Features:
+   - AsymmetricLoss & JSDCrossEntropy
+   - Augmentations Split
+   - Customized AMP
+3. Bug fixes:
+   - Since the classifier weights are not fully deleted, you may encounter an error passing in the `num_classes` when creating a pre-trained model.
+4. Refactoring:
+   - The names of many models have been refactored for better understanding.
+   - [Script](mindcv/models/vit.py) of `VisionTransformer`.
+   - [Script](train_with_func.py) of Mixed(PyNative+jit) mode training.
+5. Documentation:
+   - A guide of how to extract multiscale features from backbone.
+   - A guide of how to finetune the pre-trained model on a custom dataset.
+6. BREAKING CHANGES:
+   - We are going to drop support of MindSpore 1.x for it's EOL.
+   - Configuration `filter_bias_and_bn` will be removed and renamed as `weight_decay_filter`,
+   due to a prolonged misunderstanding of the MindSpore optimizer.
+   We will migrate the existing training recipes, but the signature change of function a will be incompatible
+   and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
+
 ## 0.2.2 (2023/6/16)
 
 1. New version `0.2.2` is released! We upgrade to support `MindSpore` v2.0 while maintaining compatibility of v1.8

mindcv/version.py

@@ -1,2 +1,2 @@
 """version init"""
-__version__ = "0.2.2"
+__version__ = "0.3.0"