docs: set long_description to the contents of README.md as the description on PyPI

RELEASE.md

@@ -1,6 +1,97 @@
-
 # Release Note
 
+- 2023/5/30
+1. New Models:
+    - AMP(O2) version of [VGG](configs/vgg)
+    - [GhostNet](configs/ghostnet)
+    - AMP(O3) version of [MobileNetV2](configs/mobilenetv2) and [MobileNetV3](configs/mobilenetv3)
+    - (x,y)_(200,400,600,800)mf of [RegNet](configs/regnet)
+    - b1g2, b1g4 & b2g4 of [RepVGG](configs/repvgg)
+    - 0.5 of [MnasNet](configs/mnasnet)
+    - b3 & b4 of [PVTv2](configs/pvt_v2)
+2. New Features:
+    - 3-Augment, Augmix, TrivialAugmentWide
+3. Bug Fixes:
+    - ViT pooling mode
+
+- 2023/04/28
+1. Add some new models, listed as following
+    - [VGG](configs/vgg)
+    - [DPN](configs/dpn)
+    - [ResNet v2](configs/resnetv2)
+    - [MnasNet](configs/mnasnet)
+    - [MixNet](configs/mixnet)
+    - [RepVGG](configs/repvgg)
+    - [ConvNeXt](configs/convnext)
+    - [Swin Transformer](configs/swintransformer)
+    - [EdgeNeXt](configs/edgenext)
+    - [CrossViT](configs/crossvit)
+    - [XCiT](configs/xcit)
+    - [CoAT](configs/coat)
+    - [PiT](configs/pit)
+    - [PVT v2](configs/pvt_v2)
+    - [MobileViT](configs/mobilevit)
+2. Bug fix:
+    - Setting the same random seed for each rank
+    - Checking if options from yaml config exist in argument parser
+    - Initializing flag variable as `Tensor` in Optimizer `Adan`
+
+## 0.2.0
+
+- 2023/03/25
+1. Update checkpoints for pretrained ResNet for better accuracy
+    - ResNet18 (from 70.09 to 70.31 @Top1 accuracy)
+    - ResNet34 (from 73.69 to 74.15 @Top1 accuracy)
+    - ResNet50 (from 76.64 to 76.69 @Top1 accuracy)
+    - ResNet101 (from 77.63 to 78.24 @Top1 accuracy)
+    - ResNet152 (from 78.63 to 78.72 @Top1 accuracy)
+2. Rename checkpoint file name to follow naming rule ({model_scale-sha256sum.ckpt}) and update download URLs.
+
+- 2023/03/05
+1. Add Lion (EvoLved Sign Momentum) optimizer from paper https://arxiv.org/abs/2302.06675
+    - To replace adamw with lion, LR is usually 3-10x smaller, and weight decay is usually 3-10x larger than adamw.
+2. Add 6 new models with training recipes and pretrained weights for
+    - [HRNet](configs/hrnet)
+    - [SENet](configs/senet)
+    - [GoogLeNet](configs/googlenet)
+    - [Inception V3](configs/inception_v3)
+    - [Inception V4](configs/inception_v4)
+    - [Xception](configs/xception)
+3. Support gradient clip
+4. Arg name `use_ema` changed to **`ema`**, add `ema: True` in yaml to enable EMA.
+
+## 0.1.1
+
+- 2023/01/10
+1. MindCV v0.1 released! It can be installed via PyPI `pip install mindcv` now.
+2. Add training recipe and trained weights of googlenet, inception_v3, inception_v4, xception
+
+## 0.1.0
+
+- 2022/12/09
+1. Support lr warmup for all lr scheduling algorithms besides cosine decay.
+2. Add repeated augmentation, which can be enabled by setting `--aug_repeats` to be a value larger than 1 (typically, 3 or 4 is a common choice).
+3. Add EMA.
+4. Improve BCE loss to support mixup/cutmix.
+
+- 2022/11/21
+1. Add visualization for loss and acc curves
+2. Support epochwise lr warmup cosine decay (previous is stepwise)
+
+- 2022/11/09
+1. Add 7 pretrained ViT models.
+2. Add RandAugment augmentation.
+3. Fix CutMix efficiency issue and CutMix and Mixup can be used together.
+4. Fix lr plot and scheduling bug.
+
+- 2022/10/12
+1. Both BCE and CE loss now support class-weight config, label smoothing, and auxiliary logit input (for networks like inception).
+
+## 0.0.1-beta
+
+- 2022/09/13
+1. Add Adan optimizer (experimental)
+
 ## MindSpore Computer Vision 0.0.1
 
 ### Models

docs/en/index.md

@@ -85,7 +85,7 @@ Below are a few code snippets for your taste.
       <img src="https://user-images.githubusercontent.com/8156835/210049681-89f68b9f-eb44-44e2-b689-4d30c93c6191.jpg" width=360 />
     </p>
 
-    Classify the dowloaded image with a pretrained SoTA model:
+    Classify the downloaded image with a pretrained SoTA model:
 
     ```pycon
     >>> !python infer.py --model=swin_tiny --image_path='./dog.jpg'
@@ -156,7 +156,7 @@ It is easy to train your model on a standard or customized dataset using `train.
 
     [Pynative mode with ms_function](https://www.mindspore.cn/tutorials/zh-CN/r1.8/advanced/pynative_graph/combine.html) is a mixed mode for comprising flexibility and efficiency in MindSpore. To apply pynative mode with ms_function for training, please run `train_with_func.py`, e.g.,
 
-    ``` shell
+    ```shell
     python train_with_func.py --model=resnet50 --dataset=cifar10 --dataset_download  --epoch_size=10
     ```
 
@@ -201,42 +201,42 @@ We provide the following jupyter notebook tutorials to help users learn to use M
 <summary> Supported algorithms </summary>
 
 * Augmentation
-	* [AutoAugment](https://arxiv.org/abs/1805.09501)
-	* [RandAugment](https://arxiv.org/abs/1909.13719)
-	* [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
-	* RandErasing (Cutout)
-	* CutMix
-	* MixUp
-	* RandomResizeCrop
-	* Color Jitter, Flip, etc
+    * [AutoAugment](https://arxiv.org/abs/1805.09501)
+    * [RandAugment](https://arxiv.org/abs/1909.13719)
+    * [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
+    * RandErasing (Cutout)
+    * CutMix
+    * MixUp
+    * RandomResizeCrop
+    * Color Jitter, Flip, etc
 * Optimizer
-	* Adam
-	* AdamW
-	* [Lion](https://arxiv.org/abs/2302.06675)
-	* Adan (experimental)
-	* AdaGrad
-	* LAMB
-	* Momentum
-	* RMSProp
-	* SGD
-	* NAdam
+    * Adam
+    * AdamW
+    * [Lion](https://arxiv.org/abs/2302.06675)
+    * Adan (experimental)
+    * AdaGrad
+    * LAMB
+    * Momentum
+    * RMSProp
+    * SGD
+    * NAdam
 * LR Scheduler
-	* Warmup Cosine Decay
-	* Step LR
-	* Polynomial Decay
-	* Exponential Decay
+    * Warmup Cosine Decay
+    * Step LR
+    * Polynomial Decay
+    * Exponential Decay
 * Regularization
-	* Weight Decay
-	* Label Smoothing
-	* Stochastic Depth (depends on networks)
-	* Dropout (depends on networks)
+    * Weight Decay
+    * Label Smoothing
+    * Stochastic Depth (depends on networks)
+    * Dropout (depends on networks)
 * Loss
-	* Cross Entropy (w/ class weight and auxiliary logit support)
-	* Binary Cross Entropy  (w/ class weight and auxiliary logit support)
-	* Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
-	* Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+    * Cross Entropy (w/ class weight and auxiliary logit support)
+    * Binary Cross Entropy  (w/ class weight and auxiliary logit support)
+    * Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+    * Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
 * Ensemble
-	* Warmup EMA (Exponential Moving Average)
+    * Warmup EMA (Exponential Moving Average)
 
 </details>
 

docs/zh/index.md

@@ -55,7 +55,7 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
 
 ## 安装
 
-详情请见[安装](./installation.md)页面
+详情请见[安装](./installation.md)页面。
 
 ## 快速入门
 
@@ -73,7 +73,7 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
 # 创建模型
 >>> network = mindcv.create_model('swin_tiny', pretrained=True)
 # 验证模型的准确率
->>> !python validate.py - -model = swin_tiny - -pretrained - -dataset = imagenet - -val_split = validation
+>>> !python validate.py --model=swin_tiny --pretrained --dataset=imagenet --val_split=validation
 {'Top_1_Accuracy': 0.808343989769821, 'Top_5_Accuracy': 0.9527253836317136, 'loss': 0.8474242982580839}
 ```
 
@@ -96,7 +96,7 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
 
 ### 模型训练
 
-通过`train.py`，用户可以很容易地在标准数据集或自定义数据集上训练模型，用户可以通过外部变量或者yaml配文件来设置训练策略（如数据增强、学习路策略）。
+通过`train.py`，用户可以很容易地在标准数据集或自定义数据集上训练模型，用户可以通过外部变量或者yaml配文件来设置训练策略（如数据增强、学习率策略）。
 
 - 单卡训练
 
@@ -133,18 +133,17 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
     !!! tip "预定义的训练策略"
         MindCV目前提前了超过20种模型训练策略，在ImageNet取得SoTA性能。
         具体的参数配置和详细精度性能汇总请见[`configs`](https://github.com/mindspore-lab/mindcv/tree/main/configs)文件夹。
-        您可以便捷将这些训练策略用于您的模型训练中以提高性能（复用或修改相应的yaml文件即可）
-
+        您可以便捷将这些训练策略用于您的模型训练中以提高性能（复用或修改相应的yaml文件即可）。
 
 - 在ModelArts/OpenI平台上训练
 
-    在[ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html)或[OpenI](https://openi.pcl.ac.cn/)云平台上进行训练，需要执行以下操作，：
+    在[ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html)或[OpenI](https://openi.pcl.ac.cn/)云平台上进行训练，需要执行以下操作：
 
     ```text
     1、在云平台上创建新的训练任务。
     2、在网站UI界面添加运行参数`config`，并指定yaml配置文件的路径。
     3、在网站UI界面添加运行参数`enable_modelarts`并设置为True。
-    4、在网站上填写其他训练信息并启动培训任务。
+    4、在网站上填写其他训练信息并启动训练任务。
     ```
 
 !!! tip "静态图和动态图模式"
@@ -160,7 +159,7 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
     python train_with_func.py --model=resnet50 --dataset=cifar10 --dataset_download --epoch_size=10
     ```
 
-    > 注：此为试验性质的训练脚本，仍在改进，在1.8.1或更早版本的MindSpore上使用此模式目前并不稳定。
+    > 注：此为试验性质的训练脚本，仍在改进，在MindSpore 1.8.1或更早版本上使用此模式目前并不稳定。
 
 ### 模型验证
 
@@ -177,7 +176,7 @@ python validate.py --model=resnet50 --dataset=imagenet --data_dir=/path/to/data
 
     ```shell
     python train.py --model=resnet50 --dataset=cifar10 \
-            --val_while_train --val_split=test --val_interval=1
+        --val_while_train --val_split=test --val_interval=1
     ```
 
     各轮次的训练损失和测试精度将保存在`{ckpt_save_dir}/results.log`中。
@@ -206,13 +205,13 @@ python validate.py --model=resnet50 --dataset=imagenet --data_dir=/path/to/data
     * [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
     * RandErasing (Cutout)
     * CutMix
-    * Mixup
+    * MixUp
     * RandomResizeCrop
     * Color Jitter, Flip, etc
 * 优化器
     * Adam
     * AdamW
-	* [Lion](https://arxiv.org/abs/2302.06675)
+    * [Lion](https://arxiv.org/abs/2302.06675)
     * Adan (experimental)
     * AdaGrad
     * LAMB

infer.py

@@ -47,7 +47,7 @@ def main():
     logits = nn.Softmax()(network(ms.Tensor(img)))[0].asnumpy()
     preds = np.argsort(logits)[::-1][:5]
     probs = logits[preds]
-    with open("./tutorials/imagenet1000_clsidx_to_labels.txt", encoding="utf-8") as f:
+    with open("./examples/data/imagenet1000_clsidx_to_labels.txt", encoding="utf-8") as f:
         idx2label = ast.literal_eval(f.read())
     # print(f"Predict result of {args.image_path}:")
     cls_prob = {}

setup.py

@@ -1,19 +1,28 @@
 #!/usr/bin/env python
 
+from pathlib import Path
+
 from setuptools import find_packages, setup
 
+# read the contents of README file
+this_directory = Path(__file__).parent
+long_description = (this_directory / "README.md").read_text()
+
+# read the `__version__` global variable in `version.py`
 exec(open("mindcv/version.py").read())
 
 setup(
     name="mindcv",
-    author="MindSpore Ecosystem",
-    author_email="mindspore-ecosystem@example.com",
+    author="MindSpore Lab",
+    author_email="mindspore-lab@example.com",
     url="https://github.com/mindspore-lab/mindcv",
     project_urls={
         "Sources": "https://github.com/mindspore-lab/mindcv",
         "Issue Tracker": "https://github.com/mindspore-lab/mindcv/issues",
     },
     description="A toolbox of vision models and algorithms based on MindSpore.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
     license="Apache Software License 2.0",
     include_package_data=True,
     packages=find_packages(include=["mindcv", "mindcv.*"]),