docs: set long_description to the contents of README.md as the descri…

…ption 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
     ```
 

docs/zh/index.md

@@ -135,7 +135,6 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
         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）
 
-
 - 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，：
@@ -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。
@@ -206,7 +205,7 @@ 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

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.*"]),