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fix error links

pull/276/head
lvmingfu 2 years ago
parent
c078e44034
commit
b61b1de49a
6 changed files with 41 additions and 66 deletions
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  1. +4
    -3
      events/summer2020/README.md
  2. +13
    -26
      security/comments_specification_en.md
  3. +20
    -33
      security/comments_specification_zh_cn.md
  4. +2
    -2
      sigs/data/README.md
  5. +1
    -1
      sigs/data/docs/official_resource.md
  6. +1
    -1
      tsc/meeting-notes/2021/2021-02-18.md

+ 4
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events/summer2020/README.md View File

@@ -1,12 +1,13 @@
# 开源软件供应链点亮计划 - 暑期 2020 # 开源软件供应链点亮计划 - 暑期 2020


[开源供应链点亮计划 - 暑期 2020](https://isrc.iscas.ac.cn/summer2020)是由中科院软件所和 [openEuler](https://openeuler.org) 社区共同举办的一项面向高校学生的暑期活动，旨在鼓励在校学生积极参与开源软件的开发维护，促进国内优秀开源软件社区的蓬勃发展。
[开源供应链点亮计划 - 暑期 2020](https://summer-ospp.ac.cn/2020/)是由中科院软件所和 [openEuler](https://openeuler.org) 社区共同举办的一项面向高校学生的暑期活动，旨在鼓励在校学生积极参与开源软件的开发维护，促进国内优秀开源软件社区的蓬勃发展。


从即日起 [MindSpore](https://www.mindspore.cn) 社区通过[开源供应链点亮计划 - 暑期 2020](https://isrc.iscas.ac.cn/summer2020)活动发布开发任务，在活动过程中有对开发任务感兴趣的在校学生会通过社区 Issue / 邮件等方式和发布任务的导师进行沟通。 2020 年 6 月 30 日导师公布选中参与任务开发的学生，从 7 月 1 日开始到 9 月 30 日之间进行为期三个月的开发，在这个过程中导师对学生进行多种形式的辅导，帮助学生完成开发任务。
从即日起 [MindSpore](https://www.mindspore.cn) 社区通过[开源供应链点亮计划 - 暑期 2020](https://summer-ospp.ac.cn/2020/)活动发布开发任务，在活动过程中有对开发任务感兴趣的在校学生会通过社区 Issue / 邮件等方式和发布任务的导师进行沟通。 2020 年 6 月 30 日导师公布选中参与任务开发的学生，从 7 月 1 日开始到 9 月 30 日之间进行为期三个月的开发，在这个过程中导师对学生进行多种形式的辅导，帮助学生完成开发任务。


更多活动介绍 更多活动介绍

1. https://docs.google.com/presentation/d/1XtMfT6XLLMw4yajz0M_Gi9WI71yk-rhWqx3xcT1q5kU 1. https://docs.google.com/presentation/d/1XtMfT6XLLMw4yajz0M_Gi9WI71yk-rhWqx3xcT1q5kU
2. https://isrc.iscas.ac.cn/summer2020
2. https://summer-ospp.ac.cn/2020/


## MindSpore 社区任务提交方式 ## MindSpore 社区任务提交方式




+ 13
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security/comments_specification_en.md View File

@@ -714,47 +714,34 @@ in which,
### C++ Example ### C++ Example


```cpp ```cpp
/// \brief Function to create a CocoDataset.
/// \note The generated dataset has multi-columns :
/// - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
/// ['iscrowd', dtype=uint32]].
/// - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd', dtype=uint32]].
/// - task='Keypoint', column: [['image', dtype=uint8], ['keypoints', dtype=float32],
/// ['num_keypoints', dtype=uint32]].
/// - task='Panoptic', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
/// ['iscrowd', dtype=uint32], ['area', dtype=uitn32]].
/// \brief Function to create a MnistDataset.
/// \note The generated dataset has two columns ["image", "label"].
/// \param[in] dataset_dir Path to the root directory that contains the dataset. /// \param[in] dataset_dir Path to the root directory that contains the dataset.
/// \param[in] annotation_file Path to the annotation json.
/// \param[in] task Set the task type of reading coco data, now support 'Detection'/'Stuff'/'Panoptic'/'Keypoint'.
/// \param[in] decode Decode the images after reading.
/// \param[in] usage Part of dataset of MNIST, can be "train", "test" or "all" (default = "all").
/// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not /// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not
/// given, a `RandomSampler` will be used to randomly iterate the entire dataset (default = RandomSampler()). /// given, a `RandomSampler` will be used to randomly iterate the entire dataset (default = RandomSampler()).
/// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used). /// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used).
/// \param[in] extra_metadata Flag to add extra meta-data to row. (default=false).
/// \return Shared pointer to the CocoDataset.
/// \return Shared pointer to the MnistDataset.
/// \par Example /// \par Example
/// \code /// \code
/// /* Define dataset path and MindData object */ /// /* Define dataset path and MindData object */
/// std::string folder_path = "/path/to/coco_dataset_directory";
/// std::string annotation_file = "/path/to/annotation_file";
/// std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file);
/// std::string folder_path = "/path/to/mnist_dataset_directory";
/// std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", std::make_shared<RandomSampler>(false, 20));
/// ///
/// /* Create iterator to read dataset */ /// /* Create iterator to read dataset */
/// std::shared_ptr<Iterator> iter = ds->CreateIterator(); /// std::shared_ptr<Iterator> iter = ds->CreateIterator();
/// std::unordered_map<std::string, mindspore::MSTensor> row; /// std::unordered_map<std::string, mindspore::MSTensor> row;
/// iter->GetNextRow(&row); /// iter->GetNextRow(&row);
/// ///
/// /* Note: In COCO dataset, each dictionary has keys "image" and "annotation" */
/// /* Note: In MNIST dataset, each dictionary has keys "image" and "label" */
/// auto image = row["image"]; /// auto image = row["image"];
/// \endcode /// \endcode
inline std::shared_ptr<CocoDataset> Coco(const std::string &dataset_dir, const std::string &annotation_file,
const std::string &task = "Detection", const bool &decode = false,
const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
const std::shared_ptr<DatasetCache> &cache = nullptr,
const bool &extra_metadata = false) {
return std::make_shared<CocoDataset>(StringToChar(dataset_dir), StringToChar(annotation_file), StringToChar(task),
decode, sampler, cache, extra_metadata);
inline std::shared_ptr<MnistDataset> MS_API
Mnist(const std::string &dataset_dir, const std::string &usage = "all",
const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
const std::shared_ptr<DatasetCache> &cache = nullptr) {
return std::make_shared<MnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
} }
``` ```


The API document page output according to the above comments is [Function mindspore::dataset::Coco](https://www.mindspore.cn/lite/api/en/master/generate/function_mindspore_dataset_Coco-1.html).
The API document page output according to the above comments is [Function mindspore::dataset::Coco](https://www.mindspore.cn/lite/api/en/master/generate/function_mindspore_dataset_Mnist-1.html).

+ 20
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security/comments_specification_zh_cn.md View File

@@ -69,7 +69,7 @@ Examples:
- `Raises`：异常信息，包含异常类型、含义等。 - `Raises`：异常信息，包含异常类型、含义等。
- `Examples`：样例代码。 - `Examples`：样例代码。


**针对算子和Cell的注释，需要在`Examples`前添加`Inputs`、`Outputs`和`Supported Platforms`三项内容。**
针对算子和Cell的注释，需要在`Examples`前添加`Inputs`、`Outputs`和`Supported Platforms`三项内容。


- `Inputs`和`Outputs`：用于描述实例化后，算子的输入和输出的类型和shape，输入名可以和样例相同。建议在注释中给出对应的数学公式。 - `Inputs`和`Outputs`：用于描述实例化后，算子的输入和输出的类型和shape，输入名可以和样例相同。建议在注释中给出对应的数学公式。
- `Supported Platforms`：用于描述算子支持的硬件平台，名称前后需添加``，存在多个时使用空格隔开。 - `Supported Platforms`：用于描述算子支持的硬件平台，名称前后需添加``，存在多个时使用空格隔开。
@@ -713,47 +713,34 @@ class BatchNorm(PrimitiveWithInfer):
### 完整示例 ### 完整示例


```cpp ```cpp
/// \brief Function to create a CocoDataset.
/// \note The generated dataset has multi-columns :
/// - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
/// ['iscrowd', dtype=uint32]].
/// - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd', dtype=uint32]].
/// - task='Keypoint', column: [['image', dtype=uint8], ['keypoints', dtype=float32],
/// ['num_keypoints', dtype=uint32]].
/// - task='Panoptic', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
/// ['iscrowd', dtype=uint32], ['area', dtype=uitn32]].
/// \brief Function to create a MnistDataset.
/// \note The generated dataset has two columns ["image", "label"].
/// \param[in] dataset_dir Path to the root directory that contains the dataset. /// \param[in] dataset_dir Path to the root directory that contains the dataset.
/// \param[in] annotation_file Path to the annotation json.
/// \param[in] task Set the task type of reading coco data, now support 'Detection'/'Stuff'/'Panoptic'/'Keypoint'.
/// \param[in] decode Decode the images after reading.
/// \param[in] usage Part of dataset of MNIST, can be "train", "test" or "all" (default = "all").
/// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not /// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not
/// given, a `RandomSampler` will be used to randomly iterate the entire dataset (default = RandomSampler()). /// given, a `RandomSampler` will be used to randomly iterate the entire dataset (default = RandomSampler()).
/// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used). /// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used).
/// \param[in] extra_metadata Flag to add extra meta-data to row. (default=false).
/// \return Shared pointer to the CocoDataset.
/// \return Shared pointer to the MnistDataset.
/// \par Example /// \par Example
/// \code /// \code
/// /* Define dataset path and MindData object */
/// std::string folder_path = "/path/to/coco_dataset_directory";
/// std::string annotation_file = "/path/to/annotation_file";
/// std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file);
/// /* Define dataset path and MindData object */
/// std::string folder_path = "/path/to/mnist_dataset_directory";
/// std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", std::make_shared<RandomSampler>(false, 20));
/// ///
/// /* Create iterator to read dataset */
/// std::shared_ptr<Iterator> iter = ds->CreateIterator();
/// std::unordered_map<std::string, mindspore::MSTensor> row;
/// iter->GetNextRow(&row);
/// /* Create iterator to read dataset */
/// std::shared_ptr<Iterator> iter = ds->CreateIterator();
/// std::unordered_map<std::string, mindspore::MSTensor> row;
/// iter->GetNextRow(&row);
/// ///
/// /* Note: In COCO dataset, each dictionary has keys "image" and "annotation" */
/// auto image = row["image"];
/// /* Note: In MNIST dataset, each dictionary has keys "image" and "label" */
/// auto image = row["image"];
/// \endcode /// \endcode
inline std::shared_ptr<CocoDataset> Coco(const std::string &dataset_dir, const std::string &annotation_file,
const std::string &task = "Detection", const bool &decode = false,
const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
const std::shared_ptr<DatasetCache> &cache = nullptr,
const bool &extra_metadata = false) {
return std::make_shared<CocoDataset>(StringToChar(dataset_dir), StringToChar(annotation_file), StringToChar(task),
decode, sampler, cache, extra_metadata);
inline std::shared_ptr<MnistDataset> MS_API
Mnist(const std::string &dataset_dir, const std::string &usage = "all",
const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
const std::shared_ptr<DatasetCache> &cache = nullptr) {
return std::make_shared<MnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
} }
``` ```


根据以上注释内容输出的API文档页面为[Function mindspore::dataset::Coco](https://www.mindspore.cn/lite/api/en/master/generate/function_mindspore_dataset_Coco-1.html)。
根据以上注释内容输出的API文档页面为[Function mindspore::dataset::Coco](https://www.mindspore.cn/lite/api/en/master/generate/function_mindspore_dataset_Mnist-1.html)。

+ 2
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sigs/data/README.md View File

@@ -23,8 +23,8 @@ This is the working repo for the Data special interest group (SIG). This repo co
## Representative videos ## Representative videos


* [mindspore data processing introduction](https://www.bilibili.com/video/BV1RZ4y1W7FL) * [mindspore data processing introduction](https://www.bilibili.com/video/BV1RZ4y1W7FL)
* [mindspore data loading and data format conversion](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/quick_start/quick_video/loading_the_dataset_and_converting_data_format.html)
* [optimize data processing](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/quick_start/quick_video/optimize_data_processing.html)
* [mindspore data loading and data format conversion](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/teaching_video/video/%E5%8A%A0%E8%BD%BD%E6%95%B0%E6%8D%AE%E9%9B%86%E4%B8%8E%E8%BD%AC%E6%8D%A2%E6%A0%BC%E5%BC%8F.mp4)
* [optimize data processing](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/teaching_video/video/%E4%BC%98%E5%8C%96%E6%95%B0%E6%8D%AE%E5%A4%84%E7%90%86.mp4)


## Main issue To be solved ## Main issue To be solved




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sigs/data/docs/official_resource.md View File

@@ -1,5 +1,5 @@
## Resource about Dataset in mindspore.cn ## Resource about Dataset in mindspore.cn


* [data loading and processing](https://www.mindspore.cn/tutorials/zh-CN/master/index.html) * [data loading and processing](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
* [dataset sample](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_sample.html)
* [dataset sample](https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset.html)
* [dataset API introduction](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.dataset.html) * [dataset API introduction](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.dataset.html)

+ 1
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tsc/meeting-notes/2021/2021-02-18.md View File

@@ -37,7 +37,7 @@ Meeting link: https://welink-meeting.zoom.us/j/448774327


## Action Items ## Action Items


1. DX MEP is added: <https://gitee.com/lyd911/community/tree/master/design/meps/mep-dx>
1. DX MEP is added: <https://gitee.com/mindspore/community/tree/master/design/meps/mep-dx>


2. Goals of DX SIG: 2. Goals of DX SIG:
2.1 Build user portraits, attract core developers 2.1 Build user portraits, attract core developers


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