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Merge pull request !48384 from 俞涵/code_docs_r1101227
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@ -16,13 +16,6 @@
- Fixed an issue of DynamicRNN execution failure in LSTM network under the scenario of computational force segmentation on Ascend platform. - Fixed an issue of DynamicRNN execution failure in LSTM network under the scenario of computational force segmentation on Ascend platform.
- Fixed DEVICE_ID cannot be set by single card train scripts parameters in mobilenet, fasterrcnn, yolo, etc. - Fixed DEVICE_ID cannot be set by single card train scripts parameters in mobilenet, fasterrcnn, yolo, etc.
## MindSpore Lite
### Bug fixes
- Fixed potential accuracy problem of arithmetic type CPU kernels at dynamical shape case.
- Fixed the Incorrect Write Address of the Deconv Quantization Operator.
### Contributors ### Contributors
Thanks goes to these wonderful people: Thanks goes to these wonderful people:
@ -31,6 +24,13 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
Contributions of any kind are welcome! Contributions of any kind are welcome!
## MindSpore Lite 1.10.0 Release Notes
### Bug fixes
- Fixed potential accuracy problem of arithmetic type CPU kernels at dynamical shape case.
- Fixed the Incorrect Write Address of the Deconv Quantization Operator.
## MindSpore 1.9.0 Release Notes ## MindSpore 1.9.0 Release Notes
### Major Features and Improvements ### Major Features and Improvements
@ -344,7 +344,15 @@ For examples:
The API pages are aggregated to <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html>. The API pages are aggregated to <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html>.
## MindSpore Lite ### Contributors
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
Contributions of any kind are welcome!
## MindSpore Lite 1.8.0 Release Notes
### Major Features and Improvements ### Major Features and Improvements
@ -357,14 +365,6 @@ The API pages are aggregated to <https://www.mindspore.cn/docs/en/master/api_pyt
- [STABLE] Support perlayer quantization, and built-in CLE to optimize perlayer quantization accuracy. - [STABLE] Support perlayer quantization, and built-in CLE to optimize perlayer quantization accuracy.
### Contributors
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
Contributions of any kind are welcome!
## MindSpore 1.7.0 Release Notes ## MindSpore 1.7.0 Release Notes
### Major Features and Improvements ### Major Features and Improvements
@ -433,7 +433,15 @@ Contributions of any kind are welcome!
- Deprecate `mindspore.SparseTensor` and use `mindspore.COOTensor` instead. ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505)) - Deprecate `mindspore.SparseTensor` and use `mindspore.COOTensor` instead. ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505))
- Add Tensor init arg `internal` for internal use. - Add Tensor init arg `internal` for internal use.
## MindSpore Lite ### Contributors
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
## MindSpore Lite 1.7.0 Release Notes
### Major Features and Improvements ### Major Features and Improvements
@ -442,14 +450,6 @@ Contributions of any kind are welcome!
- [STABLE] Support post quantization to run dynamic quantization algorithm. - [STABLE] Support post quantization to run dynamic quantization algorithm.
- [BETA] Support post quantized model to run on NVIDIA GPU. - [BETA] Support post quantized model to run on NVIDIA GPU.
## Contributors
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
# MindSpore 1.6.0 # MindSpore 1.6.0
## MindSpore 1.6.0 Release Notes ## MindSpore 1.6.0 Release Notes

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@ -16,13 +16,6 @@
- 修复昇腾平台算力切分场景下LSTM网络中DynamicRNN算子执行失败的问题。 - 修复昇腾平台算力切分场景下LSTM网络中DynamicRNN算子执行失败的问题。
- 修复mobilenet, fasterrcnn, yolo等网络单卡训练脚本DEVICE_ID在启动脚本中写死的问题。 - 修复mobilenet, fasterrcnn, yolo等网络单卡训练脚本DEVICE_ID在启动脚本中写死的问题。
## MindSpore Lite
### Bug fixes
- 修复Arithmetic类CPU算子动态shape场景下可能的计算精度问题。
- 修复Deconv int8量化算子重量化写入地址错误问题。
### 贡献者 ### 贡献者
感谢以下人员做出的贡献: 感谢以下人员做出的贡献:
@ -31,6 +24,13 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
欢迎以任何形式对项目提供贡献！ 欢迎以任何形式对项目提供贡献！
## MindSpore Lite 1.10.0 Release Notes
### Bug fixes
- 修复Arithmetic类CPU算子动态shape场景下可能的计算精度问题。
- 修复Deconv int8量化算子重量化写入地址错误问题。
## MindSpore 1.9.0 Release Notes ## MindSpore 1.9.0 Release Notes
### 主要特性和增强 ### 主要特性和增强
@ -344,7 +344,15 @@ mindspore.context、mindspore.parallel、mindspore.profiler、mindspore.train模
API页面统一汇总至：<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.html>。 API页面统一汇总至：<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.html>。
## MindSpore Lite ### 贡献者
感谢以下人员做出的贡献：
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
欢迎以任何形式对项目提供贡献！
## MindSpore Lite 1.8.0 Release Notes
### 主要特性和增强 ### 主要特性和增强
@ -357,14 +365,6 @@ API页面统一汇总至：<https://www.mindspore.cn/docs/zh-CN/master/api_pytho
- [STABLE] 后量化支持PerLayer量化，同时内置CLE算法优化精度。 - [STABLE] 后量化支持PerLayer量化，同时内置CLE算法优化精度。
### 贡献者
感谢以下人员做出的贡献：
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
欢迎以任何形式对项目提供贡献！
## MindSpore 1.7.0 Release Notes ## MindSpore 1.7.0 Release Notes
### 主要特性和增强 ### 主要特性和增强
@ -433,7 +433,15 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
- `mindspore.SparseTensor`接口废弃使用，对应新接口为`mindspore.COOTensor`。 ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505)) - `mindspore.SparseTensor`接口废弃使用，对应新接口为`mindspore.COOTensor`。 ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505))
- Tensor新增一个入参`internal`，作为框架内部使用。 - Tensor新增一个入参`internal`，作为框架内部使用。
## MindSpore Lite ### 贡献者
感谢以下人员做出的贡献:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
欢迎以任何形式对项目提供贡献！
## MindSpore Lite 1.7.0 Release Notes
### 主要特性和增强 ### 主要特性和增强
@ -441,11 +449,3 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
- [STABLE] 后量化支持动态量化算法。 - [STABLE] 后量化支持动态量化算法。
- [BETA] 后量化模型支持在英伟达GPU上执行推理。 - [BETA] 后量化模型支持在英伟达GPU上执行推理。
## 贡献者
感谢以下人员做出的贡献:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
欢迎以任何形式对项目提供贡献！

2
docs/api/api_python/dataset_audio/mindspore.dataset.audio.AmplitudeToDB.rst
View File

@ -16,7 +16,7 @@ mindspore.dataset.audio.AmplitudeToDB
- **top_db** (float, 可选) - 最小截止分贝值，取值为非负数，默认值：80.0。 - **top_db** (float, 可选) - 最小截止分贝值，取值为非负数，默认值：80.0。
异常： 异常：
- **TypeError** - 当 `stype` 的类型不为 :class:`mindspore.dataset.audio.utils.ScaleType` 。 - **TypeError** - 当 `stype` 的类型不为 :class:`mindspore.dataset.audio.ScaleType` 。
- **TypeError** - 当 `ref_value` 的类型不为float。 - **TypeError** - 当 `ref_value` 的类型不为float。
- **ValueError** - 当 `ref_value` 不为正数。 - **ValueError** - 当 `ref_value` 不为正数。
- **TypeError** - 当 `amin` 的类型不为float。 - **TypeError** - 当 `amin` 的类型不为float。

2
docs/api/api_python/mindspore.common.initializer.rst
View File

@ -64,7 +64,7 @@ mindspore.common.initializer
.. math:: .. math::
boundary = \text{gain} \times \sqrt{\frac{3}{fan\_mode}} boundary = \text{gain} \times \sqrt{\frac{3}{fan\_mode}}
:math:`gain` 是一个可选的缩放因子。如果 :math:`fan\_mode` 是'fan_in'，是权重Tensor中输入单元的数量。如果:math:`fan\_mode` 是'fan_out'，则是权重Tensor中输出单元的数量。 :math:`gain` 是一个可选的缩放因子。如果 :math:`fan\_mode` 是'fan_in'，是权重Tensor中输入单元的数量。如果 :math:`fan\_mode` 是'fan_out'，则是权重Tensor中输出单元的数量。
有关HeUniform算法，详情可参考 https://arxiv.org/abs/1502.01852。 有关HeUniform算法，详情可参考 https://arxiv.org/abs/1502.01852。

4
docs/api/api_python/mindspore/mindspore.SummaryCollector.rst
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@ -6,7 +6,7 @@ mindspore.SummaryCollector
SummaryCollector可以收集一些常用信息。 SummaryCollector可以收集一些常用信息。
它可以帮助收集loss、学习率、计算图等。 它可以帮助收集loss、学习率、计算图等。
SummaryCollector还可以允许通过 `summary算子 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.10/summary_record.html#方式二-结合summary算子和summarycollector自定义收集网络中的数据>`_ 将数据收集到summary文件中。 SummaryCollector还可以允许通过 `summary算子 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.9/summary_record.html#方式二-结合summary算子和summarycollector自定义收集网络中的数据>`_ 将数据收集到summary文件中。
.. note:: .. note::
- 使用SummaryCollector时，需要将代码放置到 `if __name__ == "__main__"` 中运行。 - 使用SummaryCollector时，需要将代码放置到 `if __name__ == "__main__"` 中运行。
@ -23,7 +23,7 @@ mindspore.SummaryCollector
- **collect_metric** (bool) - 表示是否收集训练metrics，目前只收集loss。把第一个输出视为loss，并且算出其平均数。默认值：True。 - **collect_metric** (bool) - 表示是否收集训练metrics，目前只收集loss。把第一个输出视为loss，并且算出其平均数。默认值：True。
- **collect_graph** (bool) - 表示是否收集计算图。目前只收集训练计算图。默认值：True。 - **collect_graph** (bool) - 表示是否收集计算图。目前只收集训练计算图。默认值：True。
- **collect_train_lineage** (bool) - 表示是否收集训练阶段的lineage数据，该字段将显示在MindInsight的 `lineage页面 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.10/lineage_and_scalars_comparison.html>`_ 上。默认值：True。 - **collect_train_lineage** (bool) - 表示是否收集训练阶段的lineage数据，该字段将显示在MindInsight的 `lineage页面 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.9/lineage_and_scalars_comparison.html>`_ 上。默认值：True。
- **collect_eval_lineage** (bool) - 表示是否收集评估阶段的lineage数据，该字段将显示在MindInsight的lineage页面上。默认值：True。 - **collect_eval_lineage** (bool) - 表示是否收集评估阶段的lineage数据，该字段将显示在MindInsight的lineage页面上。默认值：True。
- **collect_input_data** (bool) - 表示是否为每次训练收集数据集。目前仅支持图像数据。如果数据集中有多列数据，则第一列应为图像数据。默认值：True。 - **collect_input_data** (bool) - 表示是否为每次训练收集数据集。目前仅支持图像数据。如果数据集中有多列数据，则第一列应为图像数据。默认值：True。
- **collect_dataset_graph** (bool) - 表示是否收集训练阶段的数据集图。默认值：True。 - **collect_dataset_graph** (bool) - 表示是否收集训练阶段的数据集图。默认值：True。

2
docs/api/api_python/mindspore/mindspore.SummaryRecord.rst
View File

@ -7,7 +7,7 @@ mindspore.SummaryRecord
该方法将在一个指定的目录中创建summary文件和lineage文件，并将数据写入文件。 该方法将在一个指定的目录中创建summary文件和lineage文件，并将数据写入文件。
它通过执行 `record` 方法将数据写入文件。除了通过 `summary算子 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.10/summary_record.html#方式二-结合summary算子和summarycollector自定义收集网络中的数据>`_ 记录网络的数据外，SummaryRecord还支持通过 `自定义回调函数和自定义训练循环 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.10/summary_record.html#方式三-自定义callback记录数据>`_ 记录数据。 它通过执行 `record` 方法将数据写入文件。除了通过 `summary算子 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.9/summary_record.html#方式二-结合summary算子和summarycollector自定义收集网络中的数据>`_ 记录网络的数据外，SummaryRecord还支持通过 `自定义回调函数和自定义训练循环 <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.9/summary_record.html#方式三-自定义callback记录数据>`_ 记录数据。
.. note:: .. note::
- 使用SummaryRecord时，需要将代码放置到 `if __name__ == "__main__"` 中运行。 - 使用SummaryRecord时，需要将代码放置到 `if __name__ == "__main__"` 中运行。

2
docs/api/api_python/mindspore/mindspore.export.rst
View File

@ -28,5 +28,5 @@ mindspore.export
- **enc_mode** (Union[str, function]) - 指定加密模式，当设置 `enc_key` 时启用。 - **enc_mode** (Union[str, function]) - 指定加密模式，当设置 `enc_key` 时启用。
- 对于'AIR'和'ONNX'格式的模型，当前仅支持自定义加密导出。 - 对于'AIR'和'ONNX'格式的模型，当前仅支持自定义加密导出。
- 对于'MINDIR'格式的模型，支持的加密选项有：'AES-GCM'，'AES-CBC'和用户自定义加密算法。默认值："AES-GCM"。 - 对于'MINDIR'格式的模型，支持的加密选项有：'AES-GCM'，'AES-CBC'和用户自定义加密算法。默认值："AES-GCM"。
- 关于使用自定义加密导出的详情，请查看 `教程 <https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.10/model_encrypt_protection.html>`_。 - 关于使用自定义加密导出的详情，请查看 `教程 <https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.9/model_encrypt_protection.html>`_。
- **dataset** (Dataset) - 指定数据集的预处理方法，用于将数据集的预处理导入MindIR。 - **dataset** (Dataset) - 指定数据集的预处理方法，用于将数据集的预处理导入MindIR。

2
docs/api/api_python/mindspore/mindspore.load.rst
View File

@ -14,7 +14,7 @@ mindspore.load
- **dec_key** (bytes) - 用于解密的字节类型密钥。有效长度为 16、24 或 32。 - **dec_key** (bytes) - 用于解密的字节类型密钥。有效长度为 16、24 或 32。
- **dec_mode** (Union[str, function]) - 指定解密模式，设置dec_key时生效。可选项：'AES-GCM' | 'AES-CBC' ｜ 自定义解密函数。默认值："AES-GCM"。 - **dec_mode** (Union[str, function]) - 指定解密模式，设置dec_key时生效。可选项：'AES-GCM' | 'AES-CBC' ｜ 自定义解密函数。默认值："AES-GCM"。
- 关于使用自定义解密加载的详情，请查看 `教程 <https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.10/model_encrypt_protection.html>`_。 - 关于使用自定义解密加载的详情，请查看 `教程 <https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.9/model_encrypt_protection.html>`_。
返回： 返回：
GraphCell，一个可以由 `GraphCell` 构成的可执行的编译图。 GraphCell，一个可以由 `GraphCell` 构成的可执行的编译图。

2
docs/api/api_python/ops/mindspore.ops.func_print_.rst
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@ -10,7 +10,7 @@ mindspore.ops.print\_
.. note:: .. note::
在PyNative模式下，请使用Python print函数。在Ascend平台上的Graph模式下，bool、int和float将被转换为Tensor进行打印，str保持不变。 在PyNative模式下，请使用Python print函数。在Ascend平台上的Graph模式下，bool、int和float将被转换为Tensor进行打印，str保持不变。
该方法用于代码调试。当同时print大量数据时，为了保证主进程不受影响，可能会丢失一些数据。如果需要记录完整数据，推荐使用 `Summary` 功能，具体可查看 该方法用于代码调试。当同时print大量数据时，为了保证主进程不受影响，可能会丢失一些数据。如果需要记录完整数据，推荐使用 `Summary` 功能，具体可查看
`Summary <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.10/summary_record.html?highlight=summary#>`_ 。 `Summary <https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.9/summary_record.html?highlight=summary#>`_ 。
参数： 参数：
- **input_x** (Union[Tensor, bool, int, float, str]) - print_的输入。支持多个输入，用'，'分隔。 - **input_x** (Union[Tensor, bool, int, float, str]) - print_的输入。支持多个输入，用'，'分隔。

4
mindspore/python/mindspore/dataset/audio/transforms.py
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@ -123,15 +123,13 @@ class AmplitudeToDB(AudioTensorOperation):
ScaleType.POWER or ScaleType.MAGNITUDE. Default: ScaleType.POWER. ScaleType.POWER or ScaleType.MAGNITUDE. Default: ScaleType.POWER.
ref_value (float, optional): Multiplier reference value for generating ref_value (float, optional): Multiplier reference value for generating
`db_multiplier`. Default: 1.0. The formula is `db_multiplier`. Default: 1.0. The formula is
:math:`\text{db_multiplier} = Log10(max(\text{ref_value}, amin))`. :math:`\text{db_multiplier} = Log10(max(\text{ref_value}, amin))`.
amin (float, optional): Lower bound to clamp the input waveform, which must amin (float, optional): Lower bound to clamp the input waveform, which must
be greater than zero. Default: 1e-10. be greater than zero. Default: 1e-10.
top_db (float, optional): Minimum cut-off decibels, which must be non-negative. Default: 80.0. top_db (float, optional): Minimum cut-off decibels, which must be non-negative. Default: 80.0.
Raises: Raises:
TypeError: If `stype` is not of type :class:`mindspore.dataset.audio.utils.ScaleType`. TypeError: If `stype` is not of type :class:`mindspore.dataset.audio.ScaleType`.
TypeError: If `ref_value` is not of type float. TypeError: If `ref_value` is not of type float.
ValueError: If `ref_value` is not a positive number. ValueError: If `ref_value` is not a positive number.
TypeError: If `amin` is not of type float. TypeError: If `amin` is not of type float.

6
mindspore/python/mindspore/ops/composite/base.py
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@ -90,7 +90,7 @@ def core(fn=None, **flags):
Examples: Examples:
>>> net = Net() >>> net = Net()
>>> net = core(net, predit=True) >>> net = ops.core(net, predit=True)
>>> print(hasattr(net, '_func_graph_flags')) >>> print(hasattr(net, '_func_graph_flags'))
True True
""" """
@ -641,7 +641,7 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> from mindspore import ops >>> from mindspore import ops
>>> from mindspore import dtype as mstype >>> from mindspore import dtype as mstype
>>> from mindspore.ops.composite import MultitypeFuncGraph >>> from mindspore.ops import MultitypeFuncGraph
>>> >>>
>>> tensor_add = ops.Add() >>> tensor_add = ops.Add()
>>> add = MultitypeFuncGraph('add') >>> add = MultitypeFuncGraph('add')
@ -747,7 +747,7 @@ class HyperMap(HyperMap_):
Examples: Examples:
>>> from mindspore import Tensor, ops >>> from mindspore import Tensor, ops
>>> from mindspore.ops.composite.base import MultitypeFuncGraph, HyperMap >>> from mindspore.ops import MultitypeFuncGraph, HyperMap
>>> from mindspore import dtype as mstype >>> from mindspore import dtype as mstype
>>> nest_tensor_list = ((Tensor(1, mstype.float32), Tensor(2, mstype.float32)), >>> nest_tensor_list = ((Tensor(1, mstype.float32), Tensor(2, mstype.float32)),
... (Tensor(3, mstype.float32), Tensor(4, mstype.float32))) ... (Tensor(3, mstype.float32), Tensor(4, mstype.float32)))

1
mindspore/python/mindspore/ops/composite/math_ops.py
View File

@ -358,6 +358,7 @@ def dot(x1, x2):
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> import numpy as np
>>> import mindspore >>> import mindspore
>>> from mindspore import Tensor, ops >>> from mindspore import Tensor, ops
>>> input_x1 = Tensor(np.ones(shape=[2, 3]), mindspore.float32) >>> input_x1 = Tensor(np.ones(shape=[2, 3]), mindspore.float32)

1
mindspore/python/mindspore/ops/composite/random_ops.py
View File

@ -312,6 +312,7 @@ def poisson(shape, mean, seed=None):
Examples: Examples:
>>> from mindspore import Tensor, ops >>> from mindspore import Tensor, ops
>>> import mindspore >>> import mindspore
>>> import numpy as np
>>> # case 1: It can be broadcast. >>> # case 1: It can be broadcast.
>>> shape = (4, 1) >>> shape = (4, 1)
>>> mean = Tensor(np.array([5.0, 10.0]), mindspore.float32) >>> mean = Tensor(np.array([5.0, 10.0]), mindspore.float32)

2
mindspore/python/mindspore/ops/function/array_func.py
View File

@ -3300,7 +3300,7 @@ def broadcast_to(x, shape):
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> from mindspore.ops.function import broadcast_to >>> from mindspore.ops import broadcast_to
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> shape = (2, 3) >>> shape = (2, 3)
>>> x = Tensor(np.array([1, 2, 3]).astype(np.float32)) >>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))

2
mindspore/python/mindspore/ops/function/debug_func.py
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@ -34,7 +34,7 @@ def print_(*input_x):
This function is used for debugging. When too much data is printed at the same time, This function is used for debugging. When too much data is printed at the same time,
in order not to affect the main process, the framework may discard some data. If you need to record the in order not to affect the main process, the framework may discard some data. If you need to record the
data completely, you are recommended to use the `Summary` function, and can check data completely, you are recommended to use the `Summary` function, and can check
`Summary <https://www.mindspore.cn/mindinsight/docs/en/r1.10/summary_record.html?highlight=summary#>`_. `Summary <https://www.mindspore.cn/mindinsight/docs/en/r1.9/summary_record.html?highlight=summary#>`_.
Args: Args:
input_x (Union[Tensor, bool, int, float, str]): The inputs of print_. input_x (Union[Tensor, bool, int, float, str]): The inputs of print_.

18
mindspore/python/mindspore/ops/function/grad/grad_func.py
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@ -376,15 +376,15 @@ def jet(fn, primals, series):
>>> import numpy as np >>> import numpy as np
>>> import mindspore.nn as nn >>> import mindspore.nn as nn
>>> import mindspore as ms >>> import mindspore as ms
>>> import mindspore.ops as P >>> import mindspore.ops as ops
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> from mindspore.ops.functional import jet >>> from mindspore.ops import jet
>>> ms.set_context(mode=ms.GRAPH_MODE) >>> ms.set_context(mode=ms.GRAPH_MODE)
>>> class Net(nn.Cell): >>> class Net(nn.Cell):
... def __init__(self): ... def __init__(self):
... super().__init__() ... super().__init__()
... self.sin = P.Sin() ... self.sin = ops.Sin()
... self.exp = P.Exp() ... self.exp = ops.Exp()
... def construct(self, x): ... def construct(self, x):
... out1 = self.sin(x) ... out1 = self.sin(x)
... out2 = self.exp(out1) ... out2 = self.exp(out1)
@ -487,15 +487,15 @@ def derivative(fn, primals, order):
>>> import numpy as np >>> import numpy as np
>>> import mindspore as ms >>> import mindspore as ms
>>> import mindspore.nn as nn >>> import mindspore.nn as nn
>>> import mindspore.ops as P >>> import mindspore.ops as ops
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> from mindspore.ops.functional import derivative >>> from mindspore.ops import derivative
>>> ms.set_context(mode=ms.GRAPH_MODE) >>> ms.set_context(mode=ms.GRAPH_MODE)
>>> class Net(nn.Cell): >>> class Net(nn.Cell):
... def __init__(self): ... def __init__(self):
... super().__init__() ... super().__init__()
... self.sin = P.Sin() ... self.sin = ops.Sin()
... self.exp = P.Exp() ... self.exp = ops.Exp()
... def construct(self, x): ... def construct(self, x):
... out1 = self.sin(x) ... out1 = self.sin(x)
... out2 = self.exp(out1) ... out2 = self.exp(out1)
@ -564,6 +564,7 @@ def jvp(fn, inputs, v):
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> import numpy as np
>>> from mindspore import ops >>> from mindspore import ops
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> class Net(nn.Cell): >>> class Net(nn.Cell):
@ -690,6 +691,7 @@ def vjp(fn, inputs, v):
``Ascend`` ``GPU`` ``CPU`` ``Ascend`` ``GPU`` ``CPU``
Examples: Examples:
>>> import numpy as np
>>> from mindspore import ops >>> from mindspore import ops
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> class Net(nn.Cell): >>> class Net(nn.Cell):

4
mindspore/python/mindspore/ops/function/math_func.py
View File

@ -3394,9 +3394,9 @@ def std(input_x, axis=(), unbiased=True, keep_dims=False):
``Ascend`` ``CPU`` ``Ascend`` ``CPU``
Examples: Examples:
>>> from mindspore.ops import functional as F >>> import mindspore.ops as ops
>>> input_x = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32)) >>> input_x = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
>>> output = F.std(input_x, 1, True, False) >>> output = ops.std(input_x, 1, True, False)
>>> output_std, output_mean = output[0], output[1] >>> output_std, output_mean = output[0], output[1]
>>> print(output_std) >>> print(output_std)
[1. 2.5166116] [1. 2.5166116]

2
mindspore/python/mindspore/ops/functional.py
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@ -254,7 +254,7 @@ def vmap(fn, in_axes=0, out_axes=0):
Examples: Examples:
>>> from mindspore import Tensor >>> from mindspore import Tensor
>>> from mindspore.ops.functional import vmap >>> from mindspore import vmap
>>> def test_vmap(x, y, z): # ([a],[a],[a]) -> [a] >>> def test_vmap(x, y, z): # ([a],[a],[a]) -> [a]
... return x + y + z ... return x + y + z
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)) # [b, a] >>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)) # [b, a]

2
mindspore/python/mindspore/ops/op_info_register.py
View File

@ -42,7 +42,7 @@ def op_info_register(op_info):
op_info (Union[str, dict]): operator information in json format. op_info (Union[str, dict]): operator information in json format.
Examples: Examples:
>>> from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType >>> from mindspore.ops import op_info_register, TBERegOp, DataType
>>> abs_op_info = TBERegOp("Abs") \ >>> abs_op_info = TBERegOp("Abs") \
... .fusion_type("ELEMWISE") \ ... .fusion_type("ELEMWISE") \
... .async_flag(False) \ ... .async_flag(False) \

4
mindspore/python/mindspore/ops/operations/array_ops.py
View File

@ -3213,11 +3213,11 @@ class StridedSlice(PrimitiveWithInfer):
If the ith bit of `shrink_axis_mask` is 1, `begin`, `end` and `strides` If the ith bit of `shrink_axis_mask` is 1, `begin`, `end` and `strides`
are ignored and dimension i will be shrunk to 0. For a 5*6*7 Tensor `input_x`, are ignored and dimension i will be shrunk to 0. For a 5*6*7 Tensor `input_x`,
if `shrink_axis_mask` is 0b010`, it is equivalent to slice `x[:, 5, :]` if `shrink_axis_mask` is 0b010, it is equivalent to slice `x[:, 5, :]`
and results in an output shape of :math:`(5, 7)`. and results in an output shape of :math:`(5, 7)`.
Note: Note:
`new_axis_mask` and `shrink_axis_mask` are not recommended to `new_axis_mask` and `shrink_axis_mask` are not recommended to
use at the same time, it might incur unexpected result. use at the same time, it might incur unexpected result.
Args: Args:

1
mindspore/python/mindspore/ops/operations/comm_ops.py
View File

@ -57,6 +57,7 @@ class ReduceOp:
``Ascend`` ``GPU`` ``Ascend`` ``GPU``
Examples: Examples:
>>> import numpy as np
>>> from mindspore.communication import init >>> from mindspore.communication import init
>>> from mindspore import Tensor, ops, nn >>> from mindspore import Tensor, ops, nn
>>> from mindspore.ops import ReduceOp >>> from mindspore.ops import ReduceOp

1
mindspore/python/mindspore/ops/operations/custom_ops.py
View File

@ -319,6 +319,7 @@ class Custom(ops.PrimitiveWithInfer):
Examples: Examples:
>>> import mindspore.ops as ops >>> import mindspore.ops as ops
>>> import numpy as np >>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_kernel >>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_kernel
>>> from mindspore.common import dtype as mstype >>> from mindspore.common import dtype as mstype
>>> from mindspore.nn import Cell >>> from mindspore.nn import Cell

2
mindspore/python/mindspore/train/callback/_summary_collector.py
View File

@ -103,7 +103,7 @@ class SummaryCollector(Callback):
training computational graph is collected. Default: True. training computational graph is collected. Default: True.
- collect_train_lineage (bool): Whether to collect lineage data for the training phase, - collect_train_lineage (bool): Whether to collect lineage data for the training phase,
this field will be displayed on the `lineage page \ this field will be displayed on the `lineage page \
<https://www.mindspore.cn/mindinsight/docs/en/r1.10/lineage_and_scalars_comparison.html>`_ <https://www.mindspore.cn/mindinsight/docs/en/r1.9/lineage_and_scalars_comparison.html>`_
of MindInsight. Default: True. of MindInsight. Default: True.
- collect_eval_lineage (bool): Whether to collect lineage data for the evaluation phase, - collect_eval_lineage (bool): Whether to collect lineage data for the evaluation phase,
this field will be displayed on the lineage page of MindInsight. Default: True. this field will be displayed on the lineage page of MindInsight. Default: True.

4
mindspore/python/mindspore/train/serialization.py
View File

@ -399,7 +399,7 @@ def load(file_name, **kwargs):
- Option: 'AES-GCM', 'AES-CBC' or customized decryption. Default: 'AES-GCM'. - Option: 'AES-GCM', 'AES-CBC' or customized decryption. Default: 'AES-GCM'.
- For details of using the customized decryption, please check the `tutorial - For details of using the customized decryption, please check the `tutorial
<https://mindspore.cn/mindarmour/docs/en/r1.10/model_encrypt_protection.html>`_. <https://mindspore.cn/mindarmour/docs/en/r1.9/model_encrypt_protection.html>`_.
Returns: Returns:
GraphCell, a compiled graph that can executed by `GraphCell`. GraphCell, a compiled graph that can executed by `GraphCell`.
@ -881,7 +881,7 @@ def export(net, *inputs, file_name, file_format='AIR', **kwargs):
- For 'MINDIR', all options are supported. Option: 'AES-GCM', 'AES-CBC' or Customized encryption. - For 'MINDIR', all options are supported. Option: 'AES-GCM', 'AES-CBC' or Customized encryption.
Default: 'AES-GCM'. Default: 'AES-GCM'.
- For details of using the customized encryption, please check the `tutorial - For details of using the customized encryption, please check the `tutorial
<https://mindspore.cn/mindarmour/docs/en/r1.10/model_encrypt_protection.html>`_. <https://mindspore.cn/mindarmour/docs/en/r1.9/model_encrypt_protection.html>`_.
Examples: Examples:
>>> import mindspore as ms >>> import mindspore as ms