forked from mindspore-Ecosystem/mindspore
!48384 modify format and urls
Merge pull request !48384 from 俞涵/code_docs_r1101227
This commit is contained in:
Gitee
commit
6805d9fa35
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RELEASE.md
50
RELEASE.md
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@ -16,13 +16,6 @@
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- Fixed an issue of DynamicRNN execution failure in LSTM network under the scenario of computational force segmentation on Ascend platform.
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||||
- Fixed DEVICE_ID cannot be set by single card train scripts parameters in mobilenet, fasterrcnn, yolo, etc.
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||||
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||||
## 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.
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||||
|
||||
### Contributors
|
||||
|
||||
Thanks goes to these wonderful people:
|
||||
|
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@ -31,6 +24,13 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
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|
||||
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
|
||||
|
||||
### Major Features and Improvements
|
||||
|
|
@ -344,7 +344,15 @@ For examples:
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|||
|
||||
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
|
||||
|
||||
|
|
@ -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.
|
||||
|
||||
### 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
|
||||
|
||||
### Major Features and Improvements
|
||||
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@ -433,7 +433,15 @@ Contributions of any kind are welcome!
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|||
- Deprecate `mindspore.SparseTensor` and use `mindspore.COOTensor` instead. ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505))
|
||||
- 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
|
||||
|
||||
|
|
@ -442,14 +450,6 @@ Contributions of any kind are welcome!
|
|||
- [STABLE] Support post quantization to run dynamic quantization algorithm.
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||||
- [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 Release Notes
|
||||
|
|
|
|||
|
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@ -16,13 +16,6 @@
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- 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。
|
||||
|
||||
## 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
|
||||
|
||||
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！
|
||||
|
||||
## 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
|
||||
|
||||
### 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>。
|
||||
|
||||
## 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
|
||||
|
||||
|
|
@ -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。
|
||||
|
||||
### 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
|
||||
|
||||
### 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))
|
||||
- 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
|
||||
|
||||
|
|
@ -441,11 +449,3 @@ AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bing
|
|||
|
||||
- [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。
|
||||
|
||||
## 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！
|
||||
|
|
|
|||
|
|
@ -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。
|
||||
|
||||
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。
|
||||
- **ValueError** - 当 `ref_value` 不为正数。
|
||||
- **TypeError** - 当 `amin` 的类型不为float。
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ mindspore.common.initializer
|
|||
.. math::
|
||||
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。
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ mindspore.SummaryCollector
|
|||
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。
|
||||
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::
|
||||
- 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_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_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。
|
||||
|
|
|
|||
|
|
@ -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。
|
||||
|
||||
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::
|
||||
- 使用SummaryRecord时，需要将代码放置到 `if __name__ == "__main__"` 中运行。
|
||||
|
|
|
|||
|
|
@ -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。
|
||||
- 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"。
|
||||
- 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。
|
||||
|
|
|
|||
|
|
@ -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_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：
|
||||
GraphCell，一个可以由 `GraphCell` 构成的可执行的编译图。
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ mindspore.ops.print\_
|
|||
.. 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。
|
||||
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：
|
||||
- **input_x** (Union[Tensor, bool, int, float, str]) - print_的输入。支持多个输入，用'，'分隔。
|
||||
|
|
|
|||
|
|
@ -123,15 +123,13 @@ class AmplitudeToDB(AudioTensorOperation):
|
|||
ScaleType.POWER or ScaleType.MAGNITUDE. Default: ScaleType.POWER.
|
||||
ref_value (float, optional): Multiplier reference value for generating
|
||||
`db_multiplier`. Default: 1.0. The formula is
|
||||
|
||||
:math:`\text{db_multiplier} = Log10(max(\text{ref_value}, amin))`.
|
||||
|
||||
amin (float, optional): Lower bound to clamp the input waveform, which must
|
||||
be greater than zero. Default: 1e-10.
|
||||
top_db (float, optional): Minimum cut-off decibels, which must be non-negative. Default: 80.0.
|
||||
|
||||
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.
|
||||
ValueError: If `ref_value` is not a positive number.
|
||||
TypeError: If `amin` is not of type float.
|
||||
|
|
|
|||
|
|
@ -90,7 +90,7 @@ def core(fn=None, **flags):
|
|||
|
||||
Examples:
|
||||
>>> net = Net()
|
||||
>>> net = core(net, predit=True)
|
||||
>>> net = ops.core(net, predit=True)
|
||||
>>> print(hasattr(net, '_func_graph_flags'))
|
||||
True
|
||||
"""
|
||||
|
|
@ -641,7 +641,7 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
|
|||
>>> from mindspore import Tensor
|
||||
>>> from mindspore import ops
|
||||
>>> from mindspore import dtype as mstype
|
||||
>>> from mindspore.ops.composite import MultitypeFuncGraph
|
||||
>>> from mindspore.ops import MultitypeFuncGraph
|
||||
>>>
|
||||
>>> tensor_add = ops.Add()
|
||||
>>> add = MultitypeFuncGraph('add')
|
||||
|
|
@ -747,7 +747,7 @@ class HyperMap(HyperMap_):
|
|||
|
||||
Examples:
|
||||
>>> 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
|
||||
>>> nest_tensor_list = ((Tensor(1, mstype.float32), Tensor(2, mstype.float32)),
|
||||
... (Tensor(3, mstype.float32), Tensor(4, mstype.float32)))
|
||||
|
|
|
|||
|
|
@ -358,6 +358,7 @@ def dot(x1, x2):
|
|||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore
|
||||
>>> from mindspore import Tensor, ops
|
||||
>>> input_x1 = Tensor(np.ones(shape=[2, 3]), mindspore.float32)
|
||||
|
|
|
|||
|
|
@ -312,6 +312,7 @@ def poisson(shape, mean, seed=None):
|
|||
Examples:
|
||||
>>> from mindspore import Tensor, ops
|
||||
>>> import mindspore
|
||||
>>> import numpy as np
|
||||
>>> # case 1: It can be broadcast.
|
||||
>>> shape = (4, 1)
|
||||
>>> mean = Tensor(np.array([5.0, 10.0]), mindspore.float32)
|
||||
|
|
|
|||
|
|
@ -3300,7 +3300,7 @@ def broadcast_to(x, shape):
|
|||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.ops.function import broadcast_to
|
||||
>>> from mindspore.ops import broadcast_to
|
||||
>>> from mindspore import Tensor
|
||||
>>> shape = (2, 3)
|
||||
>>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ def print_(*input_x):
|
|||
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
|
||||
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:
|
||||
input_x (Union[Tensor, bool, int, float, str]): The inputs of print_.
|
||||
|
|
|
|||
|
|
@ -376,15 +376,15 @@ def jet(fn, primals, series):
|
|||
>>> import numpy as np
|
||||
>>> import mindspore.nn as nn
|
||||
>>> import mindspore as ms
|
||||
>>> import mindspore.ops as P
|
||||
>>> import mindspore.ops as ops
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.ops.functional import jet
|
||||
>>> from mindspore.ops import jet
|
||||
>>> ms.set_context(mode=ms.GRAPH_MODE)
|
||||
>>> class Net(nn.Cell):
|
||||
... def __init__(self):
|
||||
... super().__init__()
|
||||
... self.sin = P.Sin()
|
||||
... self.exp = P.Exp()
|
||||
... self.sin = ops.Sin()
|
||||
... self.exp = ops.Exp()
|
||||
... def construct(self, x):
|
||||
... out1 = self.sin(x)
|
||||
... out2 = self.exp(out1)
|
||||
|
|
@ -487,15 +487,15 @@ def derivative(fn, primals, order):
|
|||
>>> import numpy as np
|
||||
>>> import mindspore as ms
|
||||
>>> import mindspore.nn as nn
|
||||
>>> import mindspore.ops as P
|
||||
>>> import mindspore.ops as ops
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.ops.functional import derivative
|
||||
>>> from mindspore.ops import derivative
|
||||
>>> ms.set_context(mode=ms.GRAPH_MODE)
|
||||
>>> class Net(nn.Cell):
|
||||
... def __init__(self):
|
||||
... super().__init__()
|
||||
... self.sin = P.Sin()
|
||||
... self.exp = P.Exp()
|
||||
... self.sin = ops.Sin()
|
||||
... self.exp = ops.Exp()
|
||||
... def construct(self, x):
|
||||
... out1 = self.sin(x)
|
||||
... out2 = self.exp(out1)
|
||||
|
|
@ -564,6 +564,7 @@ def jvp(fn, inputs, v):
|
|||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import ops
|
||||
>>> from mindspore import Tensor
|
||||
>>> class Net(nn.Cell):
|
||||
|
|
@ -690,6 +691,7 @@ def vjp(fn, inputs, v):
|
|||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import ops
|
||||
>>> from mindspore import Tensor
|
||||
>>> class Net(nn.Cell):
|
||||
|
|
|
|||
|
|
@ -3394,9 +3394,9 @@ def std(input_x, axis=(), unbiased=True, keep_dims=False):
|
|||
``Ascend`` ``CPU``
|
||||
|
||||
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))
|
||||
>>> output = F.std(input_x, 1, True, False)
|
||||
>>> output = ops.std(input_x, 1, True, False)
|
||||
>>> output_std, output_mean = output[0], output[1]
|
||||
>>> print(output_std)
|
||||
[1. 2.5166116]
|
||||
|
|
|
|||
|
|
@ -254,7 +254,7 @@ def vmap(fn, in_axes=0, out_axes=0):
|
|||
|
||||
Examples:
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.ops.functional import vmap
|
||||
>>> from mindspore import vmap
|
||||
>>> def test_vmap(x, y, z): # ([a],[a],[a]) -> [a]
|
||||
... return x + y + z
|
||||
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)) # [b, a]
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ def op_info_register(op_info):
|
|||
op_info (Union[str, dict]): operator information in json format.
|
||||
|
||||
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") \
|
||||
... .fusion_type("ELEMWISE") \
|
||||
... .async_flag(False) \
|
||||
|
|
|
|||
|
|
@ -3213,11 +3213,11 @@ class StridedSlice(PrimitiveWithInfer):
|
|||
|
||||
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`,
|
||||
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)`.
|
||||
|
||||
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.
|
||||
|
||||
Args:
|
||||
|
|
|
|||
|
|
@ -57,6 +57,7 @@ class ReduceOp:
|
|||
``Ascend`` ``GPU``
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore.communication import init
|
||||
>>> from mindspore import Tensor, ops, nn
|
||||
>>> from mindspore.ops import ReduceOp
|
||||
|
|
|
|||
|
|
@ -319,6 +319,7 @@ class Custom(ops.PrimitiveWithInfer):
|
|||
Examples:
|
||||
>>> import mindspore.ops as ops
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_kernel
|
||||
>>> from mindspore.common import dtype as mstype
|
||||
>>> from mindspore.nn import Cell
|
||||
|
|
|
|||
|
|
@ -103,7 +103,7 @@ class SummaryCollector(Callback):
|
|||
training computational graph is collected. Default: True.
|
||||
- collect_train_lineage (bool): Whether to collect lineage data for the training phase,
|
||||
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.
|
||||
- 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.
|
||||
|
|
|
|||
|
|
@ -399,7 +399,7 @@ def load(file_name, **kwargs):
|
|||
|
||||
- Option: 'AES-GCM', 'AES-CBC' or customized decryption. Default: 'AES-GCM'.
|
||||
- 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:
|
||||
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.
|
||||
Default: 'AES-GCM'.
|
||||
- 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:
|
||||
>>> import mindspore as ms
|
||||
|
|
|
|||
Loading…
Reference in New Issue