mindspore.numpy.rand()
, mindspore.numpy.randn()
, mindspore.numpy.randint()
, and mindspore.ops.arange()
.mindspore.train.callback.History
in Callback.mindspore.ms_class
class decorator.__getitem__/__next__
methods of GeneratorDataset return a single NumPy object.ulimit -u 10240
to increase the number of threads/processes available to the current user when specify too many processes or threads for loading dataset may cause RuntimeError: can't start new thread.import mindspore.dataset.engine.datasets as ds
. Use import mindspore.dataset as ds
instead as recommended in mindspore doc.mindspore.ms_class
interface, as class decorator for user-defined classes. It allows MindSpore to identify user-defined classes and access their attributes and methods(!30855:Support user-defined classes by ms_class decorators)mindspore.SparseTensor
and use mindspore.COOTensor
instead. (!28505:Change SparseTensor to COOTensor)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!
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
mindspore.nn.LSTMCell
from single-layer LSTM to single-cell LSTM.mindspore.ops.Custom
to customize your own operators for Ascend(AICore, AICPU), GPU, CPU backends, and the custom type can be one of TBE, AKG, pure Python function or prebuild binary(called aot operator).mindspore.dataset.MindDataset
interface changes input parameter dataset_file(!27542:dataset: change dataset_file into dataset_files for MindDataset)MindDataset
contains the input parameter dataset_file
, which is in the singular format. It can receive a single file path or a list that stores multiple file paths. Thus It is preferred to change the input parameter dataset_file
into plural format. In addition, the input parameters of most dataset API, such as TFRecordDataset
, are in plural formart (dataset_files
). To ensure consistency, the input parameter dataset_file
of MindDataset is changed to plural formart as dataset_files
, we can see the updated version in api of mindspore.dataset.MindDataset.
mindspore.Tensor
's property virtual_flag
(!26989:change tensor.virtual_flag and parameter.is_init interfaces to inner api)mindspore.Parameter
's property is_init
(!26989:change tensor.virtual_flag and parameter.is_init interfaces to inner api)mindspore.nn.ROC
's interface roc
(!25713:ROC.roc change to inner api)shard()
interface of primitive is changed from shard(strategy)
to shard(in_strategy=None, out_strategy=None)
set_auto_parallel_context()
interface of context is changed fromset_auto_parallel_context(parallel_mode=AUTO_PARALLEL, auto_parallel_search_mode="dynamic_programming")
to set_auto_parallel_context(parallel_mode=AUTO_PARALLEL, search_mode="dynamic_programming")
mindspore.train.callback.SummaryCollector
interface's parameter collect_specified_data
add new operations collect_landscape
(!26229:add loss landscape visualization function)collect_landscape
can collect the parameters needed to create the loss landscape. we can see the updated version in api of mindspore.train.callback.SummaryCollector.
mindspore.train.callback
add new interface SummaryLandscape
(!26229:add loss landscape visualization function)SummaryLandscape
can help you to collect loss landscape information. It can create landscape in PCA direction or random direction by calculating loss. We can see the updated version in api of mindspore.train.callback.SummaryLandscape.
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, wangnan39@huawei.com, 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, zhanghaibo5@huawei.com, 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!
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
model_zoo
has been seperated to an individual repositorymodels
while
andbreak
,continue
statements of training network inGRAPH_MODE
.Configuring the recomputation of the communication operations generated by the model parallel and optimizer parallel to save the memory on the
devices. Users can pass mp_comm_recompute
and parallel_optimizer_comm_recompute
to enable the recomputation of the communication operations.
for
statement.(!23669:Fix inline pass problem in switch. )model_zoo
has been seperated to an individual repositorymodels
while
andbreak
,continue
statements of training network inGRAPH_MODE
.Configuring the recomputation of the communication operations generated by the model parallel and optimizer parallel to save the memory on the
devices. Users can pass mp_comm_recompute
and parallel_optimizer_comm_recompute
to enable the recomputation of the communication operations.
for
statement.(!23669:Fix inline pass problem in switch. )Previously, we need to set the dump path in dump config file. To make the dump feature easier to use on cloud, we support new environment parameter MS_DIAGNOSTIC_DATA_PATH
.
1.3.0 | 1.4.0 |
---|---|
path is a mandatory field. |
path field is optional. If path field is not provided or is empty string, MS_DIAGNOSTIC_DATA_PATH should be set in environment. |
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
run_check
to check whether MindSpore is working properly or not.set_indexes
to select the inputs of update
in the specified order._Loss
to an external API LossBase
as the base class of losses.Gather
.mindspore.dataset.Dataset.device_que
interface removes unused parameter prefetch_size
(!18973:Delete unused param in device_que)Previously, we have a parameter prefetch_size
in device_que
to define the prefetch number of records ahead of the user's request. But indeed this parameter is never used which means it is an ineffective parameter. Therefore, we remove this parameter in 1.3.0 and users can set this configuration by mindspore.dataset.config.set_prefetch_size.
1.2.1 | 1.3.0 |
|
|
mindspore.nn.optim.thor
interface changes to lowercase thor
and adds two parameters enable_clip_grad
and frequency
(!17212:clearn codechekc for thor)The parameter enable_clip_grad
is used for gradient clipping and another parameter frequency
is used to control the update interval of second order information matrix.
1.2.1 | 1.3.0 |
|
|
Previously, we could only dump tensor data for one or all steps. To make the dump feature easier to use, we changed the dump configuration format and dump structure. View the New Dump Tutorial.
1.2.1 | 1.3.0 |
---|---|
iteration is an int. |
iteration is a string. |
op_debug_mode is in async_dump_settings field. |
op_debug_mode is in common_dump_settings field. async_dump_settings is removed. |
Previously, Training on Device use TrainSession while Inference on Device use LiteSession. To simplify implementation, we move TrainSession functions to LiteSession as virtual function. and move APIs previous defined in train_session.h to lite_session.h.
class MS_API LiteSession {
...
static LiteSession *CreateTrainSession(const std::string &filename, const lite::Context *context,
bool train_mode = false, const lite::TrainCfg *cfg = nullptr);
static LiteSession *CreateTransferSession(const std::string &filename_backbone, const std::string &filename_head,
const lite::Context *context, bool train_mode = false,
const lite::TrainCfg *cfg = nullptr);
virtual int Train() { return mindspore::lite::RET_ERROR; }
virtual int Eval() { return mindspore::lite::RET_OK; }
virtual int SetupVirtualBatch(int virtual_batch_multiplier, float lr = -1.0f, float momentum = -1.0f) {
return mindspore::lite::RET_ERROR;
}
virtual std::vector<tensor::MSTensor *> GetPredictions() const {
std::vector<tensor::MSTensor *> outputs;
return outputs;
}
...
Previously, Training on Device uses SaveToFile API to save the training model to file. Export API was added in this release to support more format, more model type(train or interface part of the model), and save weight quant model of train.
virtual int Export(const std::string &file_name, lite::ModelType model_type = lite::MT_TRAIN,
lite::QuantizationType quant_type = lite::QT_DEFAULT, lite::FormatType = lite::FT_FLATBUFFERS) {
return mindspore::lite::RET_ERROR;
}
When Training on the device, we may need to update the model featuremap and get model featuremap.particularly in MindSpore Federated Scenario.
virtual std::vector<tensor::MSTensor *> GetFeatureMaps() const {
std::vector<tensor::MSTensor *> features;
return features;
}
virtual int UpdateFeatureMaps(const std::vector<tensor::MSTensor *> &features) { return mindspore::lite::RET_ERROR; }
Previously, if we want to create a LiteSession object, we need to call two APIs:
MSConfig config;
// config options ...
LiteSession liteSession = new LiteSession();
boolean ret = liteSession.init(config);
if (!ret) {
// handle init LiteSession failed ...
}
now we can create a LiteSession object with new API just like:
MSConfig config;
// config options ...
LiteSession liteSession = createSession(config);
if (liteSession == null) {
// handle create LiteSession failed ...
}
Previously, if we want to inference a model, we need to call APIs like:
MSConfig config;
// config options ...
LiteSession liteSession = new LiteSession();
boolean initSessionRet = liteSession.init(config);
if (!initSessionRet) {
// handle init LiteSession failed and return ...
}
Model model = new Model();
boolean loadModelRet = model.loadModel(modelMappedByteBuffer);
if (!loadModelRet) {
// handle load model failed and return ...
}
boolean compileModelRet = liteSession.compileGraph(model);
if (!loadModelRet) {
// handle compile model failed and return ...
}
model.free();
// liteSession is ready to inference model, call runGraph in LiteSession.class ...
now we can use new API just like:
MSConfig config;
// config options ...
LiteSession liteSession = createSession(modelMappedByteBuffer, config);
if (liteSession == null) {
// handle init LiteSession failed and return ...
}
// liteSession is ready to inference model, call runGraph in LiteSession.class ...
New createSession method is an API that integrates four old APIs: LiteSession.init, Model.loadModel, LiteSession.compileGraph and model.free. It is simple and efficient as it reduces one modelBuffer copy operation.
Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.
public List<MSTensor> getFeaturesMap() {
List<Long> ret = this.getFeaturesMap(this.sessionPtr);
ArrayList<MSTensor> tensors = new ArrayList<MSTensor>();
for (Long msTensorAddr : ret) {
MSTensor msTensor = new MSTensor(msTensorAddr);
tensors.add(msTensor);
}
return tensors;
}
public boolean updateFeatures(List<MSTensor> features) {
long[] inputsArray = new long[features.size()];
for (int i = 0; i < features.size(); i++) {
inputsArray[i] = features.get(i).getMSTensorPtr();
}
return this.updateFeatures(this.sessionPtr, inputsArray);
}
Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.
public boolean export(String modelFileName, int modelType, int quantizationType) {
return this.export(this.sessionPtr, modelFileName, modelType, quantizationType);
}
Align with update of C++ api in LiteSession class, add new java API to LiteSession.java Correspondingly.
public class LiteSession {
...
public static LiteSession createTrainSession(String modelName, final MSConfig config, boolean trainMode){...}
public boolean train() {...}
public boolean eval() {...}
...
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
Contributions of any kind are welcome!
isinstance
.(Ascend/GPU/CPU)export DATASET_ENABLE_NUMA=True
to configure NUMA binding. In multi-card training scenarios, the training data processing speed can be improved, thereby improving the network training efficiency.mindspore.numpy.array()
, mindspore.numpy.asarray()
, mindspore.numpy.asfarray()
, mindspore.numpy.copy()
now support GRAPH mode, but cannot accept numpy.ndarray
as input arguments anymore(!12726:Add March Numpy interfaces to mindspore)Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove numpy.ndarray
support. With that being said, users can still use Tensor
to convert numpy.ndarray
to tensors.
1.1.1 | 1.2.0 |
|
|
out
and where
(!12726:Add March Numpy interfaces to mindspore)Previously, we have incomplete support for keyword arguments out
and where
in mindspore.numpy interfaces, however, the out
argument is only functional when where
argument is also provided, and out
cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in np.where
1.1.1 | 1.2.0 |
|
|
ops.MakeRefKey
into an internal interface (!12010:Convert MakeRefKey to an internal interface)Previously MakeRefKey is an external interface that is not used, now make it an internal interface with the same usage. We do not recommend users to use this interface, and we will remove the relevant introduction of this interface from the official website.
ops.ApplyFtrl
, ops.ApplyMomentum
, ops.ApplyRMSProp
, ops.ApplyCenteredRMSProp
change the output on Ascend backend from multiple to a single. (!11895:unify mindir for different backend: the output num of optimizer ops, the backward of concat)Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.
P.FusedBatchNorm
, P.FusedBatchNormEx
deleted (!12115:IR operators of GPU and CPU are unified as batchnorm)The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.
MetaTensor
deleted (!10325:modify MetaTensor and Tensor)The MetaTensor interface has been deleted. The function of MetaTensor has been integrated into tensor.
ControlDepend
is deleted, use Depend
instead. The decorator @C.add_flags(has_effect=True)
does not work. (!13793:remove control_depend from py file)Previously, we used ControlDepend to control the execution order of multiple operators. In version 1.2.0, mindspore introduces the auto-monad side effects expression to ensure that the perform order of user's semantics is correct. Therefore, ControlDepend is deleted and Depend is recommended.
In most scenarios, if operators have IO side effects (such as print) or memory side effects (such as assign), they will be executed according to the user's semantics. In some scenarios, if the two operators A and B have no order dependency, and A must be executed before B, we recommend using Depend to specify their execution order. See the API documentation of the Depend operator for specific usage.
1.1.1 | 1.2.0 |
|
|
After the introduction of the auto-monad side effect expression feature, the decorator @C.add_flags(has_effect=True)
does not work. If the decorator is used in the script, please modify. Take the overflow identification operator (without side effects) as an example, the modification method is as follows:
1.1.1 | 1.2.0 |
|
|
1.1.1 supports only the old ABI. Currently, both the new and the old are supported.
1.1.1 | 1.2.0 |
|
|
The Context
class is refactored. For details, see the API docs.
1.1.1 | 1.2.0 |
|
|
LoadModel
is renamed Load
. No exception is thrown new but the return status should be checked.
1.1.1 | 1.2.0 |
|
|
Model
uses a non-parameter ctor now, and arguments are passed in through Build
.
1.1.1 | 1.2.0 |
|
|
MSTensor::CreateTensor
and MSTensor::CreateRefTensor
returns a native pointer now, need to be destroy by DestroyTensorPtr
.
1.1.1 | 1.2.0 |
|
|
mindspore.ops.operations.BondForceWithAtomEnergy
, mindspore.ops.operations.AngleForceWithAtomEnergy
, mindspore.ops.operations.DihedralForceWithAtomEnergy
, mindspore.ops.operations.Dihedral14LJCFForceWithAtomEnergy
, mindspore.ops.operations.LJForceWithPMEDirectForce
, mindspore.ops.operations.PMEExcludedForce
, mindspore.ops.operations.PMEReciprocalForce
,mindspore.ops.operations.BondEnergy
, mindspore.ops.operations.AngleEnergy
,mindspore.ops.operations.DihedralEnergy
, mindspore.ops.operations.Dihedral14LJEnergy
, mindspore.ops.operations.Dihedral14CFEnergy
,mindspore.ops.operations.LJEnergy
, mindspore.ops.operations.PMEEnergy
. All operators are supported in GPU
.nn.MatMul
is now deprecated in favor of ops.matmul
(!12817:numpy-native deprecate nn.MatMul)ops.matmul follows the API of numpy.matmul as closely as possible. As a function interface, ops.matmul is applied without instantiation, as opposed to nn.MatMul
, which should only be used as a class instance.
1.1.1 | 1.2.0 |
|
|
Previously, some common data structs such as CpuBindMode
and DeviceType
are in context.h, this may cause cross-dependency between headers. So we create a new header named lite_types.h for some common data structs and move CpuBindMode
and DeviceType
from context.h into lite_types.h.
lite_types.h |
|
Previously, users could not create MSTensor
or modify ``MSTensor, all MSTensor
are created and managed by framework. However users need to create or modify MSTensor sometimes such as pre-processing input data. So we provide two new interfaces in ms_tensor.h: `CreateTensor` interface for creating `MSTensor` by user and `set_shape` interface for modifying the shape of `MSTensor`.
CreateTensor |
|
set_shape |
|
Previously, users could access to data of MSTensor
by interface named MutableData
. However MutableData
is not only returning data of tensor but also allocating data for tensor if its data is nullptr. So we provide a new interfaces in ms_tensor.h named data
for returning data of tensor without allocating automatically.
data |
|
DimensionSize()
in ms_tensor.h.(!13515:cpp api modify)The interface named DimensionSize
is fuinctionally overlapped with the interface named shape
. For the simplicity of the interface, we delete DimensionSize
and recommend users to use the new interface named shape
instead.
DimensionSize() |
|
Previously, class Allocator
is in namespace mindspore::lite. Considering unified allocator interface for unified runtime API, we move Allocator
to namespace mindspore.
1.1.0 | 1.2.0 |
|
|
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa.
Contributions of any kind are welcome!
isinstance
.(Ascend/GPU/CPU)export DATASET_ENABLE_NUMA=True
to configure NUMA binding. In multi-card training scenarios, the training data processing speed can be improved, thereby improving the network training efficiency.mindspore.numpy.array()
, mindspore.numpy.asarray()
, mindspore.numpy.asfarray()
, mindspore.numpy.copy()
now support GRAPH mode, but cannot accept numpy.ndarray
as input arguments anymore(!12726:Add March Numpy interfaces to mindspore)Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove numpy.ndarray
support. With that being said, users can still use Tensor
to convert numpy.ndarray
to tensors.
1.1.1 | 1.2.0 |
|
|
out
and where
(!12726:Add March Numpy interfaces to mindspore)Previously, we have incomplete support for keyword arguments out
and where
in mindspore.numpy interfaces, however, the out
argument is only functional when where
argument is also provided, and out
cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in np.where
1.1.1 | 1.2.0 |
|
|
ops.MakeRefKey
into an internal interface (!12010:Convert MakeRefKey to an internal interface)Previously MakeRefKey is an external interface that is not used, now make it an internal interface with the same usage. We do not recommend users to use this interface, and we will remove the relevant introduction of this interface from the official website.
ops.ApplyFtrl
, ops.ApplyMomentum
, ops.ApplyRMSProp
, ops.ApplyCenteredRMSProp
change the output on Ascend backend from multiple to a single. (!11895:unify mindir for different backend: the output num of optimizer ops, the backward of concat)Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.
P.FusedBatchNorm
, P.FusedBatchNormEx
deleted (!12115:IR operators of GPU and CPU are unified as batchnorm)The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.
MetaTensor
deleted (!10325:modify MetaTensor and Tensor)The MetaTensor interface has been deleted. The function of MetaTensor has been integrated into tensor.
ControlDepend
is deleted, use Depend
instead. The decorator @C.add_flags(has_effect=True)
does not work. (!13793:remove control_depend from py file)Previously, we used ControlDepend to control the execution order of multiple operators. In version 1.2.0, mindspore introduces the auto-monad side effects expression to ensure that the perform order of user's semantics is correct. Therefore, ControlDepend is deleted and Depend is recommended.
In most scenarios, if operators have IO side effects (such as print) or memory side effects (such as assign), they will be executed according to the user's semantics. In some scenarios, if the two operators A and B have no order dependency, and A must be executed before B, we recommend using Depend to specify their execution order. See the API documentation of the Depend operator for specific usage.
1.1.1 | 1.2.0 |
|
|
After the introduction of the auto-monad side effect expression feature, the decorator @C.add_flags(has_effect=True)
does not work. If the decorator is used in the script, please modify. Take the overflow identification operator (without side effects) as an example, the modification method is as follows:
1.1.1 | 1.2.0 |
|
|
1.1.1 supports only the old ABI. Currently, both the new and the old are supported.
1.1.1 | 1.2.0 |
|
|
The Context
class is refactored. For details, see the API docs.
1.1.1 | 1.2.0 |
|
|
LoadModel
is renamed Load
. No exception is thrown new but the return status should be checked.
1.1.1 | 1.2.0 |
|
|
Model
uses a non-parameter ctor now, and arguments are passed in through Build
.
1.1.1 | 1.2.0 |
|
|
MSTensor::CreateTensor
and MSTensor::CreateRefTensor
returns a native pointer now, need to be destroy by DestroyTensorPtr
.
1.1.1 | 1.2.0 |
|
|
mindspore.ops.operations.BondForceWithAtomEnergy
, mindspore.ops.operations.AngleForceWithAtomEnergy
, mindspore.ops.operations.DihedralForceWithAtomEnergy
, mindspore.ops.operations.Dihedral14LJCFForceWithAtomEnergy
, mindspore.ops.operations.LJForceWithPMEDirectForce
, mindspore.ops.operations.PMEExcludedForce
, mindspore.ops.operations.PMEReciprocalForce
,mindspore.ops.operations.BondEnergy
, mindspore.ops.operations.AngleEnergy
,mindspore.ops.operations.DihedralEnergy
, mindspore.ops.operations.Dihedral14LJEnergy
, mindspore.ops.operations.Dihedral14CFEnergy
,mindspore.ops.operations.LJEnergy
, mindspore.ops.operations.PMEEnergy
. All operators are supported in GPU
.nn.MatMul
is now deprecated in favor of ops.matmul
(!12817:numpy-native deprecate nn.MatMul)ops.matmul follows the API of numpy.matmul as closely as possible. As a function interface, ops.matmul is applied without instantiation, as opposed to nn.MatMul
, which should only be used as a class instance.
1.1.1 | 1.2.0 |
|
|
Previously, some common data structs such as CpuBindMode
and DeviceType
are in context.h, this may cause cross-dependency between headers. So we create a new header named lite_types.h for some common data structs and move CpuBindMode
and DeviceType
from context.h into lite_types.h.
lite_types.h |
|
Previously, users could not create MSTensor
or modify ``MSTensor, all MSTensor
are created and managed by framework. However users need to create or modify MSTensor sometimes such as pre-processing input data. So we provide two new interfaces in ms_tensor.h: `CreateTensor` interface for creating `MSTensor` by user and `set_shape` interface for modifying the shape of `MSTensor`.
CreateTensor |
|
set_shape |
|
Previously, users could access to data of MSTensor
by interface named MutableData
. However MutableData
is not only returning data of tensor but also allocating data for tensor if its data is nullptr. So we provide a new interfaces in ms_tensor.h named data
for returning data of tensor without allocating automatically.
data |
|
DimensionSize()
in ms_tensor.h.(!13515:cpp api modify)The interface named DimensionSize
is fuinctionally overlapped with the interface named shape
. For the simplicity of the interface, we delete DimensionSize
and recommend users to use the new interface named shape
instead.
DimensionSize() |
|
Previously, class Allocator
is in namespace mindspore::lite. Considering unified allocator interface for unified runtime API, we move Allocator
to namespace mindspore.
1.1.0 | 1.2.0 |
|
|
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, 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, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, 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, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa.
Contributions of any kind are welcome!
ops.AvgPool
, ops.MaxPool
, ops.MaxPoolWithArgmax
change attr name from 'ksize', 'padding' to 'kernel_size', 'pad_mode' (!11350:update Pooling's attr kernel_size, pad_mode)Previously the kernel size and pad mode attrs of pooling ops are named "ksize" and "padding", which is a little puzzling and inconsistent with convolution ops. So they are rename to "kernel_size" and "pad_mode".
1.1.0 | 1.1.1 |
|
|
ops.TensorAdd
, change API name to ops.Add
(!11568:Change TensorAdd to Add)The operator name TensorAdd is not standardized, it is changed to Add. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
1.1.0 | 1.1.1 |
|
|
ops.Gelu
, ops.GeluGrad
, ops.FastGelu
, ops.FastGeluGrad
, change API name to ops.GeLU
, ops.GeLUGrad
, ops.FastGeLU
, ops.FastGeLUGrad
(!11603:fix_gelu_name)Gelu, GeluGrad, FastGelu, and FastGeluGrad names are unified into ReLU naming rules, "lu" is changed to the uppercase "LU". The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
1.1.0 | 1.1.1 |
|
|
ops.GatherV2
, change API name to ops.Gather
(!11713:Change GatherV2 to Gather)GatherV2 is changed to Gather. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
1.1.0 | 1.1.1 |
|
|
ops.Pack
、ops.Unpack
, change API name to ops.Stack
、ops.Unstack
(!11828:fix_stack_name)Pack is changed to Stack, and Unpack is changed to Unstack. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
1.1.0 | 1.1.1 |
|
|
ops.ControlDepend
, add deprecated to ControlDepend (!11844:add deprecated to ControlDepend)ControlDepend is deprecated and will be removed in a future version, use Depend instead.
1.1.0 | 1.1.1 |
|
|
ops.Depend
, add operator description and use case (!11815:Modify the description of the depend operator), (!11879:modify depend interface description)Since the ControlDepend operator will be deprecated from version 1.2, it is recommended to use the Depend operator instead.
1.1.0 | 1.1.1 |
|
|
mindspore::api
to mindspore
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
Context
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
Tensor
to MSTensor
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
Model
move setting of model options from Build
to ctor Model
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
Model
modify GetInputsInfo
, GetOutputsInfo
to GetInputs
, GetOutputs
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
Model
modify Predict
parameters type from Buffer
to MSTensor
(!11574:unifiled lite & cloud api )1.1.0 | 1.1.1 |
|
|
ops.SpaceToBatch
, ops.BatchToSpace
are deprecated in favor of ops.SpaceToBatchND
, ops.BatchToSpaceND
(!11527: unify SpaceToBatchND's attr)The ops.SpaceToBatchND
, ops.BatchToSpaceND
are more general and have same behavior as ops.SpaceToBatch
, ops.BatchToSpace
when block_shape
is a int.
ops.DepthwiseConv2dNative
is deprecated in favor of nn.Conv2D
(!11702:replace DepthWiseConv with nn.Conv2D)The ops.DepthwiseConv2dNative
is only supported by Ascend, it is recommended to directly use nn.Conv2D
. If group
is equal to in_ channels
and out_channels
, the 2D convolution layer is also a 2D depthwise convolution layer.
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa
Contributions of any kind are welcome!
Optimizer
add target interface (!6760:sparse optimizer)The usage of the sparse optimizer is changed.
The target interface is used to set the execution backend of the sparse operator.
The add_primitive_attr interface is no longer allowed.
The following optimizers add the target interface: Adam, FTRL, LazyAdam, ProximalAdagrad
1.0.1 | 1.1.0 |
|
|
export
Modify the input parameters and export's file name (!7385:integrate_export_v2, !9057:modify export file name)Export the MindSpore prediction model to a file in the specified format.
The reference includes:net
, *inputs
, file_name
, file_format
, **kwargs
.
Input parameters can be input according to specific export requirements.
Add the file name extension based on the format.
1.0.1 | 1.1.0 |
|
|
Dense
, Conv2dBnAct
, DenseBnAct
, DenseQuant
support setting the activation attribute as an instance of a class derived from nn.Cell
or Primtive
(!7581:Extension interface for dense)activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer
1.0.1 | 1.1.0 |
|
|
tensor.dim()
, tensor.size()
has been renamed to tensor.ndim
, tensor.size
(!10175:Add tensor.ndim and rename tensor.size() to tensor.size)Previously, tensor.size() and tensor.dim() were used for checking the total number of elements/dimensions in the tensor.
However, from a user's perspective, tensor.size and tensor.ndim (methods -> properties) are better choices, since they follow the numpy naming convention.
1.0.1 | 1.1.0 |
|
|
EmbeddingLookup
add a config in the interface: sparse (!8202:wide&deep backward unique)sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
1.0.1 | 1.1.0 |
|
|
nn.probability.bijector
change types of attributes from (int, float) to (float, list, numpy.ndarray, Tensor) (!8191:redesigned bijector broadcast shape and bijector's dtype logic)Attributes Type change: (int, float) -> (float, list, numpy.ndarray, Tensor).
Int type is not supported anymore. Parameters of all bijectors should be type float, list, numpy.ndarray or Tensor.
1.0.1 | 1.1.0 |
|
|
nn.probability.bijector.GumbelCDF
remove a attribute in the interface: dtype (!8191:redesigned bijector broadcast shape and bijector's dtype logic)dtype is removed from GumbelCDF and is no longer an argument of the class.
1.0.1 | 1.1.0 |
|
|
nn.layer.combined.Conv2dBnAct
, nn.layer.combined.DenseBnAct
move from nn.layer.quant to nn.layer.combined (!8187:move Conv2dBnAct,DenseBnAct to combined.py)Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now move them to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code don't need any change.
1.0.1 | 1.1.0 |
|
|
nn.layer.conv.Conv2D
, nn.layer.quant.Conv2dBnFoldQuant
, nn.layer.quant.Conv2dBnWithoutFoldQuant
change weight shape when group > 1 in Ascend platform (!9723:add ir passes to unify mindir)In Ascend platform, if group > 1, the weight shape of Conv2D change from [in_channels//group, out_channels, kernel_size, kernel_size] to [out_channels, in_channels//group, kernel_size, kernel_size]. Previously used checkpoints of the networks which use Conv2D with group > 1, such as MobileNet, can not be directly used now, need to transpose the first and second axis of the weight.
MindSpore Lite supports both weight quantization and full quantization. Currently, Weights can be quantized into 1 ~ 16 bits according to user configuration. In internal testing, quantization of networks, such as classification, detection, segmentation and transformer are well supported. To ensure high accuracy of quantized models, MindSpore Lite uses a pipeline quantization method. In the first phase, the weight and activation value are quantized using linear quantization methods, such as MIN-MAX. In the second phase, the quantization error is analyzed, and uses statistical methods to compensate loss caused by fp32 quantization to a fixed point such as Int8 to quantized models. The features of Post-training quantization are:
mobilenet_v2 | ACC (ImageNet) |
---|---|
FP32 | 71.56% |
A8W8 | 71.16% |
A8W8(without BiasCorrection) | 70.74% |
A8W7 | 71.06% |
A7W7 | 70.78% |
The above table uses the mobilenet_v2 model from TF official website. Using MindSpore Lite quantization, the precision of A8W8 (8-bit activation value quantization and 8-bit weight quantization) decreases from 0.82% to 0.4% after accuracy loss compensation, for 7-bit quantization, the precision loss is still no more than 1%.
Within MindSpore 1.1 release, the MindSpore Lite provides the following Training-on-Device (ToD) capabilities:
Deprecate Interface GetOutputsByNodeName
zhouyifengCode, huqi, JulyAi, damon0626, chenbo116, rmdyh, davidmc, gray0v0, doitH, Gogery, zymaa, xinyunfan,Adel, AGroupofProbiotocs, anthonyaje, 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, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa
Models
Third party
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, danish, Danish, dayschan, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, gongdaguo, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, root, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, Zichun, Zirui, Ziyan, zjun, ZPaC
Contributions of any kind are welcome!
auto
for AMP.Converter
CPU & GPU
Tool & example
Models
Python API
Executor
GPU platform
Data processing
Third party
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, danish, Danish, dayschan, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, gongdaguo, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, root, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, Zichun, Zirui, Ziyan, zjun, ZPaC
Contributions of any kind are welcome!
Models
Python API
Executor
GPU platform
Data processing and Pro
Third party
Thanks goes to these wonderful people:
Adel, Alexey, andy, andy_wangrui, anthonyaje, anzhengqi, askmiao, avakh, baihuawei, bingyaweng, BowenK, buxue, caifubi, CaoJian, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chenzupeng, chujinjin, cjh9368, Corleone, cristoval, danish, dengyutao, eric, Eric, ervinzhang, etone-chan, fangzehua, fary86, fuzhiye, gengdongjie, genglishuai, Giancarlo, gongdaguo, gukecai, guohongzilong, GuoMengHao, hangq, hanhaocheng, hanhuifeng2020, hanjun996, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, hongxing, huangdongrun, huanghui, huangxinjing, islam_amin, Jesse, jianghui58, jiangzhiwen, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, kai00, kingfo, kpy, kswang, laiyongqiang, leilei_snow, leopz, Li, liangzelang, lianliguang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, lingyunli63, linqingke, lirongzhen1, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuzhongkai, Lixia, lixian, liyong, lizhenyu, looop5, luoyang, lvchangquan, lvliang, lvwenyuan, lyvette, mahdi, Mahdi, mamba_ni, maning202007, Margaret_wangrui, mayang, meixiaowei, meng_chunyang, ms_yan, nhussain, panbingao, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, pengyongrong, Pengyongrong, qianlong, qujianwei, root, shenwei41, shibeiji, simson, songhonglei413, Su, sunsuodong, suteng, tao_yunhao, TFbunny, tinazhang, tom__chen, tony_liu2, tronzhang, VectorSL, wandongdong, wangdongxu, wanghua, wangmin, wangshaocong, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, wuyongkang, xiefangqi, xuanyue, Xun, xutianchun, xuyongfei, yanghaitao, yangjie159, YangLuo, yangruoqi713, yangyongjie, yangzhenzhang, yankai, yao_yf, yelihua, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zhangxuetong, zhaizhiqiang, Zhang, zhangxinfeng3, zhangxuetong, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaoting, zhaozhenlong, zhengjun10, zhongligeng, zhoufeng, zhousiyi, zhouyaqiang, zhouyuanshen, Zichun, Zirui, zjun, zongha, ZPaC, lijiaqi, liangchenghui, wangminggui
Contributions of any kind are welcome!
Models
Python API
Executor
Data processing
Third party
Thanks goes to these wonderful people:
Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu
Contributions of any kind are welcome!
Models
example
to model_zoo
, optimize network for better performance. (!1902:Fix bert scripts.)example
to model_zoo
, optimize network for better accuracy. (!2645:fix vgg16 accuracy lower then 92)example
to model_zoo
, optimize network for better performance. (!2221:modify modezoo wide&deep)Python API
Executor
GPU platform
Data processing
Third party
Thanks goes to these wonderful people:
Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu
Contributions of any kind are welcome!