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nn_ops.h 1.8 kB

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  1. /**
  2. * Copyright 2019 Huawei Technologies Co., Ltd
  3. *
  4. * Licensed under the Apache License, Version 2.0 (the "License");
  5. * you may not use this file except in compliance with the License.
  6. * You may obtain a copy of the License at
  7. *
  8. * http://www.apache.org/licenses/LICENSE-2.0
  9. *
  10. * Unless required by applicable law or agreed to in writing, software
  11. * distributed under the License is distributed on an "AS IS" BASIS,
  12. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13. * See the License for the specific language governing permissions and
  14. * limitations under the License.
  15. */
  16. /*!
  17. * \file nn_ops.h
  18. * \brief
  19. */
  20. #ifndef OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
  21. #define OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
  22. #include "graph/operator_reg.h"
  23. #include "nn_pooling_ops.h"
  24. namespace ge {
  25. /**
  26. * @brief Says whether the targets are in the top "k" predictions . \n
  27. * @par Inputs:
  28. * Three inputs, including:
  29. * @li predictions: A 2D Tensor of type float32. A "batch_size * classes" tensor.
  30. * @li targets: A 1D Tensor of type IndexNumberType. A batch_size tensor of class ids.
  31. * @li k: A 1D Tensor of the same type as "targets".
  32. * Specifies the number of top elements to look at for computing precision . \n
  33. * @par Outputs:
  34. * precision: A Tensor of type bool . \n
  35. * @attention Constraints:
  36. * @li targets must be non-negative tensor.
  37. * @par Third-party framework compatibility
  38. * @li Compatible with the TensorFlow operator InTopKV2.
  39. */
  40. REG_OP(InTopKV2)
  41. .INPUT(predictions, TensorType({DT_FLOAT}))
  42. .INPUT(targets, TensorType(IndexNumberType))
  43. .INPUT(k, TensorType({IndexNumberType}))
  44. .OUTPUT(precision, TensorType({DT_BOOL}))
  45. .OP_END_FACTORY_REG(InTopKV2)
  46. }// namespace ge
  47. #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示