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max_pool_v3_grad.h 3.0 kB

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  1. /**
  2. * Copyright 2019-2020 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 max_pool_v3_grad.h
  18. *\brief
  19. */
  20. #ifndef OPS_BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_GRAD_H_
  21. #define OPS_BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_GRAD_H_
  22. #include "graph/operator_reg.h"
  23. namespace ge {
  24. /**
  25. * @brief Computes gradients of the maxpooling function . \n
  26. * @par Inputs:
  27. * @li orig_input: A mutable NC1HWC0 tensor of type RealNumberType.
  28. * @li orig_output: A mutable NC1HWC0 tensor of type RealNumberTypex.
  29. * @li grad: A mutable NC1HWC0 tensor of type RealNumberType . \n
  30. * @par Attributes:
  31. * @li ksize: A required list of int8, int16, int32, or int64 values,
  32. * specifying the size of the window for each dimension of the input tensor.
  33. * No default value.
  34. * @li strides: A required list of int8, int16, int32, or int64 values,
  35. * specifying the stride of the sliding window for each dimension of
  36. * the input tensor. No default value.
  37. * @li padding_mode: A required string. Defaults to "CALCULATED".
  38. * @li pads:A required list of int8, int16, int32, or int64 values,
  39. * a data to caculate when padding_mode is "SAME" and "CALCULATED".
  40. * @li data_format: An optional string. Defaults to "NHWC" .
  41. * @li global_pooling bool, Whether to use the global pooling.
  42. * If global_pooling = true, kernel size and paddings will be ignored.
  43. * Default False
  44. * @li ceil_mode:global_pooling (bool) – (bool) Whether to use the global pooling.
  45. * If global_pooling = true, kernel size and paddings will be ignored.
  46. * Default False \n
  47. * @par Outputs:
  48. * y: A mutable tensor. Has the same shape and type as "x1" . \n
  49. * @attention Constraints:
  50. * @li Computing gradients of global pooling is not supported, which means
  51. * "ksize < x1".
  52. * @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63]
  53. * @par Third-party framework compatibility
  54. * Compatible with the TensorFlow operator MaxPoolGrad.
  55. */
  56. REG_OP(MaxPoolV3Grad)
  57. .INPUT(orig_input, TensorType::RealNumberType())
  58. .INPUT(orig_output, TensorType::RealNumberType())
  59. .INPUT(grad, TensorType::RealNumberType())
  60. .OUTPUT(out_grad, TensorType::RealNumberType())
  61. .REQUIRED_ATTR(ksize, ListInt)
  62. .REQUIRED_ATTR(strides, ListInt)
  63. .ATTR(padding_mod, String, "CALCULATED")
  64. .ATTR(pads, ListInt, {0, 0, 0, 0})
  65. .ATTR(data_format, String, "NCHW")
  66. .ATTR(global_pooling, Bool, false)
  67. .ATTR(ceil_mode, Bool, false)
  68. .OP_END_FACTORY_REG(MaxPoolV3Grad)
  69. } // namespace ge
  70. #endif // OPS_BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_GRAD_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