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- /**
- * Copyright 2019-2020 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- #ifndef GE_OP_REDUCE_OPS_H
- #define GE_OP_REDUCE_OPS_H
-
- #include "graph/operator_reg.h"
-
- namespace ge {
- /**
- *@brief Performs reduced batch normalization.
-
- *@par Inputs:\n
- *x: A 5D Tensor of type float16 or float32, with format NC1HWC0.
-
- *@par Outputs:
- *@li sum: A 1D Tensor of type float32 for SUM reduced "x".
- *@li square_sum: A 1D Tensor of type float32 for SUMSQ reduced "x".
-
- *@attention Constraints:\n
- * This operator is a BatchNorm fusion operator for updating the moving averages for training. \n This operator is used in conjunction with BNTrainingUpdate.
- */
- REG_OP(BNTrainingReduce)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(sum, TensorType({DT_FLOAT}))
- .OUTPUT(square_sum, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(BNTrainingReduce)
-
- /**
- *@brief Performs the backpropagation of BatchNorm.
-
- *@par Inputs:
- * Seven inputs, including: \n
- *@li grads: A 5D Tensor of type float16 or float32, with format NC1HWC0, for the gradient.
- *@li x: A 5D Tensor of type float16 or float32, with format NC1HWC0.
- *@li diff_scale: A 5D Tensor of type float32, with format NC1HWC0, for the mean of "x".
- *@li diff_offset: A 5D Tensor of type float32, with format NC1HWC0, for the variance of "x".
- *@li scale: A 5D Tensor of type float32, with format NC1HWC0.
- *@li batch_mean: A 5D Tensor of type float32, with format NC1HWC0, for the mean of "x".
- *@li batch_variance: A 5D Tensor of type float32, with format NC1HWC0, for the variance of "x".
-
- *@par Attributes:
- *epsilon: An optional float32. Defaults to "0.0001". A small float number added to the variance of "x".
-
- *@par Outputs:
- *y: A Tensor of type float16 or float32, with format NC1HWC0, for the offset of "x".
-
- *@attention Constraints:
- * The preceding layer of this operator must be BNTrainingUpdateGrad.
-
- *@see BNTrainingUpdateGrad
- */
- REG_OP(BNTrainingReduceGrad)
- .INPUT(grads, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(diff_scale, TensorType({DT_FLOAT}))
- .INPUT(diff_offset, TensorType({DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(batch_mean, TensorType({DT_FLOAT}))
- .INPUT(batch_variance, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .ATTR(epsilon, Float, 0.0001)
- .OP_END_FACTORY_REG(BNTrainingReduceGrad)
-
- /**
- *@brief Performs reduced batch normalization.
-
- *@par Inputs:\n
- * Seven inputs, including: (NC1HWC0 supported)
- *@li x: A 5D Tensor of type float16 or float32.
- *@li sum: A 1D Tensor of type float32 for the output of operator BNTrainingReduce.
- *@li square_sum: A 1D Tensor of type float32 for the output of operator BNTrainingReduce.
- *@li scale: A 1D Tensor of type float32, for the scaling factor.
- *@li offset: A 1D Tensor of type float32, for the scaling offset.
- *@li mean: A 1D Tensor of type float32, for the updated mean.
- *@li variance: A 1D Tensor of type float32, for the updated variance.
-
- *@par Attributes:
- *@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero.
- *@li factor: A required float32, specifying the weight for updating the mean and variance.
-
- *@par Outputs:\n
- * Five outputs, including: (NC1HWC0 supported)
- *@li y: A 5D Tensor of type float16 or float32, for normalized "x".
- *@li mean: A 5D Tensor of type float32, for the updated mean.
- *@li variance: A 5D Tensor of type float32, for the updated variance.
- *@li batch_mean: A 1D Tensor of type float32, for the mean of "x".
- *@li batch_variance: A 1D Tensor of type float32, for the variance of "x".
-
- *@attention Constraints:
- *@li This operator is a BatchNorm fusion operator for updating the moving averages for training. \n This operator is used in conjunction with BNTrainingReduce.
- *@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction.
- */
- REG_OP(BNTrainingUpdate)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(sum, TensorType({DT_FLOAT}))
- .INPUT(square_sum, TensorType({DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(offset, TensorType({DT_FLOAT}))
- .INPUT(mean, TensorType({DT_FLOAT}))
- .INPUT(variance, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(factor, Float)
- .REQUIRED_ATTR(epsilon, Float)
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(mean, TensorType({DT_FLOAT}))
- .OUTPUT(variance, TensorType({DT_FLOAT}))
- .OUTPUT(batch_mean, TensorType({DT_FLOAT}))
- .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(BNTrainingUpdate)
-
- /**
- *@brief Performs batch normalization for inference.
-
- *@par Inputs:\n
- * Five inputs, including: (NC1HWC0 supported)
- *@li x: A 5D Tensor of type float16 or float32.
- *@li scale: A 5D Tensor of type float32, for the scaling factor.
- *@li offset: A 5D Tensor of type float32, for the scaling offset.
- *@li mean: A 5D Tensor of type float32, for the mean.
- *@li variance: A 5D Tensor of type float32, for the variance.
-
- *@par Attributes:
- *epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.0001".
-
- *@par Outputs:\n
- *y: A 5D Tensor of type float16 or float32 for the normalized "x".
-
- *@attention Constraints:
- *For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction.
- */
- REG_OP(BNInfer)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(offset, TensorType({DT_FLOAT}))
- .INPUT(mean, TensorType({DT_FLOAT}))
- .INPUT(variance, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(epsilon, Float)
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OP_END_FACTORY_REG(BNInfer)
-
- /**
- *@brief Performs reduced batch normalization. For some scene which don't contain
- assignmoving average.
-
- *@par Inputs:\n
- * Five inputs, including: (NC1HWC0 supported)
- *@li x: A 5D Tensor of type float16 or float32.
- *@li sum: A 5D Tensor of type float32 for the output of operator BNTrainingReduce.
- *@li square_sum: A 5D Tensor of type float32 for the output of operator BNTrainingReduce.
- *@li scale: A 5D Tensor of type float32, for the scaling factor.
- *@li offset: A 5D Tensor of type float32, for the scaling offset.
-
- *@par Attributes:
- *epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero.
-
- *@par Outputs:\n
- * Three outputs, including: (NC1HWC0 supported)
- *@li y: A 5D Tensor of type float16 or float32, for normalized "x".
- *@li batch_mean: A 5D Tensor of type float32, for the mean of "x".
- *@li batch_variance: A 5D Tensor of type float32, for the variance of "x".
-
- *@attention Constraints:
- *@li This operator is used in conjunction with BNTrainingReduce.
- *@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction.
- */
- REG_OP(BNTrainingUpdateV2)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(sum, TensorType({DT_FLOAT}))
- .INPUT(square_sum, TensorType({DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(offset, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(epsilon, Float)
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(batch_mean, TensorType({DT_FLOAT}))
- .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(BNTrainingUpdateV2)
-
- REG_OP(BNTrainingUpdateV3)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(sum, TensorType({DT_FLOAT}))
- .INPUT(square_sum, TensorType({DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(offset, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(epsilon, Float)
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(batch_mean, TensorType({DT_FLOAT}))
- .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
- .OUTPUT(reserve_1, TensorType({DT_FLOAT}))
- .OUTPUT(reserve_2, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(BNTrainingUpdateV3)
-
- /**
- *@brief Performs the backpropagation of BatchNorm.
-
- *@par Inputs:
- * Four inputs, including: \n
- *@li grads: A 5D Tensor of type float16 or float32, with format NC1HWC0, for the gradient.
- *@li x: A 5D Tensor of type float16 or float32, with format NC1HWC0.
- *@li batch_mean: A 5D Tensor of type float32, with format NC1HWC0, for the mean of "x".
- *@li batch_variance: A 5D Tensor of type float32, with format NC1HWC0, for the variance of "x".
-
- *@par Attributes:
- *epsilon: An optional float32. Defaults to "0.0001". A small float number added to the variance of "x".
-
- *@par Outputs:
- *@li diff_scale: A Tensor of type float32, with format NC1HWC0, for the offset of "scale".
- *@li diff_offset: A Tensor of type float32, with format NC1HWC0, for the offset of "offset".
-
- */
- REG_OP(BNTrainingUpdateGrad)
- .INPUT(grads, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(batch_mean, TensorType({DT_FLOAT}))
- .INPUT(batch_variance, TensorType({DT_FLOAT}))
- .ATTR(epsilon, Float, 0.0001)
- .OUTPUT(diff_scale, TensorType({DT_FLOAT}))
- .OUTPUT(diff_offset, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(BNTrainingUpdateGrad)
-
- /**
- *@brief Performs the backpropagation of BatchNorm for inference.
-
- *@par Inputs:
- * Three inputs, including: \n
- *@li grads: A 5D Tensor of type loat16 or float32, with format NC1HWC0, for the gradient.
- *@li scale: A 5D Tensor of type float32, with format NC1HWC0.
- *@li batch_variance: A 5D Tensor of type float32, with format NC1HWC0. It is an output of BatchNorm.
-
- *@par Attributes:
- *epsilon: An optional float32. Defaults to "0.0001". A small float number added to the variance of "x".
-
- *@par Outputs:
- *x_backprop: A Tensor of type float16 or float32, with format NC1HWC0, for the offset of "x".
-
- *@attention Constraints:
- * The preceding layer of this operator must be operator BatchNorm.
- */
- REG_OP(BNInferGrad)
- .INPUT(grads, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(batch_variance, TensorType({DT_FLOAT}))
- .OUTPUT(x_backprop, TensorType({DT_FLOAT16,DT_FLOAT}))
- .ATTR(epsilon, Float, 0.0001)
- .OP_END_FACTORY_REG(BNInferGrad)
-
- /**
- *@brief Computes the sum of elements across dimensions of a tensor.
-
- *@par Inputs:
- * Two inputs, including: \n
- *@li x: A Tensor of type float16 or float32. Up to 8D.
- *@li axes: A 1D list or tuple of int32 or int64. Specifies the dimensions to reduce.
-
- *@par Attributes:
- *keep_dims: An optional bool. If "true", retains reduced dimensions with length 1. Defaults to "false".
-
- *@par Outputs:
- *y: The reduced tensor. Has the same type and format as input "x".
-
- */
- REG_OP(ReduceSum)
- .INPUT(x, TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceSum)
-
- /**
- *@brief Computes the sum of elements across dimensions of a tensor.
-
- *@par Inputs:
- * One input: \n
- *x: A Tensor. Up to 8D. Must be one of the following types: float16, float32, int32, int8, uint8.
-
- *@par Attributes:
- *@li axes: A required 1D list or tuple of int32 or int64. Specifies the dimensions to reduce.
- *@li keep_dims: An optional bool. If "true", retains reduced dimensions with length 1. Defaults to "false".
-
- *@par Outputs:
- *y: The reduced tensor. Has the same type and format as input "x".
-
- */
- REG_OP(ReduceSumD)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_INT32}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceSumD)
-
- /**
- *@brief Calculates the "logical sum" of elements of a tensor in a dimension.
-
- *@par Inputs:
- *One input:
- *x: A mutable Tensor. Must be one of the following types: float16,
- * float32, double. Should be a Variable Tensor.
-
- *@par Attributes:
- *@li keep_dims: A bool. If true, retains reduced dimensions with length 1.
- *@li axis: The dimensions to reduce. If None, reduces all dimensions.
- *Must be in the range [- rank (input_sensor), rank (input_sensor)).
-
- *@par Outputs:
- *y: The reduced tensor.
- */
- REG_OP(ReduceAllD)
- .INPUT(x, TensorType({DT_BOOL}))
- .OUTPUT(y, TensorType({DT_BOOL}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceAllD)
-
- /**
- *@brief Calculates the "logical sum" of elements of a tensor in a dimension.
-
- *@par Inputs:
- *Two inputs, including:
- *@li x: A mutable Tensor. Must be one of the following types: float16, float32, double. Should be a Variable Tensor.
- *@li axis: A mutable Tensor. The dimensions to reduce. If None, reduces all dimensions. Must be in the range [- rank (input_sensor), rank (input_sensor)).
-
- *@par Attributes:
- *keep_dims: A bool. If true, retains reduced dimensions with length 1.
-
- *@par Outputs:
- *y: The reduced tensor.
- */
- REG_OP(ReduceAll)
- .INPUT(x, TensorType({DT_BOOL}))
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType({DT_BOOL}))
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceAll)
-
- /**
- *@brief Reduce a tensor on a certain axis based on product..
-
- *@par Inputs:
- *Two inputs, including:
- *@li x: A mutable Tensor. Must be the type of NumberType.
- *@li axis: A mutable Tensor. The dimensions to reduce.
-
- *@par Attributes:
- *@li keep_dims: A bool. If true, retains reduced dimensions with length 1. Defaults to "False".
-
- *@par Outputs:
- *y: A Tensor. Has the same type and format as input "x".
-
- */
- REG_OP(ReduceProd)
- .INPUT(x,TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y,TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceProd)
-
- /**
- *@brief Computes the product of elements across dimensions of a tensor.
-
- *@par Inputs:
- * One input: \n
- *x: A Tensor. Must be one of the following types: float16, float, int8, uint8.
-
- *@par Attributes:
- *@li axes: A required int8, int16, int32, or int64. Specifies the dimensions to reduce. No default value.
- *@li keep_dims: An optional bool. If "True", retains reduced dimensions with length 1. Defaults to "False".
-
- *@par Outputs:
- *y: A Tensor. Has the same type and format as input "x".
-
- *@attention Constraints:
- * "keep_dims" is in the range [-rank(input_tensor), rank(input_tensor)].
-
- */
- REG_OP(ReduceProdD)
- .INPUT(x,TensorType({DT_FLOAT, DT_UINT8, DT_INT8, DT_INT32, DT_FLOAT16}))
- .OUTPUT(y,TensorType({DT_FLOAT, DT_UINT8, DT_INT8, DT_INT32, DT_FLOAT16}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceProdD)
-
- /**
- *@brief Reduces "x" along the dimensions according to "axis".
-
- *@par Inputs:
- *Two inputs, including:
- * @li x: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
- * @li axes: The dimensions to reduce. Must be one of the following types: int, list, tuple, NoneType.\n
- * - If None (the default), reduces all dimensions.\n
- * - Must be in the range [-rank(x), rank(x)).
-
- *@par Attributes:
- *keep_dims: A bool or NoneType. \n
- * - If true, retains reduced dimensions with length 1. \n
- * - If false, the rank of the tensor is reduced by 1 for each entry in axis.
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- */
- REG_OP(ReduceMean)
- .INPUT(x, TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMean)
-
- /**
- *@brief Reduces "x" along the dimensions according to "axis".
-
- *@par Inputs:
- *One input:
- * @li x: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
-
- *@par Attributes:
- *@li axes: The dimensions to reduce. Must be one of the following types: int, list, tuple, NoneType. \n
- * If None (the default), reduces all dimensions. \n
- * Must be in the range [-rank(x), rank(x)). \n
- *@li keep_dims: A bool or NoneType. \n
- * - If true, retains reduced dimensions with length 1. \n
- * - If false, the rank of the tensor is reduced by 1 for each entry in axis.
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- */
- REG_OP(ReduceMeanD)
- .INPUT(x, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT, DT_INT8, DT_UINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT, DT_INT8, DT_UINT8}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMeanD)
-
- /**
- *@brief Returns the maximum of elements across dimensions of a Tensor.
-
- *@par Inputs:
- * Two inputs, including: \n
- *@li x: A multi-dimensional Tensor of type float16, float32, or int16.
- *@li axes: A Scalar of type int32, specifying the axes information of the index with the maximum value.
-
- *@par Attributes:
- *keep_dims: A bool, specifying whether to keep dimensions for the output Tensor. Defaults to "false".
-
- *@par Outputs:
- *y: A multi-dimensional Tensor, specifying the maximum value of the corresponding axis in the tensor. Has the same type as "x". (If "keep_dims" is set to "false", the output dimensions are reduced by "dimension" compared with that of "x". Otherwise, the output has one fewer dimension than "x".)
-
- *@attention Constraints:
- * The value range of "axes" is [-dims, dims - 1]. "dims" indicates the dimension length of "x".
-
- */
- REG_OP(ReduceMax)
- .INPUT(x, TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMax)
-
- /**
- *@brief Returns the maximum of elements across dimensions of a Tensor.
-
- *@par Inputs:
- *x: A multi-dimensional Tensor of type float16, float32, or int16.
-
- *@par Attributes:
- * Two attributes, including: \n
- *@li axes: A required listint, specifying the axes information of the index with the maximum value.
- *@li keep_dims: A bool, specifying whether to keep dimensions for the output Tensor. Defaults to "false".
-
- *@par Outputs:
- *y: A multi-dimensional Tensor, specifying the maximum value of the corresponding axis in the tensor. Has the same type as "x". (If "keep_dims" is set to "false", the output dimensions are reduced by "dimension" compared with that of "x". Otherwise, the output has one fewer dimension than "x".)
-
- *@attention Constraints:
- * The value range of "axis" is [-dims, dims - 1]. "dims" indicates the dimension length of "x".
- */
- REG_OP(ReduceMaxD)
- .INPUT(x, TensorType({DT_FLOAT, DT_UINT8, DT_INT8,
- DT_FLOAT16, DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_UINT8, DT_INT8,
- DT_FLOAT16, DT_INT32}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMaxD)
-
- /**
- *@brief Computes the minimum of elements across dimensions of a tensor.
-
- *@par Inputs:
- *@li input_tensor: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
- *@li axes: A Tensor of type int8 or int32. Specifies the dimensions to reduce. Defaults to "None".
-
- *@par Attributes:\n
- *keep_dims: An optional bool. If "True", reduced dimensions will be retained. Defaults to "False".
-
- *@par Outputs:\n
- *output_tensor: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
-
- *@attention Constraints:\n
- * If "axes = None", all dimensions will be reduced. "axes" must be in the range [-rank(input_shape), rank(input_shape)).
-
- */
- REG_OP(ReduceMin)
- .INPUT(x, TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMin)
-
- /**
- *@brief Computes the minimum of elements across dimensions of a tensor.
-
- *@par Inputs:\n
- *input_min: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
-
- *@par Attributes:
- *@li axes: An optional int32, list, tuple, or NoneType value. Specifies the dimensions to reduce. Defaults to "None".
- *@li keep_dims: An optional bool or NoneType value. If "True", reduced dimensions will be retained. Defaults to "None" (equivalent to "False").
-
- *@par Outputs:\n
- *output_min: A Tensor. Must be one of the following types: float16, float32, int8, uint8.
-
- *@attention Constraints:\n
- * If "axes = None", all dimensions will be reduced. "axes" must be in the range [-rank(input_shape), rank(input_shape)).
-
- */
- REG_OP(ReduceMinD)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceMinD)
- /**
- *@brief Computes the "logical or" of elements across dimensions of a tensor.\n
- * Reduces "x" along the dimensions given in "axes".
- * Unless "keep_dims" is true, the rank of the tensor is reduced by 1 for each
- * entry in "axes". If "keep_dims" is true, the reduced dimensions
- * are retained with length 1.
- *
- * If "axes" is None, all dimensions are reduced, and a
- * tensor with a single element is returned.
- *
- *@attention Constraints:\n
- * Only support bool
- *
- *@par Inputs:
- *@li x : The boolean tensor to reduce.
- *@li axes: The dimensions to reduce. If "None" (default), reduces all
- * dimensions. Must be in the range "[-rank(x), rank(x))".
- *
- *@par Attributes:
- * keep_dims: If true, retains reduced dimensions with length 1.
- *
- *@par Outputs:
- * y: The reduced tensor
- *
- */
- REG_OP(ReduceAny)
- .INPUT(x, TensorType({DT_BOOL}))
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType({DT_BOOL}))
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceAny)
- /**
- *@brief Computes the "logical or" of elements across dimensions of a tensor.\n
- * Reduces "x" along the dimensions given in "axes".
- * Unless "keep_dims" is true, the rank of the tensor is reduced by 1 for each
- * entry in "axes". If "keep_dims" is true, the reduced dimensions
- * are retained with length 1.
- *
- * If "axis" is None, all dimensions are reduced, and a
- * tensor with a single element is returned.
- *
- *@attention Constraints:\n
- * Only support bool
- *
- *@par Inputs:
- * x: The boolean tensor to reduce.
- *
- *@par Attributes:
- *@li axes: The dimensions to reduce. If "None" (default), reduces all
- * dimensions. Must be in the range "[-rank(x), rank(x))".
- *@li keep_dims: If true, retains reduced dimensions with length 1.
- *
- *@par Outputs:
- * y: The reduced tensor
- *
- */
- REG_OP(ReduceAnyD)
- .INPUT(x, TensorType({DT_BOOL}))
- .OUTPUT(y, TensorType({DT_BOOL}))
- .REQUIRED_ATTR(axes, ListInt)
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(ReduceAnyD)
-
- /**
- *@brief Compute reduction on dimensions specified by "axis".
- *Four reduction operations are provided:
- *SUM Computes the sum of elements across specified dimensions of a tensor.
- *ASUM Computes the sum of absolute values of elements across specified dimensions of a tensor.
- *SUMSQ Computes the sum of squares of elements across specified dimensions of a tensor.
- *SUMSQ Computes the mean values of elements across specified dimensions of a tensor.
-
- *@par Inputs:
- *x: A Tensor of type float16 or float32
-
- *@par Attributes:
- *@li operation: An optional int32 from 1(SUM), 2(ASUM), 3(SUMSQ), and 4(MEAN),
- *specifying the reduction algorithm. Defaults to 1.
- *@li axis: An optional int32, specifying the first axis to reduce. Defaults to "0".
- *The value range is [-N, N-1], where N is the input tensor rank.
- *@li coeff: An optional float32, specifying the scale coefficient. Defaults to "1.0".
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
-
- *@attention Constraints: The Reduction operator supports type float16 only on the device chip.
- */
- REG_OP(Reduction)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(operation, Int, 1)
- .ATTR(axis, Int, 0)
- .ATTR(coeff, Float, 1.0)
- .OP_END_FACTORY_REG(Reduction);
-
- /**
- *@brief Computes the euclidean norm of elements across dimensions of a tensor.
-
- *@par Inputs:
- *@li input_tensor: A Tensor. Must be one of the following types: float16, float32, int32.
- *@li axes: A Tensor of type int8 or int32. Specifies the dimensions to reduce. Defaults to "None".
-
- *@par Attributes:\n
- *keep_dims: An optional bool. If "True", reduced dimensions will be retained. Defaults to "False".
-
- *@par Outputs:\n
- *output_tensor: A Tensor. Must be one of the following types: float16, float32, int32.
-
- *@attention Constraints:\n
- * If "axes = None", all dimensions will be reduced. "axes" must be in the range [-rank(input_shape), rank(input_shape)).
-
- */
- REG_OP(EuclideanNorm)
- .INPUT(x, TensorType::NumberType())
- .INPUT(axes, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::NumberType())
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(EuclideanNorm)
-
- /**
- *@brief Computes the euclidean norm of elements across dimensions of a tensor.
-
- *@par Inputs:\n
- *input_min: A Tensor. Must be one of the following types: float16, float32, int32.
-
- *@par Attributes:
- *@li axes: An optional int32, list, tuple, or NoneType value. Specifies the dimensions to reduce. Defaults to "None".
- *@li keep_dims: An optional bool or NoneType value. If "True", reduced dimensions will be retained. Defaults to "None" (equivalent to "False").
-
- *@par Outputs:\n
- *output_min: A Tensor. Must be one of the following types: float16, float32, int32.
-
- *@attention Constraints:\n
- * If "axes = None", all dimensions will be reduced. "axes" must be in the range [-rank(input_shape), rank(input_shape)).
-
- */
- REG_OP(EuclideanNormD)
- .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16}))
- .ATTR(axes, ListInt, {})
- .ATTR(keep_dims, Bool, false)
- .OP_END_FACTORY_REG(EuclideanNormD)
-
- } //namespace ge
-
-
- #endif /* GE_OP_REDUCE_OPS_H */
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