|
- /**
- * 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.
- */
-
- /*!
- * \file transformation_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_
-
- #include "graph/operator_reg.h"
-
- namespace ge {
- /**
- *@brief This operation convert output dataType and shape
-
- *@par Inputs:
- *The input handle must have the resource type. Inputs include:
- *@li x:A list of Tensor objects. One or more tensors from which
- the enqueued tensors should be taken . \n
-
- *@par Outputs:
- *@li y:A list of Tensor objects. One or more tensors from which
- the enqueued tensors should be taken . \n
-
- *@par Attributes:
- *@li type: An optional ge::DataType. It refers to the target data type of outputs . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow QueueIsClosed operator.
- */
-
- REG_OP(Bitcast)
- .INPUT(x, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT32, DT_UINT8,
- DT_INT64, DT_UINT64, DT_INT16, DT_UINT16, DT_DOUBLE, DT_COMPLEX64,
- DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32}))
- .OUTPUT(y, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT32, DT_UINT8,
- DT_INT64, DT_UINT64, DT_INT16, DT_UINT16, DT_DOUBLE, DT_COMPLEX64,
- DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32}))
- .REQUIRED_ATTR(type, Type)
- .OP_END_FACTORY_REG(Bitcast)
-
- /**
- *@brief Convert tensor format from HWCN to C1HWNCoC0 . \n
-
- *@par Inputs:
- *x: A Tensor. Must be 4D Tensor of type float16, float32, int32, uint16, with format HWCN . \n
-
- *@par Outputs:
- *y: A 6D Tensor. Has the same type as "x", with format C1HWNCoC0.
- */
- REG_OP(DepthwiseWeight4DTo6D)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_UINT16}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_UINT16}))
- .OP_END_FACTORY_REG(DepthwiseWeight4DTo6D)
-
- /**
- *@brief Convert tensor format from C1HWNCoC0 to HWCN . \n
-
- *@par Inputs:
- *x: A Tensor. Must be 6D Tensor of type float16, float32, int32, uint16, with format C1HWNCoC0 . \n
-
- *@par Attributes:
- *channel_size: An optional int, specifying the channel size of 4D Tensor with format HWCN . \n
-
- *@par Outputs:
- *y: A 4D Tensor. Has the same type as "x", with format HWCN.
- */
- REG_OP(DepthwiseWeight6DTo4D)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_UINT16}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_UINT16}))
- .ATTR(channel_size, Int, 16)
- .OP_END_FACTORY_REG(DepthwiseWeight6DTo4D)
-
- /**
- *@brief Permutes the dimensions according to perm.
- The returned tensor's dimension i will correspond to the input dimension perm[i] . \n
-
- *@par Inputs:
- *x: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64 . \n
-
- *@par Attributes:
- *perm: A permutation of the dimensions of "x" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- *@par Restrictions:
- *Warning: THIS FUNCTION IS DEPRECATED. Please use Transpose instead.
- */
- REG_OP(TransposeD)
- .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_FLOAT16, DT_FLOAT}))
- .REQUIRED_ATTR(perm, ListInt)
- .OP_END_FACTORY_REG(TransposeD)
-
- /**
- *@brief Permutes the dimensions according to perm.
- The returned tensor's dimension i will correspond to the input dimension perm[i] . \n
-
- *@par Inputs:
- *Two inputs, including:
- *@li x: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
- *@li perm: A Tensor of type int32 or int64. A permutation of the dimensions of "x" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@par Third-party framework compatibility
- *Compatible with the TensorFlow operator Transpose.
- */
- REG_OP(Transpose)
- .INPUT(x, TensorType::BasicType())
- .INPUT(perm, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .OP_END_FACTORY_REG(Transpose)
-
- /**
- *@brief Doing format_transfer for various data format only
- support "NHWC/NCHW" to "NC1HWC0" and "NC1HWC0" to "NHWC/NCHW"
- "NCHW" to "FRACTAL_Zn" or "FRACTAL_Zn" to "NCHW".
- "HWCN" to "FRACTAL_Zn" or "FRACTAL_Zn" to "HWCN" . \n
-
- *@par Inputs:
- *src: A Tensor dtype of all types . \n
-
- *@par Attributes:
- *@li src_format: A string source data format, can be "NHWC", "NCHW", "FRACTAL_Zn" etc.
- *@li dst_format: A string target data format, can be "NC1HWC0", "NCHW", "FRACTAL_Zn" etc.
- *@li group: A required int32, default value is 1. \n
-
- *@par Outputs:
- *dst: A Tensor dtype of all types.
- */
- REG_OP(TransData)
- .INPUT(src, TensorType::BasicType())
- .OUTPUT(dst, TensorType::BasicType())
- .REQUIRED_ATTR(src_format, String)
- .REQUIRED_ATTR(dst_format, String)
- .ATTR(group, Int, 1)
- .OP_END_FACTORY_REG(TransData)
-
- /**
- *@brief Permutes the dimensions according to order.
- The returned tensor's dimension i will correspond to the input dimension order[i] . \n
-
- *@par Inputs:
- *x: A Tensor. Must be one of the following types: float16, float32 . \n
-
- *@par Attributes:
- *order: A permutation of the dimensions of "x".Type is int32.support any axis transformation.Defaults to "{0}"
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- */
- REG_OP(Permute)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(order, ListInt, {0})
- .OP_END_FACTORY_REG(Permute)
-
- /**
- *@brief Flattens the inputs. Reserves axis 0 and flattens the input tensors
- * along axis 1 . \n
-
- *@par Inputs:
- *One input:
- *x: A multi-dimensional Tensor. Must be one of the following types:
- * int8, uint8, int16, uint16, int32, uint32, int64,uint64, float16, float32 . \n
-
- *@par Outputs:
- *y: A 2D flattened Tensor (Reserves axis 0 and flattens the input tensors
- * along axis 1). Must be one of the following data types: int8, uint8, int16,
- * uint16, int32, uint32, int64,uint64, float16, float32 . \n
-
- *@par Third-party framework compatibility
- * Compatible with TensorFlow operator Flatten.
- */
- REG_OP(Flatten)
- .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64,
- DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64,
- DT_FLOAT, DT_FLOAT16}))
- .OUTPUT(y, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64,
- DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64,
- DT_FLOAT, DT_FLOAT16}))
- .OP_END_FACTORY_REG(Flatten)
-
- /**
- *@brief Permutes and crops the input tensor . \n
-
- *@par Inputs:
- * Three inputs, including:
- *@li x: A 5D Tensor of type float16 or int8 or uint8, with format NC1HWC0.
- *@li block_shape: A 1D list or tuple of int32 or int64.
- *@li crops: A 2D list or tuple of int32 or int64. Specifies the amount to
- *crop from start and end dimensions after permutation . \n
-
- *@par Outputs:
- *y: A Tensor with format NC1HWC0. Has the same type as input "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator BatchToSpaceND.
- */
- REG_OP(BatchToSpaceND)
- .INPUT(x, TensorType::BasicType())
- .INPUT(block_shape, TensorType::IndexNumberType())
- .INPUT(crops, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .OP_END_FACTORY_REG(BatchToSpaceND)
-
- /**
- *@brief Permutes and crops the input tensor . \n
-
- *@par Inputs:
- * One input:
- *x: A 5D Tensor of type float16 or int8 or uint8, with format NC1HWC0 . \n
-
- *@par Attributes:
- *@li block_shape: A required 1D list or tuple of int32 or int64.
- *@li crops: A required 2D list or tuple of int32 or int64. Specifies the amount to crop
- * from the start and end dimensions after permutation . \n
-
- *@par Outputs:
- *y: A Tensor with format NC1HWC0. Has the same type as input "x".
-
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator BatchToSpaceND.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use BatchToSpaceND instead.
- */
- REG_OP(BatchToSpaceNDD)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_shape, ListInt)
- .REQUIRED_ATTR(crops, ListInt)
- .OP_END_FACTORY_REG(BatchToSpaceNDD)
-
- /**
- *@brief Pads and permutes the input tensor . \n
-
- *@par Inputs:
- * Three inputs, including:
- *@li x: A 5D Tensor of type float16 or float32, with format NC1HWC0.
- *@li block_shape: A 1D list or tuple of int32 or int64.
- *@li paddings: A 2D list or tuple of int32 or int64. Specifies the padding for the start and end dimensions after permutation . \n
-
- *@par Outputs:
- *y: A Tensor with format NC1HWC0. Has the same type as input "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator SpaceToBatchND.
- */
- REG_OP(SpaceToBatchND)
- .INPUT(x, TensorType::BasicType())
- .INPUT(block_shape, TensorType::IndexNumberType())
- .INPUT(paddings, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .OP_END_FACTORY_REG(SpaceToBatchND)
-
- /**
- *@brief Pads and permutes the input tensor . \n
-
- *@par Inputs:
- * One input:
- *x: A 5D Tensor of type float16 or float32, with format NC1HWC0 . \n
-
- *@par Attributes:
- *@li block_shape: A required 1D list or tuple of int32 or int64.
- *@li paddings: A required 2D list or tuple of int32 or int64. Specifies the padding for the start and end dimensions after permutation . \n
-
- *@par Outputs:
- *y: A Tensor with format NC1HWC0. Has the same type as input "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator SpaceToBatchND.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use SpaceToBatchND instead.
- */
- REG_OP(SpaceToBatchNDD)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_shape, ListInt)
- .REQUIRED_ATTR(paddings, ListInt)
- .OP_END_FACTORY_REG(SpaceToBatchNDD)
-
- /**
- *@brief Outputs a copy of the input tensor where values from the "height" and
- * "width" dimensions are moved to the "depth" dimension . \n
-
- *@par Inputs:
- *x: An NHWC Tensor. Must be one of the following types:
- * float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8,
- * int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
-
-
- *@par Attributes:
- *@li block_size: A required int, specifying the input block size.
- *@li data_format: An optional string, specifying the data format. Defaults to
- * "NHWC" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as input "x".
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator SpaceToDepth.
- */
- REG_OP(SpaceToDepth)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_size, Int)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(SpaceToDepth)
-
- /**
- *@brief Rearranges data from depth into blocks of spatial data . \n
-
- *@par Inputs:
- *x: A Tensor. Must be one of the following types: float16, float32, double, int32, uint8,
- * int16, int8, complex64, int64, qint8, quint8, qint32, qint16, quint16, uint16,
- * complex128, uint32, uint64
-
- *@par Attributes:
- *Two attributes, including:
- * @li block_size: An int >= 2, specifying the size of the spatial block.
- * @li data_format: An optional string, specifying the data format. Defaults to "NHWC" . \n
-
- *@par Outputs:
- *y: A Tensor of the same type as "x" . \n
-
- *@par Third-party framework compatibility:
- * Compatible with TensorFlow operator DepthToSpace.
- */
- REG_OP(DepthToSpace)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_size, Int)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(DepthToSpace)
-
- /**
- *@brief Permutes data into spatial data blocks and then prunes them . \n
-
- *@par Inputs:
- *@li x: A 4D Tensor with format NHWC.
- *@li crops: A 1D list or tuple of int32 or int64 . \n
-
- *Must be one of the following types: float16, float32
-
- *@par Attributes:
- *block_size: A required int8, int16, int32, or int64. No default value . \n
-
- *@par Outputs:
- *y: A 4D Tensor with format NHWC,
-
- * of type float16 or float32 . \n
-
- *@attention Constraints:
- *@li The size of the first dimension of input "x" must be divisible by (block_size * block_size).
- *@li "crops" is a 4Dshape [batch, height, width, depth], height = height_pad - crop_top - crop_bottom,
- *width = width_pad - crop_left - crop_right.
- *@li block_size > 2
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator BatchToSpace.
- */
- REG_OP(BatchToSpace)
- .INPUT(x, TensorType::BasicType())
- .INPUT(crops, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_size, Int)
- .OP_END_FACTORY_REG(BatchToSpace)
-
- /**
- *@brief Rearrange the batch (permutes) data into spatial data blocks, and then crop them . \n
-
- *@par Inputs:
- * One input:
- *x: An Tensor of shape [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth].
- *The batch size of the input tensor must be divisible by (block size * block size).
- *Must be one of the following types: float16, float32, double, int64, int32, uint8, uint16, uint32, uint64,
- *int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32 . \n
-
- *@par Attributes:
- *@li block_size: Must be one of the following types: `int32`, `int64`.
- *@li crops: An Tensor. Must be one of the following types: int32, Int64.
- *2D tensor with non negative integer of shape [2, 2]. It specifies how many
- *elements are clipped from the intermediate result of spatial dimension . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type and format as input "x" . \n
-
- *@attention Constraints:
- *@li The size of the first dimension of input "x" must be divisible by (block_size * block_size).
- *@li "crops" is a 2D tensor of non-negative integers with shape (2, 2).
- *@li block_size > 2
-
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator BatchToSpace.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use BatchToSpace instead.
- */
- REG_OP(BatchToSpaceD)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_INT8, DT_INT16, DT_COMPLEX64,
- DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_INT8, DT_INT16, DT_COMPLEX64,
- DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32}))
- .REQUIRED_ATTR(block_size, Int)
- .REQUIRED_ATTR(crops, ListInt)
- .OP_END_FACTORY_REG(BatchToSpaceD)
-
- /**
- *@brief Outputs a copy of the input tensor where values from the "height" and
- * "width" dimensions are padded and rearranged to the "batch" dimension . \n
-
- *@par Inputs:
- * Two inputs, including:
- *@li x: An NHWC Tensor. Must be one of the following types:
- * float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8,
- * int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
- *@li paddings: A 2D tensor of type int, specifying the input . \n
-
- *@par Attributes:
- *block_size: A required int, specifying the input block size . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as input "x".
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator SpaceToBatch.
- */
- REG_OP(SpaceToBatch)
- .INPUT(x, TensorType::BasicType())
- .INPUT(paddings, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_size, Int)
- .OP_END_FACTORY_REG(SpaceToBatch)
-
- /**
- *@brief Outputs a copy of the input tensor where values from the "height" and "width" dimensions are padded and rearranged to the "batch" dimension . \n
-
- *@par Inputs:
- *x: An NHWC Tensor. Must be one of the following types: float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
-
-
- *@par Attributes:
- *@li block_size: A required int, specifying the input block size.
- *@li paddings: A 2D tensor. All data types are supported . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as input "x".
- *@par Third-party framework compatibility
- *@ Compatible with the TensorFlow operator SpaceToBatch.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use SpaceToBatch instead.
- */
- REG_OP(SpaceToBatchD)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(block_size, Int)
- .REQUIRED_ATTR(paddings, ListInt)
- .OP_END_FACTORY_REG(SpaceToBatchD)
-
- /**
- * @brief Unpacks the given dimension of a rank-R Tensor "x" into rank-(R-1)
- * tensors . \n
-
- * @par Inputs:
- * x: A rank-R tensor (R > 0) of type BasicType, with format ND or NC1HWC0 . \n
-
- * @par Attributes:
- * @li num: A required int, specifying the number of tensors to be unpacked to.
- * Defaults to "None".
- * @li axis: An optional int, specifying the axis to unpack along. The value range
- * is [-R, R) . \n
-
- * @par Outputs:
- * y: Dynamic output. The list of Tensor objects unpacked from "x", of type BasicType . \n
-
- * @attention Constraints:
- * @li If "num" is not specified, it is inferred from the shape of "x".
- * @li For the ND format, "axis" is in the range [-R, R); For the NC1HWC0 format,
- * "axis" must not be 2, 3, -2, or -3 . \n
-
- * @par Third-party framework compatibility
- * Compatible with the TensorFlow operator Unpack.
- */
- REG_OP(Unpack)
- .INPUT(x, TensorType::BasicType())
- .DYNAMIC_OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(num, Int)
- .ATTR(axis, Int, 0)
- .OP_END_FACTORY_REG(Unpack)
-
- /**
- * @brief Extract "patches" from "images" and stacks them in the "depth"
- * dimension of the output . \n
-
- * @par Inputs:
- * x: A 4D Tensor with shape [batch, in_rows, in_cols, depth], Must be one of the
- * following types:float32, double, int32, uint8, int16, int8, int64, uint16,
- * float16, uint32, uint64
-
- * @par Attributes:
- * @li ksizes: A required list or tuple. The size of the sliding window for each
- * dimension of images.
- * @li strides: A required list or tuple. How far the centers of two consecutive
- * patches are in the images. Must be: [1, stride_rows, stride_cols, 1].
- * @li rates: A required list or tuple. Must be: [1, rate_rows, rate_cols, 1].
- * This is the input stride, specifying how far two consecutive patch
- * samples are in the input. Equivalent to extracting patches
- * with patch_sizes_eff = patch_sizes + (patch_sizes - 1) *
- * (rates - 1), followed by subsampling them spatially by a factor of rates.
- * This is equivalent to rate in dilated (a.k.a. Atrous) convolutions.
- * @li padding: A required string. The type of padding algorithm to use,
- support "SAME" or "VALID". \n
- * @li data_format: A required string. The format of input, only supported NHWC. \n
-
- * @par Outputs:
- * y: A 4D Tensor with shape [batch, out_rows, out_cols, ksize_rows *
- * ksize_cols * depth] containing image patches with size ksize_rows x ksize_cols
- * x depth vectorized in the "depth" dimension. Note "out_rows" and "out_cols"
- * are the dimensions of the output patches . \n
-
- * @attention Constraints:
- * "ksizes", "strides" and "rates" are lists of integers . \n
-
- * @par Third-party framework compatibility
- * Compatible with the TensorFlow operator ExtractImagePatches.
- */
- REG_OP(ExtractImagePatches)
- .INPUT(x, TensorType::RealNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .REQUIRED_ATTR(ksizes, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(rates, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(ExtractImagePatches)
-
- /**
- * @brief Extract "patches" from "input" and put them in the "depth"
- * dimension of the output . \n
-
- * @par Inputs:
- * x: A 5D Tensor with shape [batch, in_planes, in_rows, in_cols, depth] . \n
-
- * @par Attributes:
- * @li ksizes: A required list or tuple. The size of the sliding window for each
- * dimension of "x".
- * @li strides: A required list or tuple. How far the centers of two consecutive
- * patches are in "x". Must be: [1, stride_planes, stride_rows, stride_cols, 1].
- * @li padding: A required string. The type of padding algorithm to use ,
- * support "SAME" or "VALID" . \n
- * @li data_format: An optional string. The format of input, only supported NDHWC. \n
-
- * @par Outputs:
- * Output: A 5D Tensor with shape [batch, out_planes, out_rows, out_cols, ksize_planes *
- * ksize_rows * ksize_cols * depth] containing patches with size (ksize_rows * ksize_cols
- * * depth) vectorized in the "depth" dimension. Note "out_planes", "out_rows" and "out_cols"
- * are the dimensions of the output patches . \n
-
- * @attention Constraints:
- * "ksizes" and "strides" are lists of integers.
- * @par Third-party framework compatibility
- * Compatible with the TensorFlow operator ExtractVolumePatches.
- */
- REG_OP(ExtractVolumePatches)
- .INPUT(x, TensorType::REALNUMBERTYPE())
- .OUTPUT(y, TensorType::REALNUMBERTYPE())
- .REQUIRED_ATTR(ksizes, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NDHWC")
- .OP_END_FACTORY_REG(ExtractVolumePatches)
-
- /**
- *@brief Confuse reshape and transpose . \n
-
- *@par Inputs:
- *x: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64 . \n
-
- *@par Attributes:
- *@li perm: A permutation of the dimensions of "x".
- *@li shape: The shape of the input.
- *@li transpose_first: If True, the transpose is first, otherwise the reshape is first . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use ConfusionTranspose instead.
- */
- REG_OP(ConfusionTransposeD)
- .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_UINT32, DT_UINT64, DT_FLOAT16, DT_FLOAT}))
- .REQUIRED_ATTR(perm, ListInt)
- .REQUIRED_ATTR(shape, ListInt)
- .REQUIRED_ATTR(transpose_first, Bool)
- .OP_END_FACTORY_REG(ConfusionTransposeD)
-
- /**
- *@brief Confuse reshape and transpose . \n
-
- *@par Inputs:
- *@li x: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
- *@li shape: The shape of the input . \n
-
- *@par Attributes:
- *@li perm: A permutation of the dimensions of "x".
- *@li transpose_first: If True, the transpose is first, otherwise the reshape is first . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(ConfusionTranspose)
- .INPUT(x, TensorType::BasicType())
- .INPUT(shape, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(perm, ListInt)
- .REQUIRED_ATTR(transpose_first, Bool)
- .OP_END_FACTORY_REG(ConfusionTranspose)
-
- /**
- *@brief Flattens the input tensor to one-dimensional . \n
-
- *@par Inputs:
- *x: An ND tensor. All data types are supported . \n
-
- *@par Attributes:
- *@li axis: An optional int32, specifying the first axis to flatten. All preceding axes are retained in the output. Defaults to "1".
- *@li end_axis: An optional int32, specifying the last axis to flatten. All following axes are retained in the output. Defaults to "-1" . \n
-
- *@par Outputs:
- *y: The flattened ND tensor. All data types are supported . \n
-
- *@attention Constraints:
- * "axis" and "end_axis" must be within the dimension range of the input. This operator cannot be directly called by the acllopExecute API.
- *@par Third-party framework compatibility
- * Compatible with the Caffe operator Flatten.
- */
- REG_OP(FlattenV2)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
- DT_INT32, DT_UINT32, DT_INT64, DT_UINT64}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
- DT_INT32, DT_UINT32, DT_INT64, DT_UINT64}))
- .ATTR(axis, Int, 1)
- .ATTR(end_axis, Int, -1)
- .OP_END_FACTORY_REG(FlattenV2)
-
- /**
- *@brief Compress large weight to small one. Usually inserted before Conv2d.
- *
- *@par Inputs:
- *weight: A tensor before compress. Must be one of the following types: DT_INT8, DT_FLOAT16
- *
- *@par Outputs:
- *@li weight_compress: A tensor after compress. Must be one of the following types: DT_INT8, DT_FLOAT16
- *@li compress_index: A tensor. Must be one of the following types: DT_INT8
- *
- *@par Attributes:
- *compress_parameters: A required int8, specifying the compressing block.
- *
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(Compress)
- .INPUT(weight, TensorType({DT_INT8, DT_FLOAT16}))
- .OUTPUT(weight_compress, TensorType({DT_INT8, DT_FLOAT16}))
- .OUTPUT(compress_index, TensorType({DT_INT8}))
- .REQUIRED_ATTR(compress_parameters, ListInt)
- .OP_END_FACTORY_REG(Compress)
-
- /**
- *@brief Compress large weight to small one. Usually inserted before FullyConnection.
- *
- *@par Inputs:
- *weight: A tensor before compress. Must be one of the following types: DT_INT8, DT_FLOAT16
- *
- *@par Outputs:
- *@li weight_compress: A tensor after compress. Must be one of the following types: DT_INT8, DT_FLOAT16
- *@li compress_index: A tensor. Must be one of the following types: DT_INT8
- *
- *@par Attributes:
- *compress_parameters: A required int8, specifying the compressing block.
- *
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(CompressFcOp)
- .INPUT(weight, TensorType({DT_INT8}))
- .OUTPUT(weight_compress, TensorType({DT_INT8}))
- .OUTPUT(compress_index, TensorType({DT_INT8}))
- .REQUIRED_ATTR(compress_parameters, ListInt)
- .OP_END_FACTORY_REG(CompressFcOp)
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_
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