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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_TRANSFORMATION_OPS_H
- #define GE_OP_TRANSFORMATION_OPS_H
-
- #include "../graph/operator_reg.h"
-
- namespace ge {
- 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)
-
- 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.\n
- The returned tensor's dimension i will correspond to the input dimension perm[i].
-
- *@par Inputs:
- *x: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
-
- *@par Attributes:
- *perm: A permutation of the dimensions of "x".
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- */
- REG_OP(TransposeD)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .ATTR(perm, ListInt, {})
- .OP_END_FACTORY_REG(TransposeD)
-
- /**
- *@brief Permutes the dimensions according to perm.\n
- The returned tensor's dimension i will correspond to the input dimension perm[i].
-
- *@par Inputs:
- *@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".
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
- */
- REG_OP(Transpose)
- .INPUT(x, TensorType::BasicType())
- .INPUT(perm, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::BasicType())
- .OP_END_FACTORY_REG(Transpose)
-
- 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)
-
- 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)
-
- 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)
-
- 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)
-
- 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)
-
- 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)
-
- 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.
-
- *@par Inputs:
- *x: A 4D Tensor with format NC1HWC0. \n
-
- *Must be one of the following types: float16, float32
-
- *@par Attributes:
- *@li crops: A required list of int8, int16, int32, or int64. No default value.
- *@li block_size: A required int8, int16, int32, or int64. No default value.
-
- *@par Outputs:
- *y: A 4D Tensor with format NC1HWC0, \n
-
- * of type float16 or float32.
-
- *@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
- */
- 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.
-
- *@par Inputs:
- * One input:
- *x: An Tensor of shape [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth].\n
- *The batch size of the input tensor must be divisible by (block size * block size).
-
- *@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.\n
- *2D tensor with non negative integer of shape [2, 2]. It specifies how many\n
- *elements are clipped from the intermediate result of spatial dimension.
-
- *@par Outputs:
- *y: A Tensor. Has the same type and format as input "x".
-
- *@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
- */
- 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)
-
- 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)
-
- 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 "value" into rank-(R-1)
- * tensors.
-
- * @par Inputs:
- * @ value: A rank-R tensor (R > 0) of type BasicType, with format ND or NC1HWC0.
-
- * @par Attributes:
- * @li num: An optional int, specifying the number of tensors to be unpacked to.
- * Defaults to "None".
- * @li axis: A required int, specifying the axis to unpack along. The value range
- * is [-R, R).
-
- * @par Outputs:
- * output: The list of Tensor objects unpacked from "value", of type BasicType.
-
- * @attention Constraints:
- * @li If "num" is not specified, it is inferred from the shape of "value".
- * @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.
- */
- REG_OP(Unpack)
- .INPUT(value, TensorType::BasicType())
- .DYNAMIC_OUTPUT(output, 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.
-
- * @par Inputs:
- * images: A 4D Tensor with shape [batch, in_rows, in_cols, depth].
-
- * @par Attributes:
- * @li ksizes: The size of the sliding window for each dimension of images.
- * @li strides: How far the centers of two consecutive patches are in the images.\n
- * Must be: [1, stride_rows, stride_cols, 1].
- * @li rates: Must be: [1, rate_rows, rate_cols, 1]. This is the input stride,\n
- * specifying how far two consecutive patch samples are in the input. Equivalent\n
- * to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) *\n
- * (rates - 1), followed by subsampling them spatially by a factor of rates. This\n
- * is equivalent to rate in dilated (a.k.a. Atrous) convolutions.
- * @li padding: The type of padding algorithm to use.
-
- * @par Outputs:
- * Output: A 4D Tensor with shape [batch, out_rows, out_cols, ksize_rows *\n
- * ksize_cols * depth] containing image patches with size ksize_rows x ksize_cols\n
- * x depth vectorized in the "depth" dimension. Note "out_rows" and "out_cols"\n
- * are the dimensions of the output patches.
-
- * @attention Constraints:
- * "ksizes", "strides" and "rates" are lists of integers.
- */
- REG_OP(ExtractImagePatches)
- .INPUT(images, TensorType::REALNUMBERTYPE())
- .OUTPUT(y, TensorType::REALNUMBERTYPE())
- .ATTR(ksizes, ListInt, {1,3,3,1})
- .ATTR(strides, ListInt, {1,1,1,1})
- .ATTR(rates, ListInt, {1,1,1,1})
- .ATTR(padding, String, "SAME")
- .OP_END_FACTORY_REG(ExtractImagePatches)
-
- REG_OP(ConfusionTransposeD)
- .INPUT(x, TensorType::BasicType())
- .OUTPUT(y, TensorType::BasicType())
- .REQUIRED_ATTR(perm, ListInt)
- .REQUIRED_ATTR(shape, ListInt)
- .REQUIRED_ATTR(transpose_first, Bool)
- .OP_END_FACTORY_REG(ConfusionTransposeD)
-
- 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)
-
- 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)
- } // namespace ge
-
- #endif // GE_OP_TRANSFORMATION_OPS_H
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