1956 lines
70 KiB
C++
1956 lines
70 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019-2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#ifndef LIBND4J_HEADERS_PARITY_H
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#define LIBND4J_HEADERS_PARITY_H
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#include <ops/declarable/headers/common.h>
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namespace nd4j {
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namespace ops {
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/**
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* This operation returns index of max element in a given NDArray (optionally: along given dimension(s))
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* Expected input:
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* 0: N-dimensional array
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* 1: optional axis vector
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*
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* Int args:
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* 0: optional axis
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*/
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#if NOT_EXCLUDED(OP_argmax)
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DECLARE_CUSTOM_OP(argmax, 1, 1, false, 0, -2);
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#endif
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/**
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* This operation returns index of min element in a given NDArray (optionally: along given dimension(s))
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* Expected input:
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* 0: N-dimensional array
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* 1: optional axis vector
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*
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* Int args:
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* 0: optional axis
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*/
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#if NOT_EXCLUDED(OP_argmin)
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DECLARE_CUSTOM_OP(argmin, 1, 1, false, 0, -2);
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#endif
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/**
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* This operation provides various normalization modes:
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* 0: frobenius
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* 1: euclidean (norm2)
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* 2: norm1
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* 3: norm2
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* 4: inf-norm
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* 5: p-norm
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*
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* Expected arguments:
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* input: N-dimensional array
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*
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*
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* Int args:
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* 0...: axis
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*
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* T args:
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* 0: norm mode
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* 1: p for p-norm
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*/
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#if NOT_EXCLUDED(OP_norm)
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DECLARE_REDUCTION_OP(norm, 1, 1, false, 1, -2);
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#endif
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/**
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* Inserts elements provided by diagonal array into the main diagonal of innermost matrices of input array
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*
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* Input arrays:
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* 0: input array, considered as batch of matrices
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* 1: diagonal array containing elements to be inserted into input array,
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* following rank condition should be satisfied: diagonal_rank = input_rank - 1,
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* the shapes of diagonal and input arrays must be equal except last dimension of input array,
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* for example if input_shape = [A,B,C,D] then diagonal_shape = [A,B,C],
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* also last dimension of diagonal array should be equal to smaller of last and last but one input dimensions
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* that is: diagonal_shape[-1] = min(input_shape[-1], input_shape[-2])
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*
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* Output array:
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* 0: has the same shape as input, corresponding diagonal elements are substituted
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*/
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#if NOT_EXCLUDED(OP_matrix_set_diag)
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DECLARE_CONFIGURABLE_OP(matrix_set_diag, 2, 1, false, 0, 0);
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#endif
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/**
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* Inserts elements provided by diagonal array into the main diagonal of innermost matrices of output array,
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* rest output elements are set to zeros
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*
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* Input array:
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* diagonal: array containing elements to be inserted into output array,
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* following rank condition is present: diagonal_rank = ouput_rank - 1
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*
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* Output array:
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* 0: is considered as batch of matrices, if for example diagonal array has shape [A,B,C] then output array has shape [A,B,C,C]
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*/
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DECLARE_CUSTOM_OP(matrix_diag, 1, 1, false, 0, 0);
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/**
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* This op calculates regularized incomplete beta integral Ix(a, b).
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* Implementation is based on two algorithms depending on input values of a and b:
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* - when a and b are both > maxValue (3000.), then Gauss-Legendre quadrature method is applied
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* - when a and b are both <= maxValue (3000.), then modified Lentz’s algorithm for continued fractions is applied
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*
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* Input arrays:
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* a: defines power t^{a-1}, must be > 0, type float.
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* b: defines power (1-t)^{b-1}, must be > 0, type float.
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* x: defines upper limit of integration, must be within (0 <= x <= 1) range, type float.
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*
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* Output array:
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* 0: values of regularized incomplete beta integral that corresponds to variable upper limit x, type float
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*
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* Three input and one output arrays must have the same shape
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*/
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#if NOT_EXCLUDED(OP_betainc)
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DECLARE_CONFIGURABLE_OP(betainc, 3, 1, false, 0, 0);
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#endif
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/**
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* This operation is added for compatibility purposes mostly.
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* PLEASE NOTE: Please consider using Add instead
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* Expected arguments:
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* 0: N-dimensional input
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* 1: bias vector
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*/
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#if NOT_EXCLUDED(OP_biasadd)
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DECLARE_CUSTOM_OP(biasadd, 2, 1, true, 0, 0);
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DECLARE_CUSTOM_OP(biasadd_bp, 3, 2, false, 0, 0);
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#endif
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/**
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* Returns a diagonal tensor with a given diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros.
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*/
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#if NOT_EXCLUDED(OP_diag)
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DECLARE_CUSTOM_OP(diag, 1, 1, false, 0, 0);
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#endif
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/**
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* Returns a diagonal tensor with a given diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros.
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*/
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#if NOT_EXCLUDED(OP_diag_part)
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DECLARE_CUSTOM_OP(diag_part, 1, 1, false, 0, 0);
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#endif
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/**
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* Returns a diagonal vector for any submatricies with in a given tensor.
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* It is an op inverse to matrix_set_giag.
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* Using input tensor as batched 2D diagonals flat them to vector (1D) with diagonal values.
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*
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* Input : batched tensor with rank >=2
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* Output: tensor with rank lesser by 1 from input
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*/
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#if NOT_EXCLUDED(OP_matrix_diag_part)
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DECLARE_CUSTOM_OP(matrix_diag_part, 1, 1, false, 0, 0);
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#endif
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/**
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* QR decomposition: A = QR, where Q is ortogonal (Q * QT = I) and R is upper triangular.
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* For A (MxN) Q is M x M and R is (NxN).
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*
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* Input :
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* 0 - float (or complex float) tensor with shape {.,..,...,M,N} - batch of float matricies
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*
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* Output:
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* 0 - float tensor with shape {.,..,...,MxN} - batch of ortogonal matricies {Qs}
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* 1 - float tensor with shape {.,..,...,NxN} - batch of upper triangular matricies {Rs}
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*/
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#if NOT_EXCLUDED(OP_qr)
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DECLARE_CUSTOM_OP(qr, 1, 2, false, 0, 0);
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#endif
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/**
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* This operation takes 2 arrays: original values, and values to be excluded. And returns 2 arrays: values left after exclusion, and indices in original array for surivals.
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* Expected arguments:
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* 0: vector with original values
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* 1: vector with values to exclude
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*/
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#if NOT_EXCLUDED(OP_listdiff)
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DECLARE_CUSTOM_OP(listdiff, 2, 2, false, 0, 0);
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#endif
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/**
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* This operation applies Add operation to specific inputs wrt indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_add)
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DECLARE_OP(scatter_add, 3, 1, true);
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#endif
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/**
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* This operation applies Subtract operation to specific inputs wrt indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_sub)
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DECLARE_OP(scatter_sub, 3, 1, true);
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#endif
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/**
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* This operation applies Multiply operation to specific inputs wrt indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_mul)
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DECLARE_OP(scatter_mul, 3, 1, true);
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#endif
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/**
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* This operation applies Divide operation to specific inputs wrt indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_div)
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DECLARE_OP(scatter_div, 3, 1, true);
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#endif
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/**
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* This operation applies Assign operation to specific inputs wrt indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_upd)
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DECLARE_OP(scatter_upd, 3, 1, true);
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#endif
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/**
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* This operation applies Max operation to specific inputs through given indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_max)
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DECLARE_OP(scatter_max, 3, 1, true);
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#endif
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/**
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* This operation applies Min operation to specific inputs through given indices
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* Expected arguments:
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* input: array to be updated
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* indices: array containing indexes for first dimension of input
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_min)
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DECLARE_OP(scatter_min, 3, 1, true);
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#endif
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/**
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* This operation scatter "updates" elements into new output array according to given "indices"
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* Expected arguments:
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* indices: array containing elements/slices indexes of output array to put "updates" elements into, the rest output elements will be zeros
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* updates: array containing elements to be inserted into output array
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* shape: contains shape of output array
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*/
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#if NOT_EXCLUDED(OP_scatter_nd)
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DECLARE_CUSTOM_OP(scatter_nd, 3, 1, false, 0, 0);
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#endif
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/**
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* This operation scatter "updates" elements into input array along given "indices"
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* Expected arguments:
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* input: array to be updated
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* indices: array containing elements/slices indexes of input array to put "updates" elements into
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* updates: array containing elements to be inserted into input array
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*/
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#if NOT_EXCLUDED(OP_scatter_nd_update)
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DECLARE_OP(scatter_nd_update, 3, 1, true);
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#endif
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/**
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* This operation adds "updates" elements to input array along given "indices"
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* Expected arguments:
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* input: array to be updated
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* indices: array containing elements/slices indexes of input array to add "updates" elements to
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_add)
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DECLARE_OP(scatter_nd_add, 3, 1, true);
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#endif
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/**
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* This operation subtract "updates" elements from input array along given "indices"
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* Expected arguments:
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* input: array to be updated
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* indices: array containing elements/slices indexes of input array to subtract "updates" elements from
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* updates: array containing elements to be interfered with input
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*/
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#if NOT_EXCLUDED(OP_scatter_sub)
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DECLARE_OP(scatter_nd_sub, 3, 1, true);
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#endif
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/**
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* This operation takes input's shape, and returns new NDArray filled with specified value
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* Expected arguments:
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* input: N-dimensional array
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*
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* T args:
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* 0: scalar value, used to fill NDArray
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*/
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#if NOT_EXCLUDED(OP_fill_as)
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DECLARE_CONFIGURABLE_OP(fill_as, 1, 1, true, 1, 0);
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#endif
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/**
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* This operation applies element-wise rint (round to integral value) operation
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*/
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#if NOT_EXCLUDED(OP_rint)
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DECLARE_OP(rint, 1, 1, true);
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#endif
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/**
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* This operation returns unique elements from input array as vector, and their original indices in input array
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* Expected input:
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* input: N-dimensional array
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*/
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#if NOT_EXCLUDED(OP_unique)
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DECLARE_CUSTOM_OP(unique, 1, 2, false, 0, 0);
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#endif
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/**
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* This operation returns 3 1D arrays for given 1D array with unique element count and indexes
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* input:
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* 0 - 1D array
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*
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* output:
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* 0 - 1D array with unique values
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* 1 - 1D array with ids for values in array above
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* 2 - 1D array with counts for values in array above
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*/
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#if NOT_EXCLUDED(OP_unique_with_counts)
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DECLARE_CUSTOM_OP(unique_with_counts, 1, 3, false, 0, 0);
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#endif
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/**
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* This operation splits input NDArray into multiple TADs along given dimensions
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* Expected arguments:
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* input: N-dimensional array
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*
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* Int args:
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* 0..: TAD axis
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*/
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#if NOT_EXCLUDED(OP_tear)
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DECLARE_CUSTOM_OP(tear, 1, -1, false, 0, -1);
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#endif
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/**
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* This op does the same as tear, just uses different input format:
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* @tparam T
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*/
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#if NOT_EXCLUDED(OP_unstack)
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DECLARE_CUSTOM_OP(unstack, 1, -1, false, 0, 1);
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#endif
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/**
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* This operation extracts a strided (optionally) slice from a tensor,
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*/
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#if NOT_EXCLUDED(OP_strided_slice)
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DECLARE_CUSTOM_OP(strided_slice, 1, 1, false, 0, 5); // TODO: new op type needed. that returns VIEW
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DECLARE_CUSTOM_OP(strided_slice_bp, 2, 1, false, 0, 5);
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#endif
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/**
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* This operation extracts a slice from a tensor.
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*
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*/
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#if NOT_EXCLUDED(OP_slice)
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DECLARE_CUSTOM_OP(slice, 1, 1, false, 0, -2);
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DECLARE_CUSTOM_OP(slice_bp, 2, 1, false, 0, -2);
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#endif
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/**
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* This operation generate sequences. Basically from......to, with step used as increment.
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* Expected arguments:
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* start: optional scalar with starting value
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* stop: optional scalar with end value
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* step: optional scalar witn step value
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*
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* Int args: (optional)
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* 0: optional scalar with starting value
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* 1: optional scalar with end value
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* 1: optional scalar witn step value
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*
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* T args: (optional)
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* 0: optional scalar with starting value
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* 1: optional scalar with end value
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* 1: optional scalar witn step value
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*/
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#if NOT_EXCLUDED(OP_range)
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DECLARE_CUSTOM_OP(range, -2, 1, false, -2, -2);
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#endif
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/**
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* This operation return one-hot encoded n-dimensional array
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* Expected arguments:
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* input: N-dimensional array
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*
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* T args:
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* 0: 'on' value
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* 1: 'off' value
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*
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* Int args:
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* 0: depth
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* 1: axis
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*/
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#if NOT_EXCLUDED(OP_onehot)
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DECLARE_CUSTOM_OP(onehot, 1, 1, false, -2, -2);
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#endif
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/**
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* This operation calculate the confusion matrix for a
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* pair of prediction and label 1-D arrays.
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* Expected arguments:
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* Input arrays:
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* 0 - predictions: 1-D array
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* 1 - labels: 1-D array
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* 2 - weights : optional
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* Int args:
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* 0 - num_classes: optional
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*
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*/
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#if NOT_EXCLUDED(OP_confusion_matrix)
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DECLARE_CUSTOM_OP(confusion_matrix, 2, 1, false, 0, -2);
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#endif
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/**
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* This operation stacks a list of rank tensors into one rank-(R+1) tensor.
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* Expected arguments:
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* 0...: N-Dimensional arrays to stack
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*
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*/
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#if NOT_EXCLUDED(OP_stack)
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DECLARE_CUSTOM_OP(stack, -1, 1, false, 0, 0);
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#endif
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/**
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* This operation returns length of input array
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* Expected arguments:
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* input: N-dimensional array
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*
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* TODO: make this operation reduction, to allow TAD -> size
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*/
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#if NOT_EXCLUDED(OP_size)
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DECLARE_CUSTOM_OP(size, 1, 1, false, 0, 0); // add DeclarableScalarOp?
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#endif
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/**
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* This operation returns rank of input array as scalar value.
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*/
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#if NOT_EXCLUDED(OP_rank)
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DECLARE_CUSTOM_OP(rank, 1, 1, false, 0, 0); // ^
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#endif
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#if NOT_EXCLUDED(OP_broadcastgradientargs)
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DECLARE_OP(broadcastgradientargs, 2, 2, true);
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#endif
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/**
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* This operation takes input's shape, and returns new NDArray filled with zeros
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* Expected arguments:
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* input: N-dimensional array
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*
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*/
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#if NOT_EXCLUDED(OP_zeros_as)
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DECLARE_CUSTOM_OP(zeros_as, 1, 1, false, 0, 0);
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#endif
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/**
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* This operation takes input's shape, and returns new NDArray filled with ones
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* Expected arguments:
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* input: N-dimensional array
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*
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*/
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#if NOT_EXCLUDED(OP_ones_as)
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DECLARE_CUSTOM_OP(ones_as, 1, 1, false, 0, 0);
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#endif
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/**
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* This operation applies element-wise pow(x, 2) to the given input
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* Expected arguments:
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* input: N-Dimensional array
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*/
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#if NOT_EXCLUDED(OP_square)
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DECLARE_OP(square, 1, 1, true);
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#endif
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/**
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* This op calculates Hurwitz zeta function zeta(x, q) = sum_{n=0}^{inf} (q + n)^{-x}
|
||
* Implementation is based on Euler-Maclaurin summation formula
|
||
*
|
||
* Input arrays:
|
||
* x: define power {-x}, must be > 1, type float.
|
||
* q: define summand in denominator, must be > 0, type float.
|
||
*
|
||
* Output array:
|
||
* 0: corresponding values of Hurwitz zeta function
|
||
*
|
||
* Two input and one output arrays must have the same shape
|
||
*/
|
||
#if NOT_EXCLUDED(OP_zeta)
|
||
DECLARE_CONFIGURABLE_OP(zeta, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates polygamma function psi^(n)(x). Implementation is based on serial representation written in
|
||
* terms of the Hurwitz zeta function: polygamma = (-1)^{n+1} * n! * zeta(n+1, x).
|
||
*
|
||
* Input arrays:
|
||
* 0: n - define derivative order (n+1), type integer (however currently is implemented as float casted to integer)
|
||
* 1: x - abscissa points where to evaluate the polygamma function, type float
|
||
*
|
||
* Output array:
|
||
* 0: values of polygamma function at corresponding x, type float
|
||
*
|
||
* Two input and one output arrays have the same shape
|
||
*/
|
||
#if NOT_EXCLUDED(OP_polygamma)
|
||
DECLARE_CONFIGURABLE_OP(polygamma, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates lgamma function lgamma(x) = log(Gamma(x))
|
||
*
|
||
* Input arrays:
|
||
* 0: x - input matrix
|
||
*
|
||
* Output array:
|
||
* 0: log of Gamma(x)
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_lgamma)
|
||
DECLARE_OP(lgamma, 1, 1, true);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates digamma function psi(x) = derivative of log(Gamma(x))
|
||
*
|
||
* Input arrays:
|
||
* 0: x - abscissa points where to evaluate the digamma function, type float
|
||
*
|
||
* Output array:
|
||
* 0: values of digamma function at corresponding x, type float
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_digamma)
|
||
DECLARE_CONFIGURABLE_OP(digamma, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This operation takes shape as first argument, and returns new NDArray filled with specific scalar value.
|
||
* Input arrays:
|
||
* 0 - shape vector
|
||
* 1 - optional scalar NDArray
|
||
*
|
||
* T arguments:
|
||
* 0 - optional scalar value
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_fill)
|
||
DECLARE_CUSTOM_OP(fill, 1, 1, false, -2, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This operation splits given NDArray into chunks of specific size, along given dimension
|
||
* Input arrays:
|
||
* 0 - input array
|
||
* 1 - array of sizes
|
||
* 2 - optional axis
|
||
*
|
||
* Integer arguments:
|
||
* 0 - optional axis
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_split_v)
|
||
DECLARE_CUSTOM_OP(split_v, 2, -1, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* This operation splits given NDArray into chunks of specific size, along given dimension
|
||
* 0 - input array
|
||
* 1 - optional axis
|
||
*
|
||
* Integer arguments:
|
||
* 0 - number of splits
|
||
* 1 - optional axis
|
||
*/
|
||
#if NOT_EXCLUDED(OP_split)
|
||
DECLARE_CUSTOM_OP(split, 1, -1, false, 0, 1);
|
||
#endif
|
||
|
||
|
||
/**
|
||
* This operation adjusts image hue by delta
|
||
* Input arrays:
|
||
* 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
|
||
* 1 - optional argument, input scalar-array containing delta
|
||
*
|
||
* T arguments:
|
||
* 0 - optional argument, delta value
|
||
*
|
||
* Int arguments:
|
||
* 0 - optional argument, corresponds to dimension with 3 channels
|
||
*/
|
||
#if NOT_EXCLUDED(OP_adjust_hue)
|
||
DECLARE_CONFIGURABLE_OP(adjust_hue, 1, 1, true, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This operation adjusts image saturation by delta
|
||
* Input arrays:
|
||
* 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
|
||
* 1 - optional argument, input scalar-array containing saturation factor
|
||
*
|
||
* T arguments:
|
||
* 0 - optional argument, saturation factor
|
||
*
|
||
* Int arguments:
|
||
* 0 - optional argument, corresponds to dimension with 3 channels
|
||
*/
|
||
#if NOT_EXCLUDED(OP_adjust_saturation)
|
||
DECLARE_CONFIGURABLE_OP(adjust_saturation, 1, 1, true, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This operation adjusts image contrast by given factor ( z = (x - mean) * factor + mean )
|
||
* Input arrays:
|
||
* 0 - input array with rank >= 3, must have last one dimension equal 3, that is dimension containing channels.
|
||
* 1 - optional argument, input scalar-array containing saturation contrast factor
|
||
*
|
||
* T arguments:
|
||
* 0 - optional argument, contrast factor
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_adjust_contrast)
|
||
DECLARE_CONFIGURABLE_OP(adjust_contrast, 1, 1, true, 0, 0);
|
||
DECLARE_CONFIGURABLE_OP(adjust_contrast_v2, 1, 1, true, 0, 0);
|
||
#endif
|
||
|
||
|
||
|
||
|
||
/**
|
||
* This operation rearranges data from depth into blocks of spatial data. This is the reverse transformation
|
||
* of space_to_depth op. This op output is a copy of the input tensor where values from the depth dimension
|
||
* are moved in spatial blocks to the height and width dimensions. Int attr 0 indicates the input
|
||
* block size and how the data is moved.
|
||
* Input:
|
||
* 0 - 4D tensor on given type
|
||
* Output:
|
||
* 0 - 4D tensor of given type and proper shape
|
||
*
|
||
* Int arguments:
|
||
* 0 - block size
|
||
* 1 - output data format: 0 ("NHWC"): shape{ batch, height, width, channels }
|
||
* 1 ("NCHW"): shape{ batch, channels, height, width }
|
||
* 2 ("NCHW_VECT_C"): int8 shape{ batch, channels / 4, height, width, 4 }
|
||
* optional (default 0)
|
||
*/
|
||
#if NOT_EXCLUDED(OP_depth_to_space)
|
||
DECLARE_CUSTOM_OP(depth_to_space, 1, 1, false, 0, -1);
|
||
#endif
|
||
|
||
/**
|
||
* This operation rearranges blocks of spatial data, into depth.This op output is a copy of the input tensor
|
||
* where values from the height and width dimensions are moved to the depth dimension. Int attr 0 indicates
|
||
* the input block size.
|
||
*
|
||
* Input:
|
||
* - 4D tensor of given type
|
||
* Output:
|
||
* - 4D tensor
|
||
*
|
||
* Int arguments:
|
||
* 0 - block size
|
||
* 1 - output data format: 0 ("NHWC"): shape{ batch, height, width, channels }
|
||
* 1 ("NCHW"): shape{ batch, channels, height, width }
|
||
* 2 ("NCHW_VECT_C"): int8 shape{ batch, channels / 4, height, width, 4 }
|
||
* optional (default 0)
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_space_to_depth)
|
||
DECLARE_CUSTOM_OP(space_to_depth, 1, 1, false, 0, -1);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates cross-product between input arguments
|
||
* Input arguments
|
||
* 0 - vector or tensor A
|
||
* 1 - vector or tensor B
|
||
*/
|
||
#if NOT_EXCLUDED(OP_cross)
|
||
DECLARE_OP(cross, 2, 1, false);
|
||
#endif
|
||
|
||
/**
|
||
* Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op
|
||
* outputs a copy of the input tensor where values from the height and width dimensions are moved to the
|
||
* batch dimension. After the zero-padding, both height and width of the input must be divisible by the block
|
||
* size.
|
||
*
|
||
* Inputs:
|
||
* 0 - input tensor
|
||
* 1 - 2D paddings tensor (shape {M, 2})
|
||
*
|
||
* Output:
|
||
* - result tensor
|
||
*
|
||
* Int args:
|
||
* 0 - block size (M)
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_space_to_batch)
|
||
DECLARE_CUSTOM_OP(space_to_batch, 2, 1, false, 0, 1);
|
||
#endif
|
||
|
||
/*
|
||
* This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape
|
||
* block_shape, and interleaves these blocks with the "batch" dimension (0) such that in the output,
|
||
* the spatial dimensions [1, ..., M] correspond to the position within the grid, and the batch dimension
|
||
* combines both the position within a spatial block and the original batch position. Prior to division into
|
||
* blocks, the spatial dimensions of the input are optionally zero padded according to paddings.
|
||
*
|
||
* Inputs:
|
||
* 0 - input (N-D tensor)
|
||
* 1 - block_shape - int 1D tensor with M length
|
||
* 2 - paddings - int 2D tensor with shape {M, 2}
|
||
*
|
||
* Output:
|
||
* - N-D tensor with the same type as input 0.
|
||
*
|
||
* */
|
||
#if NOT_EXCLUDED(OP_space_to_batch_nd)
|
||
DECLARE_CUSTOM_OP(space_to_batch_nd, 3, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
*
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_batch_to_space)
|
||
DECLARE_CUSTOM_OP(batch_to_space, 2, 1, false, 0, 1);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_batch_to_space_nd)
|
||
DECLARE_CUSTOM_OP(batch_to_space_nd, 3, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* top_k operation returns a vector of k top values for
|
||
* given NDArray as tensor with default boolean (true)
|
||
* as sort for result index array
|
||
* will be sorted by the values in descending order.
|
||
* The first parameter is a NDArray for working.
|
||
* The second is k (default 1) - optional
|
||
* The third is boolean value(default is true) (0 - as is, 1 - sorted by value) optional
|
||
*/
|
||
#if NOT_EXCLUDED(OP_top_k)
|
||
DECLARE_CUSTOM_OP(top_k, 1, 2, false, 0, -1);
|
||
#endif
|
||
|
||
/**
|
||
* in_top_k operation returns a vector of k boolean values for
|
||
* given NDArray as 2D matrix of predicted in the NDArray k top values
|
||
* The first parameter is a NDArray of predicted values (2d array).
|
||
* The second is NDArray as vector of indeces k top values will be search.
|
||
* The third is k
|
||
*/
|
||
#if NOT_EXCLUDED(OP_in_top_k)
|
||
DECLARE_CUSTOM_OP(in_top_k, 2, 1, true, 1, 1);
|
||
#endif
|
||
|
||
/**
|
||
* moments operation calculate a mean and variation for given NDArray
|
||
* with reduce a result according to axis array given.
|
||
* For full axis the result is both mean and variance of all members in array.
|
||
* Otherwise there are two NDArrays with means and variances for
|
||
* Axes can be put as the second NDArray or as int vector.
|
||
*
|
||
* the optional flag "keep_dims" can be set as T param
|
||
*/
|
||
#if NOT_EXCLUDED(OP_moments)
|
||
DECLARE_CUSTOM_OP(moments, 1, 2, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* embedding_lookup - search for submatrices in given matrix and retunts them
|
||
* accordingly to index array given.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_embedding_lookup)
|
||
DECLARE_CUSTOM_OP(embedding_lookup, 2, 1, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* dynamic_partition - partition a input tensor onto num_partitions
|
||
* accordingly to index array given.
|
||
*
|
||
* the first param - NDArray to be partitioned.
|
||
* the second param - index array
|
||
* the third param (integer param) - num or partitions.
|
||
*
|
||
* returns a num of NDArrays as output
|
||
*/
|
||
#if NOT_EXCLUDED(OP_dynamic_partition)
|
||
DECLARE_CUSTOM_OP(dynamic_partition, 2, 1, false, 0, 1);
|
||
#endif
|
||
|
||
#if NOT_EXCLUDED(OP_dynamic_partition_bp)
|
||
DECLARE_CUSTOM_OP(dynamic_partition_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* dynamic_stitch - merge partitions from the second param a input tensor
|
||
* into a single tensor accordingly to index array given.
|
||
*
|
||
* the first param - index array
|
||
* the second params - tensors to be merged
|
||
*
|
||
* returns a num of NDArrays as output
|
||
*
|
||
* the operation is inversion od dynamic_partition
|
||
*/
|
||
#if NOT_EXCLUDED(OP_dynamic_stitch)
|
||
DECLARE_CUSTOM_OP(dynamic_stitch, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* zero_fraction op.
|
||
* compute a fraction of zeros in given array
|
||
*
|
||
* input param - an array (tensor)
|
||
* output value - a real number with given type (e.g. float or double)
|
||
*/
|
||
#if NOT_EXCLUDED(OP_zero_fraction)
|
||
DECLARE_CUSTOM_OP(zero_fraction, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* xw_plus_b op.
|
||
* multiply two first matrices and add third vector to each row of result
|
||
*
|
||
* input params:
|
||
* - 2D matrix NxM
|
||
* - 2D matrix MxN
|
||
* - 1D vector with N elements
|
||
* output value - 2D matrix NxN as multiply of matrixes and add vector
|
||
*/
|
||
#if NOT_EXCLUDED(OP_xw_plus_b)
|
||
DECLARE_CUSTOM_OP(xw_plus_b, 3, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This operation is missed due it simplicy.
|
||
* Input and output params are the same after operation.
|
||
* Input - NDArray, output - NDArray with the same shape.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_stop_gradient)
|
||
DECLARE_OP(stop_gradient, 1, 1, true);
|
||
#endif
|
||
|
||
#if NOT_EXCLUDED(OP_parallel_stack)
|
||
DECLARE_CUSTOM_OP(parallel_stack, -1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* normalize_moments operation normalize already calculated mean and variation
|
||
* accordingly to shift and count.
|
||
* input params:
|
||
* - count of data
|
||
* - tensor with mean
|
||
* - tensor with variance (the same shape as before)
|
||
*
|
||
* - optional floating point param shift.
|
||
*
|
||
* returns a normalized pair mean and variance with the same shapes as input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_normalize_moments)
|
||
DECLARE_CUSTOM_OP(normalize_moments, 3, 2, false, 1, 0);
|
||
#endif
|
||
|
||
/**
|
||
* sufficient_statistics operation return calculated mean and variation with data count.
|
||
* this operation is invert for moments
|
||
* accordingly to shift and count.
|
||
* input params:
|
||
* - input tensor
|
||
* - axes vector
|
||
*
|
||
*
|
||
* - optional floating point param shift.
|
||
* - optional int (as bool) keep_dimension
|
||
*
|
||
* returns four tensors:
|
||
* - scalar tensor (data count)
|
||
* - sum elements of input (accross axises)
|
||
* - sum of squares of input (accross axises)
|
||
* - shift (if was given by input floating param)
|
||
*/
|
||
#if NOT_EXCLUDED(OP_sufficient_statistics)
|
||
DECLARE_CUSTOM_OP(sufficient_statistics, 2, 3, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates weighted logarithmic loss of input
|
||
* Input arguments
|
||
* 0 - target
|
||
* 1 - input
|
||
* 2 - weights (scalar or vector with same as last dimension)
|
||
*
|
||
* return value - a tensor with the same shape as target or input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_weighted_cross_entropy_with_logits)
|
||
DECLARE_OP(weighted_cross_entropy_with_logits, 3, 1, true);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates dropout of input
|
||
* Input arguments
|
||
* 0 - input tensor
|
||
* 1 - noise_shape - (vector with shape to reduce) - optional
|
||
*
|
||
* int parameter - seed for random numbers
|
||
* T parameter - probability (should be between 0 and 1)
|
||
* return value - a tensor with the same shape as target or input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_dropout)
|
||
DECLARE_CONFIGURABLE_OP(dropout, 1, 1, true, 1, 1);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_dropout_bp)
|
||
DECLARE_CONFIGURABLE_OP(dropout_bp, 2, 1, false, 1, 1);
|
||
#endif
|
||
|
||
/* Calculates alpha weighted dropout
|
||
T params:
|
||
0 - drop probability
|
||
1 - alpha value
|
||
2 - alpha' value
|
||
3 - beta value
|
||
*/
|
||
#if NOT_EXCLUDED(OP_alpha_dropout_bp)
|
||
DECLARE_CONFIGURABLE_OP(alpha_dropout_bp, 2, 1, false, 4, 1);
|
||
#endif
|
||
|
||
|
||
/**
|
||
* bincount operation return a vector with element counted.
|
||
*
|
||
* input params:
|
||
* - input tensor - only int part are accepted
|
||
* - weights - the same shape tensor with integer weights for element (optional)
|
||
* default weight - 1,1,1..,1 for all values in the tensor
|
||
*
|
||
* optional ints:
|
||
* - min_length - zero or greater
|
||
* - max_length - between min_length and max(input) + 1
|
||
*
|
||
* returns four tensors:
|
||
* - vector tensor with length to min(max_len, max(input) + 1) with count
|
||
* of values in indexed place
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_bincount)
|
||
DECLARE_CUSTOM_OP(bincount, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* broadcast_dynamic_shape op.
|
||
*
|
||
* input params:
|
||
* 0 - the first shape (vector with shape)
|
||
* 1 - the second shape (vector with shape)
|
||
*
|
||
* return value:
|
||
* vector with broadcasted shape
|
||
*/
|
||
#if NOT_EXCLUDED(OP_broadcast_dynamic_shape)
|
||
DECLARE_CUSTOM_OP(broadcast_dynamic_shape, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* matrix_determinant op.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with dimension (x * y * z * ::: * M * M)
|
||
*
|
||
* return value:
|
||
* tensor with dimension (x * y * z * ::: *) with determinant for all
|
||
* M x M matricies
|
||
*/
|
||
#if NOT_EXCLUDED(OP_matrix_determinant)
|
||
DECLARE_CUSTOM_OP(matrix_determinant, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* log_matrix_determinant op.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with dimension (x * y * z * ::: * M * M)
|
||
*
|
||
* return value:
|
||
* tensor with dimension (x * y * z * ::: *) with log determinant for all
|
||
* M x M matricies
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_log_matrix_determinant)
|
||
DECLARE_CUSTOM_OP(log_matrix_determinant, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* logdet op. Logarithm of the determinant of hermitian positive matricies.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with dimension (x * y * z * ::: * M * M)
|
||
*
|
||
* return value:
|
||
* tensor with dimension (x * y * z * ::: *) with log determinant for all
|
||
* M x M matricies
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_logdet)
|
||
DECLARE_CUSTOM_OP(logdet, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* matrix_inverse op. - make inverse for all 2D square matricies found in the input tensor
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with dimension (x * y * z * ::: * M * M)
|
||
*
|
||
* return value:
|
||
* tensor with dimension (x * y * z * ::: * M * M) with inverse M x M matricies in it
|
||
*/
|
||
#if NOT_EXCLUDED(OP_matrix_inverse)
|
||
DECLARE_OP(matrix_inverse, 1, 1, true);
|
||
#endif
|
||
|
||
/**
|
||
* triangular_solve op. - reverse Gaussian method for solve systems of linear equations.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with dimension (x * y * z * ::: * M * M) - left parts of equations
|
||
* 1 - the tensor with dimension (x * y * z * ::: * M * K) - right parts of equations
|
||
*
|
||
* boolean args:
|
||
* 0 - lower - default is true (optional) - left part is lower triangular matrix
|
||
* 1 - adjoint - default is false (optional) - indicate input matrix or its adjoint (hermitian addition) should be used
|
||
*
|
||
* return value:
|
||
* tensor with dimension (x * y * z * ::: * M * K) with solutions
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_triangular_solve)
|
||
DECLARE_CUSTOM_OP(triangular_solve, 2, 1, true, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* lu op. - make LUP decomposition of given batch of 2D square matricies
|
||
*
|
||
* input params:
|
||
* 0 - float tensor with dimension (x * y * z * ::: * M * M)
|
||
*
|
||
* return value:
|
||
* 0 - float tensor with dimension (x * y * z * ::: * M * M) with LU M x M matricies in it
|
||
* 1 - int (32 or 64) batched vector of permutations with length M - shape (x * y * z * ::: * M)
|
||
*
|
||
* int argument:
|
||
* 0 - data type of output permutaion vector (int32 or int64), optional, default INT32
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_matrix_inverse)
|
||
DECLARE_CUSTOM_OP(lu, 1, 2, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* sequence_mask op. - make mask for given tensor filled by (j > x[i_1, i_2,...,i_n]) -> z[i_1, i_2,...,i_n,j]
|
||
*
|
||
* input params:
|
||
* 0 - the ND-tensor filled by integer-like values
|
||
*
|
||
* optional int param - maxlength (maxlength >= max(x)). By default maxlength = max(x).
|
||
* return value:
|
||
* (N+1)D tensor filled by 0 and 1 accordingly the mask
|
||
*/
|
||
#if NOT_EXCLUDED(OP_sequence_mask)
|
||
DECLARE_CUSTOM_OP(sequence_mask, 1, 1, false, 0, 0);
|
||
#endif
|
||
/**
|
||
* segment_max op. - make a tensor filled by max values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with max values according to indices sets.
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_segment_max)
|
||
DECLARE_CUSTOM_OP(segment_max, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_segment_max_bp)
|
||
DECLARE_CUSTOM_OP(segment_max_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* segment_min op. - make a tensor filled by min values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with min values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_segment_min)
|
||
DECLARE_CUSTOM_OP(segment_min, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_segment_min_bp)
|
||
DECLARE_CUSTOM_OP(segment_min_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* segment_sum op. - make a tensor filled by sum of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with sum of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_segment_sum)
|
||
DECLARE_CUSTOM_OP(segment_sum, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_segment_sum_bp)
|
||
DECLARE_CUSTOM_OP(segment_sum_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* segment_prod op. - make a tensor filled by product of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with product of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_segment_prod)
|
||
DECLARE_CUSTOM_OP(segment_prod, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_segment_prod_bp)
|
||
DECLARE_CUSTOM_OP(segment_prod_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
/**
|
||
* segment_mean op. - make a tensor filled by average of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with average of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_segment_mean)
|
||
DECLARE_CUSTOM_OP(segment_mean, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_segment_mean_bp)
|
||
DECLARE_CUSTOM_OP(segment_mean_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_max op. - make a tensor filled by max values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* return value:
|
||
* tensor with max values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_max)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_max, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_max_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_max_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_min op. - make a tensor filled by min values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* integer param:
|
||
* 0 - num of segments
|
||
*
|
||
* return value:
|
||
* tensor with min values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_min_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_min, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_min_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_min_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_sum op. - make a tensor filled by sum of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* integer param:
|
||
* 0 - num of segments
|
||
*
|
||
* return value:
|
||
* tensor with sum of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_sum)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_sum, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_sum_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_sum_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_prod op. - make a tensor filled by product of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* integer param:
|
||
* 0 - num of segments
|
||
*
|
||
* return value:
|
||
* tensor with product of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_prod)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_prod, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_prod_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_prod_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_mean op. - make a tensor filled by average of values according to index tensor given.
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* integer param:
|
||
* 0 - num of segments
|
||
*
|
||
* return value:
|
||
* tensor with average of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_mean)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_mean, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_mean_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_mean_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* unsorted_segment_sqrt_n op. - computes the sum along segments of a tensor divided by the sqrt(N).
|
||
*
|
||
* input params:
|
||
* 0 - the tensor with data;
|
||
* 1 - the tensor with indices.
|
||
*
|
||
* integer param:
|
||
* 0 - num of segments
|
||
*
|
||
* return value:
|
||
* tensor with average of values according to indices sets.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_sqrt)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_sqrt_n, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_unsorted_segment_sqrt_n_bp)
|
||
DECLARE_CUSTOM_OP(unsorted_segment_sqrt_n_bp, 3, 2, false, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* extract_image_patches op - Extract patches from images and put them in the "depth" output dimension.
|
||
*
|
||
* input params:
|
||
* 0 - images tensor (4D)
|
||
*
|
||
* int params:
|
||
* 0 - ksize_rows
|
||
* 1 - ksize_cols
|
||
* 2 - strides_rows
|
||
* 3 - strides_cols
|
||
* 4 - rates_rows
|
||
* 5 - rates_cols
|
||
* 6 - padding_type - 0 - equiv 'VALID', 1 - 'SAME'
|
||
*/
|
||
#if NOT_EXCLUDED(OP_extract_image_patches)
|
||
DECLARE_CUSTOM_OP(extract_image_patches, 1, 1, false, 0, 7);
|
||
#endif
|
||
|
||
/**
|
||
* draw_bounding_boxes op - modified input image with given colors exept given boxes.
|
||
*
|
||
* input params:
|
||
* 0 - images tensor (4D) with shape {batch, width, height, channels}, where channes is 1 (BW image),
|
||
* 3 (RGB) or 4 (RGBA)
|
||
* 1 - boxes tensor (3D) with shape {batch, number_of_boxes, 4} where last dimension encoded as
|
||
* (y_min, x_min, y_max, x_max), all values in between 0. and 1.
|
||
* 2 - colours tensor (2D) with shape {number_of_boxes, channels} -- bordering color set (palette)
|
||
*
|
||
* output:
|
||
* 0 - 4D tensor with same shape as images (input 0)
|
||
*/
|
||
#if NOT_EXCLUDED(OP_draw_bounding_boxes)
|
||
DECLARE_OP(draw_bounding_boxes, 3, 1, true);
|
||
#endif
|
||
|
||
/**
|
||
* roll - op porting from numpy (https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.roll.html)
|
||
*
|
||
* input params:
|
||
* 0 - NDArray
|
||
*
|
||
* int params:
|
||
* 0 - shift
|
||
* 1 - axe 1
|
||
* 2 - axe 2
|
||
* ...
|
||
* N - axe N
|
||
*
|
||
* All axes are optional and should be between 0 and input->rankOf(). Of course, all axes can be repeated.
|
||
*
|
||
* output:
|
||
* 0 - NDArray with the same shape as input.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_roll)
|
||
DECLARE_CONFIGURABLE_OP(roll, 1, 1, true, 0, 1);
|
||
#endif
|
||
|
||
/**
|
||
* lin_space - op porting from TF (https://www.tensorflow.org/api_docs/python/tf/lin_space)
|
||
*
|
||
* input params:
|
||
* 0 - startVal - NDArray scalar (float point)
|
||
* 1 - finishVal - NDArray scalar (float point)
|
||
* 2 - numOfElements - NDArray scalar (integer)
|
||
*
|
||
* output:
|
||
* 0 - 1D NDArray with the same type as input and length as given with numOfElements param.
|
||
*/
|
||
#if NOT_EXCLUDED(OP_lin_space)
|
||
DECLARE_CUSTOM_OP(lin_space, 3, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* reduction_sum - tf.reduction_sum operation
|
||
*
|
||
* input params:
|
||
* 0 - NDArray
|
||
*
|
||
* T_ARG param (optional):
|
||
* 0 - keep_dims != 0.
|
||
*
|
||
* int params (optional):
|
||
* 0 - axe 1
|
||
* 1 - axe 2
|
||
* ...
|
||
* N-1 axe N
|
||
*
|
||
* All axes are optional and should be between 0 and input->rankOf() - 1
|
||
*
|
||
* output:
|
||
* 0 - NDArray with reduces shape accordingly to axes (the scalar in default case).
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_sum)
|
||
DECLARE_CUSTOM_OP(reduce_sum, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
#if NOT_EXCLUDED(OP_reduce_sum_bp)
|
||
DECLARE_CUSTOM_OP(reduce_sum_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* reduction_prod - tf.reduction_prod operation
|
||
*
|
||
* input params:
|
||
* 0 - NDArray
|
||
*
|
||
* T_ARG param (optional):
|
||
* 0 - keep_dims != 0.
|
||
*
|
||
* int params (optional):
|
||
* 0 - axe 1
|
||
* 1 - axe 2
|
||
* ...
|
||
* N-1 axe N
|
||
*
|
||
* All axes are optional and should be between 0 and input->rankOf() - 1
|
||
*
|
||
* output:
|
||
* 0 - NDArray with reduces shape accordingly to axes (the scalar in default case).
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_prod)
|
||
DECLARE_CUSTOM_OP(reduce_prod, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
#if NOT_EXCLUDED(OP_reduce_prod_bp)
|
||
DECLARE_CUSTOM_OP(reduce_prod_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates min of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate mins for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate min along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated mins
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_min)
|
||
DECLARE_CUSTOM_OP(reduce_min, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_min_bp)
|
||
DECLARE_CUSTOM_OP(reduce_min_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates max of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate maxes for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate max along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated maxes
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_max)
|
||
DECLARE_CUSTOM_OP(reduce_max, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_max_bp)
|
||
DECLARE_CUSTOM_OP(reduce_max_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates norm1 of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate norm1 for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate norm1 along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated norm1
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_norm1)
|
||
DECLARE_CUSTOM_OP(reduce_norm1, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_norm1_bp)
|
||
DECLARE_CUSTOM_OP(reduce_norm1_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates norm2 of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate norm2 for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate norm2 along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated norm2
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_norm2)
|
||
DECLARE_CUSTOM_OP(reduce_norm2, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_norm2_bp)
|
||
DECLARE_CUSTOM_OP(reduce_norm2_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
|
||
/**
|
||
* This op calculates squared norm of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate squared norm for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate squared norm along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated norm
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_sqnorm)
|
||
DECLARE_CUSTOM_OP(reduce_sqnorm, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_sqnorm_bp)
|
||
DECLARE_CUSTOM_OP(reduce_sqnorm_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates norm max of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate norm max for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate norm max along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated norm
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_norm_max)
|
||
DECLARE_CUSTOM_OP(reduce_norm_max, 1, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_reduce_norm_max_bp)
|
||
DECLARE_CUSTOM_OP(reduce_norm_max_bp, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op calculates mean of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate mean for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate mean along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated means
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_mean)
|
||
DECLARE_CUSTOM_OP(reduce_mean, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
#if NOT_EXCLUDED(OP_reduce_mean_bp)
|
||
DECLARE_CUSTOM_OP(reduce_mean_bp, 2, 1, false, 0, 0)
|
||
#endif
|
||
/**
|
||
* This op calculates sample variance of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate mean for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
* biasCorrected - if non zero, then bias correction will be applied, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate mean along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated means
|
||
*/
|
||
DECLARE_CUSTOM_OP(reduce_variance, 1, 1, false, 0, 0);
|
||
DECLARE_CUSTOM_OP(reduce_variance_bp, 2, 1, false, 0, 0)
|
||
|
||
/**
|
||
* This op calculates sample standard deviation of elements along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate mean for
|
||
*
|
||
* float arguments:
|
||
* keepDims: if non zero, then keep reduced dimensions with length = 1, default value is zero
|
||
* biasCorrected - if non zero, then bias correction will be applied, default value is zero
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate mean along, default corresponds to empty list in which case calculation is performed for all dimensions and scalar is returned
|
||
*
|
||
* output array:
|
||
* reduced tensor with calculated means
|
||
*/
|
||
DECLARE_CUSTOM_OP(reduce_stdev, 1, 1, false, 0, 0);
|
||
DECLARE_CUSTOM_OP(reduce_stdev_bp, 2, 1, false, 0, 0)
|
||
|
||
/**
|
||
* This op calculates backprop dot for two tensors along given dimensions
|
||
*
|
||
* input array:
|
||
* x: tensor to calculate dot for
|
||
* y: tensor to calculate dot for
|
||
* z: tensor with gradient output of the FF dot for x and y
|
||
*
|
||
* int arguments:
|
||
* list of integers - dimensions to calculate dot along,
|
||
* default corresponds to empty list in which case calculation
|
||
* is performed for all dimensions and scalar is returned.
|
||
*
|
||
* output array:
|
||
* the tensor with calculated backproped dots
|
||
*
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_reduce_dot_bp)
|
||
DECLARE_CUSTOM_OP(reduce_dot_bp, 3, 2, false, 0, 0);
|
||
#endif
|
||
/**
|
||
* reduce_logsumexp - tf.reduce_logsumexe operation
|
||
*
|
||
* input params:
|
||
* 0 - NDArray (input)
|
||
* 1 - 1D NDArray (axis) (optional) - integer array
|
||
*
|
||
* T_ARG param (optional):
|
||
* 0 - keep_dims != 0.
|
||
*
|
||
* int params (optional):
|
||
* 0 - axe 1
|
||
* 1 - axe 2
|
||
* ...
|
||
* N-1 axe N
|
||
*
|
||
* CAUTION: All axes are optional and should be between 0 and input->rankOf() - 1
|
||
* and put either with second param or as integers but not both
|
||
*
|
||
* output:
|
||
* 0 - NDArray with reduces shape accordingly to axes (the scalar in default case).
|
||
*/
|
||
#if NOT_EXCLUDED(OP_reduce_logsumexp)
|
||
DECLARE_CUSTOM_OP(reduce_logsumexp, 1, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op make bilinear or nearest neighbor interpolated resize for given tensor
|
||
*
|
||
* input array:
|
||
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels) numeric type
|
||
* 1 - 2D-Tensor with shape (num_boxes, 4) float type
|
||
* 2 - 1D-Tensor with shape (num_boxes) int type
|
||
* 3 - 1D-Tensor with 2 values (newWidth, newHeight) (optional) int type
|
||
*
|
||
* float arguments (optional)
|
||
* 0 - exprapolation_value (optional) default 0.f
|
||
*
|
||
* int arguments: (optional)
|
||
* 0 - mode (default 0 - bilinear interpolation)
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with resized to crop_size images given - float type
|
||
*/
|
||
#if NOT_EXCLUDED(OP_crop_and_resize)
|
||
DECLARE_CUSTOM_OP(crop_and_resize, 4, 1, false, -1, -1);
|
||
#endif
|
||
|
||
/**
|
||
* This op make bilinear interpolated resize for given tensor
|
||
*
|
||
* input array:
|
||
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
|
||
* 1 - 1D-Tensor with 2 values (newWidth, newHeight) (optional)
|
||
*
|
||
* int arguments: (optional)
|
||
* 0 - new width
|
||
* 1 - new height
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with calculated backproped dots
|
||
*
|
||
* CAUTION: either size tensor or a pair of int params should be provided.
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_resize_bilinear)
|
||
DECLARE_CUSTOM_OP(resize_bilinear, 1, 1, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* This op make nearest neighbor interpolated resize for given tensor
|
||
*
|
||
* input array:
|
||
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
|
||
* 1 - 1D-Tensor with 2 values (newWidth, newHeight) (optional)
|
||
*
|
||
* int arguments: (optional)
|
||
* 0 - new width
|
||
* 1 - new height
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
|
||
*
|
||
* CAUTION: either size tensor or a pair of int params should be provided.
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_resize_nearest_neighbor)
|
||
DECLARE_CUSTOM_OP(resize_nearest_neighbor, 1, 1, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* This op make bicubic interpolated resize for given tensor
|
||
*
|
||
* input array:
|
||
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
|
||
* 1 - 1D-Tensor with 2 values (newWidth, newHeight)
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_resize_bicubic)
|
||
DECLARE_CUSTOM_OP(resize_bicubic, 1, 1, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* This op make area interpolated resize (as OpenCV INTER_AREA algorithm) for given tensor
|
||
*
|
||
* input array:
|
||
* 0 - images - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
|
||
* 1 - size - 1D-Tensor with 2 values (newWidth, newHeight) (if missing a pair of integer args should be provided).
|
||
*
|
||
* int args: - proveded only when size tensor is missing
|
||
* 0 - new height
|
||
* 1 - new width
|
||
* boolean args:
|
||
* 0 - align_corners - optional (default is false)
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
|
||
*
|
||
*/
|
||
#if NOT_EXCLUDED(OP_resize_area)
|
||
DECLARE_CUSTOM_OP(resize_area, 1, 1, false, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* This op make interpolated resize for given tensor with given algorithm.
|
||
* Supported algorithms are bilinear, bicubic, nearest_neighbor.
|
||
* Need to implement to full compatibility with TF: lanczos5, gaussian, area and mitchellcubic
|
||
*
|
||
* input array:
|
||
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
|
||
* 1 - 1D-Tensor with 2 values (newWidth, newHeight)
|
||
*
|
||
* optional int args:
|
||
* 0 - algorithm - bilinear by default
|
||
* optional bool args:
|
||
* 0 - preserve_aspect_ratio - default False
|
||
* 1 - antialias - default False
|
||
*
|
||
* output array:
|
||
* the 4D-Tensor with resized by given algorithm image (shape is {batch, newWidth, newHeight, channels})
|
||
*
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_image_resize)
|
||
DECLARE_CUSTOM_OP(image_resize, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* Copy a tensor setting everything outside a central band in each innermost matrix
|
||
*
|
||
* input array:
|
||
* x: given tensor with shape {..., M, N} - as vector (matrix) of matricies MxN
|
||
*
|
||
* int arguments:
|
||
* lower band
|
||
* upper band
|
||
*
|
||
* output array:
|
||
* matrix with given bands between lower and upper diagonals
|
||
*
|
||
*/
|
||
|
||
#if NOT_EXCLUDED(OP_matrix_band_part)
|
||
DECLARE_CONFIGURABLE_OP(matrix_band_part, 1, 1, true, 0, 2);
|
||
#endif
|
||
|
||
|
||
#if NOT_EXCLUDED(OP_Assert)
|
||
DECLARE_OP(Assert, 1, 1, false);
|
||
#endif
|
||
|
||
/**
|
||
* image.non_max_suppression ops.
|
||
* input:
|
||
* 0 - boxes - 2D-tensor with shape (num_boxes, 4) by float type
|
||
* 1 - scales - 1D-tensor with shape (num_boxes) by float type
|
||
* 2 - output_size - 0D-tensor by int type (optional)
|
||
* float args:
|
||
* 0 - overlap_threshold - threshold value for overlap checks (optional, by default 0.5)
|
||
* 1 - score_threshold - the threshold for deciding when to remove boxes based on score (optional, by default -inf)
|
||
* int args:
|
||
* 0 - output_size - as arg 2 used for same target. Eigher this or arg 2 should be provided.
|
||
*
|
||
* output:
|
||
* - vector with size M, where M <= output_size by int type
|
||
*
|
||
* */
|
||
#if NOT_EXCLUDED(OP_image_non_max_suppression)
|
||
DECLARE_CUSTOM_OP(non_max_suppression, 2, 1, false, 0, 0);
|
||
#endif
|
||
#if NOT_EXCLUDED(OP_image_non_max_suppression_v3)
|
||
DECLARE_CUSTOM_OP(non_max_suppression_v3, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/*
|
||
* image.non_max_suppression_overlaps op.
|
||
* input:
|
||
* 0 - boxes - 2D-tensor with shape (num_boxes, 4) by float type
|
||
* 1 - scales - 1D-tensor with shape (num_boxes) by float type
|
||
* 2 - output_size - 0D-tensor by int type (optional)
|
||
* float args:
|
||
* 0 - overlap_threshold - threshold value for overlap checks (optional, by default 0.5)
|
||
* 1 - score_threshold - the threshold for deciding when to remove boxes based on score (optional, by default -inf)
|
||
* int args:
|
||
* 0 - output_size - as arg 2 used for same target. Eigher this or arg 2 should be provided.
|
||
*
|
||
* output:
|
||
* 0 - 1D integer tensor with shape [M], epresenting the selected indices from the overlaps tensor, where M <= max_output_size
|
||
* */
|
||
#if NOT_EXCLUDED(OP_image_non_max_suppression_overlaps)
|
||
DECLARE_CUSTOM_OP(non_max_suppression_overlaps, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/*
|
||
* cholesky op - decomposite positive square symetric matrix (or matricies when rank > 2).
|
||
* input:
|
||
* 0 - matricies - tensor with shape (..., N, N) by float type
|
||
*
|
||
* output - lower triangular matrix (matricies when rank > 2) with the same shape as input.
|
||
* */
|
||
#if NOT_EXCLUDED(OP_cholesky)
|
||
DECLARE_OP(cholesky, 1, 1, true);
|
||
#endif
|
||
/*
|
||
* nth_element - apply nth_element for last dimension of input tensor
|
||
* input array:
|
||
* 0 - input array
|
||
* 1 - scalar tensor with n for operation. n should be less than last dimension
|
||
*
|
||
* output:
|
||
* 0 - NDArray with the same shape as input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_nth_element)
|
||
DECLARE_CUSTOM_OP(nth_element, 2, 1, false, 0, 0);
|
||
#endif
|
||
|
||
/**
|
||
* This op checks for Inf/NaN values within input array, and throws exception if there's at least one
|
||
*/
|
||
#if NOT_EXCLUDED(OP_check_numerics)
|
||
DECLARE_CUSTOM_OP(check_numerics, 2, 1, true, 0, 0);
|
||
#endif
|
||
/**
|
||
* fake_quant_with_min_max_vals - tf.quantization.fake_quant_with_min_max_vars
|
||
*
|
||
* input params:
|
||
* 0 - NDArray (input)
|
||
* 1 - 0D Tensor - min value
|
||
* 2 - 0D Tensor - max value
|
||
*
|
||
* int params (optional):
|
||
* 0 - num_bits (allowed interval [2, 16], default 8)
|
||
* 1 - narrow_range (default False)
|
||
*
|
||
* output:
|
||
* 0 - NDArray with the same shape as input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_fake_quant_with_min_max_vars)
|
||
DECLARE_CONFIGURABLE_OP(fake_quant_with_min_max_vars, 3, 1, true, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* fake_quant_with_min_max_vals_per_channel - tf.quantization.fake_quant_with_min_max_vars_per_channel
|
||
*
|
||
* input params:
|
||
* 0 - NDArray (input) - at least 2D.
|
||
* 1 - 1D Tensor - min values (min length equals to last dim of input)
|
||
* 2 - 1D Tensor - max value (length equals to min)
|
||
*
|
||
* int params (optional):
|
||
* 0 - num_bits (allowed interval [2, 16], default 8)
|
||
* 1 - narrow_range (default False)
|
||
*
|
||
* output:
|
||
* 0 - NDArray with the same shape as input
|
||
*/
|
||
#if NOT_EXCLUDED(OP_fake_quant_with_min_max_vars_per_channel)
|
||
DECLARE_CONFIGURABLE_OP(fake_quant_with_min_max_vars_per_channel, 3, 1, true, 0, -2);
|
||
#endif
|
||
|
||
/**
|
||
* compare_and_bitpack - compare with greater and pack result with uint8
|
||
*
|
||
* input params:
|
||
* 0 - NDArray (input)
|
||
* 1 - 0D Tensor - threshold
|
||
*
|
||
*
|
||
* output:
|
||
* 0 - NDArray with the same shape as input and type uint8
|
||
*/
|
||
#if NOT_EXCLUDED(OP_compare_and_bitpack)
|
||
DECLARE_CUSTOM_OP(compare_and_bitpack, 2, 1, false, 0, 0);
|
||
#endif
|
||
}
|
||
}
|
||
|
||
#endif
|