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Legacy API changes (#441)
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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

142 lines
5.9 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by raver119 on 30.11.17.
//
#include <ops/declarable/helpers/col2im.h>
#include <execution/Threads.h>
namespace sd {
namespace ops {
namespace helpers {
// [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW]
template <typename T>
void col2im_(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) {
auto imBuff = output.bufferAsT<T>();
auto colBuff = input.bufferAsT<T>();
auto imShapeBuffer = output.shapeInfo();
auto colShapeBuffer = input.shapeInfo();
auto colShape = shape::shapeOf(colShapeBuffer);
auto colStride = shape::stride(colShapeBuffer);
auto imShape = shape::shapeOf(imShapeBuffer);
auto imStride = shape::stride(imShapeBuffer);
const int bS = imShape[0];
const int iC = imShape[1];
const int kH = colShape[2];
const int kW = colShape[3];
const int oH = colShape[4];
const int oW = colShape[5];
const Nd4jLong colStride0 = colStride[0];
const Nd4jLong colStride1 = colStride[1];
const Nd4jLong colStride2 = colStride[2];
const Nd4jLong colStride3 = colStride[3];
const Nd4jLong colStride4 = colStride[4];
const Nd4jLong colStride5 = colStride[5];
const Nd4jLong imStride0 = imStride[0];
const Nd4jLong imStride1 = imStride[1];
const Nd4jLong imStride2 = imStride[2];
const Nd4jLong imStride3 = imStride[3];
// if (shape::order(colShapeBuffer) == 'c' && shape::order(imShapeBuffer) == 'c' && shape::strideDescendingCAscendingF(colShapeBuffer) && shape::strideDescendingCAscendingF(imShapeBuffer)) {
if (false) {
auto func = PRAGMA_THREADS_FOR_2D {
T const* col;
T* im;
int imRow, imCol;
for (auto b = start_x; b < stop_x; b += inc_x) {
for (auto c = start_y; c < stop_y; c += inc_y) {
for (int kRow = 0; kRow < kH; ++kRow) {
for (int kCol = 0; kCol < kW; ++kCol) {
for (int colH = 0; colH < oH; ++colH) {
for (int colW = 0; colW < oW; ++colW) {
imRow = (-pH + kRow * dH) + colH * sH;
imCol = (-pW + kCol * dW) + colW * sW;
col = colBuff + b * colStride0 + c * colStride1 + kRow * colStride2 + kCol * colStride3 + colH * colStride4 + colW * colStride5;
im = imBuff + b * imStride0 + c * imStride1 + imRow * imStride2 + imCol * imStride3;
if (static_cast<unsigned>(imRow) < static_cast<unsigned>(iH) &&
static_cast<unsigned>(imCol) < static_cast<unsigned>(iW))
*im += *col;
}
}
}
}
}
}
};
samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1);
}
else {
auto func = PRAGMA_THREADS_FOR {
T *col, *im;
for (auto b = start; b < stop; b++) {
T *im0 = imBuff + b * imStride0;
T const* col4 = colBuff + b * colStride0;
for (int colH = 0; colH < oH; ++colH, col4 += colStride4) {
T const* col5 = col4;
for (int colW = 0; colW < oW; ++colW, col5 += colStride5) {
T const* col1 = col5;
T *im1 = im0;
for (int c = 0; c < iC; ++c, col1 += colStride1, im1 += imStride1) {
int imRow = (-pH + colH * sH);
T const* col2 = col1;
T *im2 = im1 + imRow * imStride2;
for (int kRow = 0;
kRow < kH; ++kRow, col2 += colStride2, imRow += dH, im2 += dH * imStride2) {
int imCol = -pW + colW * sW;
T const* col3 = col2;
T *im3 = im2 + imCol * imStride3;
for (int kCol = 0;
kCol < kW; ++kCol, col3 += colStride3, imCol += dW, im3 += dW * imStride3) {
if (static_cast<unsigned>(imRow) < static_cast<unsigned>(iH) &&
static_cast<unsigned>(imCol) < static_cast<unsigned>(iW))
*im3 += *col3;
}
}
}
}
}
}
};
samediff::Threads::parallel_tad(func, 0, bS);
}
}
void col2im(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) {
BUILD_SINGLE_SELECTOR(input.dataType(), col2im_, (context, input, output, sH, sW, pH, pW, iH, iW, dH, dW), FLOAT_TYPES);
}
}
}
}