329 lines
21 KiB
C++
329 lines
21 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
|
|
******************************************************************************/
|
|
|
|
//
|
|
// @author raver119@gmail.com
|
|
// @author Yurii Shyrma
|
|
//
|
|
|
|
#include <system/op_boilerplate.h>
|
|
#if NOT_EXCLUDED(OP_deconv2d)
|
|
|
|
#include <ops/declarable/CustomOperations.h>
|
|
#include <helpers/MmulHelper.h>
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
#include <ops/declarable/helpers/im2col.h>
|
|
#include <ops/declarable/helpers/col2im.h>
|
|
#include <ops/declarable/helpers/addBias.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
|
|
CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
auto output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
|
|
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
|
|
|
|
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
if(!isNCHW)
|
|
output = new NDArray(output->permute({0, 3, 1, 2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
|
|
|
|
std::vector<int> colPermut;
|
|
if(1 == wFormat)
|
|
colPermut = {1, 2, 3, 0, 4, 5};
|
|
else
|
|
colPermut = {2, 3, 1, 0, 4, 5};
|
|
|
|
if(isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
|
|
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
|
|
|
|
NDArray columns(input->ordering(), {bS, oC, kH, kW, iH, iW}, input->dataType(), block.launchContext());
|
|
|
|
//----- calculation of output -----//
|
|
// NHWC: [kH, kW, oC, iC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW]
|
|
// NHWC: [iC, oC, kH, kW] x [bS, iH, iW, iC] = [oC, kH, kW, bS, iH, iW]
|
|
// NHWC: [iC, kH, kW, oC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW]
|
|
sd::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, colPermut);
|
|
LaunchContext* ctx = block.launchContext();
|
|
helpers::col2im(*ctx, columns, *output, sH, sW, pH, pW, oH, oW, dH, dW); // [bS, oC, kH, kW, iH, iW] is de-convoluted to [bS, oC, oH, oW]
|
|
|
|
//----- add biases if required -----//
|
|
if(bias)
|
|
// output->applyBroadcast(broadcast::Add, {1}, bias);
|
|
helpers::addBias(block, *output, *bias, *output, true);
|
|
|
|
if(!isNCHW)
|
|
delete output;
|
|
|
|
return Status::OK();
|
|
}
|
|
DECLARE_TYPES(deconv2d) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(sd::DataType::ANY)
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(deconv2d) {
|
|
|
|
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
|
|
auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
|
|
|
|
const int rank = 4;
|
|
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, shape::rank(inputShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, shape::rank(weightsShapeInfo));
|
|
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
|
|
|
|
int indIOioC, indIiH, indWoC(0 == wFormat ? 2 : (1 == wFormat ? 1 : 3));
|
|
if(!isNCHW) {
|
|
indIOioC = 3; indIiH = 1;
|
|
}
|
|
else {
|
|
indIOioC = 1; indIiH = 2;
|
|
}
|
|
|
|
const int bS = inputShapeInfo[1]; // batch size
|
|
const int iH = inputShapeInfo[indIiH+1]; // input height
|
|
const int iW = inputShapeInfo[indIiH+2]; // input width
|
|
const int iC = inputShapeInfo[indIOioC+1]; // input channels
|
|
const int oC = weightsShapeInfo[indWoC+1]; // output channels
|
|
|
|
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if (biasShapeInfo)
|
|
REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
|
|
|
|
int oH, oW; // output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
Nd4jLong outputShape[4];
|
|
|
|
outputShape[0] = bS;
|
|
if (isNCHW) {
|
|
outputShape[1] = oC;
|
|
outputShape[2] = oH;
|
|
outputShape[3] = oW;
|
|
} else {
|
|
outputShape[1] = oH;
|
|
outputShape[2] = oW;
|
|
outputShape[3] = oC;
|
|
}
|
|
|
|
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(weightsShapeInfo), shape::order(inputShapeInfo), outputShape, 4)));
|
|
}
|
|
|
|
DECLARE_TYPES(deconv2d_bp) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(sd::DataType::ANY)
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
CUSTOM_OP_IMPL(deconv2d_bp, 3, 2, false, 0, 9) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of weights array must be equal to 4 , but got %i instead !", weights->rankOf());
|
|
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
|
|
|
|
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
|
|
|
|
int trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
|
|
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if(bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
if(isSameMode){ // SAME
|
|
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
|
|
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
|
|
}
|
|
|
|
// ----- calculation of gradI -> pass it through conv2d_ff ----- //
|
|
sd::ops::conv2d conv2d;
|
|
const Nd4jStatus status = conv2d.execute({gradO, weights}, {gradI}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, !isNCHW, wFormat}, {});
|
|
if (status != ND4J_STATUS_OK)
|
|
return status;
|
|
|
|
// -----prepare permutation arrays and axes for dot product ----- //
|
|
std::vector<int> inputAxes;
|
|
|
|
if(!isNCHW) {
|
|
gradO = new NDArray(gradO->permute({0, 3, 1, 2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
|
|
inputAxes = {0, 1, 2}; // bS, iH, iW
|
|
}
|
|
else
|
|
inputAxes = {0, 2, 3}; // bS, iH, iW
|
|
|
|
std::vector<int> gradWAxes; // empty for wFormat = 1
|
|
if(0 == wFormat)
|
|
gradWAxes = {3, 2, 0, 1};
|
|
else if(2 == wFormat)
|
|
gradWAxes = {0, 3, 1, 2};
|
|
|
|
// ----- calculation of gradW ----- //
|
|
NDArray columns(input->ordering(), {bS, oC, kH, kW, iH, iW}, input->dataType(), block.launchContext());
|
|
|
|
LaunchContext* ctx = block.launchContext();
|
|
helpers::im2col(*ctx, *gradO, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, oC, oH, oW] is convoluted to [bS, oC, kH, kW, iH, iW]
|
|
MmulHelper::tensorDot(input, &columns, gradW, inputAxes, {0, 4, 5}, gradWAxes); // [bS, iC, iH, iW]/[bS, iH, iW, iC] x [bS, oC, kH, kW, iH, iW] = [iC, oC, kH, kW]
|
|
|
|
// ----- calculation of gradB ----- //
|
|
if(gradB) {
|
|
if(gradB->rankOf() == 2)
|
|
gradB = new NDArray(gradB->reshape(gradB->ordering(), {gradB->lengthOf()}, false));
|
|
gradO->reduceAlongDimension(reduce::Sum, *gradB, {0, 2, 3}); // sum over bS, oH, oW
|
|
if(gradB != OUTPUT_VARIABLE(2))
|
|
delete gradB;
|
|
}
|
|
|
|
if(!isNCHW)
|
|
delete gradO;
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(deconv2d_bp) {
|
|
|
|
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
|
|
Nd4jLong const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
|
|
auto gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
|
|
|
|
const int rank = 4;
|
|
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, shape::rank(inputShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DECONV2D_BP OP: rank of weights array must be equal to %i , but got %i instead !", rank, shape::rank(weightsShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(gradOShapeInfo) == rank, 0, "CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, shape::rank(gradOShapeInfo));
|
|
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
|
|
|
|
int indIOioC, indIiH, indOoH, indWoC(0 == wFormat ? 2 : (1 == wFormat ? 1 : 3));
|
|
if(!isNCHW) {
|
|
indIOioC = 3; indIiH = 1; indOoH = 1;
|
|
}
|
|
else {
|
|
indIOioC = 1; indIiH = 2; indOoH = 2;
|
|
}
|
|
|
|
const int bS = inputShapeInfo[1]; // batch size
|
|
const int iH = inputShapeInfo[indIiH+1]; // input height
|
|
const int iW = inputShapeInfo[indIiH+2]; // input width
|
|
const int iC = inputShapeInfo[indIOioC+1]; // input channels
|
|
const int oC = weightsShapeInfo[indWoC+1]; // output channels
|
|
|
|
int trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
|
|
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0, "CUSTOM DECONV2D_BP OP: wrong shape of output gradients next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
|
|
REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if(biasShapeInfo)
|
|
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
|
|
|
|
auto gradIShapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
auto gradWShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
|
|
auto shapes = SHAPELIST(CONSTANT(gradIShapeInfo), CONSTANT(gradWShapeInfo));
|
|
|
|
if (biasShapeInfo != nullptr) {
|
|
auto gradBShapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
shapes->push_back(CONSTANT(gradBShapeInfo));
|
|
}
|
|
|
|
return shapes;
|
|
}
|
|
|
|
|
|
|
|
}
|
|
}
|
|
|
|
#endif |