/*******************************************************************************
* 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 Yurii Shyrma, created on 06.03.2018
//
#ifndef LIBND4J_CONVO_OPS_H
#define LIBND4J_CONVO_OPS_H
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_conv2d)
#include <op_boilerplate.h>
#include <memory>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/OpRegistrator.h>
#include <declarable/helpers/convolutions.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(conv2d, 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, iC, oC] always
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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
bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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 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, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::conv2d(block, input, weights, bias, output, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
return Status::OK();
}
DECLARE_SHAPE_FN(conv2d) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC] always
auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
//output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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 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
const int rank = 4; // 4
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
int indIOioC, indIiH, indWoC(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::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if (biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
Nd4jLong* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
int oH, oW; // output height, width
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
outputShapeInfo[0] = rank;
outputShapeInfo[1] = bS;
if (isNCHW) {
outputShapeInfo[2] = oC;
outputShapeInfo[3] = oH;
outputShapeInfo[4] = oW;
} else {
outputShapeInfo[2] = oH;
outputShapeInfo[3] = oW;
outputShapeInfo[4] = oC;
}
ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
return SHAPELIST(CONSTANT(outputShapeInfo));
}
DECLARE_TYPES(conv2d) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_TYPES(conv2d_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv2d_bp, 3, 2, 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, iC, oC] always
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] (NCHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
int kH = INT_ARG(0); // filter(kernel) height
int kW = INT_ARG(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
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
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, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::conv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
return Status::OK();
}
DECLARE_SHAPE_FN(conv2d_bp) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC] always
auto 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] (NCHW), epsilon_next
const int rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
const int kH = INT_ARG(0); // filter(kernel) height
const int kW = INT_ARG(1); // filter(kernel) width
const int sH = INT_ARG(2); // strides height
const int sW = INT_ARG(3); // strides width
const int pH = INT_ARG(4); // paddings height
const int pW = INT_ARG(5); // paddings width
const int dH = INT_ARG(6); // dilations height
const int dW = INT_ARG(7); // dilations width
const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int indIOioC, indIiH, indOoH, indWoC(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::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if(biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D_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());
if(biasShapeInfo) {
auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
}
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) {
auto gradIShape = INPUT_VARIABLE(0); // [4]
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
int kH = INT_ARG(0); // filter(kernel) height
int kW = INT_ARG(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
const int rank = gradO->rankOf();
REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradIShape->rankOf() == 1, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to 1, but got %i instead !", gradIShape->rankOf());
REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: length of array with output shape must be equal to 4, but got %i instead !", gradIShape->lengthOf());
// create empty conv2d input array
std::vector<Nd4jLong> gradIShapeAsVector(rank);
for(int i = 0; i < rank; ++i)
gradIShapeAsVector[i] = gradIShape->e<Nd4jLong>(i);
NDArray input(gradO->ordering(), gradIShapeAsVector, gradO->dataType(), block.launchContext());
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, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
return Status::OK();
}
DECLARE_TYPES(conv2d_input_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(conv2d_input_bp) {
auto gradIShapeShapeInfo = inputShape->at(0); // [4]
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC] always
auto gradOShapeInfo = inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
const int rank = 4;
REQUIRE_TRUE(gradIShapeShapeInfo[0] == 1, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to %i, but got %i instead !", 1, gradIShapeShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
const int kH = INT_ARG(0); // filter(kernel) height
const int kW = INT_ARG(1); // filter(kernel) width
const int sH = INT_ARG(2); // strides height
const int sW = INT_ARG(3); // strides width
const int pH = INT_ARG(4); // paddings height
const int pW = INT_ARG(5); // paddings width
const int dH = INT_ARG(6); // dilations height
const int dW = INT_ARG(7); // dilations width
const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int indIOioC, indIiH, indWoC(3), indOoH;
if(!isNCHW) {
indIOioC = 3; indIiH = 1; indOoH = 1;
}
else {
indIOioC = 1; indIiH = 2; indOoH = 2;
}
std::vector<Nd4jLong> gradIShape = INPUT_VARIABLE(0)->template asVectorT<Nd4jLong>();
const int bS = gradIShape[0]; // batch size
const int iH = gradIShape[indIiH]; // input height
const int iW = gradIShape[indIiH+1]; // input width
const int iC = gradIShape[indIOioC]; // input channels
const int oC = weightsShapeInfo[indWoC+1]; // output channels
int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
Nd4jLong* gradIshapeInfo(nullptr);
ALLOCATE(gradIshapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
gradIshapeInfo[0] = rank;
gradIshapeInfo[1] = bS;
if (isNCHW) {
gradIshapeInfo[2] = iC;
gradIshapeInfo[3] = iH;
gradIshapeInfo[4] = iW;
} else {
gradIshapeInfo[2] = iH;
gradIshapeInfo[3] = iW;
gradIshapeInfo[4] = iC;
}
ShapeUtils::updateStridesAndType(gradIshapeInfo, gradOShapeInfo, shape::order(gradOShapeInfo));
return SHAPELIST(CONSTANT(gradIshapeInfo));
}
}
}
#endif
#endif //LIBND4J_CONVO_OPS_H