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cavis/libnd4j/include/ops/declarable/generic/nn/convo/sconv2d.cpp

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/*******************************************************************************
* 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, created on 29/10/17.
// @author Yurii Shyrma, changed on 20.03.2018
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_sconv2d)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>
#include <memory>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(sconv2d, 2, 1, false, 0, 9) {
NDArray *input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
NDArray *weightsDepth = INPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
NDArray *weightsPoint = nullptr; // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
NDArray *bias = nullptr; // [oC], if weightsPoint=nullptr then oC = iC*mC
NDArray *output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
if(block.width() == 3) {
if((INPUT_VARIABLE(2))->rankOf() == 4)
weightsPoint = INPUT_VARIABLE(2);
else
bias = INPUT_VARIABLE(2);
}
else if(block.width() == 4) {
weightsPoint = INPUT_VARIABLE(2);
bias = INPUT_VARIABLE(3);
}
REQUIRE_TRUE(input->rankOf() == 4, 0, " SCONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weightsDepth->rankOf() == 4, 0, " SCONV2D OP: rank of weightsDepth array must be equal to 4, but got %i instead !", weightsDepth->rankOf());
if(weightsPoint)
REQUIRE_TRUE(weightsPoint->rankOf() == 4, 0, " SCONV2D OP: rank of weightsPoint array must be equal to 4, but got %i instead !", weightsPoint->rankOf());
if(bias)
REQUIRE_TRUE(bias->rankOf() == 1 || bias->rankOf() == 2, 0, " SCONV2D OP: rank of biases array must be equal to 1 or 2, but got %i instead !", bias->rankOf());;
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
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weightsDepth->sizeAt(indWmC); // channels multiplier
std::vector<Nd4jLong> expectedWeightsDShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(weightsDepth->isSameShape(expectedWeightsDShape), 0, " SCONV2D OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str());
if(weightsPoint) {
std::vector<Nd4jLong> expectedWeightsPShape = ConvolutionUtils::expectWeightsShape(wFormat, 1, 1, iC*mC, oC);
REQUIRE_TRUE(weightsPoint->isSameShape(expectedWeightsPShape), 0, " SCONV2D OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str());
}
if (bias)
REQUIRE_TRUE(oC == bias->lengthOf(), 0, " SCONV2D OP: length of bias array must be equal to outChannels, but got %i instead", bias->lengthOf());
if (iC == 1) {
nd4j_debug("SCONV2D OP: for input_channels = 1 this op is equivalent to standard conv2d\n","");
ConvolutionUtils::conv2d(block, input, weightsDepth, bias, output, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW, wFormat);
return Status::OK();
}
ConvolutionUtils::sconv2d(block, input, weightsDepth, weightsPoint, bias, output, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW, wFormat);
return Status::OK();
}
DECLARE_TYPES(sconv2d) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(sconv2d) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weightsDShapeInfo = inputShape->at(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
Nd4jLong* weightsPShapeInfo = nullptr; // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
Nd4jLong* biasShapeInfo = nullptr; // [oC], oC = iC*mC if weightsPoint=nullptr
if(block.width() == 3)
if(inputShape->at(2)[0] == 4)
weightsPShapeInfo = inputShape->at(2);
else
biasShapeInfo = inputShape->at(2);
else if(block.width() == 4) {
weightsPShapeInfo = inputShape->at(2);
biasShapeInfo = inputShape->at(3);
}
const int rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "SCONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(weightsDShapeInfo[0] == rank, 0, "SCONV2D OP: rank of weightsDepth array must be equal to %i, but got %i instead !", rank, weightsDShapeInfo[0]);
if(weightsPShapeInfo)
REQUIRE_TRUE(weightsPShapeInfo[0] == rank, 0, "SCONV2D OP: rank of weightsPoint array must be equal to %i, but got %i instead !", rank, weightsPShapeInfo[0]);
if(biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2, 0, "SCONV2D OP: rank of biases array must be <= 2, but got %i instead !", biasShapeInfo[0]);;
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): 1-NHWC, 0-NCHW
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
int indIOioC, indIiH, indWmC(0 == wFormat ? 3 : 0);
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 mC = weightsDShapeInfo[indWmC+1]; // channel multiplier
const int oC = weightsPShapeInfo ? weightsPShapeInfo[indWmC+1] : iC*mC; // output channels (oC or iC*mC)
std::vector<Nd4jLong> expectedWeightsDShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsDShapeInfo, expectedWeightsDShape), 0, "SCONV2D OP: wrong shape of depth weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str());
if(weightsPShapeInfo) {
std::vector<Nd4jLong> expectedWeightsPShape = ConvolutionUtils::expectWeightsShape(wFormat, 1, 1, iC*mC, oC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsPShapeInfo, expectedWeightsPShape), 0, "SCONV2D OP: wrong shape of point array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str());
}
if (biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "SCONV2D 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::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
Nd4jLong* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);
outputShapeInfo[0] = 4;
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, weightsDShapeInfo, shape::order(inputShapeInfo));
return SHAPELIST(CONSTANT(outputShapeInfo));
}
DECLARE_TYPES(sconv2d_bp) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(sconv2d_bp, 3, 2, false, 0, 9) {
NDArray *input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
NDArray *gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
NDArray *weightsDepth = INPUT_VARIABLE(2); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
NDArray *weightsPoint = nullptr; // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
NDArray *bias = nullptr; // [oC], oC = iC*mC if weightsPoint=nullptr
NDArray *gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
NDArray *gradWD = OUTPUT_NULLIFIED(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
NDArray *gradWP = nullptr; // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
NDArray *gradB = nullptr; // [oC]
if(block.width() == 4) {
if((INPUT_VARIABLE(3))->rankOf() == 4) {
weightsPoint = INPUT_VARIABLE(3);
gradWP = OUTPUT_NULLIFIED(2);
}
else {
bias = INPUT_VARIABLE(3);
gradB = OUTPUT_NULLIFIED(2);
}
}
else if(block.width() == 5) {
weightsPoint = INPUT_VARIABLE(3);
bias = INPUT_VARIABLE(4);
gradWP = OUTPUT_NULLIFIED(2);
gradB = OUTPUT_NULLIFIED(3);
}
REQUIRE_TRUE(input->rankOf() == 4, 0, " SCONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 4, 0, " SCONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
REQUIRE_TRUE(weightsDepth->rankOf() == 4, 0, " SCONV2D_BP OP: rank of weightsDepth array must be equal to 4 !, but got %i instead !", weightsDepth->rankOf());
if(weightsPoint) {
REQUIRE_TRUE(weightsPoint->rankOf() == 4, 0, " SCONV2D_BP OP: rank of weightsPoint array must be equal to 4, but got %i instead !", weightsPoint->rankOf());
REQUIRE_TRUE(gradWP->rankOf() == 4, 0, " SCONV2D_BP OP: rank of weightsPoint gradients array must be equal to 4, but got %i instead !", gradWP->rankOf());
}
if(bias) {
REQUIRE_TRUE(bias->rankOf() == 1 || bias->rankOf() == 2, 0, " SCONV2D_BP OP: rank of biases array must be equal to 1 or 2, but got %i instead !", bias->rankOf());
REQUIRE_TRUE(gradB->rankOf() == 1 || gradB->rankOf() == 2, 0, " SCONV2D_BP OP: rank of biases gradientsarray must be equal to 1 or 2, but got %i instead !", gradB->rankOf());
}
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
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weightsDepth->sizeAt(indWmC); // channels multiplier
std::vector<Nd4jLong> expectedWeightsDShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(weightsDepth->isSameShape(expectedWeightsDShape), 0, " SCONV2D_BP OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str());
REQUIRE_TRUE(gradWD->isSameShape(expectedWeightsDShape), 0, " SCONV2D_BP OP: wrong shape of gradWD array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(gradWD).c_str());
if(weightsPoint) {
std::vector<Nd4jLong> expectedWeightsPShape = ConvolutionUtils::expectWeightsShape(wFormat, 1, 1, iC*mC, oC);
REQUIRE_TRUE(weightsPoint->isSameShape(expectedWeightsPShape), 0, " SCONV2D_BP OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str());
REQUIRE_TRUE(gradWP->isSameShape(expectedWeightsPShape), 0, " SCONV2D_BP OP: wrong shape of gradWP array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(gradWP).c_str());
}
if (bias) {
REQUIRE_TRUE(oC == bias->lengthOf(), 0, " SCONV2D_BP OP: length of bias array must be equal to outChannels, but got %i instead", bias->lengthOf());
REQUIRE_TRUE(oC == gradB->lengthOf(), 0, " SCONV2D_BP OP: length of biases gradients array must be equal to outChannels, but got %i instead", gradB->lengthOf());
}
// if (iC == 1) {
// nd4j_debug(" SCONV2D_BP OP: for input_channels=1 this op is equivalent to standard conv2d_bp \n","");
// sd::ops::conv2d_bp op;
// return op.execute(&block);
// }
// ----- if weightsPoint is present, perform pointwise backprop first and calculate gradWP at this step ----- //
if (weightsPoint){
auto resultFFShape = isNCHW ? std::vector<Nd4jLong>({bS, mC*iC, oH, oW}) : std::vector<Nd4jLong>({bS, oH, oW, mC*iC});
auto resultFF = NDArrayFactory::create_(input->ordering(), resultFFShape, input->dataType(), block.launchContext());
ConvolutionUtils::sconv2d(block, input, weightsDepth, nullptr, nullptr, resultFF, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW, wFormat);
auto gradIDepthShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC*mC,oH,oW, 0,indIOioC,indIiH,indIiH+1});
auto gradIDepth = NDArrayFactory::create_(resultFF->ordering(), gradIDepthShape, resultFF->dataType(), block.launchContext()); // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
ConvolutionUtils::conv2dBP(block, resultFF, weightsPoint, bias, gradO, gradIDepth, gradWP, gradB, 1,1, 1,1, 0,0, 1,1, isSameMode, isNCHW, wFormat); // in this case oH=iH and oW=iW
gradO = gradIDepth;
bias = gradB = nullptr; // if pointwise backprop was done then don't calculate gradB at depthwise_conv2d_bp step
delete resultFF;
}
// ----- apply depthwise_conv2d_bp ----- //
ConvolutionUtils::depthwiseConv2dBP(block, input, weightsDepth, bias, gradO, gradI, gradWD, gradB, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW, wFormat);
if(weightsPoint)
delete gradO;
return Status::OK();
}
DECLARE_SHAPE_FN(sconv2d_bp) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto gradOShapeInfo = inputShape->at(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto weightsDShapeInfo = inputShape->at(2); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
Nd4jLong* weightsPShapeInfo = nullptr; // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
Nd4jLong* biasShapeInfo = nullptr; // [oC], oC = iC*mC if weightsPoint=nullptr
if(block.width() == 4) {
if(inputShape->at(3)[0] == 4)
weightsPShapeInfo = inputShape->at(3);
else
biasShapeInfo = inputShape->at(3);
}
else if(block.width() == 5) {
weightsPShapeInfo = inputShape->at(3);
biasShapeInfo = inputShape->at(4);
}
const int rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, " SCONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, " SCONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
REQUIRE_TRUE(weightsDShapeInfo[0] == rank, 0, " SCONV2D_BP OP: rank of weightsDepth array must be equal to %i, but got %i instead !", rank, weightsDShapeInfo[0]);
if(weightsPShapeInfo)
REQUIRE_TRUE(weightsPShapeInfo[0] == rank, 0, " SCONV2D_BP OP: rank of weightsPoint array must be equal to %i, but got %i instead !", rank, weightsPShapeInfo[0]);
if(biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] ==1 || biasShapeInfo[0] == 2, 0, " SCONV2D_BP OP: rank of biases array must be 1 or 2, but got %i instead !", biasShapeInfo[0]);;
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
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
int indIOioC, indIiH, indWmC(0 == wFormat ? 3 : 0);
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 mC = weightsDShapeInfo[indWmC+1]; // channel multiplier
const int oC = weightsPShapeInfo ? weightsPShapeInfo[indWmC+1] : iC*mC; // output channels (oC or iC*mC)
int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::vector<Nd4jLong> expectedGradOShapeInfo = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indIiH,indIiH+1});
REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShapeInfo), 0, "SCONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShapeInfo).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
std::vector<Nd4jLong> expectedWeightsDShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsDShapeInfo, expectedWeightsDShape), 0, "SCONV2D_BP OP: wrong shape of depth weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str());
if(weightsPShapeInfo) {
std::vector<Nd4jLong> expectedWeightsPShape = ConvolutionUtils::expectWeightsShape(wFormat, 1, 1, iC*mC, oC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsPShapeInfo, expectedWeightsPShape), 0, "SCONV2D_BP OP: wrong shape of point array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str());
}
if (biasShapeInfo)
REQUIRE_TRUE((biasShapeInfo[0] == 1 || biasShapeInfo[0] == 2) && oC == shape::length(biasShapeInfo), 0, "SCONV2D_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 gradWDshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsDShapeInfo, gradOShapeInfo, false, block.getWorkspace());
Nd4jLong* gradWPshapeInfo(nullptr), *gradBshapeInfo(nullptr);
if(weightsPShapeInfo && biasShapeInfo) {
gradWPshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsPShapeInfo, gradOShapeInfo, false, block.getWorkspace());
gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWDshapeInfo), CONSTANT(gradWPshapeInfo), CONSTANT(gradBshapeInfo));
}
if(weightsPShapeInfo && !biasShapeInfo) {
gradWPshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsPShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWDshapeInfo), CONSTANT(gradWPshapeInfo));
}
if(!weightsPShapeInfo && biasShapeInfo) {
gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWDshapeInfo), CONSTANT(gradBshapeInfo));
}
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWDshapeInfo));
}
}
}
#endif
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