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

/* ******************************************************************************
 *
 *
 * 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.
 *
 *  See the NOTICE file distributed with this work for additional
 *  information regarding copyright ownership.
 * 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 const* weightsPShapeInfo = nullptr;              // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
    Nd4jLong const* 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 const* weightsPShapeInfo = nullptr;                      // [1, 1, iC*mC, oC], [oC, iC*mC, 1, 1], [oC, 1, 1, iC*mC]
    Nd4jLong const* 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