cavis/libnd4j/include/ops/declarable/generic/convo/conv2d.cpp

/*******************************************************************************
 * 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