/*******************************************************************************
 * 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 (iuriish@yahoo.com), created on 05.09.2018
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_deconv3d)

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>
#include <ops/declarable/helpers/addBias.h>
#include <helpers/MmulHelper.h>

namespace sd {
namespace ops  {

CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) {

    auto input   = INPUT_VARIABLE(0);                                    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(1);                                    // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
    auto bias    = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]

    auto output  = OUTPUT_VARIABLE(0);                                   // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)

    REQUIRE_TRUE(input->rankOf()   == 5, 0, "CUSTOM DECONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
    REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());

    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));    // filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));    // filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));    // filter(kernel) width
    int sD = INT_ARG(3);                                                            // strides depth
    int sH = INT_ARG(4);                                                            // strides height
    int sW = INT_ARG(5);                                                            // strides width
    int pD = INT_ARG(6);                                                            // paddings depth
    int pH = INT_ARG(7);                                                            // paddings height
    int pW = INT_ARG(8);                                                            // paddings width
    int dD = INT_ARG(9);                                                            // dilations depth
    int dH = INT_ARG(10);                                                           // dilations height
    int dW = INT_ARG(11);                                                           // dilations width
    int isSameMode = INT_ARG(12);                                                   // 0-SAME,  1-VALID
    int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;            // INT_ARG(13): 1-NDHWC, 0-NCDHW
    int wFormat = block.getIArguments()->size() > 14 ? INT_ARG(14) : 0;             // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]

    int bS, iC, iD, iH, iW, oC, oD, oH, oW;                     // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
    int indIOioC, indIOioD, indWoC, indWiC, indWkD;             // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);

    std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
    REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
    if (bias)
        REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());

    if(!isNCDHW)
        output = new NDArray(output->permute({0, 4, 1, 2, 3}));                 // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]

    std::vector<int> colPermut;
    if(1 == wFormat)
        colPermut = {1,2,3,4,0,5,6,7};
    else
        colPermut = {2,3,4,1,0,5,6,7};

    if(isSameMode)         // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
        ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);

    NDArray columns(input->ordering(), {bS, oC, kD, kH, kW, iD, iH, iW}, input->dataType(),  block.launchContext());

    //----- calculation of output -----//
    // [kD, kH, kW, oC, iC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW]
    // [iC, oC, kD, kH, kW] x [bS, iD, iH, iW, iC] = [oC, kD, kH, kW, bS, iD, iH, iW]
    // [iC, kD, kH, kW, oC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW]
    sd::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, colPermut);       // [bS, oC, kD, kH, kW, iD, iH, iW] -> [kD, kH, kW, oC, bS, iD, iH, iW]
    ConvolutionUtils::col2vol(block, columns, *output, sD, sH, sW, pD, pH, pW, dD, dH, dW);     // [bS, oC, kD, kH, kW, iD, iH, iW] is de-convoluted to [bS, oC, oD, oH, oW]

    //----- add biases if required -----//
    if(bias)
        // output->applyBroadcast(broadcast::Add,{1}, bias);
        helpers::addBias(block, *output, *bias, *output, true);

    if(!isNCDHW)
        delete output;

    return Status::OK();

}

   DECLARE_TYPES(deconv3d) {
        getOpDescriptor()
                ->setAllowedInputTypes(0, sd::DataType::ANY)
                ->setAllowedInputTypes(1, {ALL_FLOATS})
                ->setAllowedInputTypes(2, {ALL_FLOATS})
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

DECLARE_SHAPE_FN(deconv3d) {

    auto inputShapeInfo   = inputShape->at(0);                                    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NDCHW)
    auto weightsShapeInfo = inputShape->at(1);                                    // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
    auto biasShapeInfo    = block.width() > 2 ? inputShape->at(2) : nullptr;      // [oC]

    const int rank = 5;
    REQUIRE_TRUE(shape::rank(inputShapeInfo)   == rank, 0, "CUSTOM DECONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, shape::rank(inputShapeInfo));
    REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DECONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, shape::rank(weightsShapeInfo));

    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
    int sD = INT_ARG(3);                                                        // strides depth
    int sH = INT_ARG(4);                                                        // strides height
    int sW = INT_ARG(5);                                                        // strides width
    int pD = INT_ARG(6);                                                        // paddings depth
    int pH = INT_ARG(7);                                                        // paddings height
    int pW = INT_ARG(8);                                                        // paddings width
    int dD = INT_ARG(9);                                                        // dilations depth
    int dH = INT_ARG(10);                                                       // dilations height
    int dW = INT_ARG(11);                                                       // dilations width
    int isSameMode = INT_ARG(12);                                               // 0-SAME,  1-VALID
    int isNCDHW  = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;       // INT_ARG(13): 1-NDHWC, 0-NCDHW
    int wFormat = block.getIArguments()->size() > 14 ? INT_ARG(14) : 0;         // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]

    int indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4));
    if(!isNCDHW) {
        indIOioC = 4; indIiD = 1;
    }
    else {
        indIOioC = 1; indIiD = 2;
    }

    const int bS = inputShapeInfo[1];                           // batch size
    const int iD = inputShapeInfo[indIiD+1];                    // input depth
    const int iH = inputShapeInfo[indIiD+2];                    // input height
    const int iW = inputShapeInfo[indIiD+3];                    // input width
    const int iC = inputShapeInfo[indIOioC+1];                  // input channels
    const int oC = weightsShapeInfo[indWoC+1];                  // output channels

    std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
    REQUIRE_TRUE(shape::shapeEquals(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
    if (biasShapeInfo)
        REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));

    int oD, oH, oW;                                         // output depth, height, width
    ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);

    Nd4jLong* outputShapeInfo = nullptr;
    ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);

    outputShapeInfo[0] = rank;
    outputShapeInfo[1] = bS;

    if (isNCDHW) {
        outputShapeInfo[2] = oC;
        outputShapeInfo[3] = oD;
        outputShapeInfo[4] = oH;
        outputShapeInfo[5] = oW;
    } else {
        outputShapeInfo[2] = oD;
        outputShapeInfo[3] = oH;
        outputShapeInfo[4] = oW;
        outputShapeInfo[5] = oC;
    }

    ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));

    return SHAPELIST(CONSTANT(outputShapeInfo));
}


//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(deconv3d_bp, 3, 2, false, 0, 13) {

    auto input   = INPUT_VARIABLE(0);                                                // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(1);                                                // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
    auto bias    = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
    auto gradO   = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next

    auto gradI = OUTPUT_VARIABLE(0);                                                 // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
    auto gradW = OUTPUT_VARIABLE(1);                                                 // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
    auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]

    REQUIRE_TRUE(input->rankOf()   == 5, 0, "CUSTOM DECONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
    REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D_BP OP: rank of weights array must be equal to 5 , but got %i instead !", weights->rankOf());
    REQUIRE_TRUE(gradO->rankOf()   == 5, 0, "CUSTOM DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !", gradO->rankOf());


    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
    int sD = INT_ARG(3);                                                        // strides depth
    int sH = INT_ARG(4);                                                        // strides height
    int sW = INT_ARG(5);                                                        // strides width
    int pD = INT_ARG(6);                                                        // paddings depth
    int pH = INT_ARG(7);                                                        // paddings height
    int pW = INT_ARG(8);                                                        // paddings width
    int dD = INT_ARG(9);                                                        // dilations depth
    int dH = INT_ARG(10);                                                       // dilations height
    int dW = INT_ARG(11);                                                       // dilations width
    int isSameMode = INT_ARG(12);                                               // 0-SAME,  1-VALID
    int isNCDHW  = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;       // INT_ARG(13): 1-NDHWC, 0-NCDHW
    int wFormat = block.getIArguments()->size() > 14 ? INT_ARG(14) : 0;         // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]

    int bS, iC, iD, iH, iW, oC, oD, oH, oW;                     // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
    int indIOioC, indIOioD, indWoC, indWiC, indWkD;             // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);

    int trueoD, trueoH, trueoW;          // true output height, width
    ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);

    std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW,  0,indIOioC,indIOioD,indIOioD+1,indIOioD+2});
    std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
    REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,  "CUSTOM DECONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
    REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
    if(bias)
        REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());

    if(isSameMode)               // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
        ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);

     // ----- calculation of gradI -> pass it through conv3d_ff ----- //
    sd::ops::conv3dnew conv3d;
    const Nd4jStatus status = conv3d.execute({gradO, weights}, {gradI}, {}, {kD,kH,kW,  sD,sH,sW,  pD,pH,pW,  dD,dH,dW,  isSameMode,  !isNCDHW, wFormat}, {});
    if (status != ND4J_STATUS_OK)
        return status;

    // -----prepare permutation arrays and axes for dot product ----- //
    std::vector<int> inputAxesForDot;

    if(!isNCDHW) {
        gradO = new NDArray(gradO->permute({0, 4, 1, 2, 3}));                   // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
        inputAxesForDot = {0, 1, 2, 3};                                         // bS, iD, iH, iW
    }
    else
        inputAxesForDot = {0, 2, 3, 4};                                         // bS, iD, iH, iW

    std::vector<int> gradWAxes;     // empty for wFormat = 1
    if(0 == wFormat)
        gradWAxes = {4,3,0,1,2};
    else if(2 == wFormat)
        gradWAxes = {0,4,1,2,3};

    // ----- calculation of gradW ----- //
    auto columns = NDArrayFactory::create(input->ordering(), {bS, oC, kD, kH, kW, iD, iH, iW},  input->dataType(), block.launchContext());
    ConvolutionUtils::vol2col(block, *gradO, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW);     // [bS, oC, oD, oH, oW] is deconvoluted to [bS, oC, kD, kH, kW, iD, iH, iW]
    MmulHelper::tensorDot(input, &columns, gradW, inputAxesForDot, {0, 5, 6, 7}, gradWAxes);   // [bS, iC, iD, iH, iW]/[bS, iD, iH, iW, iC] x [bS, oC, kD, kH, kW, iD, iH, iW] = [iC, oC, kD, kH, kW]

    // ----- calculation of gradB ----- //
    if(gradB) {
        if(gradB->rankOf() == 2)
            gradB = new NDArray(gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}, false));
        gradO->reduceAlongDimension(reduce::Sum, *gradB, {0, 2, 3, 4});                                // sum over bS, oD, oH, oW
        if(gradB != OUTPUT_VARIABLE(2))
            delete gradB;
    }

    if(!isNCDHW)
        delete gradO;

    return Status::OK();
}

 DECLARE_TYPES(deconv3d_bp) {
        getOpDescriptor()
                ->setAllowedInputTypes(0, sd::DataType::ANY)
                ->setAllowedInputTypes(1, {ALL_FLOATS})
                ->setAllowedInputTypes(2, {ALL_FLOATS})
                ->setAllowedInputTypes(3, {ALL_FLOATS})
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

DECLARE_SHAPE_FN(deconv3d_bp) {

    auto inputShapeInfo   = inputShape->at(0);                                                // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weightsShapeInfo = inputShape->at(1);                                                // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
    auto biasShapeInfo    = block.width() > 3 ? inputShape->at(2) : nullptr;             // [oC]
    auto gradOShapeInfo   = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);   // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next

    const int rank = 5;
    REQUIRE_TRUE(shape::rank(inputShapeInfo)   == rank, 0, "CUSTOM DECONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, shape::rank(inputShapeInfo));
    REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DECONV3D_BP OP: rank of weights array must be equal to %i , but got %i instead !", rank, shape::rank(weightsShapeInfo));
    REQUIRE_TRUE(shape::rank(gradOShapeInfo)   == rank, 0, "CUSTOM DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, shape::rank(gradOShapeInfo));

    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
    int sD = INT_ARG(3);                                                        // strides depth
    int sH = INT_ARG(4);                                                        // strides height
    int sW = INT_ARG(5);                                                        // strides width
    int pD = INT_ARG(6);                                                        // paddings depth
    int pH = INT_ARG(7);                                                        // paddings height
    int pW = INT_ARG(8);                                                        // paddings width
    int dD = INT_ARG(9);                                                        // dilations depth
    int dH = INT_ARG(10);                                                       // dilations height
    int dW = INT_ARG(11);                                                       // dilations width
    int isSameMode = INT_ARG(12);                                               // 0-SAME,  1-VALID
    int isNCDHW  = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;       // INT_ARG(13): 1-NDHWC, 0-NCDHW
    int wFormat = block.getIArguments()->size() > 14 ? INT_ARG(14) : 0;         // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]

    int indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4));
    if(!isNCDHW) {
        indIOioC = 4; indIiD = 1;
    }
    else {
        indIOioC = 1; indIiD = 2;
    }

    const int bS = inputShapeInfo[1];                           // batch size
    const int iD = inputShapeInfo[indIiD+1];                    // input depth
    const int iH = inputShapeInfo[indIiD+2];                    // input height
    const int iW = inputShapeInfo[indIiD+3];                    // input width
    const int iC = inputShapeInfo[indIOioC+1];                  // input channels
    const int oC = weightsShapeInfo[indWoC+1];                  // output channels

    int trueoD, trueoH, trueoW;          // true output depth, height, width
    ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);

    std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW,  0,indIOioC,indIiD,indIiD+1,indIiD+2});
    std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
    REQUIRE_TRUE(shape::shapeEquals(5, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0,  "CUSTOM DECONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
    REQUIRE_TRUE(shape::shapeEquals(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
    if(biasShapeInfo)
        REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));

    auto gradIShapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo,   gradOShapeInfo, false, block.getWorkspace());
    auto gradWShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());

    auto shapes = SHAPELIST(CONSTANT(gradIShapeInfo), CONSTANT(gradWShapeInfo));

    if (biasShapeInfo != nullptr) {
        auto gradBShapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
        shapes->push_back(CONSTANT(gradBShapeInfo));
    }

    return shapes;
}



}
}

#endif