cavis/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.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 raver119@gmail.com
// @author Yurii Shyrma
//

#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_deconv2d)

#include <ops/declarable/CustomOperations.h>
#include <MmulHelper.h>
#include <declarable/helpers/convolutions.h>
#include <ops/declarable/helpers/im2col.h>
#include <ops/declarable/helpers/col2im.h>
#include <ops/declarable/helpers/addBias.h>

namespace nd4j {
namespace ops  {

CUSTOM_OP_IMPL(deconv2d, 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, oC, iC] 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)

    REQUIRE_TRUE(input->rankOf()   == 4, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
    REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());

    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 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 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, indWoC, indWiC, indWkH, indOoH);

    std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
    REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D 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 DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());

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

    if(isSameMode)          // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
        ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);

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

    //----- calculation of output -----//
    // NHWC: [kH, kW, oC, iC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW]
    // NCHW: [kH, kW, oC, iC] x [bS, iC, iH, iW] = [kH, kW, oC, bS, iH, iW]
    nd4j::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 1, 0, 4, 5});
    LaunchContext* ctx = block.launchContext();
    helpers::col2im(*ctx, columns, *output, sH, sW, pH, pW, oH, oW, dH, dW);     // [bS, oC, kH, kW, iH, iW] is de-convoluted to [bS, oC, oH, oW]

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

     if(!isNCHW)
        delete output;

    return Status::OK();
}
    DECLARE_TYPES(deconv2d) {
        getOpDescriptor()
                ->setAllowedInputTypes(nd4j::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

DECLARE_SHAPE_FN(deconv2d) {

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

    const int rank = 4;
    REQUIRE_TRUE(shape::rank(inputShapeInfo)   == rank, 0, "CUSTOM DECONV2D 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 DECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, shape::rank(weightsShapeInfo));

    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
    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 indIOioC, indIiH, indWoC(2);
    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::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
    REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV2D 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 DECONV2D 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::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

    Nd4jLong outputShape[4];

    outputShape[0] = bS;
    if (isNCHW) {
        outputShape[1] = oC;
        outputShape[2] = oH;
        outputShape[3] = oW;
    } else {
        outputShape[1] = oH;
        outputShape[2] = oW;
        outputShape[3] = oC;
    }

    return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(weightsShapeInfo), shape::order(inputShapeInfo), outputShape, 4)));
}

    DECLARE_TYPES(deconv2d_bp) {
        getOpDescriptor()
                ->setAllowedInputTypes(nd4j::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(deconv2d_bp, 3, 2, false, 0, 9) {

    auto input   = INPUT_VARIABLE(0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(1);                                                // [kH, kW, oC, iC] 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] (NCDHW), epsilon_next

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

    REQUIRE_TRUE(input->rankOf()   == 4, 0, "CUSTOM DECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
    REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of weights array must be equal to 4 , but got %i instead !", weights->rankOf());
    REQUIRE_TRUE(gradO->rankOf()   == 4, 0, "CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());


    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 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 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, indWoC, indWiC, indWkH, indOoH);

    int trueoH, trueoW;          // true output height, width
    ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

    std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW,  0,indIOioC,indOoH,indOoH+1});
    std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
    REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,  "CUSTOM DECONV2D_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 DECONV2D_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 DECONV2D_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){                       // SAME
        //Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
        ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
    }


     // ----- calculation of gradI -> pass it through conv2d_ff ----- //
    nd4j::ops::conv2d conv2d;
    const Nd4jStatus status = conv2d.execute({gradO, weights}, {gradI}, {}, {kH,kW,  sH,sW,  pH,pW,  dH,dW,  isSameMode,  !isNCHW}, {});
    if (status != ND4J_STATUS_OK)
        return status;

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

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

    // ----- calculation of gradW ----- //
    NDArray columns(input->ordering(), {bS, oC, kH, kW, iH, iW}, input->dataType(), block.launchContext());

    LaunchContext* ctx = block.launchContext();
    helpers::im2col(*ctx, *gradO, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext()));  // [bS, oC, oH, oW] is convoluted to [bS, oC, kH, kW, iH, iW]
    MmulHelper::tensorDot(input, &columns, gradW, inputAxesForDot, {0, 4, 5}, {3, 2, 0, 1});     // [bS, iC, iH, iW]/[bS, iH, iW, iC] x [bS, oC, kH, kW, iH, iW] = [iC, oC, kH, kW]

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

    if(!isNCHW)
        delete gradO;

    return Status::OK();
}

DECLARE_SHAPE_FN(deconv2d_bp) {

    auto inputShapeInfo   = inputShape->at(0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
    auto weightsShapeInfo = inputShape->at(1);                                                // [kH, kW, oC, iC] always
    Nd4jLong* biasShapeInfo    = block.width() > 3 ? inputShape->at(2) : nullptr;             // [oC]
    Nd4jLong* gradOShapeInfo   = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);   // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next

    const int rank = 4;
    REQUIRE_TRUE(shape::rank(inputShapeInfo)   == rank, 0, "CUSTOM DECONV2D_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 DECONV2D_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 DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, shape::rank(gradOShapeInfo));

    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
    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 indIOioC, indIiH, indWoC(2), indOoH;
    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::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

    std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW,  0,indIOioC,indOoH,indOoH+1});
    std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
    REQUIRE_TRUE(shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0,  "CUSTOM DECONV2D_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(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV2D_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 DECONV2D_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