/*******************************************************************************
* 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>
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::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DECONV2D 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 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) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, 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: [iC, oC, kH, kW] x [bS, iC, iH, iW] = [oC, kH, kW, 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);
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(inputShapeInfo[0] == rank, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM DECONV2D 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 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::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM DECONV2D_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 DECONV2D_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 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
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, 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(inputShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_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 DECONV2D_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 DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
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::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM DECONV2D_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 DECONV2D_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 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