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cavis/libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp

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/*******************************************************************************
* 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)
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_depthwise_conv2d)
#include <system/op_boilerplate.h>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(depthwise_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, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] = iC*mC
auto output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D 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 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(oC = iC*mC), 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 = weights->sizeAt(indWmC); // channels multiplier
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
REQUIRE_TRUE(output->sizeAt(indIOioC) == iC*mC, 0, "CUSTOM DEPTHWISECONV2D OP: the output_channels must be equal to input_channels * channels_multiplier = %i !", iC*mC);
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::depthwiseConv2d(block, input, weights, bias, output, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW,wFormat);
return Status::OK();
}
DECLARE_TYPES(depthwise_conv2d) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(depthwise_conv2d) {
Nd4jLong* inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
Nd4jLong* weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
Nd4jLong* biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] = iC*mC
const int rank = 4;
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D 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 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 = shape::sizeAt(inputShapeInfo, 0); // batch size
const int iH = shape::sizeAt(inputShapeInfo, indIiH); // input height
const int iW = shape::sizeAt(inputShapeInfo, indIiH+1); // input width
const int iC = shape::sizeAt(inputShapeInfo, indIOioC); // input channels
const int mC = shape::sizeAt(weightsShapeInfo, indWmC); // channels multiplier(oC = iC*mC)
const int oC = iC*mC; // output channels
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "DEPTHWISECONV2D 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, "DEPTHWISECONV2D 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 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] = 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(depthwise_conv2d_bp) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(depthwise_conv2d_bp, 3, 2, false, 0, 9) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
auto gradW = OUTPUT_NULLIFIED(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_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 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(oC = iC*mC), 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 = weights->sizeAt(indWmC); // channels multiplier
int trueoH, trueoW; // correct output height, width
ConvolutionUtils::calcOutSizePool2D(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 = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DEPTHWISECONV2D_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 DEPTHWISECONV2D_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 DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::depthwiseConv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW, wFormat);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(depthwise_conv2d_bp) {
Nd4jLong* inputShapeInfo = inputShape->at(0);
Nd4jLong* weightsShapeInfo = inputShape->at(1);
Nd4jLong* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr;
Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);
const int rank = 4;
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D_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 DEPTHWISECONV2D_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 DEPTHWISECONV2D_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 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 = shape::sizeAt(inputShapeInfo, 0); // batch size
const int iH = shape::sizeAt(inputShapeInfo, indIiH); // input height
const int iW = shape::sizeAt(inputShapeInfo, indIiH+1); // input width
const int iC = shape::sizeAt(inputShapeInfo, indIOioC); // input channels
const int mC = shape::sizeAt(weightsShapeInfo, indWmC); // channels multiplier(oC = iC*mC)
const int oC = iC*mC; // output channels
int trueoH, trueoW; // correct output height, width
ConvolutionUtils::calcOutSizePool2D(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,indIiH,indIiH+1});
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
REQUIRE_TRUE(shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0, "CUSTOM DEPTHWISECONV2D_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 DEPTHWISECONV2D_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(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));
auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
if(biasShapeInfo) {
Nd4jLong* gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
}
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}
}
}
#endif
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