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cavis/libnd4j/include/ops/declarable/generic/nn/pooling/pnormpool2d.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 raver119@gmail.com, created on 29/10/17.
// @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_pnormpool2d)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(pnormpool2d, 1, 1, false, 0, 10) {
REQUIRE_OK(this->validateInputLengthMatch(block));
REQUIRE_OK(this->validateInputDimensionsMatch(block));
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
REQUIRE_TRUE(input->rankOf() == 4, 0, "PNORMPOOL2D op: input should have rank of 4, but got %i instead", input->rankOf());
auto kY = INT_ARG(0);
auto kX = INT_ARG(1);
auto sY = INT_ARG(2);
auto sX = INT_ARG(3);
auto pY = INT_ARG(4);
auto pX = INT_ARG(5);
auto dY = INT_ARG(6);
auto dX = INT_ARG(7);
bool isSameMode = static_cast<bool>(INT_ARG(8));
auto extraParam0 = INT_ARG(9);
REQUIRE_TRUE(dY != 0 && dX != 0, 0, "PNORMPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dY, dX);
int oY = 0;
int oX = 0;
int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW
if(!isNCHW) {
input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
output = new NDArray(output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
}
const auto inY = static_cast<int>(input->sizeAt(2));
const auto inX = static_cast<int>(input->sizeAt(3));
ConvolutionUtils::calcOutSizePool2D(oY, oX, kY, kX, sY, sX, pY, pX, dY, dX, inY, inX, isSameMode);
if (isSameMode)
ConvolutionUtils::calcPadding2D(pY, pX, oY, oX, inY, inX, kY, kX, sY, sX, dY, dX);
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
ConvolutionUtils::pooling2d(block, *input, *output, kY, kX, sY, sX, pY, pX, dY, dX, PoolingType::PNORM_POOL, extraParam0);
if(!isNCHW) {
delete input;
delete output;
}
return Status::OK();
}
DECLARE_SYN(PnormPool2D, pnormpool2d);
DECLARE_SYN(PnormPool, pnormpool2d);
DECLARE_SYN(pnormpool, pnormpool2d);
DECLARE_TYPES(pnormpool2d) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(pnormpool2d) {
auto inShape = inputShape->at(0);
auto shapeOf = shape::shapeOf(inShape);
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
std::vector<int> argI = *(block.getIArguments());
int kH = INT_ARG(0);
int kW = INT_ARG(1);
int sH = INT_ARG(2);
int sW = INT_ARG(3);
int pH = INT_ARG(4);
int pW = INT_ARG(5);
int dH = INT_ARG(6);
int dW = INT_ARG(7);
int isSameMode = INT_ARG(8);
int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "PNORMPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
int bS = shapeOf[0];
int iC = isNCHW ? shapeOf[1] : shapeOf[3];
int iH = isNCHW ? shapeOf[2] : shapeOf[1];
int iW = isNCHW ? shapeOf[3] : shapeOf[2];
char order = shape::order(inShape); // output order must be equal to input order
// calculate output Height/Width
int oH, oW;
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
// allocate memory for new shape
Nd4jLong newShape[4];
newShape[0] = bS;
if (isNCHW) {
newShape[1] = iC;
newShape[2] = oH;
newShape[3] = oW;
} else {
newShape[1] = oH;
newShape[2] = oW;
newShape[3] = iC;
}
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), order, newShape, 4)));
}
DECLARE_TYPES(pnormpool2d_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(pnormpool2d_bp, 2, 1, false, 1, 10) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto gradO = INPUT_VARIABLE(1); // [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 pnorm = INT_ARG(9);
int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW
// FIXME: double?
double eps = T_ARG(0);
REQUIRE_TRUE(input->rankOf() == 4, 0, "PNORMPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf());
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "PNORMPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1}));
std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1}));
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "PNORMPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "PNORMPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str());
if(!isNCHW) {
input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradI = new NDArray(gradI->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradO = new NDArray(gradO->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
}
// if(isSameMode) // SAME
// ConvolutionUtils<T>::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
// NDArray<T> columnsWrongShape(input->ordering(), {bS, iC, oH, oW, kH, kW}, input->getWorkspace());
// NDArray<T>* columns = columnsWrongShape.permute({0, 1, 4, 5, 2, 3}); // [bS, iC, oH, oW, kH, kW] -> [bS, iC, kH, kW, oH, oW]
// NDArray<T>* gradOVector = gradO->reshape('c', {(int) gradO->lengthOf(), 1});
// NDArray<T>* columns2d = columnsWrongShape.reshape('c', {bS*iC*oH*oW, kH*kW});
// NDArray<T> pNorm(columns2d->getShapeInfo(), block.getWorkspace());
// input->template applyTransform<simdOps::Im2col<T>>(columns, std::vector<T>({(T)kH, (T)kW, (T)sH, (T)sW, (T)pH, (T)pW, (T)dH, (T)dW, (T)0.f, (T)0.f}).data());
// columns2d->template applyTransform<simdOps::Abs<T>>(&pNorm);
// pNorm.template applyTransform<simdOps::Pow<T>>(&pNorm, std::vector<T>({(T)pnorm}).data());
// NDArray<T>* denomVec = pNorm.sum({1});
// denomVec->template applyTransform<simdOps::Pow<T>>(std::vector<T>({(T)1. - (T)1. / pnorm}).data());
// denomVec->template applyScalar<simdOps::Max<T>>(eps); // in case of 0
// denomVec->template applyPairwiseTransform<simdOps::ReverseDivide<T>>(gradOVector, denomVec, nullptr);
// if(pnorm != 2) {
// T extraParams[] = {(T)1. - (T)2. / pnorm};
// pNorm.template applyTransform<simdOps::Pow<T>>(std::vector<T>({(T)1. - (T)2. / pnorm}).data());
// *columns2d *= pNorm;
// }
// columns2d->muliColumnVector(denomVec);
// columns->template applyTransform<simdOps::Col2Im<T>>(gradI, std::vector<T>({(T)sH, (T)sW, (T)pH, (T)pW, (T)iH, (T)iW, (T)dH, (T)dW}).data());
ConvolutionUtils::pooling2dBP(block, *input, *gradO, *gradI, kH, kW, sH, sW, pH, pW, dH, dW, 2, pnorm);
if(!isNCHW) {
delete input;
delete gradI;
delete gradO;
}
return Status::OK();
}
DECLARE_SHAPE_FN(pnormpool2d_bp) {
REQUIRE_TRUE(inputShape->at(0)[0] == 4, 0, "PNORMPOOL2D_BP op: input array must be 4D, but got %i instead!", inputShape->at(0)[0]);
REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0, "PNORMPOOL2D_BP op: output's gradient array (next epsilon) must be 4D, but got %i instead!", inputShape->at(1)[0]);
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1)))));
}
}
}
#endif
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