221 lines
10 KiB
C++
221 lines
10 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com, created on 29/10/17.
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// @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_avgpool2d)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(avgpool2d, 1, 1, false, 0, 10) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_NULLIFIED(0);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
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const auto kH = INT_ARG(0);
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const auto kW = INT_ARG(1);
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const auto sH = INT_ARG(2);
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const auto sW = INT_ARG(3);
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auto pH = INT_ARG(4);
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auto pW = INT_ARG(5);
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const auto dH = INT_ARG(6);
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const auto dW = INT_ARG(7);
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const auto isSameMode = static_cast<bool>(INT_ARG(8));
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const auto extraParam0 = INT_ARG(9);
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const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D op: input should have rank of 4, but got %i instead", input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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int oH = 0;
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int oW = 0;
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const int iH = static_cast<int>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
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const int iW = static_cast<int>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
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if(!isNCHW) {
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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output = new NDArray(output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
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}
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ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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if (isSameMode)
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
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ConvolutionUtils::pooling2d(block, *input, *output, kH, kW, sH, sW, pH, pW, dH, dW, PoolingType::AVG_POOL, extraParam0);
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if(!isNCHW) {
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delete input;
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delete output;
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}
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return Status::OK();
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}
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DECLARE_SYN(AvgPool2D, avgpool2d);
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DECLARE_SYN(AvgPool, avgpool2d);
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DECLARE_SYN(avgpool, avgpool2d);
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DECLARE_TYPES(avgpool2d) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(avgpool2d) {
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auto inShape = inputShape->at(0);
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auto shapeOf = shape::shapeOf(inShape);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
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auto argI = *(block.getIArguments());
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const int kH = INT_ARG(0);
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const int kW = INT_ARG(1);
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const int sH = INT_ARG(2);
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const int sW = INT_ARG(3);
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const int pH = INT_ARG(4);
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const int pW = INT_ARG(5);
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const int dH = INT_ARG(6);
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const int dW = INT_ARG(7);
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const int isSameMode = INT_ARG(8);
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const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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const int bS = shapeOf[0];
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const int iD = isNCHW ? shapeOf[1] : shapeOf[3];
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const int iH = isNCHW ? shapeOf[2] : shapeOf[1];
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const int iW = isNCHW ? shapeOf[3] : shapeOf[2];
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const char order = shape::order(inShape); // output order must be equal to input order
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// calculate output Height/Width
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int oH, oW;
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ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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// allocate memory for new shape
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Nd4jLong newShape[4];
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if (isNCHW) {
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newShape[0] = bS;
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newShape[1] = iD;
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newShape[2] = oH;
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newShape[3] = oW;
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} else {
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newShape[0] = bS;
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newShape[1] = oH;
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newShape[2] = oW;
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newShape[3] = iD;
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}
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), shape::order(inShape), newShape, 4)));
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}
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DECLARE_TYPES(avgpool2d_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(avgpool2d_bp, 2, 1, false, 0, 10) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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int kH = INT_ARG(0); // filter(kernel) height
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int kW = INT_ARG(1); // filter(kernel) width
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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int extraParam0 = INT_ARG(9);
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int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1});
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std::vector<Nd4jLong> expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1});
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str());
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if(!isNCHW) {
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradI = new NDArray(gradI->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradO = new NDArray(gradO->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
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}
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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// NDArray<T> columnsWrongShape(input->ordering(), {bS, iC, oH, oW, kH, kW}, input->getWorkspace());
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// NDArray<T>* columns = columnsWrongShape.permute({0, 1, 4, 5, 2, 3}); // [bS, iC, oH, oW, kH, kW] -> [bS, iC, kH, kW, oH, oW]
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// NDArray<T>* gradOVector = gradO->reshape('c', {(int) gradO->lengthOf(), 1});
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// NDArray<T>* columns2d = columnsWrongShape.reshape('c', {bS*iC*oH*oW, kH*kW});
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// columns2d->addiColumnVector(gradOVector);
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// 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());
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// *gradI /= kH*kW;
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
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ConvolutionUtils::pooling2dBP(block, *input, *gradO, *gradI, kH, kW, sH, sW, pH, pW, dH, dW, 1, extraParam0);
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if(!isNCHW) {
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delete input;
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delete gradI;
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delete gradO;
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}
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return Status::OK();
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}
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DECLARE_SHAPE_FN(avgpool2d_bp) {
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REQUIRE_TRUE(inputShape->at(0)[0] == 4, 0, "AVGPOOL2D_BP op: input array must be 4D, but got %i instead!", inputShape->at(0)[0]);
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REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0, "AVGPOOL2D_BP op: output's gradient array (next epsilon) must be 4D, but got %i instead!", inputShape->at(1)[0]);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1)))));
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}
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}
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}
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#endif
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