chore: import upstream snapshot with attribution
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wehub-resource-sync
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/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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
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// mode;
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const LongType kH = INT_ARG(0);
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const LongType kW = INT_ARG(1);
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const LongType sH = INT_ARG(2);
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const LongType sW = INT_ARG(3);
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LongType pH = INT_ARG(4);
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LongType pW = INT_ARG(5);
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const LongType dH = INT_ARG(6);
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const LongType 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",
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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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LongType oH = 0;
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LongType oW = 0;
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const LongType iH = static_cast<LongType>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
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const LongType iW = static_cast<LongType>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
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if (!isNCHW) {
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std::vector<sd::LongType> perm = {0,3,1,2};
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input = input->permute(perm, false, false); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] - permute() already returns NDArray*
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output = output->permute(perm, false, false); // [bS, oH, oW, iC] -> [bS, iC, oH, oW] - permute() already returns NDArray*
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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) 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 -
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// poolingMode; 9 - divisor;
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ConvolutionUtils::pooling2d(block, *input, *output, kH, kW, sH, sW, pH, pW, dH, dW, AVG_POOL,
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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()->setAllowedInputTypes(ANY)->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
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// mode;
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const LongType kH = INT_ARG(0);
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const LongType kW = INT_ARG(1);
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const LongType sH = INT_ARG(2);
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const LongType sW = INT_ARG(3);
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const LongType pH = INT_ARG(4);
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const LongType pW = INT_ARG(5);
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const LongType dH = INT_ARG(6);
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const LongType 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 LongType bS = shapeOf[0];
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const LongType iD = isNCHW ? shapeOf[1] : shapeOf[3];
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const LongType iH = isNCHW ? shapeOf[2] : shapeOf[1];
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const LongType 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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LongType 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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LongType *newShape = new LongType[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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auto ret = SHAPELIST(ConstantShapeHelper::getInstance().bufferForShapeInfo(ArrayOptions::dataType(inShape),
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shape::order(inShape),
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4,
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newShape)->primary());
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delete[] newShape;
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return ret;
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}
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DECLARE_TYPES(avgpool2d_bp) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->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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LongType kH = INT_ARG(0); // filter(kernel) height
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LongType kW = INT_ARG(1); // filter(kernel) width
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType 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",
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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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LongType bS, iC, iH, iW, oC, oH,
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oW; // batch size, input channels, input height/width, output channels, output height/width;
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LongType 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,
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indWiC, indWoC, indWkH, indOoH);
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std::vector<LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1});
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std::vector<LongType> expectedGradIShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, iH, iW, 0, indIOioC, indIiH, indIiH + 1});
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REQUIRE_TRUE(
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gradO->isSameShape(expectedGradOShape), 0,
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"AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(
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gradI->isSameShape(expectedGradIShape), 0,
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"AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str());
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if (!isNCHW) {
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std::vector<sd::LongType> perm = {0,3,1,2};
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input = input->permute(perm, false, false); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] - permute() already returns NDArray*
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gradI = gradI->permute(perm, false, false); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] - permute() already returns NDArray*
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gradO = gradO->permute(perm, false, false); // [bS, oH, oW, iC] -> [bS, iC, oH, oW] - permute() already returns NDArray*
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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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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 -
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// 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!",
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inputShape->at(0)[0]);
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REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0,
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"AVGPOOL2D_BP op: output's gradient array (next epsilon) must be 4D, but got %i instead!",
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inputShape->at(1)[0]);
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auto desc = new ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1)), false);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(desc));
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}
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} // namespace ops
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} // namespace sd
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#endif
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