wehub-resource-sync
242 lines
12 KiB
C++
242 lines
12 KiB
C++
/* ******************************************************************************
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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 Yurii Shyrma (iuriish@yahoo.com), created on 01.03.2018
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_avgpool3dnew)
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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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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(avgpool3dnew, 1, 1, false, 0, 14) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto output = OUTPUT_NULLIFIED(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW)
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LongType kD = INT_ARG(0); // filter(kernel) depth
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LongType kH = INT_ARG(1); // filter(kernel) height
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LongType kW = INT_ARG(2); // filter(kernel) width
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LongType sD = INT_ARG(3); // strides depth
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LongType sH = INT_ARG(4); // strides height
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LongType sW = INT_ARG(5); // strides width
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LongType pD = INT_ARG(6); // paddings depth
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LongType pH = INT_ARG(7); // paddings height
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LongType pW = INT_ARG(8); // paddings width
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LongType dD = INT_ARG(9); // dilations depth
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LongType dH = INT_ARG(10); // dilations height
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LongType dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int extraParam0 = INT_ARG(13);
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW OP: rank of input array must be equal to 5, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"AVGPOOL3DNEW OP: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC,
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indIOioD, indWiC, indWoC, indWkD);
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std::vector<LongType> expectedOutputShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
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REQUIRE_TRUE(output->isSameShape(expectedOutputShape), 0,
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"AVGPOOL3DNEW OP: wrong shape of output array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedOutputShape).c_str(), ShapeUtils::shapeAsString(output).c_str());
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if (!isNCDHW) {
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std::vector<sd::LongType> perm = {0, 4, 1, 2, 3};
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input = input->permute(perm, false, false); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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output =output->permute(perm, false, false); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
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}
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if (isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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// T extraParams[] = {};
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ConvolutionUtils::pooling3d(block, *input, *output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1, extraParam0);
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if (!isNCDHW) {
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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_TYPES(avgpool3dnew) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(avgpool3dnew) {
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LongType kD = INT_ARG(0); // filter(kernel) depth
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LongType kH = INT_ARG(1); // filter(kernel) height
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LongType kW = INT_ARG(2); // filter(kernel) width
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LongType sD = INT_ARG(3); // strides depth
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LongType sH = INT_ARG(4); // strides height
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LongType sW = INT_ARG(5); // strides width
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LongType pD = INT_ARG(6); // paddings depth
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LongType pH = INT_ARG(7); // paddings height
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LongType pW = INT_ARG(8); // paddings width
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LongType dD = INT_ARG(9); // dilations depth
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LongType dH = INT_ARG(10); // dilations height
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LongType dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"AVGPOOL3DNEW op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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auto inputShapeInfo = inputShape->at(0);
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LongType idxID, idxIC;
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if (isNCDHW) {
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idxID = 2;
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idxIC = 1;
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} else {
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idxID = 1;
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idxIC = 4;
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}
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LongType bS = inputShapeInfo[1]; // batch size
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LongType iC = inputShapeInfo[idxIC + 1]; // input channels
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LongType iD = inputShapeInfo[idxID + 1]; // input depth
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LongType iH = inputShapeInfo[idxID + 2]; // input height
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LongType iW = inputShapeInfo[idxID + 3]; // input width
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LongType oD, oH, oW; // output depth, height, width
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ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW,
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isSameMode);
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LongType outputShape[5];
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outputShape[0] = bS;
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if (isNCDHW) {
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outputShape[1] = iC;
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outputShape[2] = oD;
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outputShape[3] = oH;
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outputShape[4] = oW;
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} else {
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outputShape[1] = oD;
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outputShape[2] = oH;
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outputShape[3] = oW;
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outputShape[4] = iC;
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}
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// TF DOC: A Tensor. Has the same type as input.
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// TF DOC: A Tensor. Has the same type as input.
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auto ret = SHAPELIST(ConstantShapeHelper::getInstance().bufferForShapeInfo(ArrayOptions::dataType(inputShapeInfo),
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shape::order(inputShapeInfo),
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5,
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outputShape)->primary());
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return ret;
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}
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DECLARE_TYPES(avgpool3dnew_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(avgpool3dnew_bp, 2, 1, false, 0, 14) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_NULLIFIED(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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const LongType kD = INT_ARG(0); // filter(kernel) depth
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const LongType kH = INT_ARG(1); // filter(kernel) height
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const LongType kW = INT_ARG(2); // filter(kernel) width
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const LongType sD = INT_ARG(3); // strides depth
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const LongType sH = INT_ARG(4); // strides height
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const LongType sW = INT_ARG(5); // strides width
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LongType pD = INT_ARG(6); // paddings depth
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LongType pH = INT_ARG(7); // paddings height
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LongType pW = INT_ARG(8); // paddings width
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const LongType dD = INT_ARG(9); // dilations depth
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const LongType dH = INT_ARG(10); // dilations height
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const LongType dW = INT_ARG(11); // dilations width
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const int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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const int extraParam0 = INT_ARG(13); // define what divisor to use while averaging
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const int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW_BP op: input should have rank of 5, but got %i instead",
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input->rankOf());
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"AVGPOOL3DNEW_BP op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC,
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indIOioD, indWiC, indWoC, indWkD);
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std::vector<LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
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std::vector<LongType> expectedGradIShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, iD, iH, iW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
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"AVGPOOL3DNEW_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s "
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"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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"AVGPOOL3DNEW_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 (!isNCDHW) {
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std::vector<sd::LongType> perm = {0, 4, 1, 2, 3};
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input = input->permute(perm, false, false); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradI = gradI->permute(perm, false, false); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradO =gradO->permute(perm, false, false); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
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}
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if (isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, 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::pooling3dBP(block, *input, *gradO, *gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1,
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extraParam0);
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if (!isNCDHW) {
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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(avgpool3dnew_bp) {
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auto ret = SHAPELIST(ConstantShapeHelper::getInstance().castToDataType(inputShape->at(0),
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ArrayOptions::dataType(inputShape->at(1))));
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return ret;
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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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