153 lines
9.2 KiB
C++
153 lines
9.2 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
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// @author Yurii Shyrma
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_deconv2d)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(deconv2d_tf, 3, 1, false, 0, 9) {
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auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto gradIShape = INPUT_VARIABLE(0); // [4] - shape of input of conv2d (that is shape of gradI)
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
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int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(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 isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
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const int rank = gradO->rankOf();
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REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM DECONV2D_TF OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
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REQUIRE_TRUE(gradIShape->rankOf() == 1, 0, "CUSTOM DECONV2D_TF OP: rank of array with output shape must be equal to 1, but got %i instead !", gradIShape->rankOf());
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REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0, "CUSTOM DECONV2D_TF OP: length of array with output shape must be equal to 4, but got %i instead !", gradIShape->lengthOf());
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// create empty conv2d input array
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NDArray input(gradO->ordering(), gradIShape->asVectorT<Nd4jLong>(), gradO->dataType(), block.launchContext());
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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, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM DECONV2D_TF OP: wrong shape of input array, basing on array with output shape expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DECONV2D_TF OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
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ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
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return Status::OK();
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}
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DECLARE_TYPES(deconv2d_tf) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(deconv2d_tf) {
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auto gradOShapeInfo = inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC] always
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auto gradIShapeShapeInfo = inputShape->at(0); // [4]
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const int rank = 4;
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_TF OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_TF OP: rank of input array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
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REQUIRE_TRUE(gradIShapeShapeInfo[0] == 1, 0, "CUSTOM DECONV2D_TF OP: rank of array with output shape must be equal to %i, but got %i instead !", 1, gradIShapeShapeInfo[0]);
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const int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height
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const int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width
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const int sH = INT_ARG(2); // strides height
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const int sW = INT_ARG(3); // strides width
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const int pH = INT_ARG(4); // paddings height
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const int pW = INT_ARG(5); // paddings width
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const int dH = INT_ARG(6); // dilations height
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const int dW = INT_ARG(7); // dilations width
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const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
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int indIOioC, indIiH, indWoC(3), indOoH;
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if(!isNCHW) {
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indIOioC = 3; indIiH = 1; indOoH = 1;
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}
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else {
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indIOioC = 1; indIiH = 2; indOoH = 2;
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}
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std::vector<Nd4jLong> gradIShape = INPUT_VARIABLE(0)->template asVectorT<Nd4jLong>();
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const int bS = gradIShape[0]; // batch size
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const int iH = gradIShape[indIiH]; // input height
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const int iW = gradIShape[indIiH+1]; // input width
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const int iC = gradIShape[indIOioC]; // input channels
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const int oC = weightsShapeInfo[indWoC+1]; // output channels
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const int oH = gradOShapeInfo[indOoH+1]; // input height
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const int oW = gradOShapeInfo[indOoH+2]; // input width
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int trueiH, trueiW; // output height, width
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ConvolutionUtils::calcOutSizeDeconv2D(trueiH, trueiW, kH, kW, sH, sW, pH, pW, dH, dW, oH, oW, isSameMode);
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std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,trueiH,trueiW, 0,indIOioC,indIiH,indIiH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradIShape), 0, "CUSTOM DECONV2D_TF OP: wrong shape of array with output shape, expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradIShape).c_str());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM DECONV2D_TF OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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Nd4jLong shape[4];
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shape[0] = bS;
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if (isNCHW) {
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shape[1] = iC;
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shape[2] = iH;
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shape[3] = iW;
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} else {
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shape[1] = iH;
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shape[2] = iW;
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shape[3] = iC;
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}
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(weightsShapeInfo), shape::order(gradOShapeInfo), 4, shape));
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}
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}
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}
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#endif |