wehub-resource-sync
192 lines
10 KiB
C++
192 lines
10 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 raver119@gmail.com
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// @author Yurii Shyrma
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//
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#include <system/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 sd {
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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], [oC, iC, kH, kW], [oC, kH, kW, iC]
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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_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(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 isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
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int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
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const LongType rank = gradO->rankOf();
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REQUIRE_TRUE(weights->rankOf() == rank, 0,
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"CUSTOM DECONV2D_TF OP: rank of weights array must be equal to 4, but got %i instead !",
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weights->rankOf());
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REQUIRE_TRUE(gradIShape->rankOf() == 1, 0,
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"CUSTOM DECONV2D_TF OP: rank of array with output shape must be equal to 1, but got %i instead !",
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gradIShape->rankOf());
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REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0,
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"CUSTOM DECONV2D_TF OP: length of array with output shape must be equal to 4, but got %i instead !",
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gradIShape->lengthOf());
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auto nonConst = const_cast<NDArray *>(gradIShape);
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std::vector<sd::LongType> gradIShapeVector = nonConst->template asVectorT<sd::LongType>();
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// create empty conv2d input array
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NDArray input(gradO->ordering(), gradIShapeVector, gradO->dataType(), block.launchContext());
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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, wFormat, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWiC, indWoC, indWkH, indOoH);
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LongType 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::vector<sd::LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
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"CUSTOM DECONV2D_TF OP: wrong shape of input array, basing on array with output shape expected is %s, "
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"but got %s instead !",
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ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM DECONV2D_TF OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(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,
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dH, dW, isSameMode, isNCHW, wFormat);
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return sd::Status::OK;
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}
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DECLARE_TYPES(deconv2d_tf) {
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getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->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], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto gradIShapeShapeInfo = inputShape->at(0); // [4]
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const int rank = 4;
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REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
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"CUSTOM DECONV2D_TF OP: rank of weights array must be equal to %i, but got %i instead !", rank,
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shape::rank(weightsShapeInfo));
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REQUIRE_TRUE(shape::rank(gradOShapeInfo) == rank, 0,
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"CUSTOM DECONV2D_TF OP: rank of input array must be equal to %i, but got %i instead !", rank,
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shape::rank(gradOShapeInfo));
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REQUIRE_TRUE(shape::rank(gradIShapeShapeInfo) == 1, 0,
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"CUSTOM DECONV2D_TF OP: rank of array with output shape must be equal to %i, but got %i instead !", 1,
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shape::rank(gradIShapeShapeInfo));
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const LongType kH =
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INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) height
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const LongType kW =
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INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) width
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const LongType sH = INT_ARG(2); // strides height
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const LongType sW = INT_ARG(3); // strides width
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const LongType pH = INT_ARG(4); // paddings height
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const LongType pW = INT_ARG(5); // paddings width
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const LongType dH = INT_ARG(6); // dilations height
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const LongType 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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const int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
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LongType indIOioC, indIiH, indWoC(0 == wFormat ? 3 : 0), indOoH;
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if (!isNCHW) {
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indIOioC = 3;
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indIiH = 1;
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indOoH = 1;
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} else {
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indIOioC = 1;
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indIiH = 2;
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indOoH = 2;
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}
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std::vector<sd::LongType> gradIShape = INPUT_VARIABLE(0)->template asVectorT<sd::LongType>();
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const LongType bS = gradIShape[0]; // batch size
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const LongType iH = gradIShape[indIiH]; // input height
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const LongType iW = gradIShape[indIiH + 1]; // input width
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const LongType iC = gradIShape[indIOioC]; // input channels
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const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
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const LongType oH = gradOShapeInfo[indOoH + 1]; // input height
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const LongType oW = gradOShapeInfo[indOoH + 2]; // input width
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LongType 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::vector<sd::LongType> expectedGradIShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, trueiH, trueiW, 0, indIOioC, indIiH, indIiH + 1});
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
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REQUIRE_TRUE(expectedGradIShape == gradIShape, 0,
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"CUSTOM DECONV2D_TF OP: wrong shape of array with output shape, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradIShape).c_str());
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REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
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shape::shapeOf(weightsShapeInfo)),
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0, "CUSTOM DECONV2D_TF OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
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ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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sd::LongType 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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auto ret = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(weightsShapeInfo),shape::order(gradOShapeInfo),4,shape,0);
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return SHAPELIST(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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