wehub-resource-sync
408 lines
22 KiB
C++
408 lines
22 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 <helpers/MmulHelper.h>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/addBias.h>
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#include <ops/declarable/helpers/col2im.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <ops/declarable/helpers/im2col.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) {
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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 weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"CUSTOM DECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"CUSTOM DECONV2D OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
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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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sd::LongType pH = INT_ARG(4); // paddings height
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sd::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): 0-NCHW, 1-NHWC
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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, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
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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, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWoC, indWiC, indWkH, indOoH);
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM DECONV2D 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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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
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"instead !",
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oC, bias->rankOf(), bias->lengthOf());
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std::vector<LongType> outputPermute = {0,3,1,2};
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if (!isNCHW) output = output->permute(outputPermute, false, false); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
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std::vector<LongType> colPermut;
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if (1 == wFormat)
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colPermut = {1, 2, 3, 0, 4, 5};
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else
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colPermut = {2, 3, 1, 0, 4, 5};
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if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not
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// deconv) forward pass
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ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
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std::vector<sd::LongType> colShape = {bS, oC, kH, kW, iH, iW};
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NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
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//----- calculation of output -----//
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// NHWC: [kH, kW, oC, iC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW]
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// NHWC: [iC, oC, kH, kW] x [bS, iH, iW, iC] = [oC, kH, kW, bS, iH, iW]
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// NHWC: [iC, kH, kW, oC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW]
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std::vector<LongType> firstDims = {indWiC};
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std::vector<LongType> secondDims = {indIOioC};
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sd::MmulHelper::tensorDot(weights, input, &columns, firstDims, secondDims, colPermut);
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LaunchContext* ctx = block.launchContext();
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helpers::col2im(*ctx, &columns, output, sH, sW, pH, pW, oH, oW, dH,
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dW); // [bS, oC, kH, kW, iH, iW] is de-convoluted to [bS, oC, oH, oW]
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//----- add biases if required -----//
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if (bias)
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helpers::addBias(block, *output, *bias, *output, true);
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if (!isNCHW) delete output;
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return sd::Status::OK;
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}
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DECLARE_TYPES(deconv2d) {
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getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(deconv2d) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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const int rank = 4;
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REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
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"CUSTOM DECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank,
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shape::rank(inputShapeInfo));
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REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
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"CUSTOM DECONV2D 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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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(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, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
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LongType indIOioC, indIiH, indWoC(0 == wFormat ? 2 : (1 == wFormat ? 1 : 3));
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if (!isNCHW) {
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indIOioC = 3;
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indIiH = 1;
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} else {
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indIOioC = 1;
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indIiH = 2;
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}
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const LongType bS = inputShapeInfo[1]; // batch size
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const LongType iH = inputShapeInfo[indIiH + 1]; // input height
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const LongType iW = inputShapeInfo[indIiH + 2]; // input width
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const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
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const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
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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 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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if (biasShapeInfo)
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REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0,
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"CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
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"instead !",
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oC, biasShapeInfo[0], shape::length(biasShapeInfo));
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LongType oH, oW; // output height, width
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ConvolutionUtils::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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sd::LongType outputShape[4];
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outputShape[0] = bS;
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if (isNCHW) {
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outputShape[1] = oC;
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outputShape[2] = oH;
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outputShape[3] = oW;
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} else {
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outputShape[1] = oH;
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outputShape[2] = oW;
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outputShape[3] = oC;
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}
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auto desc = new ShapeDescriptor(ArrayOptions::dataType(weightsShapeInfo), shape::order(inputShapeInfo), outputShape, 4);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(desc));
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}
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DECLARE_TYPES(deconv2d_bp) {
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getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(deconv2d_bp, 3, 2, false, 0, 9) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto gradO = block.width() > 3
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? INPUT_VARIABLE(3)
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: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI
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auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"CUSTOM DECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"CUSTOM DECONV2D_BP 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(
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gradO->rankOf() == 4, 0,
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"CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !",
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gradO->rankOf());
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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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sd::LongType pH = INT_ARG(4); // paddings height
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sd::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, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
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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, indWoC, indWiC, indWkH, indOoH);
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LongType trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizeDeconv2D(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, oC, iC);
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
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"CUSTOM DECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got "
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"%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_BP 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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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CUSTOM DECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
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"%i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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if (isSameMode) { // SAME
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// Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward
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// pass
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ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
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}
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// ----- calculation of gradI -> pass it through conv2d_ff ----- //
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sd::ops::conv2d conv2d;
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const sd::Status status =
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conv2d.execute({gradO, weights}, {gradI}, {}, {kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, !isNCHW, wFormat}, {});
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if (status != sd::Status::OK) return status;
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// -----prepare permutation arrays and axes for dot product ----- //
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std::vector<LongType> inputAxes;
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if (!isNCHW) {
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std::vector<LongType> permuteDims = {0,3,1,2};
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gradO = gradO->permute(permuteDims, false, false); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
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inputAxes = {0, 1, 2}; // bS, iH, iW
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} else
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inputAxes = {0, 2, 3}; // bS, iH, iW
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std::vector<LongType> gradWAxes; // empty for wFormat = 1
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if (0 == wFormat)
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gradWAxes = {3, 2, 0, 1};
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else if (2 == wFormat)
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gradWAxes = {0, 3, 1, 2};
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std::vector<sd::LongType> colShape = {bS, oC, kH, kW, iH, iW};
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// ----- calculation of gradW ----- //
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NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
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LaunchContext* ctx = block.launchContext();
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NDArray *zero = NDArrayFactory::create(0.f, input->getContext());
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helpers::im2col(
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*ctx, *gradO, columns, kH, kW, sH, sW, pH, pW, dH, dW,
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*zero ); // [bS, oC, oH, oW] is convoluted to [bS, oC, kH, kW, iH, iW]
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std::vector<LongType> mulDims = {0,4,5};
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MmulHelper::tensorDot(input, &columns, gradW, inputAxes, mulDims,
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gradWAxes); // [bS, iC, iH, iW]/[bS, iH, iW, iC] x [bS, oC, kH, kW, iH, iW] = [iC, oC, kH, kW]
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// ----- calculation of gradB ----- //
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if (gradB) {
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std::vector<LongType> bShape = {gradB->lengthOf()};
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if (gradB->rankOf() == 2) gradB = gradB->reshape(gradB->ordering(), bShape, false);
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std::vector<sd::LongType> axesForReduction = {0, 2, 3}; // bS, oH, oW
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gradO->reduceAlongDimension(reduce::Sum, gradB, &axesForReduction); // sum over bS, oH, oW
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if (gradB != OUTPUT_VARIABLE(2)) delete gradB;
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}
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if (!isNCHW) delete gradO;
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delete zero;
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return sd::Status::OK;
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}
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DECLARE_SHAPE_FN(deconv2d_bp) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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sd::LongType const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
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auto gradOShapeInfo = block.width() > 3
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? inputShape->at(3)
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: inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
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const int rank = 4;
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REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
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"CUSTOM DECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
|
|
shape::rank(inputShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV2D_BP OP: rank of weights array must be equal to %i , but got %i instead !", rank,
|
|
shape::rank(weightsShapeInfo));
|
|
REQUIRE_TRUE(
|
|
shape::rank(gradOShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !",
|
|
rank, shape::rank(gradOShapeInfo));
|
|
|
|
LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) height
|
|
LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) width
|
|
LongType sH = INT_ARG(2); // strides height
|
|
LongType sW = INT_ARG(3); // strides width
|
|
LongType pH = INT_ARG(4); // paddings height
|
|
LongType pW = INT_ARG(5); // paddings width
|
|
LongType dH = INT_ARG(6); // dilations height
|
|
LongType dW = INT_ARG(7); // dilations width
|
|
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, oC, iC], 1 - [iC, oC, kH, kW], 2 - [iC, kH, kW, oC]
|
|
|
|
LongType indIOioC, indIiH, indOoH, indWoC(0 == wFormat ? 2 : (1 == wFormat ? 1 : 3));
|
|
if (!isNCHW) {
|
|
indIOioC = 3;
|
|
indIiH = 1;
|
|
indOoH = 1;
|
|
} else {
|
|
indIOioC = 1;
|
|
indIiH = 2;
|
|
indOoH = 2;
|
|
}
|
|
|
|
const LongType bS = inputShapeInfo[1]; // batch size
|
|
const LongType iH = inputShapeInfo[indIiH + 1]; // input height
|
|
const LongType iW = inputShapeInfo[indIiH + 2]; // input width
|
|
const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
|
|
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
|
|
|
|
LongType trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape =
|
|
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(
|
|
shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0,
|
|
"CUSTOM DECONV2D_BP OP: wrong shape of output gradients next epsilon) array, expected is %s, but got %s instead "
|
|
"!",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
|
|
REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
|
|
shape::shapeOf(weightsShapeInfo)),
|
|
0, "CUSTOM DECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
|
|
ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if (biasShapeInfo)
|
|
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
|
|
"CUSTOM DECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
|
|
"%i instead !",
|
|
oC, biasShapeInfo[0], shape::length(biasShapeInfo));
|
|
|
|
auto gradIShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
auto gradWShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
|
|
auto shapes = SHAPELIST(CONSTANT(gradIShapeInfo), CONSTANT(gradWShapeInfo));
|
|
|
|
if (biasShapeInfo != nullptr) {
|
|
auto gradBShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
shapes->push_back(CONSTANT(gradBShapeInfo));
|
|
}
|
|
|
|
return shapes;
|
|
}
|
|
|
|
} // namespace ops
|
|
} // namespace sd
|
|
|
|
#endif
|