wehub-resource-sync
453 lines
25 KiB
C++
453 lines
25 KiB
C++
/*
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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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//
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// @author Yurii Shyrma, created on 05.02.2018
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv3dnew)
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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/convolutions.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
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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 weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
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REQUIRE_TRUE(input->rankOf() == 5, 0,
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"CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0,
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"CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
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LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
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LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(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 paddingMode = INT_ARG(12); // 0-SAME, 1-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int wFormat = block.getIArguments()->size() > 14
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? INT_ARG(14)
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: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
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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, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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REQUIRE_TRUE(paddingMode < 2, 0,
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"CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM CONV3D 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(
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bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
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sd_debug("MKL-DNN is not used for conv3dnew!\n", 0);
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std::vector<LongType> permutForOutput;
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std::vector<LongType> permuteDims = {0,4,1,2,3};
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if (isNCDHW)
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permutForOutput = {0, 2, 3, 4, 1}; // [bS, oC, oD, oH, oW] -> [bS, oD, oH, oW, oC]
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else
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input = input->permute(permuteDims, false, false);
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std::vector<LongType> wAxes;
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if (0 == wFormat)
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wAxes = {3, 0, 1, 2};
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else if (1 == wFormat)
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wAxes = {1, 2, 3, 4};
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else
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wAxes = {4, 1, 2, 3};
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std::vector<sd::LongType> colShape = {bS, iC, kD, kH, kW, oD, oH, oW};
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NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
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ConvolutionUtils::vol2col(block, input, &columns, sD, sH, sW, pD, pH, pW, dD, dH,
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dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
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// [bS, iC, kD, kH, kW, oD, oH, oW] x [kD, kH, kW, iC, oC] = [bS, oD, oH, oW, oC]
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// [bS, iC, kD, kH, kW, oD, oH, oW] x [oC, iC, kD, kH, kW] = [bS, oD, oH, oW, oC]
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// [bS, iC, kD, kH, kW, oD, oH, oW] x [oC, kD, kH, kW, iC] = [bS, oD, oH, oW, oC]
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std::vector<LongType> mulDims = {1,2,3,4};
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MmulHelper::tensorDot(&columns, weights, output, mulDims, wAxes, permutForOutput);
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if (bias)
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helpers::addBias(block, *output, *bias, *output, isNCDHW);
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if (!isNCDHW) delete input;
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return sd::Status::OK;
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}
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DECLARE_TYPES(conv3dnew) {
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getOpDescriptor()
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->setAllowedInputTypes(0, sd::DataType::ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(conv3dnew) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) depth
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LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) height
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LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(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 paddingMode = INT_ARG(12); // 1-SAME, 0-VALID;
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int wFormat = block.getIArguments()->size() > 14
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? INT_ARG(14)
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: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
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const int rank = 5;
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REQUIRE_TRUE(paddingMode < 2, 0,
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"CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
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"CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
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"CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
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weightsShapeInfo);
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LongType indIOioC, indIiD, indWoC(0 == wFormat ? 4 : 0);
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if (!isNCDHW) {
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indIOioC = 4;
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indIiD = 1;
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} else {
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indIOioC = 1;
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indIiD = 2;
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}
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LongType bS = inputShapeInfo[1]; // batch size
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LongType iD = inputShapeInfo[indIiD + 1]; // input depth
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LongType iH = inputShapeInfo[indIiD + 2]; // input height
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LongType iW = inputShapeInfo[indIiD + 3]; // input width
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LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
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LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
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"CUSTOM CONV3D 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(
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biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
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"CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
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oC, biasShapeInfo[0], shape::length(biasShapeInfo));
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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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paddingMode);
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sd::LongType* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), sd::LongType);
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outputShapeInfo[0] = rank;
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outputShapeInfo[1] = bS;
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if (isNCDHW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oD;
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outputShapeInfo[4] = oH;
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outputShapeInfo[5] = oW;
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} else {
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outputShapeInfo[2] = oD;
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outputShapeInfo[3] = oH;
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outputShapeInfo[4] = oW;
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outputShapeInfo[5] = oC;
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}
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ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
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return SHAPELIST(CONSTANT(outputShapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
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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 weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
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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, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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REQUIRE_TRUE(input->rankOf() == 5, 0,
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"CUSTOM CONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0,
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"CUSTOM CONV3D_BP OP: rank of weights array must be equal to 5, but got %i instead !",
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weights->rankOf());
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REQUIRE_TRUE(
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gradO->rankOf() == 5, 0,
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"CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !",
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gradO->rankOf());
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LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
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LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
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LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(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 paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int wFormat = block.getIArguments()->size() > 14
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? INT_ARG(14)
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: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
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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, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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LongType trueoD, trueoH, trueoW; // true output depth/height/width
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ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
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iW, paddingMode);
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REQUIRE_TRUE(paddingMode < 2, 0,
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"CUSTOM CONV3D_BP OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
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{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
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REQUIRE_TRUE(
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gradO->isSameShape(expectedGradOShape), 0,
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"CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, 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(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM CONV3D_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 CONV3D_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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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
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sd_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);
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std::vector<LongType> gradOaxesForDot;
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std::vector<LongType> permute = {0, 4, 1, 2, 3};
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if (!isNCDHW) {
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gradOaxesForDot = {0, 1, 2, 3}; // bS, oD, oH, oW
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input =input->permute(permute, false, false); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradI = gradI->permute(permute, false, false); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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} else {
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gradOaxesForDot = {0, 2, 3, 4}; // bS, oD, oH, oW
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}
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std::vector<LongType> wPermut, colPermut;
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if (0 == wFormat) {
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wPermut = {3, 0, 1, 2, 4};
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colPermut = {2, 3, 4, 1, 0, 5, 6, 7};
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} else if (1 == wFormat) {
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wPermut = {1, 2, 3, 4, 0};
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colPermut = {1, 2, 3, 4, 0, 5, 6, 7};
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} else {
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wPermut = {4, 1, 2, 3, 0};
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colPermut = {2, 3, 4, 1, 0, 5, 6, 7};
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}
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std::vector<sd::LongType> colShape = {bS, iC, kD, kH, kW, oD, oH, oW};
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// ----- calculation of gradW and gradB ----- //
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NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
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ConvolutionUtils::vol2col(block, input, &columns, sD, sH, sW, pD, pH, pW, dD, dH,
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dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
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|
|
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std::vector<LongType> mulDims = {0,5,6,7};
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MmulHelper::tensorDot(
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&columns, gradO, gradW, mulDims, gradOaxesForDot,
|
|
wPermut); // [bS, iC, kD, kH, kW, oD, oH, oW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [iC, kD, kH, kW, oC]
|
|
|
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//----- calculation of gradO -----//
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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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gradO->reduceAlongDimension(reduce::Sum, gradB, &gradOaxesForDot); // sum over bS oD oH oW
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if (gradB != OUTPUT_VARIABLE(2)) delete gradB;
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|
}
|
|
|
|
//----- calculation of gradI -----//
|
|
// [kD, kH, kW, iC, oC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
|
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// [oC, iC, kD, kH, kW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
|
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// [oC, kD, kH, kW, iC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
|
|
std::vector<LongType> firstDims = {indWoC};
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std::vector<LongType> secondDims = {indIOioC};
|
|
|
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MmulHelper::tensorDot(weights, gradO, &columns, firstDims, secondDims, colPermut);
|
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ConvolutionUtils::col2vol(block, columns, *gradI, sD, sH, sW, pD, pH, pW, dD, dH,
|
|
dW); // columns [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to [bS, iC, iD, iH, iW]
|
|
|
|
if (!isNCDHW) {
|
|
delete input;
|
|
delete gradI;
|
|
}
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
DECLARE_TYPES(conv3dnew_bp) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(0, sd::DataType::ANY)
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS})
|
|
->setAllowedInputTypes(3, {ALL_FLOATS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(conv3dnew_bp) {
|
|
auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
|
|
sd::LongType const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
|
|
sd::LongType const* gradOShapeInfo =
|
|
block.width() > 3
|
|
? inputShape->at(3)
|
|
: inputShape->at(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(2))); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
|
|
|
|
const int rank = 5;
|
|
REQUIRE_TRUE(paddingMode < 2, 0,
|
|
"CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
|
|
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
|
|
"CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
|
|
inputShapeInfo);
|
|
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
|
|
"CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
|
|
weightsShapeInfo);
|
|
REQUIRE_TRUE(
|
|
gradOShapeInfo[0] == rank, 0,
|
|
"CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !",
|
|
rank, gradOShapeInfo);
|
|
|
|
sd::LongType indIOioC, indIiD, indWoC(0 == wFormat ? 4 : 0);
|
|
if (!isNCDHW) {
|
|
indIOioC = 4;
|
|
indIiD = 1;
|
|
} else {
|
|
indIOioC = 1;
|
|
indIiD = 2;
|
|
}
|
|
|
|
LongType bS = inputShapeInfo[1]; // batch size
|
|
LongType iD = inputShapeInfo[indIiD + 1]; // input depth
|
|
LongType iH = inputShapeInfo[indIiD + 2]; // input height
|
|
LongType iW = inputShapeInfo[indIiD + 3]; // input width
|
|
LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
|
|
LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
|
|
|
|
LongType trueoD, trueoH, trueoW; // true output depth/height/width
|
|
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
|
|
iW, paddingMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
|
|
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIiD, indIiD + 1, indIiD + 2});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(
|
|
ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0,
|
|
"CUSTOM CONV3D_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(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
|
|
"CUSTOM CONV3D_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 CONV3D_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());
|
|
|
|
if (biasShapeInfo) {
|
|
auto gradBshapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
|
|
}
|
|
|
|
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
|
|
}
|
|
} // namespace ops
|
|
} // namespace sd
|
|
|
|
#endif
|