/* ****************************************************************************** * * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * See the NOTICE file distributed with this work for additional * information regarding copyright ownership. * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author Yurii Shyrma (iuriish@yahoo.com), created on 05.09.2018 // #include #if NOT_EXCLUDED(OP_deconv3d) #include #include #include #include namespace sd { namespace ops { CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC] auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW) REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM DECONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf()); LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) depth LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) height LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(weights->sizeAt(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 isSameMode = INT_ARG(12); // 0-SAME, 1-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, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC] LongType bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD); std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i " "instead !", oC, bias->rankOf(), bias->lengthOf()); std::vector outputPerm = {0, 4, 1, 2, 3}; if (!isNCDHW) output = output->permute(outputPerm, false, false); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW] std::vector colPermut; if (1 == wFormat) colPermut = {1, 2, 3, 4, 0, 5, 6, 7}; else colPermut = {2, 3, 4, 1, 0, 5, 6, 7}; if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not // deconv) forward pass ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW); std::vector columnsShape = {bS, oC, kD, kH, kW, iD, iH, iW}; NDArray columns(input->ordering(),columnsShape, input->dataType(), block.launchContext()); //----- calculation of output -----// // [kD, kH, kW, oC, iC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW] // [iC, oC, kD, kH, kW] x [bS, iD, iH, iW, iC] = [oC, kD, kH, kW, bS, iD, iH, iW] // [iC, kD, kH, kW, oC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW] std::vector indWiCShape = {indWiC}; std::vector indIOioCShape = {indIOioC}; sd::MmulHelper::tensorDot(weights, input, &columns, indWiCShape, indIOioCShape, colPermut); // [bS, oC, kD, kH, kW, iD, iH, iW] -> [kD, kH, kW, oC, bS, iD, iH, iW] ConvolutionUtils::col2vol(block, columns, *output, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, oC, kD, kH, kW, iD, iH, iW] is de-convoluted to [bS, oC, oD, oH, oW] //----- add biases if required -----// if (bias) helpers::addBias(block, *output, *bias, *output, true); //if (!isNCDHW) delete output; return sd::Status::OK; } DECLARE_TYPES(deconv3d) { getOpDescriptor() ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(deconv3d) { auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NDCHW) auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC] auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] const sd::LongType rank = 5; REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DECONV3D 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 DECONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, shape::rank(weightsShapeInfo)); LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(0))); // filter(kernel) depth LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(1))); // filter(kernel) height LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(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 isSameMode = INT_ARG(12); // 0-SAME, 1-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, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC] LongType indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4)); if (!isNCDHW) { indIOioC = 4; indIiD = 1; } else { indIOioC = 1; indIiD = 2; } const LongType bS = inputShapeInfo[1]; // batch size const LongType iD = inputShapeInfo[indIiD + 1]; // input depth const LongType iH = inputShapeInfo[indIiD + 2]; // input height const LongType iW = inputShapeInfo[indIiD + 3]; // input width const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC); REQUIRE_TRUE(shape::shapeEquals(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV3D 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(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i " "instead !", oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo)); LongType oD, oH, oW; // output depth, height, width ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); std::vector outputShape; if (isNCDHW) { outputShape = {bS,oC,oD,oH,oW}; } else { outputShape = {bS,oD,oH,oW,oC}; } ShapeDescriptor *shapeDescriptor = new ShapeDescriptor(ArrayOptions::dataType(inputShapeInfo), shape::order(inputShapeInfo), outputShape); auto outputShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(shapeDescriptor); delete shapeDescriptor; return SHAPELIST(outputShapeInfo); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(deconv3d_bp, 3, 2, false, 0, 13) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC] auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC] auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM DECONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D_BP OP: rank of weights array must be equal to 5 , but got %i instead !", weights->rankOf()); REQUIRE_TRUE( gradO->rankOf() == 5, 0, "CUSTOM DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !", gradO->rankOf()); LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) depth LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) height LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(weights->sizeAt(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 isSameMode = INT_ARG(12); // 0-SAME, 1-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, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC] LongType bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD); LongType trueoD, trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx( {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}); std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DECONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got " "%s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, " "%i instead !", oC, bias->rankOf(), bias->lengthOf()); if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not // deconv) forward pass ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW); // ----- calculation of gradI -> pass it through conv3d_ff ----- // sd::ops::conv3dnew conv3d; const sd::Status status = conv3d.execute({gradO, weights}, {gradI}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, !isNCDHW, wFormat}, {}); if (status != sd::Status::OK) return status; // -----prepare permutation arrays and axes for dot product ----- // std::vector inputAxesForDot; if (!isNCDHW) { std::vector grad0Permute = {0,4,1,2,3}; gradO = gradO->permute(grad0Permute, false, false); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW] inputAxesForDot = {0, 1, 2, 3}; // bS, iD, iH, iW } else inputAxesForDot = {0, 2, 3, 4}; // bS, iD, iH, iW std::vector gradWAxes; // empty for wFormat = 1 if (0 == wFormat) gradWAxes = {4, 3, 0, 1, 2}; else if (2 == wFormat) gradWAxes = {0, 4, 1, 2, 3}; // ----- calculation of gradW ----- // auto columns = NDArrayFactory::create(input->ordering(), {bS, oC, kD, kH, kW, iD, iH, iW}, input->dataType(), block.launchContext()); ConvolutionUtils::vol2col(block, gradO, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, oC, oD, oH, oW] is deconvoluted to [bS, oC, kD, kH, kW, iD, iH, iW] std::vector mulDims = {0,5,6,7}; MmulHelper::tensorDot(input, columns, gradW, inputAxesForDot, mulDims, gradWAxes); // [bS, iC, iD, iH, iW]/[bS, iD, iH, iW, iC] x [bS, oC, kD, kH, kW, iD, iH, iW] = // [iC, oC, kD, kH, kW] // ----- calculation of gradB ----- // if (gradB) { std::vector biasShape = {gradB->lengthOf()}; if (gradB->rankOf() == 2) gradB = gradB->reshape(gradB->ordering(), biasShape, false); std::vector dims = {{0, 2, 3, 4}}; gradO->reduceAlongDimension(reduce::Sum, gradB, &dims); // sum over bS, oD, oH, oW if (gradB != OUTPUT_VARIABLE(2)) delete gradB; } if (!isNCDHW) delete gradO; delete columns; return sd::Status::OK; } DECLARE_TYPES(deconv3d_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(deconv3d_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, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC] auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC] auto 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 const int rank = 5; REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DECONV3D_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 DECONV3D_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 DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, shape::rank(gradOShapeInfo)); LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(0))); // filter(kernel) depth LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(1))); // filter(kernel) height LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(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 isSameMode = INT_ARG(12); // 0-SAME, 1-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, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC] LongType indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4)); if (!isNCDHW) { indIOioC = 4; indIiD = 1; } else { indIOioC = 1; indIiD = 2; } const LongType bS = inputShapeInfo[1]; // batch size const LongType iD = inputShapeInfo[indIiD + 1]; // input depth const LongType iH = inputShapeInfo[indIiD + 2]; // input height const LongType iW = inputShapeInfo[indIiD + 3]; // input width const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels LongType trueoD, trueoH, trueoW; // true output depth, height, width ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx( {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIiD, indIiD + 1, indIiD + 2}); std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC); REQUIRE_TRUE( shape::shapeEquals(5, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0, "CUSTOM DECONV3D_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(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DECONV3D_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 DECONV3D_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