/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * 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. * * 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 raver119@gmail.com // @author Yurii Shyrma // #include #if NOT_EXCLUDED(OP_deconv2d) #include #include #include #include #include namespace nd4j { namespace ops { CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC] always auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW) REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf()); int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0));// filter(kernel) height int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1));// filter(kernel) width int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int 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): 0-NCHW, 1-NHWC int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC}); REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); if(!isNCHW) output = output->permute({0, 3, 1, 2}); // [bS, oH, oW, oC] -> [bS, oC, oH, oW] if(isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); NDArray columns(input->ordering(), {bS, oC, kH, kW, iH, iW}, input->dataType(), block.launchContext()); //----- calculation of output -----// // NHWC: [kH, kW, oC, iC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW] // NCHW: [iC, oC, kH, kW] x [bS, iC, iH, iW] = [oC, kH, kW, bS, iH, iW] nd4j::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); LaunchContext* ctx = block.launchContext(); helpers::col2im(*ctx, columns, *output, sH, sW, pH, pW, oH, oW, dH, dW); // [bS, oC, kH, kW, iH, iW] is de-convoluted to [bS, oC, oH, oW] //----- add biases if required -----// if(bias) output->applyBroadcast(broadcast::Add, {1}, bias); if(!isNCHW) delete output; return Status::OK(); } DECLARE_TYPES(deconv2d) { getOpDescriptor() ->setAllowedInputTypes(nd4j::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(deconv2d) { auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC] always auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] const int rank = 4; REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM DECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]); REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]); int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int 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 indIOioC, indIiH, indWoC(2); if(!isNCHW) { indIOioC = 3; indIiH = 1; } else { indIOioC = 1; indIiH = 2; } const int bS = inputShapeInfo[1]; // batch size const int iH = inputShapeInfo[indIiH+1]; // input height const int iW = inputShapeInfo[indIiH+2]; // input width const int iC = inputShapeInfo[indIOioC+1]; // input channels const int oC = weightsShapeInfo[indWoC+1]; // output channels std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC}); REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM DECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); int oH, oW; // output height, width ConvolutionUtils::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); Nd4jLong outputShape[4]; outputShape[0] = bS; if (isNCHW) { outputShape[1] = oC; outputShape[2] = oH; outputShape[3] = oW; } else { outputShape[1] = oH; outputShape[2] = oW; outputShape[3] = oC; } return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(weightsShapeInfo), shape::order(inputShapeInfo), outputShape, 4))); } DECLARE_TYPES(deconv2d_bp) { getOpDescriptor() ->setAllowedInputTypes(nd4j::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(deconv2d_bp, 3, 2, false, 0, 9) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC] always auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, oC, iC] always auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of weights array must be equal to 4 , but got %i instead !", weights->rankOf()); REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf()); int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0));// filter(kernel) height int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1));// filter(kernel) width int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int 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 bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH); int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC}); REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM DECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); if(bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_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) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); // ----- calculation of gradI -> pass it through conv2d_ff ----- // nd4j::ops::conv2d conv2d; const Nd4jStatus status = conv2d.execute({gradO, weights}, {gradI}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, !isNCHW}, {}); if (status != ND4J_STATUS_OK) return status; // -----prepare permutation arrays and axes for dot product ----- // std::vector inputAxesForDot; if(!isNCHW) { gradO = gradO->permute({0, 3, 1, 2}); // [bS, oH, oW, oC] -> [bS, oC, oH, oW] inputAxesForDot = {0, 1, 2}; // bS, iH, iW } else inputAxesForDot = {0, 2, 3}; // bS, iH, iW // ----- calculation of gradW ----- // NDArray columns(input->ordering(), {bS, oC, kH, kW, iH, iW}, input->dataType(), block.launchContext()); LaunchContext* ctx = block.launchContext(); helpers::im2col(*ctx, *gradO, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, oC, oH, oW] is convoluted to [bS, oC, kH, kW, iH, iW] MmulHelper::tensorDot(input, &columns, gradW, inputAxesForDot, {0, 4, 5}, {3, 2, 0, 1}); // [bS, iC, iH, iW]/[bS, iH, iW, iC] x [bS, oC, kH, kW, iH, iW] = [iC, oC, kH, kW] // ----- calculation of gradB ----- // if(gradB) { if(gradB->rankOf() == 2) gradB = gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}); gradO->reduceAlongDimension(reduce::Sum, gradB, {0, 2, 3}); // sum over bS, oH, oW if(gradB != OUTPUT_VARIABLE(2)) delete gradB; } if(!isNCHW) delete gradO; return ND4J_STATUS_OK; } DECLARE_SHAPE_FN(deconv2d_bp) { auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW) auto weightsShapeInfo = inputShape->at(1); // [kH, kW, oC, iC] always Nd4jLong* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC] Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next const int rank = 4; REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]); REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_BP OP: rank of weights array must be equal to %i , but got %i instead !", rank, weightsShapeInfo[0]); REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM DECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]); int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int 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 indIOioC, indIiH, indWoC(2), indOoH; if(!isNCHW) { indIOioC = 3; indIiH = 1; indOoH = 1; } else { indIOioC = 1; indIiH = 2; indOoH = 2; } const int bS = inputShapeInfo[1]; // batch size const int iH = inputShapeInfo[indIiH+1]; // input height const int iW = inputShapeInfo[indIiH+2]; // input width const int iC = inputShapeInfo[indIOioC+1]; // input channels const int oC = weightsShapeInfo[indWoC+1]; // output channels int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, oC, iC}); REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM DECONV2D_BP OP: wrong shape of output gradients next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM DECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", 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; } } } #endif