68ea5f3688
* correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
396 lines
25 KiB
C++
396 lines
25 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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 Yurii Shyrma, created on 06.03.2018
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//
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#ifndef LIBND4J_CONVO_OPS_H
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#define LIBND4J_CONVO_OPS_H
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv2d)
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#include <op_boilerplate.h>
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#include <memory>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(conv2d, 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, iC, oC] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
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int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", 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, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
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ConvolutionUtils::conv2d(block, input, weights, bias, output, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(conv2d) {
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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, iC, oC] always
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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//output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int 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 kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0)); // filter(kernel) height
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int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1)); // filter(kernel) width
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const int rank = 4; // 4
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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int indIOioC, indIiH, indWoC(3);
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if(!isNCHW) {
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indIOioC = 3; indIiH = 1;
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}
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else {
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indIOioC = 1; indIiH = 2;
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}
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const int bS = inputShapeInfo[1]; // batch size
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const int iH = inputShapeInfo[indIiH+1]; // input height
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const int iW = inputShapeInfo[indIiH+2]; // input width
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const int iC = inputShapeInfo[indIOioC+1]; // input channels
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const int oC = weightsShapeInfo[indWoC+1]; // output channels
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if (biasShapeInfo)
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REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
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Nd4jLong* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
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int oH, oW; // output height, width
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ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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outputShapeInfo[0] = rank;
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outputShapeInfo[1] = bS;
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if (isNCHW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oH;
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outputShapeInfo[4] = oW;
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} else {
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outputShapeInfo[2] = oH;
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outputShapeInfo[3] = oW;
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outputShapeInfo[4] = 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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DECLARE_TYPES(conv2d) {
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getOpDescriptor()
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->setAllowedInputTypes(0, nd4j::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_TYPES(conv2d_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv2d_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] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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int kH = INT_ARG(0); // filter(kernel) height
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int kW = INT_ARG(1); // filter(kernel) width
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int 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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REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM CONV2D_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, "CUSTOM CONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
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REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_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());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", 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, "CUSTOM CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
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ConvolutionUtils::conv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(conv2d_bp) {
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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, iC, oC] always
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auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
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auto gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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const int rank = 4;
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
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const int kH = INT_ARG(0); // filter(kernel) height
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const int kW = INT_ARG(1); // filter(kernel) width
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const int sH = INT_ARG(2); // strides height
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const int sW = INT_ARG(3); // strides width
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const int pH = INT_ARG(4); // paddings height
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const int pW = INT_ARG(5); // paddings width
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const int dH = INT_ARG(6); // dilations height
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const int dW = INT_ARG(7); // dilations width
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const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int indIOioC, indIiH, indOoH, indWoC(3);
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if(!isNCHW) {
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indIOioC = 3; indIiH = 1; indOoH = 1;
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}
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else {
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indIOioC = 1; indIiH = 2; indOoH = 2;
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}
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const int bS = inputShapeInfo[1]; // batch size
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const int iH = inputShapeInfo[indIiH+1]; // input height
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const int iW = inputShapeInfo[indIiH+2]; // input width
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const int iC = inputShapeInfo[indIOioC+1]; // input channels
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const int oC = weightsShapeInfo[indWoC+1]; // output channels
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_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());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if(biasShapeInfo)
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REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
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auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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if(biasShapeInfo) {
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auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
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}
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return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) {
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auto gradIShape = INPUT_VARIABLE(0); // [4]
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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int kH = INT_ARG(0); // filter(kernel) height
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int kW = INT_ARG(1); // filter(kernel) width
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int 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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const int rank = gradO->rankOf();
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REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
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REQUIRE_TRUE(gradIShape->rankOf() == 1, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to 1, but got %i instead !", gradIShape->rankOf());
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REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: length of array with output shape must be equal to 4, but got %i instead !", gradIShape->lengthOf());
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// create empty conv2d input array
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std::vector<Nd4jLong> gradIShapeAsVector(rank);
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for(int i = 0; i < rank; ++i)
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gradIShapeAsVector[i] = gradIShape->e<Nd4jLong>(i);
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NDArray input(gradO->ordering(), gradIShapeAsVector, gradO->dataType(), block.launchContext());
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_INPUT_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());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
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ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW);
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return Status::OK();
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}
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DECLARE_TYPES(conv2d_input_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(conv2d_input_bp) {
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auto gradIShapeShapeInfo = inputShape->at(0); // [4]
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC] always
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auto gradOShapeInfo = inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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const int rank = 4;
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REQUIRE_TRUE(gradIShapeShapeInfo[0] == 1, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to %i, but got %i instead !", 1, gradIShapeShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV2D_INPUT_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
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const int kH = INT_ARG(0); // filter(kernel) height
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const int kW = INT_ARG(1); // filter(kernel) width
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const int sH = INT_ARG(2); // strides height
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const int sW = INT_ARG(3); // strides width
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const int pH = INT_ARG(4); // paddings height
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const int pW = INT_ARG(5); // paddings width
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const int dH = INT_ARG(6); // dilations height
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const int dW = INT_ARG(7); // dilations width
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const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int indIOioC, indIiH, indWoC(3), indOoH;
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if(!isNCHW) {
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indIOioC = 3; indIiH = 1; indOoH = 1;
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}
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else {
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indIOioC = 1; indIiH = 2; indOoH = 2;
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}
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std::vector<Nd4jLong> gradIShape = INPUT_VARIABLE(0)->template asVectorT<Nd4jLong>();
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const int bS = gradIShape[0]; // batch size
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const int iH = gradIShape[indIiH]; // input height
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const int iW = gradIShape[indIiH+1]; // input width
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const int iC = gradIShape[indIOioC]; // input channels
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const int oC = weightsShapeInfo[indWoC+1]; // output channels
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_INPUT_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());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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Nd4jLong* gradIshapeInfo(nullptr);
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ALLOCATE(gradIshapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
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|
|
|
gradIshapeInfo[0] = rank;
|
|
gradIshapeInfo[1] = bS;
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|
if (isNCHW) {
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|
gradIshapeInfo[2] = iC;
|
|
gradIshapeInfo[3] = iH;
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gradIshapeInfo[4] = iW;
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} else {
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gradIshapeInfo[2] = iH;
|
|
gradIshapeInfo[3] = iW;
|
|
gradIshapeInfo[4] = iC;
|
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}
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|
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ShapeUtils::updateStridesAndType(gradIshapeInfo, gradOShapeInfo, shape::order(gradOShapeInfo));
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|
|
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return SHAPELIST(CONSTANT(gradIshapeInfo));
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|
}
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}
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}
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#endif
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#endif //LIBND4J_CONVO_OPS_H
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