97 lines
3.9 KiB
C++
97 lines
3.9 KiB
C++
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/*******************************************************************************
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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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// Created by george@skymind.io on 2/21/2018.
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//
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/segment.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(segment_sum, 2, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto idxSegments = INPUT_VARIABLE(1);
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auto segmentedOutput = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(idxSegments->isVector(), 0, "segment_sum: segment indexes array should be a vector, but it rank is %i.", idxSegments->rankOf());
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REQUIRE_TRUE(idxSegments->lengthOf() == input->sizeAt(0), 0, "segment_sum: segment indexes array length should be equal to the input first dimension, but %i != %i.", idxSegments->lengthOf(), input->sizeAt(0));
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auto expected = NDArrayFactory::create(input->dataType(), 0.f, block.launchContext());
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auto wrong = NDArrayFactory::create(input->dataType(), 0.f, block.launchContext());
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REQUIRE_TRUE(helpers::segmentIndicesValidate(block.launchContext(), idxSegments, expected, wrong), 0, "segment_sum: segment indices should be arranged, but %2.1f > %2.1f", expected.e<float>(0), wrong.e<float>(0));
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segmentedOutput->nullify();
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helpers::segmentSumFunctor(block.launchContext(), input, idxSegments, segmentedOutput);
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return ND4J_STATUS_OK;
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}
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DECLARE_SHAPE_FN(segment_sum) {
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auto idxVector = INPUT_VARIABLE(1);
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auto in = inputShape->at(0);
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int outRank = shape::rank(in);
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Nd4jLong* outputShape = nullptr;
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int val = (*idxVector).e<int>(idxVector->lengthOf() - 1);
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int numOfClasses = static_cast<int>(val) + 1;
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ALLOCATE(outputShape, block.getWorkspace(), shape::shapeInfoLength(outRank), Nd4jLong);
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outputShape[0] = outRank;
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outputShape[1] = numOfClasses;
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for(int i = 1; i < outRank; ++i)
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outputShape[i + 1] = shape::sizeAt(in, i);
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ShapeUtils::updateStridesAndType(outputShape, in, shape::order(in));
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return SHAPELIST(CONSTANT(outputShape));
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}
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CUSTOM_OP_IMPL(segment_sum_bp, 3, 2, false, 0, 0) {
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return helpers::segmentSumFunctorBP(block.launchContext(), INPUT_VARIABLE(0), INPUT_VARIABLE(1), INPUT_VARIABLE(2), OUTPUT_VARIABLE(0));
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}
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DECLARE_SHAPE_FN(segment_sum_bp){
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Nd4jLong* in = inputShape->at(0);
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Nd4jLong* inIdx = inputShape->at(1);
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Nd4jLong* outShape;
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Nd4jLong* outIndex;
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COPY_SHAPE(in, outShape);
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COPY_SHAPE(inIdx, outIndex);
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return SHAPELIST(CONSTANT(outShape), CONSTANT(outIndex));
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}
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DECLARE_TYPES(segment_sum) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setSameMode(true);
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}
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DECLARE_TYPES(segment_sum_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes(0, {ALL_FLOATS})
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->setAllowedOutputTypes(1, {ALL_INTS})
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->setSameMode(false);
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}
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}
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}
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