105 lines
3.9 KiB
C++
105 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 01.11.2017.
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_stack)
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/stack.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(stack, -1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0;
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if(dim < 0)
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dim += input->rankOf() + 1;
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// input validation
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// check whether shapes of all input array are the same
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for (int i = 0; i < (int) block.width() - 1; ++i)
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REQUIRE_TRUE(shape::equalsSoft((INPUT_VARIABLE(i))->getShapeInfo(), (INPUT_VARIABLE(i+1))->getShapeInfo()), 0, "STACK op: the shapes of all input arrays must be the same !");
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REQUIRE_TRUE(dim <= input->rankOf(), 0, "STACK op: the input dimension parameter must be <= rank of input arrays shapes (rank=%i), but got %i instead !", input->shapeOf(), dim);
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std::vector<NDArray*> inArrs(block.width());
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for(int i = 0; i < block.width(); ++i)
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inArrs[i] = INPUT_VARIABLE(i);
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helpers::stack(block.launchContext(), inArrs, output, dim);
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// remove unity from output shape if input arrays are vectors
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// if(input->isVector()) {
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// std::vector<int> outShape(output->shapeOf(), output->shapeOf() + output->rankOf());
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// outShape.erase(find(outShape.begin(), outShape.end(), 1));
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// output->reshapei(output->ordering(), outShape);
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// if(dim != 0 && (int)block.width() == 1) // such is implemented by tensorFlow
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// output->permutei({1, 0});
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// output->getShapeInfo()[output->rankOf()*2 + 2] = 1;
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// }
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return Status::OK();
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}
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DECLARE_SYN(pack, stack);
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DECLARE_SYN(Pack, stack);
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DECLARE_TYPES(stack) {
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//getOpDescriptor()->setSameMode(true);
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getOpDescriptor()
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->setAllowedInputTypes(DataType::ANY)
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->setAllowedOutputTypes(DataType::ANY);
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}
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DECLARE_SHAPE_FN(stack) {
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// check whether input dimension is within rank range
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auto inShapeInfo = inputShape->at(0);
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int rank = shape::rank(inShapeInfo);
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int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0;
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if(dim < 0 )
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dim += rank + 1;
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REQUIRE_TRUE(dim <= inShapeInfo[0], 0, "STACK op: the input dimension parameter must be <= rank of input arrays shapes (rank=%i), but got %i instead !", inShapeInfo[0], dim);
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if(rank == 0) {
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return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(block.width(), ArrayOptions::dataType(inShapeInfo)));
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}
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//the rank of output ShapeInfo is larger by one compared to input ShapeInfo
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std::vector<Nd4jLong> outShape(inShapeInfo + 1, inShapeInfo + 1 + rank);
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// insert (int) block.width() at dim position of input shape to get output shape
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outShape.insert(outShape.begin() + Nd4jLong(dim), (Nd4jLong) block.width());
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShapeInfo), shape::order(inShapeInfo), outShape)));
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}
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// 1) 1х4 + 1х4 = 2х1х4 (along dim=0) = 2x4
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// 2) 1х4 + 1х4 = 1х2х4 (along dim=1) = 2x4
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// 3) 4х1 + 4х1 = 2х4x1 (along dim=0) = 2x4
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// 4) 4х1 + 4х1 = 4х2x1 (along dim=1) = 4x2
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}
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}
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#endif
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