cavis/libnd4j/include/ops/declarable/generic/parity_ops/unstack.cpp

140 lines
5.4 KiB
C++

/*******************************************************************************
* 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
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_unstack)
#include <ops/declarable/CustomOperations.h>
#include <helpers/ConstantTadHelper.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(unstack, 1, -1, false, 0, 1) {
auto input = INPUT_VARIABLE(0);
auto dim = INT_ARG(0);
if (dim < 0)
dim += input->rankOf();
REQUIRE_TRUE(dim < input->rankOf(), 0, "Unstack dimension should be lower then rank of input %i, but got dimension=%i !", input->rankOf(), dim);
REQUIRE_TRUE(dim >= 0, 0, "Unstack dimension should be non-negative value, but got %i !", dim);
if(input->isEmpty())
return Status::OK();
std::vector<int> dims;
for (int e = 0; e < input->rankOf(); e++)
if (e != dim)
dims.emplace_back(e);
if (dims.size() == 0 && input->rankOf() == 1) { // split vector into lenthOf scalars
for (Nd4jLong e = 0; e < input->lengthOf(); e++) {
auto outE = OUTPUT_VARIABLE(e);
outE->assign(input->e(e));
}
}
auto tads = input->allTensorsAlongDimension(dims);
//nd4j_printf("Tad size: %d\n",tads.size());
for (int e = 0; e < tads.size(); e++) {
//nd4j_printf("Calling assign at index %d\n",e);
auto outE = OUTPUT_VARIABLE(e);
auto tadAtE = tads.at(e);
outE->assign(tadAtE);
this->storeResult(block, e, *outE);
}
return Status::OK();
}
DECLARE_SYN(unpack, unstack);
DECLARE_SHAPE_FN(unstack) {
auto inShape = inputShape->at(0);
auto dim = INT_ARG(0);
if (dim < 0)
dim += shape::rank(inShape);
REQUIRE_TRUE(dim < inShape[0], 0, "UNSTACK op: dimension should be lower then rank of input %i, but got dimension=%i !", inShape[0], dim);
REQUIRE_TRUE(dim >= 0, 0, "UNSTACK op: dimension should be non-negative value, but got %i !", dim);
if(ArrayOptions::arrayType(inShape) == ArrayType::EMPTY) {
if(shape::shapeOf(inShape)[dim] == 0)
return SHAPELIST();
const Nd4jLong numTads = shape::shapeOf(inShape)[dim];
std::vector<Nd4jLong> outShape;
for(uint i = 0; i < shape::rank(inShape); ++i)
if(i != dim)
outShape.push_back(shape::shapeOf(inShape)[i]);
auto result = SHAPELIST();
for(uint i = 0; i < numTads; ++i)
result->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), outShape));
return result;
}
std::vector<int> dims;
for (int e = 0; e < shape::rank(inShape); e++)
if (e != dim)
dims.emplace_back(e);
if (dims.size() == 0 && shape::rank(inShape) == 1) { // split vector into lenthOf scalars
//
auto result = SHAPELIST();
for (Nd4jLong e = 0; e < shape::length(inShape); e++)
result->push_back(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inShape)));
return result;
}
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(inShape, dims);
auto numTads = tadPack.numberOfTads();
std::vector<Nd4jLong> shape(shape::rank(tadPack.primaryShapeInfo()));
for (int e = 0; e < shape::rank(tadPack.primaryShapeInfo()); e++)
shape[e] = shape::shapeOf(tadPack.primaryShapeInfo())[e];
// remove leading and trailing 1
if (inShape[0] == 2 && shape.size() == 2) {
if (shape[0] == 1) {
shape.erase(shape.begin());
} else if (shape[1] == 1) {
shape.erase(shape.end());
}
}
auto result = SHAPELIST();
for (int e = 0; e < numTads; e++) {
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), shape);
result->push_back(newShape);
}
return result;
}
DECLARE_TYPES(unstack) {
getOpDescriptor()
->setAllowedInputTypes({ALL_FLOATS, ALL_INTS})
->setSameMode(true);
}
}
}
#endif