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

161 lines
5.8 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 Yurii Shyrma (iuriish@yahoo.com), created on 01.06.2018
//
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_mean, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
auto dimensions = *block.getIArguments();
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
}
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);
REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
for(const auto& item : dimensions)
REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_MEAN OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item);
input->reduceAlongDimension(reduce::Mean, *output, dimensions, keepDims);
return Status::OK();
}
DECLARE_SHAPE_FN(reduce_mean) {
auto dimensions = *block.getIArguments();
auto in = inputShape->at(0);
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(shape::rank(in), axesVector, dimensions);
}
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);
REQUIRE_TRUE(dimensions.size() <= in[0], 0, "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
for(const auto& item : dimensions)
REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0, "REDUCE_MEAN OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , inputShape->at(0)[0], inputShape->at(0)[0], item);
auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(in), dimensions, in, keepDims, false, block.getWorkspace());
return SHAPELIST(outShapeInfo);
}
DECLARE_TYPES(reduce_mean) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_mean_bp, 2, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto gradO = INPUT_VARIABLE(1);
auto gradI = OUTPUT_VARIABLE(0);
auto dimensions = *block.getIArguments();
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
}
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);
REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
for(const auto& item : dimensions)
REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_MEAN_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item);
if(gradO->lengthOf() == 1) {
gradI->assign(gradO->e(0) / input->lengthOf());
}
else {
gradI->assign((gradO->lengthOf() + 0.) / input->lengthOf());
if(!keepDims) {
auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), dimensions, *input, true, false, block.getWorkspace());
*gradI *= gradO->reshape(gradO->ordering(), ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims)); // for example could be something like [a,b] -> [1,a,1,b]
}
else
*gradI *= *gradO;
}
return Status::OK();
}
DECLARE_SHAPE_FN(reduce_mean_bp) {
auto in = inputShape->at(0);
auto dimensions = *block.getIArguments();
auto rank = shape::rank(in);
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(rank, axesVector, dimensions);
}
REQUIRE_TRUE(dimensions.size() <= rank, 0, "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
for(const auto& item : dimensions)
REQUIRE_TRUE(item >= -rank || item < rank, 0, "REDUCE_MEAN_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , rank, rank, item);
Nd4jLong* gradIshapeInfo(nullptr);
COPY_SHAPE(inputShape->at(0), gradIshapeInfo);
return SHAPELIST(CONSTANT(gradIshapeInfo));
}
DECLARE_TYPES(reduce_mean_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}