cavis/libnd4j/include/ops/declarable/generic/activations/prelu.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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 24.07.2018
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_prelu)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/activations.h>
#include <numeric>
namespace nd4j {
namespace ops {
////////////////////////////////////////////////////////////////////////
CONFIGURABLE_OP_IMPL(prelu, 2, 1, true, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto alpha = INPUT_VARIABLE(1);
auto output = OUTPUT_VARIABLE(0);
std::vector<int> sharedAxes = *block.getIArguments();
const int inputRank = input->rankOf();
const int alphaRank = alpha->rankOf();
const int numSharedAxes = sharedAxes.size(); // can be zero as well
const Nd4jLong inputLen = input->lengthOf();
const Nd4jLong alphaLen = alpha->lengthOf();
const std::vector<Nd4jLong> inputShape = input->getShapeAsVector();
const std::vector<Nd4jLong> alphaShape = alpha->getShapeAsVector();
//***** input validation *****//
std::vector<Nd4jLong> expectedAlphaShape(&inputShape[1], &inputShape[inputRank]);
REQUIRE_TRUE(inputRank > 1, 0, "PRELU OP: wrong rank of input array, expected rank should be > 1, but got %i instead !", inputRank);
for(int i = 0; i < numSharedAxes; ++i) {
if(sharedAxes[i] <= 0)
sharedAxes[i] += inputRank - 1;
REQUIRE_TRUE(1 <= sharedAxes[i] && sharedAxes[i] <= inputRank - 1, 0, "PRELU OP: wrong axis value %i in sharedAxes at position %i, axis value must be within range [1, input_rank-1] !", sharedAxes[i], i);
expectedAlphaShape[sharedAxes[i] - 1] = 1;
}
Nd4jLong product = 1;
for(const auto& item : expectedAlphaShape)
product *= item;
REQUIRE_TRUE(product == alphaLen, 0, "PRELU OP: wrong shape of alpha array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedAlphaShape).c_str(), ShapeUtils::shapeAsString(alphaShape).c_str());
// ***** end of validation ***** //
if(alphaShape != expectedAlphaShape)
alpha = alpha->reshape(alpha->ordering(), expectedAlphaShape);
helpers::prelu(block.launchContext(), *input, *alpha, *output);
if(alphaShape != expectedAlphaShape)
delete alpha;
return Status::OK();
}
DECLARE_TYPES(prelu) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedOutputTypes(0, {ALL_FLOATS});
}
////////////////////////////////////////////////////////////////////////
CONFIGURABLE_OP_IMPL(prelu_bp, 3, 2, true, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto alpha = INPUT_VARIABLE(1);
auto dLdO = INPUT_VARIABLE(2);
auto dLdI = OUTPUT_VARIABLE(0);
auto dLdA = OUTPUT_VARIABLE(1);
std::vector<int> sharedAxes = *block.getIArguments();
const int inputRank = input->rankOf();
const int alphaRank = alpha->rankOf();
const int numSharedAxes = sharedAxes.size(); // can be zero as well
const Nd4jLong inputLen = input->lengthOf();
const Nd4jLong alphaLen = alpha->lengthOf();
const std::vector<Nd4jLong> inputShape = input->getShapeAsVector();
const std::vector<Nd4jLong> alphaShape = alpha->getShapeAsVector();
//***** input validation *****//
// temporary limitation imposed by Yurii
REQUIRE_TRUE(inputRank <= MAX_RANK/2, 0, "rank of input array should be <= MAX_RANK/2, but got %i instead!", inputRank);
REQUIRE_TRUE(input->lengthOf() / alpha->lengthOf() <= MAX_RANK*2, 0, "the length of input array should be no more than MAX_RANK*2 times the alpha array length, but got %lld and %lld correspondingly!", input->lengthOf(), alpha->lengthOf());
std::vector<Nd4jLong> expectedAlphaShape(&inputShape[1], &inputShape[inputRank]);
REQUIRE_TRUE(inputRank > 1, 0, "PRELU_BP OP: wrong rank of input array, expected rank should be > 1, but got %i instead !", inputRank);
for(int i = 0; i < numSharedAxes; ++i) {
if(sharedAxes[i] <= 0)
sharedAxes[i] += inputRank - 1;
REQUIRE_TRUE(1 <= sharedAxes[i] && sharedAxes[i] <= inputRank - 1, 0, "PRELU_BP OP: wrong axis value %i in sharedAxes at position %i, axis value must be within range [1, input_rank-1] !", sharedAxes[i], i);
expectedAlphaShape[sharedAxes[i] - 1] = 1;
}
Nd4jLong product = 1;
for(const auto& item : expectedAlphaShape)
product *= item;
REQUIRE_TRUE(product == alphaLen, 0, "PRELU_BP OP: wrong shape of alpha array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedAlphaShape).c_str(), ShapeUtils::shapeAsString(alphaShape).c_str());
// ***** end of validation ***** //
if(alphaShape != expectedAlphaShape) {
alpha = alpha->reshape(alpha->ordering(), expectedAlphaShape);
dLdA = dLdA->reshape(dLdA->ordering(), expectedAlphaShape);
}
helpers::preluBP(block.launchContext(), *input, *alpha, *dLdO, *dLdI, *dLdA);
if(alphaShape != expectedAlphaShape) {
delete alpha;
delete dLdA;
}
return Status::OK();
}
DECLARE_TYPES(prelu_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedInputTypes(2, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedOutputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
}
}
}
#endif