/*
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
// Contains the implementation of the operations.
#define LOG_TAG "Operations"
#include "CpuOperationUtils.h"
#include "Operations.h"
#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
#include "tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h"
#include "Tracing.h"
namespace android {
namespace nn {
bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape) {
NNTRACE_TRANS("floorFloat16");
std::vector<float> inputDataFloat32(getNumberOfElements(shape));
convertFloat16ToFloat32(inputData, &inputDataFloat32);
std::vector<float> outputDataFloat32(getNumberOfElements(shape));
floorFloat32(inputDataFloat32.data(), outputDataFloat32.data(), shape);
convertFloat32ToFloat16(outputDataFloat32, outputData);
return true;
}
bool floorFloat32(const float* inputData, float* outputData, const Shape& shape) {
NNTRACE_TRANS("floorFloat32");
tflite::Dims<4> dim = convertShapeToDims(shape);
NNTRACE_COMP_SWITCH("optimized_ops::Floor");
tflite::optimized_ops::Floor(inputData, dim, outputData, dim);
return true;
}
bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
const Shape& axisShape, bool keepDims, _Float16* outputData,
const Shape& outputShape) {
NNTRACE_TRANS("meanFloat16");
std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
convertFloat16ToFloat32(inputData, &inputDataFloat32);
std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
meanGeneric<float, float>(inputDataFloat32.data(), inputShape, axis, axisShape, keepDims,
outputDataFloat32.data(), outputShape);
convertFloat32ToFloat16(outputDataFloat32, outputData);
return true;
}
template <typename T, typename U>
bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape,
bool keepDims, T* outputData, const Shape& outputShape) {
NNTRACE_TRANS("meanGeneric");
// Creates a temp index to iterate through input data.
int32_t* scratchBuffer = new int32_t[getNumberOfDimensions(inputShape)];
// Creates a temp tensor to store resolved axis given input data.
int32_t axisSize = static_cast<int32_t>(getSizeOfDimension(axisShape, 0));
int32_t* resolvedAxis = new int32_t[axisSize];
bool result = true;
U* tempSumBuffer = new (std::nothrow) U[getNumberOfElements(outputShape)];
if (!tempSumBuffer) {
LOG(ERROR) << "Failed to allocate tempSumBuffer for MEAN";
result = false;
} else {
NNTRACE_COMP_SWITCH("optimized_ops::Mean");
tflite::reference_ops::Mean<T, U>(
inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()),
getNumberOfDimensions(inputShape), outputData,
reinterpret_cast<const int*>(outputShape.dimensions.data()),
getNumberOfDimensions(outputShape), axis, axisSize, keepDims, scratchBuffer,
resolvedAxis, tempSumBuffer);
delete[] tempSumBuffer;
}
delete[] scratchBuffer;
delete[] resolvedAxis;
return result;
}
template bool meanGeneric<float, float>(float* inputData, const Shape& inputShape,
const int32_t* axis, const Shape& axisShape, bool keepDims,
float* outputData, const Shape& outputShape);
template bool meanGeneric<uint8_t, int32_t>(uint8_t* inputData, const Shape& inputShape,
const int32_t* axis, const Shape& axisShape,
bool keepDims, uint8_t* outputData,
const Shape& outputShape);
} // namespace nn
} // namespace android