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/*
* Copyright (C) 2018 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.
*/
#define LOG_TAG "Operations"
#include <algorithm>
#include <utility>
#include "Tile.h"
#include "HalInterfaces.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace tile {
namespace {
using namespace hal;
template <typename T>
void CopyMultipleTimes(const T* in_data, int32_t in_size, int32_t multiplier, T* out_data) {
for (int i = 0; i < multiplier; ++i) {
const T* in_end = in_data + in_size;
T* new_out_data = std::copy(in_data, in_end, out_data);
in_data = out_data;
out_data = new_out_data;
}
}
template <typename T, typename M>
std::pair<int, int> TileOneDimension(const Shape& input_shape, const T* in_data,
const M* multipliers, T* out_data, int dimension) {
const int dimension_size = input_shape.dimensions[dimension];
if (dimension == input_shape.dimensions.size() - 1) {
CopyMultipleTimes(in_data, dimension_size, multipliers[dimension], out_data);
return std::make_pair(dimension_size,
dimension_size * static_cast<int>(multipliers[dimension]));
}
int total_stride_size = 0, total_tiled_stride_size = 0;
const T* copy_from_data = in_data;
T* copy_to_data = out_data;
for (int i = 0; i < dimension_size; ++i) {
int stride_size = 0, tiled_stride_size = 0;
std::tie(stride_size, tiled_stride_size) = TileOneDimension(
input_shape, copy_from_data, multipliers, copy_to_data, dimension + 1);
copy_from_data += stride_size;
copy_to_data += tiled_stride_size;
total_stride_size += stride_size;
total_tiled_stride_size += tiled_stride_size;
}
CopyMultipleTimes(out_data, total_tiled_stride_size, multipliers[dimension] - 1,
out_data + total_tiled_stride_size);
return std::make_pair(total_stride_size, total_tiled_stride_size * multipliers[dimension]);
}
template <typename T>
void tileImpl(const T* inputData, const Shape& inputShape, const int32_t* multiples, T* outputData,
const Shape& outputShape) {
TileOneDimension(inputShape, inputData, multiples, outputData, 0);
}
} // namespace
bool prepare(const Shape& input, const int32_t* multiples, const Shape& multiplesShape,
Shape* output) {
output->type = input.type;
output->offset = input.offset;
output->scale = input.scale;
output->dimensions.assign(input.dimensions.begin(), input.dimensions.end());
for (size_t i = 0; i < output->dimensions.size(); ++i) {
output->dimensions[i] *= multiples[i];
}
return true;
}
bool eval(const uint8_t* inputData, const Shape& inputShape, const int32_t* multiples,
uint8_t* outputData, const Shape& outputShape) {
NNTRACE_TRANS("tile::eval");
#define ANDROID_NN_IMPL_TILE(operandType, dataType) \
case operandType: { \
NNTRACE_COMP_SWITCH("tileImpl::" #dataType); \
tileImpl(reinterpret_cast<const dataType*>(inputData), inputShape, multiples, \
reinterpret_cast<dataType*>(outputData), outputShape); \
return true; \
}
switch (inputShape.type) {
ANDROID_NN_IMPL_TILE(OperandType::TENSOR_FLOAT16, _Float16);
ANDROID_NN_IMPL_TILE(OperandType::TENSOR_FLOAT32, float);
ANDROID_NN_IMPL_TILE(OperandType::TENSOR_INT32, int32_t);
ANDROID_NN_IMPL_TILE(OperandType::TENSOR_QUANT8_ASYMM, uint8_t);
ANDROID_NN_IMPL_TILE(OperandType::TENSOR_QUANT8_ASYMM_SIGNED, int8_t);
default:
LOG(ERROR) << "Unsupported data type";
return false;
}
#undef ANDROID_NN_IMPL_TILE
}
} // namespace tile
} // namespace nn
} // namespace android
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