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/*
* Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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.
*/
#include "SplitV.h"
#include "Utils.h"
#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
namespace luci_interpreter
{
namespace kernels
{
SplitV::SplitV(const Tensor *input, const Tensor *size_splits, const Tensor *axis,
std::vector<Tensor *> outputs)
: Kernel({input, size_splits, axis}, std::move(outputs))
{
}
void SplitV::configure()
{
assert(axis()->shape().num_elements() == 1);
_axis_value = getTensorData<int32_t>(axis())[0];
if (_axis_value < 0)
_axis_value += input()->shape().num_dims();
assert(_axis_value >= 0 && _axis_value < input()->shape().num_dims());
auto num_split = static_cast<int32_t>(_outputs.size());
auto sizes_data = getTensorData<int32_t>(size_splits());
assert(size_splits()->shape().num_dims() == 1);
int32_t sum = 0;
const auto num_dims_size_spits = size_splits()->shape().dim(0);
int32_t count_neg_dim = 0;
for (int32_t i = 0; i < num_dims_size_spits - 1; ++i)
{
if (sizes_data[i] != -1)
{
sum += sizes_data[i];
}
else
{
count_neg_dim++;
}
}
assert(count_neg_dim < 2);
assert(size_splits()->shape().num_elements() == num_split);
auto output_shape = input()->shape();
for (int32_t i = 0; i < num_split; ++i)
{
if (sizes_data[i] == -1)
{
output_shape.dim(_axis_value) = input()->shape().dim(_axis_value) - sum;
}
else
{
output_shape.dim(_axis_value) = sizes_data[i];
}
_outputs[i]->resize(output_shape);
}
}
void SplitV::execute() const
{
tflite::SplitParams params{};
params.num_split = _outputs.size();
params.axis = _axis_value;
#define TF_LITE_SPLIT(scalar) \
{ \
VectorOfTensors<scalar, false> all_outputs(_outputs); \
tflite::optimized_ops::Split(params, getTensorShape(input()), getTensorData<scalar>(input()), \
all_outputs.shapes(), all_outputs.data()); \
}
switch (input()->element_type())
{
case DataType::FLOAT32:
TF_LITE_SPLIT(float);
break;
case DataType::U8:
TF_LITE_SPLIT(uint8_t);
break;
case DataType::S16:
TF_LITE_SPLIT(int16_t);
break;
default:
throw std::runtime_error("Unsupported type.");
}
#undef TF_LITE_SPLIT
}
} // namespace kernels
} // namespace luci_interpreter
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