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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2019 The TensorFlow Authors. 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 "kernels/Slice.h"
#include "Utils.h"
#include "PALSlice.h"
#include <cassert>
#include <cstring>
namespace luci_interpreter
{
namespace kernels
{
const int max_dim = 4;
Slice::Slice(const Tensor *input, const Tensor *begin, const Tensor *size, Tensor *output)
: Kernel({input, begin, size}, {output})
{
}
template <typename T>
Shape calculateOutputShape(const Tensor *input, const Tensor *begin, const Tensor *size)
{
Shape output_shape = Shape(input->shape().num_dims());
for (int idx = 0; idx < input->shape().num_dims(); idx++)
{
T size_value = getTensorData<T>(size)[idx];
if (size_value < 0)
{
if (size_value != -1)
{
assert(false && "Invalid size.");
}
size_value = input->shape().dim(idx) - getTensorData<T>(begin)[idx];
}
else
{
if (input->shape().dim(idx) < getTensorData<T>(begin)[idx] + size_value)
{
assert(false && "Invalid begin and size.");
}
}
output_shape.dim(idx) = static_cast<int>(size_value);
}
return output_shape;
}
template <typename T>
void getBeginAndSizeVectors(int dimensions, const Tensor *begin, const Tensor *size,
std::vector<int> *begins, std::vector<int> *sizes)
{
for (int idx = dimensions - 1; idx >= 0; --idx)
{
begins->push_back(getTensorData<T>(begin)[idx]);
sizes->push_back(getTensorData<T>(size)[idx]);
}
}
void Slice::configure()
{
assert(input()->element_type() == output()->element_type());
assert(begin()->element_type() == DataType::S32 || begin()->element_type() == DataType::S64);
assert(size()->element_type() == DataType::S32 || size()->element_type() == DataType::S64);
assert(begin()->shape().num_dims() == 1);
assert(size()->shape().num_dims() == 1);
assert(input()->shape().num_dims() <= max_dim);
// TODO: enable it only if kernel with dynamic shapes
if (begin()->element_type() == DataType::S32)
{
output()->resize(calculateOutputShape<int32_t>(input(), begin(), size()));
}
else if (begin()->element_type() == DataType::S64)
{
output()->resize(calculateOutputShape<int64_t>(input(), begin(), size()));
}
else
{
assert(false && "Unsupported type.");
}
}
void Slice::execute() const
{
std::vector<int> begins;
begins.reserve(max_dim);
std::vector<int> sizes;
sizes.reserve(max_dim);
if (begin()->element_type() == DataType::S32)
{
getBeginAndSizeVectors<int32_t>(input()->shape().num_dims(), begin(), size(), &begins, &sizes);
}
else if (begin()->element_type() == DataType::S64)
{
getBeginAndSizeVectors<int64_t>(input()->shape().num_dims(), begin(), size(), &begins, &sizes);
}
else
{
assert(false && "Unsupported begin type.");
}
for (int i = input()->shape().num_dims(); i < max_dim; ++i)
{
begins.push_back(0);
sizes.push_back(1);
}
assert(begins.size() == 4);
assert(sizes.size() == 4);
tflite::SliceParams op_params{};
op_params.begin_count = 4;
op_params.size_count = 4;
for (int i = 0; i < 4; i++)
{
op_params.begin[i] = begins[3 - i];
op_params.size[i] = sizes[3 - i];
}
switch (input()->element_type())
{
case DataType::FLOAT32:
luci_interpreter_pal::Slice(op_params, getTensorShape(input()), getTensorData<float>(input()),
getTensorShape(output()), getTensorData<float>(output()));
break;
case DataType::U8:
luci_interpreter_pal::Slice(op_params, getTensorShape(input()),
getTensorData<uint8_t>(input()), getTensorShape(output()),
getTensorData<uint8_t>(output()));
break;
case DataType::S8:
luci_interpreter_pal::Slice(op_params, getTensorShape(input()),
getTensorData<int8_t>(input()), getTensorShape(output()),
getTensorData<int8_t>(output()));
break;
default:
assert(false && "Unsupported input type.");
}
}
} // namespace kernels
} // namespace luci_interpreter
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