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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2018 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/Squeeze.h"
#include "kernels/Utils.h"
namespace luci_interpreter
{
namespace kernels
{
Squeeze::Squeeze(const Tensor *input, Tensor *output, const SqueezeParams ¶ms)
: KernelWithParams<SqueezeParams>({input}, {output}, params)
{
}
void Squeeze::configure()
{
int input_num_dims = input()->shape().num_dims();
int num_squeeze_dims = params().squeeze_dims.size();
assert(input_num_dims <= 8);
bool should_squeeze[8] = {false};
int num_squeezed_dims = 0;
if (num_squeeze_dims == 0)
{
for (int idx = 0; idx < input_num_dims; ++idx)
{
if (input()->shape().dim(idx) == 1)
{
should_squeeze[idx] = true;
++num_squeezed_dims;
}
}
}
else
{
for (int idx = 0; idx < num_squeeze_dims; ++idx)
{
int current = params().squeeze_dims[idx] < 0 ? params().squeeze_dims[idx] + input_num_dims
: params().squeeze_dims[idx];
assert(current >= 0 && current < input_num_dims && input()->shape().dim(current) == 1);
if (!should_squeeze[current])
++num_squeezed_dims;
should_squeeze[current] = true;
}
}
// TODO: enable it only if kernel with dynamic shapes
Shape output_shape(input_num_dims - num_squeezed_dims);
for (int in_idx = 0, out_idx = 0; in_idx < input_num_dims; ++in_idx)
{
if (!should_squeeze[in_idx])
{
output_shape.dim(out_idx++) = input()->shape().dim(in_idx);
}
}
output()->resize(output_shape);
}
void Squeeze::execute() const
{
assert(input()->shape().num_elements() == output()->shape().num_elements());
const auto *input_data = input()->data<void>();
auto *output_data = output()->data<void>();
std::memcpy(output_data, input_data,
getDataTypeSize(input()->element_type()) * input()->shape().num_elements());
}
} // namespace kernels
} // namespace luci_interpreter
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