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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 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/L2Pool2D.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
L2Pool2D::L2Pool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms)
: KernelWithParams<Pool2DParams>({input}, {output}, params)
{
}
void L2Pool2D::configure()
{
LUCI_INTERPRETER_CHECK(input()->shape().num_dims() == 4);
LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type());
int batches = input()->shape().dim(0);
int height = input()->shape().dim(1);
int width = input()->shape().dim(2);
int channels_out = input()->shape().dim(3);
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params().padding;
int out_width, out_height;
out_width = computeOutputSize(padding, width, params().filter_width, params().stride_width, 1);
out_height =
computeOutputSize(padding, height, params().filter_height, params().stride_height, 1);
_padding_width =
computePadding(params().stride_width, 1, width, params().filter_width, out_width);
_padding_height =
computePadding(params().stride_height, 1, height, params().filter_height, out_height);
LUCI_INTERPRETER_CHECK(input()->element_type() == DataType::FLOAT32);
output()->resize({batches, out_height, out_width, channels_out});
}
void L2Pool2D::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
float activation_min, activation_max;
calculateActivationRange(params().activation, &activation_min, &activation_max);
tflite::PoolParams op_params;
op_params.stride_height = params().stride_height;
op_params.stride_width = params().stride_width;
op_params.filter_height = params().filter_height;
op_params.filter_width = params().filter_width;
op_params.padding_values.height = _padding_height;
op_params.padding_values.width = _padding_width;
op_params.float_activation_min = activation_min;
op_params.float_activation_max = activation_max;
tflite::optimized_ops::L2Pool(op_params, getTensorShape(input()),
getTensorData<float>(input()), getTensorShape(output()),
getTensorData<float>(output()));
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
} // namespace kernels
} // namespace luci_interpreter
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