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/*
* Copyright (c) 2020 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 "kernels/AveragePool2D.h"
#include "kernels/Utils.h"
#include "PALAveragePool2d.h"
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
AveragePool2D::AveragePool2D(const Tensor *input, Tensor *output, Tensor *scratchpad,
const Pool2DParams ¶ms)
: KernelWithParams<Pool2DParams>({input}, {output, scratchpad}, params)
{
}
void AveragePool2D::configure()
{
if (input()->element_type() != output()->element_type())
{
throw std::runtime_error("Input Tensor and Output Tensor Type must be same");
}
if (input()->shape().num_dims() != 4)
{
throw std::runtime_error("Input Tensor Shape must be 4-D");
}
const Shape &input_shape = input()->shape();
const int32_t batches = input_shape.dim(0);
const int32_t input_height = input_shape.dim(1);
const int32_t input_width = input_shape.dim(2);
const int32_t depth = input_shape.dim(3);
const int32_t output_height =
computeOutputSize(_params.padding, input_height, _params.filter_height, _params.stride_height);
const int32_t output_width =
computeOutputSize(_params.padding, input_width, _params.filter_width, _params.stride_width);
_padding_height =
computePadding(_params.stride_height, 1, input_height, _params.filter_height, output_height);
_padding_width =
computePadding(_params.stride_width, 1, input_width, _params.filter_width, output_width);
if (input()->element_type() == DataType::U8)
{
LUCI_INTERPRETER_CHECK(std::abs(output()->scale() - input()->scale()) <= 1.0e-6);
LUCI_INTERPRETER_CHECK(output()->zero_point() == input()->zero_point());
}
else if (input()->element_type() == DataType::S16)
{
LUCI_INTERPRETER_CHECK(std::abs(output()->scale() - input()->scale()) <= 1.0e-6);
LUCI_INTERPRETER_CHECK(input()->zero_point() == 0 && output()->zero_point() == 0);
}
else if (input()->element_type() == DataType::S8)
{
LUCI_INTERPRETER_CHECK(std::abs(output()->scale() - input()->scale()) <= 1.0e-6);
LUCI_INTERPRETER_CHECK(output()->zero_point() == input()->zero_point());
}
output()->resize({batches, output_height, output_width, depth});
auto scratchpad = getOutputTensors()[1];
luci_interpreter_pal::SetupScratchpadTensor(scratchpad, input()->element_type(),
getTensorShape(input()), getTensorShape(output()));
}
void AveragePool2D::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
case DataType::S16:
evalSInt16();
break;
case DataType::S8:
evalSInt8();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void AveragePool2D::evalFloat() const
{
float activation_min{};
float activation_max{};
calculateActivationRange(_params.activation, &activation_min, &activation_max);
tflite::PoolParams params{};
params.padding_values.height = _padding_height;
params.padding_values.width = _padding_width;
params.stride_height = _params.stride_height;
params.stride_width = _params.stride_width;
params.filter_height = _params.filter_height;
params.filter_width = _params.filter_width;
params.float_activation_min = activation_min;
params.float_activation_max = activation_max;
tflite::reference_ops::AveragePool(params, getTensorShape(input()), getTensorData<float>(input()),
getTensorShape(output()), getTensorData<float>(output()));
}
void AveragePool2D::evalQuantized() const
{
int32_t activation_min{};
int32_t activation_max{};
calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
tflite::PoolParams params{};
params.padding_values.height = _padding_height;
params.padding_values.width = _padding_width;
params.stride_height = _params.stride_height;
params.stride_width = _params.stride_width;
params.filter_height = _params.filter_height;
params.filter_width = _params.filter_width;
params.quantized_activation_min = activation_min;
params.quantized_activation_max = activation_max;
tflite::reference_ops::AveragePool(params, getTensorShape(input()),
getTensorData<uint8_t>(input()), getTensorShape(output()),
getTensorData<uint8_t>(output()));
}
void AveragePool2D::evalSInt8() const
{
int32_t activation_min{};
int32_t activation_max{};
calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
tflite::PoolParams params{};
params.padding_values.height = _padding_height;
params.padding_values.width = _padding_width;
params.stride_height = _params.stride_height;
params.stride_width = _params.stride_width;
params.filter_height = _params.filter_height;
params.filter_width = _params.filter_width;
params.quantized_activation_min = activation_min;
params.quantized_activation_max = activation_max;
auto scratchpad = getOutputTensors()[1];
int8_t *scratchpad_data = nullptr;
if (scratchpad->is_allocatable())
scratchpad_data = scratchpad->data<int8_t>();
luci_interpreter_pal::AveragePool<int8_t>(
params, getTensorShape(input()), getTensorData<int8_t>(input()), getTensorShape(output()),
getTensorData<int8_t>(output()), getTensorShape(scratchpad), scratchpad_data);
}
void AveragePool2D::evalSInt16() const
{
int32_t activation_min{};
int32_t activation_max{};
calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
tflite::PoolParams params{};
params.padding_values.height = _padding_height;
params.padding_values.width = _padding_width;
params.stride_height = _params.stride_height;
params.stride_width = _params.stride_width;
params.filter_height = _params.filter_height;
params.filter_width = _params.filter_width;
params.quantized_activation_min = activation_min;
params.quantized_activation_max = activation_max;
tflite::reference_integer_ops::AveragePool(
params, getTensorShape(input()), getTensorData<int16_t>(input()), //
getTensorShape(output()), getTensorData<int16_t>(output()));
}
} // namespace kernels
} // namespace luci_interpreter
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