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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/InstanceNorm.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/common.h>
#include <cmath>
namespace luci_interpreter
{
namespace kernels
{
InstanceNorm::InstanceNorm(const Tensor *input, const Tensor *gamma, const Tensor *beta,
Tensor *output, const InstanceNormParams ¶ms)
: KernelWithParams<InstanceNormParams>({input, gamma, beta}, {output}, params)
{
}
void InstanceNorm::configure()
{
LUCI_INTERPRETER_CHECK(input()->shape().num_dims() == 4);
LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type());
LUCI_INTERPRETER_CHECK(gamma()->element_type() == input()->element_type());
LUCI_INTERPRETER_CHECK(beta()->element_type() == input()->element_type());
output()->resize(input()->shape());
}
void InstanceNorm::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void InstanceNorm::evalFloat() const
{
float activation_min, activation_max;
calculateActivationRange(params().activation, &activation_min, &activation_max);
auto input_shape = getTensorShape(input());
auto output_shape = getTensorShape(output());
const int32_t batches = tflite::MatchingDim(input_shape, 0, output_shape, 0);
const int32_t heights = tflite::MatchingDim(input_shape, 1, output_shape, 1);
const int32_t widths = tflite::MatchingDim(input_shape, 2, output_shape, 2);
const int32_t channels = tflite::MatchingDim(input_shape, 3, output_shape, 3);
const float *input_data = getTensorData<float>(input());
const float *gamma_data = getTensorData<float>(gamma());
const float *beta_data = getTensorData<float>(beta());
float *output_data = getTensorData<float>(output());
for (int32_t batch = 0; batch < batches; batch++)
{
for (int32_t channel = 0; channel < channels; channel++)
{
double sum = 0.0f;
double square_sum = 0.0f;
int32_t size = heights * widths;
for (int32_t height = 0; height < heights; height++)
{
for (int32_t width = 0; width < widths; width++)
{
double input_val = input_data[tflite::Offset(input_shape, batch, height, width, channel)];
sum += input_val;
square_sum += (input_val * input_val);
}
}
double mean = sum / size;
double var = square_sum / size - mean * mean;
double gamma = gamma_data[channel];
double beta = beta_data[channel];
double a = gamma / (std::sqrt(var + params().epsilon));
double b = -mean * a + beta;
for (int32_t height = 0; height < heights; height++)
{
for (int32_t width = 0; width < widths; width++)
{
double input_value =
input_data[tflite::Offset(output_shape, batch, height, width, channel)];
double output_value = input_value * a + b;
output_data[tflite::Offset(output_shape, batch, height, width, channel)] =
tflite::ActivationFunctionWithMinMax((float)output_value, activation_min,
activation_max);
}
}
}
}
}
} // namespace kernels
} // namespace luci_interpreter
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