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/*
* Copyright (c) 2019 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 "InstanceNormLayer.h"
#include "OperationUtils.h"
#include "ncnn/layer/instance_norm.h"
namespace neurun
{
namespace backend
{
namespace srcn
{
namespace kernel
{
InstanceNormLayer::InstanceNormLayer()
: _inputData(), _gammaData(), _betaData(), _outputData(), _inputDescr(), _gammaDescr(),
_betaDescr(), _outputDescr(), _epsilon(1e-5), _activation(ir::Activation::NONE),
_inputType(OperandType::FLOAT32), _backendLayout(ir::Layout::UNKNOWN)
{
// DO NOTHING
}
void InstanceNormLayer::instanceNormFloat32()
{
// Call kernel for NCHW data layout
if (_backendLayout == ir::Layout::NCHW)
{
// Supports single batch only
assert(_inputDescr.dimensions[0] == 1);
const int input_channels = _inputDescr.dimensions[1];
const int input_height = _inputDescr.dimensions[2];
const int input_width = _inputDescr.dimensions[3];
nnfw::ncnn::Mat in_mat(input_width, input_height, input_channels, _inputData.f);
const int gamma_channels = _gammaDescr.dimensions[0];
nnfw::ncnn::Mat gamma_mat(gamma_channels, _gammaData.f);
const int beta_channels = _betaDescr.dimensions[0];
nnfw::ncnn::Mat beta_mat(beta_channels, _betaData.f);
assert(_outputDescr.dimensions[0] == 1);
const int output_channels = _outputDescr.dimensions[1];
const int output_height = _outputDescr.dimensions[2];
const int output_width = _outputDescr.dimensions[3];
nnfw::ncnn::Mat out_mat(output_width, output_height, output_channels, _outputData.f);
if (_activation == ir::Activation::NONE)
{
nnfw::ncnn::ncnn_instance_norm_rowmajor(in_mat, out_mat, gamma_mat, beta_mat, input_channels,
_epsilon);
}
else if (_activation == ir::Activation::RELU)
{
nnfw::ncnn::ncnn_instance_norm_with_relu_rowmajor(in_mat, out_mat, gamma_mat, beta_mat,
input_channels, _epsilon, 0.f);
}
else
{
std::runtime_error("Unsupported activation type");
}
}
// Call kernel for NHWC data layout
else if (_backendLayout == ir::Layout::NHWC)
{
// Supports single batch only
assert(_inputDescr.dimensions[0] == 1);
const int input_height = _inputDescr.dimensions[1];
const int input_width = _inputDescr.dimensions[2];
const int input_channels = _inputDescr.dimensions[3];
nnfw::ncnn::Mat in_mat(input_channels, input_width, input_height, _inputData.f);
const int gamma_channels = _gammaDescr.dimensions[0];
nnfw::ncnn::Mat gamma_mat(gamma_channels, _gammaData.f);
const int beta_channels = _betaDescr.dimensions[0];
nnfw::ncnn::Mat beta_mat(beta_channels, _betaData.f);
assert(_outputDescr.dimensions[0] == 1);
const int output_height = _outputDescr.dimensions[1];
const int output_width = _outputDescr.dimensions[2];
const int output_channels = _outputDescr.dimensions[3];
nnfw::ncnn::Mat out_mat(output_channels, output_width, output_height, _outputData.f);
if (_activation == ir::Activation::NONE)
{
nnfw::ncnn::ncnn_instance_norm_colmajor(in_mat, out_mat, gamma_mat, beta_mat, input_channels,
_epsilon);
}
else if (_activation == ir::Activation::RELU)
{
nnfw::ncnn::ncnn_instance_norm_with_relu_colmajor(in_mat, out_mat, gamma_mat, beta_mat,
input_channels, _epsilon, 0.f);
}
{
std::runtime_error("Unsupported activation type");
}
}
else
{
std::runtime_error("Unsupported backend layout");
}
}
void InstanceNormLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
uint8_t *gammaData, const TensorDescriptor gammaDescr,
uint8_t *betaData, const TensorDescriptor betaDescr,
uint8_t *outputData, const TensorDescriptor outputDescr,
float epsilon, ir::Activation activation,
ir::Layout backendLayout)
{
_inputData.u8 = inputData;
_inputDescr = inputDescr;
_gammaData.u8 = gammaData;
_gammaDescr = gammaDescr;
_betaData.u8 = betaData;
_betaDescr = betaDescr;
_outputData.u8 = outputData;
_outputDescr = outputDescr;
_epsilon = epsilon;
_activation = activation;
_backendLayout = backendLayout;
}
void InstanceNormLayer::run()
{
if (_inputType == OperandType::FLOAT32)
{
instanceNormFloat32();
}
else if (_inputType == OperandType::QUANT8_ASYMM)
{
throw std::runtime_error("NYI");
}
}
} // namespace kernel
} // namespace srcn
} // namespace backend
} // namespace neurun
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