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/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2019 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.
*/
#ifndef __NNFW_CKER_REFERENCE_DEPTHWISE_CONV_HYBRID_H__
#define __NNFW_CKER_REFERENCE_DEPTHWISE_CONV_HYBRID_H__
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
namespace reference_integer_ops
{
inline void DepthwiseConvHybridPerChannel(const DepthwiseConvParams ¶ms,
float *scaling_factors_ptr, const Shape &input_shape,
const int8_t *input_data, const Shape &filter_shape,
const int8_t *filter_data, const Shape &bias_shape,
const float *bias_data, const Shape &output_shape,
float *output_data, const float *per_channel_scale,
int32_t *input_offset)
{
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_activation_max;
// Check dimensions of the tensors.
assert(input_shape.DimensionsCount() == 4);
assert(filter_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int bias_depth = bias_shape.FlatSize();
UNUSED_RELEASE(output_depth);
UNUSED_RELEASE(bias_shape);
assert(output_depth == input_depth * depth_multiplier);
assert(bias_depth == output_depth);
for (int batch = 0; batch < batches; ++batch)
{
for (int out_y = 0; out_y < output_height; ++out_y)
{
for (int out_x = 0; out_x < output_width; ++out_x)
{
for (int in_channel = 0; in_channel < input_depth; ++in_channel)
{
for (int m = 0; m < depth_multiplier; ++m)
{
const int output_channel = m + in_channel * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y)
{
for (int filter_x = 0; filter_x < filter_width; ++filter_x)
{
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y = in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height);
if (is_point_inside_image)
{
int32_t input_val =
input_data[Offset(input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val =
filter_data[Offset(filter_shape, 0, filter_y, filter_x, output_channel)];
acc += filter_val * (input_val - input_offset[batch]);
}
}
}
float acc_float = static_cast<float>(acc);
acc_float *= per_channel_scale[output_channel] * scaling_factors_ptr[batch];
if (bias_data && output_channel < bias_depth)
{
acc_float += bias_data[output_channel];
}
output_data[Offset(output_shape, batch, out_y, out_x, output_channel)] =
ActivationFunctionWithMinMax(acc_float, output_activation_min, output_activation_max);
}
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_REFERENCE_DEPTHWISE_CONV_HYBRID_H__
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