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/*
* Copyright (c) 2019 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.
*/
#ifndef __NNFW_CKER_DEPTHWISE_CONV_H__
#define __NNFW_CKER_DEPTHWISE_CONV_H__
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
struct DepthwiseConvParams
{
PaddingType padding_type;
PaddingValues padding_values;
int16_t stride_width;
int16_t stride_height;
int16_t dilation_width_factor;
int16_t dilation_height_factor;
int16_t depth_multiplier;
// uint8 inference params.
// TODO(b/65838351): Use smaller types if appropriate.
int32_t input_offset;
int32_t weights_offset;
int32_t output_offset;
int32_t output_multiplier;
int output_shift;
// uint8, etc, activation params.
int32_t quantized_activation_min;
int32_t quantized_activation_max;
// float activation params.
float float_activation_min;
float float_activation_max;
};
inline void DepthwiseConv(const DepthwiseConvParams ¶ms, const Shape &input_shape,
const uint8_t *input_data, const Shape &filter_shape,
const uint8_t *filter_data, const Shape &bias_shape,
const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data)
{
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 int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
assert(input_shape.DimensionsCount() == 4);
assert(filter_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
assert(output_activation_min <= output_activation_max);
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);
assert(output_depth == input_depth * depth_multiplier);
assert(bias_shape.FlatSize() == output_depth);
UNUSED_RELEASE(output_depth);
UNUSED_RELEASE(bias_shape);
for (int b = 0; b < batches; ++b)
{
for (int out_y = 0; out_y < output_height; ++out_y)
{
for (int out_x = 0; out_x < output_width; ++out_x)
{
for (int ic = 0; ic < input_depth; ++ic)
{
for (int m = 0; m < depth_multiplier; m++)
{
const int oc = m + ic * 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;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height))
{
int32_t input_val = input_data[Offset(input_shape, b, in_y, in_x, ic)];
int32_t filter_val = filter_data[Offset(filter_shape, 0, filter_y, filter_x, oc)];
acc += (filter_val + filter_offset) * (input_val + input_offset);
}
}
}
if (bias_data)
{
acc += bias_data[oc];
}
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, b, out_y, out_x, oc)] = static_cast<uint8_t>(acc);
}
}
}
}
}
}
inline void DepthwiseConv(const DepthwiseConvParams ¶ms, const Shape &input_shape,
const float *input_data, const Shape &filter_shape,
const float *filter_data, const Shape &bias_shape, const float *bias_data,
const Shape &output_shape, float *output_data)
{
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;
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);
assert(output_depth == input_depth * depth_multiplier);
assert(bias_shape.FlatSize() == output_depth);
UNUSED_RELEASE(output_depth);
UNUSED_RELEASE(bias_shape);
for (int b = 0; b < batches; ++b)
{
for (int out_y = 0; out_y < output_height; ++out_y)
{
for (int out_x = 0; out_x < output_width; ++out_x)
{
for (int ic = 0; ic < input_depth; ++ic)
{
for (int m = 0; m < depth_multiplier; m++)
{
const int oc = m + ic * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
float total = 0.f;
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;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height))
{
float input_value = input_data[Offset(input_shape, b, in_y, in_x, ic)];
float filter_value = filter_data[Offset(filter_shape, 0, filter_y, filter_x, oc)];
total += (input_value * filter_value);
}
}
}
float bias_value = 0.0f;
if (bias_data)
{
bias_value = bias_data[oc];
}
output_data[Offset(output_shape, b, out_y, out_x, oc)] = ActivationFunctionWithMinMax(
total + bias_value, output_activation_min, output_activation_max);
}
}
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_DEPTHWISE_CONV_H__
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