1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
|
/*
* 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_AVERAGE_POOL_H__
#define __NNFW_CKER_AVERAGE_POOL_H__
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
struct AveragePoolParams
{
FusedActivationFunctionType activation;
PaddingType padding_type;
PaddingValues padding_values;
int stride_height;
int stride_width;
int filter_height;
int filter_width;
// 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 AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
const float *input_data, const Shape &output_shape, float *output_data)
{
assert(input_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
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)
{
const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
int filter_count = (filter_y_end - filter_y_start) * (filter_x_end - filter_x_start);
if (filter_count <= 0)
{
continue;
}
for (int channel = 0; channel < depth; ++channel)
{
float total = 0.f;
for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y)
{
for (int filter_x = filter_x_start; filter_x < filter_x_end; ++filter_x)
{
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
total += input_data[Offset(input_shape, batch, in_y, in_x, channel)];
}
}
const float average = total / (float)filter_count;
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
ActivationFunctionWithMinMax(average, params.float_activation_min,
params.float_activation_max);
}
}
}
}
}
inline void AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
const uint8_t *input_data, const Shape &output_shape, uint8_t *output_data)
{
assert(params.quantized_activation_min <= params.quantized_activation_max);
assert(input_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
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)
{
const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
int filter_count = (filter_y_end - filter_y_start) * (filter_x_end - filter_x_start);
if (filter_count <= 0)
{
continue;
}
for (int channel = 0; channel < depth; ++channel)
{
int32_t acc = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y)
{
for (int filter_x = filter_x_start; filter_x < filter_x_end; ++filter_x)
{
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
acc += input_data[Offset(input_shape, batch, in_y, in_x, channel)];
}
}
acc = (acc + filter_count / 2) / filter_count;
acc = std::max(acc, params.quantized_activation_min);
acc = std::min(acc, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<uint8_t>(acc);
}
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_AVERAGE_POOL_H__
|
