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
|
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_H
#include "../dnn_interface.h"
#include "libavformat/avio.h"
/**
* the enum value of DNNLayerType should not be changed,
* the same values are used in convert_from_tensorflow.py
* and, it is used to index the layer execution function pointer.
*/
typedef enum {
DLT_INPUT = 0,
DLT_CONV2D = 1,
DLT_DEPTH_TO_SPACE = 2,
DLT_MIRROR_PAD = 3,
DLT_MAXIMUM = 4,
DLT_COUNT
} DNNLayerType;
typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | DOT_INPUT} DNNOperandType;
typedef struct Layer{
DNNLayerType type;
/**
* a layer can have multiple inputs and one output.
* 4 is just a big enough number for input operands (increase it if necessary),
* do not use 'int32_t *input_operand_indexes', so we don't worry about mem leaks.
*/
int32_t input_operand_indexes[4];
int32_t output_operand_index;
void *params;
} Layer;
typedef struct DnnOperand{
/**
* there are two memory layouts, NHWC or NCHW, so we use dims,
* dims[0] is Number.
*/
int32_t dims[4];
/**
* input/output/intermediate operand of the network
*/
DNNOperandType type;
/**
* support different kinds of data type such as float, half float, int8 etc,
* first support float now.
*/
DNNDataType data_type;
/**
* NHWC if 1, otherwise NCHW.
* let's first support NHWC only, this flag is for extensive usage.
*/
int8_t isNHWC;
/**
* to avoid possible memory leak, do not use char *name
*/
char name[128];
/**
* data pointer with data length in bytes.
* usedNumbersLeft is only valid for intermediate operand,
* it means how many layers still depend on this operand,
* todo: the memory can be reused when usedNumbersLeft is zero.
*/
void *data;
int32_t length;
int32_t usedNumbersLeft;
}DnnOperand;
typedef struct InputParams{
int height, width, channels;
} InputParams;
// Represents simple feed-forward convolutional network.
typedef struct ConvolutionalNetwork{
Layer *layers;
int32_t layers_num;
DnnOperand *operands;
int32_t operands_num;
int32_t *output_indexes;
uint32_t nb_output;
} ConvolutionalNetwork;
DNNModel *ff_dnn_load_model_native(const char *model_filename);
DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output);
void ff_dnn_free_model_native(DNNModel **model);
int32_t calculate_operand_data_length(const DnnOperand *oprd);
int32_t calculate_operand_dims_count(const DnnOperand *oprd);
#endif
|
