FFmpeg coverage


Directory: ../../../ffmpeg/
File: src/libavfilter/dnn/dnn_backend_native_layer_dense.c
Date: 2022-12-09 07:38:14
Exec Total Coverage
Lines: 35 90 38.9%
Functions: 1 2 50.0%
Branches: 13 52 25.0%

Line Branch Exec Source
1 /*
2 * Copyright (c) 2020
3 *
4 * This file is part of FFmpeg.
5 *
6 * FFmpeg is free software; you can redistribute it and/or
7 * modify it under the terms of the GNU Lesser General Public
8 * License as published by the Free Software Foundation; either
9 * version 2.1 of the License, or (at your option) any later version.
10 *
11 * FFmpeg is distributed in the hope that it will be useful,
12 * but WITHOUT ANY WARRANTY; without even the implied warranty of
13 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 * Lesser General Public License for more details.
15 *
16 * You should have received a copy of the GNU Lesser General Public
17 * License along with FFmpeg; if not, write to the Free Software
18 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
19 */
20
21 #include "libavutil/avassert.h"
22 #include "dnn_backend_native_layer_dense.h"
23
24 int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
25 {
26 DenseParams *dense_params;
27 int kernel_size;
28 int dnn_size = 0;
29 dense_params = av_malloc(sizeof(*dense_params));
30 if (!dense_params)
31 return 0;
32
33 dense_params->activation = (int32_t)avio_rl32(model_file_context);
34 dense_params->input_num = (int32_t)avio_rl32(model_file_context);
35 dense_params->output_num = (int32_t)avio_rl32(model_file_context);
36 dense_params->has_bias = (int32_t)avio_rl32(model_file_context);
37 dnn_size += 16;
38
39 kernel_size = dense_params->input_num * dense_params->output_num;
40 dnn_size += kernel_size * 4;
41 if (dense_params->has_bias)
42 dnn_size += dense_params->output_num * 4;
43
44 if (dnn_size > file_size || dense_params->input_num <= 0 ||
45 dense_params->output_num <= 0){
46 av_freep(&dense_params);
47 return 0;
48 }
49
50 dense_params->kernel = av_malloc(kernel_size * sizeof(float));
51 if (!dense_params->kernel) {
52 av_freep(&dense_params);
53 return 0;
54 }
55 for (int i = 0; i < kernel_size; ++i) {
56 dense_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
57 }
58
59 dense_params->biases = NULL;
60 if (dense_params->has_bias) {
61 dense_params->biases = av_malloc(dense_params->output_num * sizeof(float));
62 if (!dense_params->biases){
63 av_freep(&dense_params->kernel);
64 av_freep(&dense_params);
65 return 0;
66 }
67 for (int i = 0; i < dense_params->output_num; ++i){
68 dense_params->biases[i] = av_int2float(avio_rl32(model_file_context));
69 }
70 }
71
72 layer->params = dense_params;
73
74 layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
75 layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
76 dnn_size += 8;
77
78 if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
79 return 0;
80 }
81
82 return dnn_size;
83 }
84
85 1 int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
86 int32_t output_operand_index, const void *parameters, NativeContext *ctx)
87 {
88 float *output;
89 1 int32_t input_operand_index = input_operand_indexes[0];
90 1 int number = operands[input_operand_index].dims[0];
91 1 int height = operands[input_operand_index].dims[1];
92 1 int width = operands[input_operand_index].dims[2];
93 1 int channel = operands[input_operand_index].dims[3];
94 1 const float *input = operands[input_operand_index].data;
95 1 const DenseParams *dense_params = parameters;
96
97 1 int src_linesize = width * channel;
98 1 DnnOperand *output_operand = &operands[output_operand_index];
99 1 output_operand->dims[0] = number;
100 1 output_operand->dims[1] = height;
101 1 output_operand->dims[2] = width;
102 1 output_operand->dims[3] = dense_params->output_num;
103 1 output_operand->data_type = operands[input_operand_index].data_type;
104 1 output_operand->length = ff_calculate_operand_data_length(output_operand);
105
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1 if (output_operand->length <= 0) {
106 av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
107 return AVERROR(EINVAL);
108 }
109 1 output_operand->data = av_realloc(output_operand->data, output_operand->length);
110
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1 if (!output_operand->data) {
111 av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
112 return AVERROR(ENOMEM);
113 }
114 1 output = output_operand->data;
115
116
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1 av_assert0(channel == dense_params->input_num);
117
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6 for (int y = 0; y < height; ++y) {
119
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35 for (int x = 0; x < width; ++x) {
120
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120 for (int n_filter = 0; n_filter < dense_params->output_num; ++n_filter) {
121
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90 if (dense_params->has_bias)
122 90 output[n_filter] = dense_params->biases[n_filter];
123 else
124 output[n_filter] = 0.f;
125
126
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360 for (int ch = 0; ch < dense_params->input_num; ++ch) {
127 float input_pel;
128 270 input_pel = input[y * src_linesize + x * dense_params->input_num + ch];
129 270 output[n_filter] += input_pel * dense_params->kernel[n_filter*dense_params->input_num + ch];
130 }
131
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90 switch (dense_params->activation){
132 case RELU:
133 output[n_filter] = FFMAX(output[n_filter], 0.0);
134 break;
135 90 case TANH:
136 90 output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
137 90 break;
138 case SIGMOID:
139 output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
140 break;
141 case NONE:
142 break;
143 case LEAKY_RELU:
144 output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
145 }
146 }
147 30 output += dense_params->output_num;
148 }
149 }
150 1 return 0;
151 }
152