FFmpeg
dnn_backend_native_layer_avgpool.c
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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
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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 /**
22  * @file
23  * DNN native backend implementation.
24  */
25 
26 #include "libavutil/avassert.h"
28 
29 int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
30 {
31  AvgPoolParams *avgpool_params;
32  int dnn_size = 0;
33  avgpool_params = av_malloc(sizeof(*avgpool_params));
34  if(!avgpool_params)
35  return 0;
36 
37  avgpool_params->strides = (int32_t)avio_rl32(model_file_context);
38  avgpool_params->padding_method = (int32_t)avio_rl32(model_file_context);
39  avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context);
40  dnn_size += 12;
41 
42  if (dnn_size > file_size || avgpool_params->kernel_size <= 0 || avgpool_params->strides <=0){
43  av_freep(&avgpool_params);
44  return 0;
45  }
46 
47  layer->params = avgpool_params;
48  layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
49  layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
50  dnn_size += 8;
51 
52  if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
53  return 0;
54  }
55  return dnn_size;
56 }
57 
58 int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
59  int32_t output_operand_index, const void *parameters, NativeContext *ctx)
60 {
61  float *output;
62  int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area;
63  int32_t input_operand_index = input_operand_indexes[0];
64  int number = operands[input_operand_index].dims[0];
65  int height = operands[input_operand_index].dims[1];
66  int width = operands[input_operand_index].dims[2];
67  int channel = operands[input_operand_index].dims[3];
68  const float *input = operands[input_operand_index].data;
69  const AvgPoolParams *avgpool_params = (const AvgPoolParams *)parameters;
70 
71  int kernel_strides = avgpool_params->strides;
72  int src_linesize = width * channel;
73  DnnOperand *output_operand = &operands[output_operand_index];
74 
75  /**
76  * When padding_method = SAME, the tensorflow will only padding the hald number of 0 pxiels
77  * except the remainders.
78  * Eg: assuming the input height = 1080, the strides = 11, so the remainders = 1080 % 11 = 2
79  * and if ksize = 5: it will fill (5 - 2) >> 1 = 1 line before the first line of input image,
80  * and 5 - 2 - 1 = 2 lines after the last line of input image.
81  * and if ksize = 7: it will fill (7 - 2) >> 1 = 2 lines before the first line of input image,
82  * and 7 - 2 - 2 = 3 lines after the last line of input image.
83  */
84  if (avgpool_params->padding_method == SAME) {
85  height_end = height;
86  width_end = width;
87  height_radius = avgpool_params->kernel_size - ((height - 1) % kernel_strides + 1);
88  width_radius = avgpool_params->kernel_size - ((width - 1) % kernel_strides + 1);
89  height_radius = height_radius < 0 ? 0 : height_radius >> 1;
90  width_radius = width_radius < 0 ? 0 : width_radius >> 1;
91  output_height = ceil(height / (kernel_strides * 1.0));
92  output_width = ceil(width / (kernel_strides * 1.0));
93  } else {
94  av_assert0(avgpool_params->padding_method == VALID);
95  height_end = height - avgpool_params->kernel_size + 1;
96  width_end = width - avgpool_params->kernel_size + 1;
97  height_radius = 0;
98  width_radius = 0;
99  output_height = ceil((height - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
100  output_width = ceil((width - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
101  }
102 
103  output_operand->dims[0] = number;
104  output_operand->dims[1] = output_height;
105  output_operand->dims[2] = output_width;
106  // not support pooling in channel dimension now
107  output_operand->dims[3] = channel;
108  output_operand->data_type = operands[input_operand_index].data_type;
109  output_operand->length = calculate_operand_data_length(output_operand);
110  if (output_operand->length <= 0) {
111  av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
112  return DNN_ERROR;
113  }
114  output_operand->data = av_realloc(output_operand->data, output_operand->length);
115  if (!output_operand->data) {
116  av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
117  return DNN_ERROR;
118  }
119  output = output_operand->data;
120 
121  for (int y = 0; y < height_end; y += kernel_strides) {
122  for (int x = 0; x < width_end; x += kernel_strides) {
123  for (int n_channel = 0; n_channel < channel; ++n_channel) {
124  output[n_channel] = 0.0;
125  kernel_area = 0;
126  for (int kernel_y = 0; kernel_y < avgpool_params->kernel_size; ++kernel_y) {
127  for (int kernel_x = 0; kernel_x < avgpool_params->kernel_size; ++kernel_x) {
128  float input_pel;
129  int y_pos = y + (kernel_y - height_radius);
130  int x_pos = x + (kernel_x - width_radius);
131  if (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) {
132  input_pel = 0.0;
133  } else {
134  kernel_area++;
135  input_pel = input[y_pos * src_linesize + x_pos * channel + n_channel];
136  }
137  output[n_channel] += input_pel;
138  }
139  }
140  output[n_channel] /= kernel_area;
141  }
142  output += channel;
143  }
144  }
145 
146  return 0;
147 }
Bytestream IO Context.
Definition: avio.h:161
void * av_realloc(void *ptr, size_t size)
Allocate, reallocate, or free a block of memory.
Definition: mem.c:134
static __device__ float ceil(float a)
Definition: cuda_runtime.h:176
int32_t input_operand_indexes[4]
a layer can have multiple inputs and one output.
#define av_assert0(cond)
assert() equivalent, that is always enabled.
Definition: avassert.h:37
#define av_malloc(s)
filter_frame For filters that do not use the this method is called when a frame is pushed to the filter s input It can be called at any time except in a reentrant way If the input frame is enough to produce output
#define height
DNNDataType data_type
support different kinds of data type such as float, half float, int8 etc, first support float now...
#define av_log(a,...)
#define AV_LOG_ERROR
Something went wrong and cannot losslessly be recovered.
Definition: log.h:194
unsigned int avio_rl32(AVIOContext *s)
Definition: aviobuf.c:754
void * data
data pointer with data length in bytes.
simple assert() macros that are a bit more flexible than ISO C assert().
int32_t dims[4]
there are two memory layouts, NHWC or NCHW, so we use dims, dims[0] is Number.
#define width
DNN inference functions interface for native backend.
int32_t
AVFormatContext * ctx
Definition: movenc.c:48
int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const void *parameters, NativeContext *ctx)
and forward the test the status of outputs and forward it to the corresponding return FFERROR_NOT_READY If the filters stores internally one or a few frame for some input
int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
channel
Use these values when setting the channel map with ebur128_set_channel().
Definition: ebur128.h:39
int32_t calculate_operand_data_length(const DnnOperand *oprd)
void * params
#define av_freep(p)
int32_t output_operand_index