git.sesse.net Git - ffmpeg/blob - libavfilter/dnn/dnn_backend_native_layer_conv2d.c

   1 /*
   2  * Copyright (c) 2018 Sergey Lavrushkin
   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 "libavutil/thread.h"
  23 #include "libavutil/cpu.h"
  24 #include "dnn_backend_native_layer_conv2d.h"
  25
  26 #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
  27
  28 //struct to pass parameters
  29 typedef struct ThreadCommonParam{
  30     DnnOperand *operands;
  31     const int32_t *input_operand_indexes;
  32     int32_t output_operand_index;
  33     const void *parameters;
  34     NativeContext *ctx;
  35     float *output_data;
  36 } ThreadCommonParam;
  37
  38 typedef struct ThreadParam{
  39     ThreadCommonParam *thread_common_param;
  40     int thread_start, thread_end;
  41 #if HAVE_PTHREAD_CANCEL
  42     pthread_t thread;
  43 #endif
  44 } ThreadParam;
  45
  46 int ff_dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
  47 {
  48     ConvolutionalParams *conv_params;
  49     int kernel_size;
  50     int dnn_size = 0;
  51     conv_params = av_malloc(sizeof(*conv_params));
  52     if (!conv_params)
  53         return 0;
  54
  55     conv_params->dilation = (int32_t)avio_rl32(model_file_context);
  56     conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
  57     conv_params->activation = (int32_t)avio_rl32(model_file_context);
  58     conv_params->input_num = (int32_t)avio_rl32(model_file_context);
  59     conv_params->output_num = (int32_t)avio_rl32(model_file_context);
  60     conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
  61     conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
  62     dnn_size += 28;
  63
  64     kernel_size = conv_params->input_num * conv_params->output_num *
  65                       conv_params->kernel_size * conv_params->kernel_size;
  66     dnn_size += kernel_size * 4;
  67     if (conv_params->has_bias)
  68         dnn_size += conv_params->output_num * 4;
  69
  70     if (dnn_size > file_size || conv_params->input_num <= 0 ||
  71         conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
  72         av_freep(&conv_params);
  73         return 0;
  74     }
  75
  76     conv_params->kernel = av_malloc_array(kernel_size, sizeof(*conv_params->kernel));
  77     if (!conv_params->kernel) {
  78         av_freep(&conv_params);
  79         return 0;
  80     }
  81     for (int i = 0; i < kernel_size; ++i) {
  82         conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
  83     }
  84
  85     conv_params->biases = NULL;
  86     if (conv_params->has_bias) {
  87         conv_params->biases = av_malloc_array(conv_params->output_num, sizeof(*conv_params->biases));
  88         if (!conv_params->biases){
  89             av_freep(&conv_params->kernel);
  90             av_freep(&conv_params);
  91             return 0;
  92         }
  93         for (int i = 0; i < conv_params->output_num; ++i){
  94             conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
  95         }
  96     }
  97
  98     layer->params = conv_params;
  99
 100     layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
 101     layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
 102     dnn_size += 8;
 103
 104     if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
 105         return 0;
 106     }
 107
 108     return dnn_size;
 109 }
 110
 111 static void * dnn_execute_layer_conv2d_thread(void *threadarg)
 112 {
 113     //pass parameters
 114     ThreadParam *thread_param = threadarg;
 115     ThreadCommonParam *thread_common_param = thread_param->thread_common_param;
 116     DnnOperand *operands = thread_common_param->operands;
 117     int32_t input_operand_index = thread_common_param->input_operand_indexes[0];
 118     int height = operands[input_operand_index].dims[1];
 119     int width = operands[input_operand_index].dims[2];
 120     int channel = operands[input_operand_index].dims[3];
 121     const float *input = operands[input_operand_index].data;
 122     const ConvolutionalParams *conv_params = thread_common_param->parameters;
 123
 124     int radius = conv_params->kernel_size >> 1;
 125     int src_linesize = width * conv_params->input_num;
 126     int filter_linesize = conv_params->kernel_size * conv_params->input_num;
 127     int filter_size = conv_params->kernel_size * filter_linesize;
 128     int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
 129
 130     float *output = thread_common_param->output_data;
 131     output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_param->thread_start - pad_size);
 132
 133     av_assert0(channel == conv_params->input_num);
 134
 135     for (int y = thread_param->thread_start; y < thread_param->thread_end; ++y) {
 136         for (int x = pad_size; x < width - pad_size; ++x) {
 137             for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
 138                 if (conv_params->has_bias)
 139                     output[n_filter] = conv_params->biases[n_filter];
 140                 else
 141                     output[n_filter] = 0.f;
 142
 143                 for (int ch = 0; ch < conv_params->input_num; ++ch) {
 144                     for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
 145                         for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
 146                             float input_pel;
 147                             if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
 148                                 int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
 149                                 int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
 150                                 input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
 151                             } else {
 152                                 int y_pos = y + (kernel_y - radius) * conv_params->dilation;
 153                                 int x_pos = x + (kernel_x - radius) * conv_params->dilation;
 154                                 input_pel = (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) ? 0.0 :
 155                                                    input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
 156                             }
 157
 158
 159                             output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
 160                                                                                 kernel_x * conv_params->input_num + ch];
 161                         }
 162                     }
 163                 }
 164                 switch (conv_params->activation){
 165                 case RELU:
 166                     output[n_filter] = FFMAX(output[n_filter], 0.0);
 167                     break;
 168                 case TANH:
 169                     output[n_filter] = 2.0f  / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
 170                     break;
 171                 case SIGMOID:
 172                     output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
 173                     break;
 174                 case NONE:
 175                     break;
 176                 case LEAKY_RELU:
 177                     output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
 178                 }
 179             }
 180             output += conv_params->output_num;
 181         }
 182     }
 183     return (void *)DNN_SUCCESS;
 184 }
 185
 186
 187 int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
 188                                 int32_t output_operand_index, const void *parameters, NativeContext *ctx)
 189 {
 190     int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
 191         ? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
 192 #if HAVE_PTHREAD_CANCEL
 193     int thread_stride;
 194 #endif
 195     ThreadParam **thread_param = av_malloc_array(thread_num, sizeof(*thread_param));
 196     ThreadCommonParam thread_common_param;
 197     const ConvolutionalParams *conv_params = parameters;
 198     int height = operands[input_operand_indexes[0]].dims[1];
 199     int width = operands[input_operand_indexes[0]].dims[2];
 200     int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
 201     DnnOperand *output_operand = &operands[output_operand_index];
 202     void *tmp;
 203
 204     output_operand->dims[0] = operands[input_operand_indexes[0]].dims[0];
 205     output_operand->dims[1] = height - pad_size * 2;
 206     output_operand->dims[2] = width - pad_size * 2;
 207     output_operand->dims[3] = conv_params->output_num;
 208     output_operand->data_type = operands[input_operand_indexes[0]].data_type;
 209     output_operand->length = ff_calculate_operand_data_length(output_operand);
 210     if (output_operand->length <= 0) {
 211         av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
 212         return DNN_ERROR;
 213     }
 214     tmp = av_realloc(output_operand->data, output_operand->length);
 215     if (!tmp) {
 216         av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
 217         return DNN_ERROR;
 218     }
 219     output_operand->data = tmp;
 220     thread_common_param.output_data = output_operand->data;
 221     thread_common_param.operands = operands;
 222     thread_common_param.input_operand_indexes = input_operand_indexes;
 223     thread_common_param.output_operand_index = output_operand_index;
 224     thread_common_param.parameters = parameters;
 225     thread_common_param.ctx = ctx;
 226
 227 #if HAVE_PTHREAD_CANCEL
 228     thread_stride = (height - pad_size * 2) / thread_num;
 229     //create threads
 230     for (int i = 0; i < thread_num; i++){
 231         thread_param[i] = av_malloc(sizeof(*thread_param[0]));
 232         thread_param[i]->thread_common_param = &thread_common_param;
 233         thread_param[i]->thread_start = thread_stride * i + pad_size;
 234         thread_param[i]->thread_end = (i == thread_num - 1) ? (height - pad_size) : (thread_param[i]->thread_start + thread_stride);
 235         pthread_create(&thread_param[i]->thread, NULL, dnn_execute_layer_conv2d_thread, (void *)thread_param[i]);
 236     }
 237
 238     //join threads, res gets function return
 239     for (int i = 0; i < thread_num; i++){
 240         pthread_join(thread_param[i]->thread, NULL);
 241     }
 242
 243     //release memory
 244     for (int i = 0; i < thread_num; i++){
 245         av_freep(&thread_param[i]);
 246     }
 247 #else
 248     thread_param[0] = av_malloc(sizeof(*thread_param[0]));
 249     thread_param[0]->thread_common_param = &thread_common_param;
 250     thread_param[0]->thread_start = pad_size;
 251     thread_param[0]->thread_end = height - pad_size;
 252     dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
 253     av_freep(&thread_param[0]);
 254 #endif
 255
 256     av_freep(&thread_param);
 257     return DNN_SUCCESS;
 258 }