git.sesse.net Git - ffmpeg/blob - libavfilter/dnn/dnn_backend_tf.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 /**
  22  * @file
  23  * DNN tensorflow backend implementation.
  24  */
  25
  26 #include "dnn_backend_tf.h"
  27 #include "dnn_backend_native.h"
  28 #include "dnn_backend_native_layer_conv2d.h"
  29 #include "dnn_backend_native_layer_depth2space.h"
  30 #include "libavformat/avio.h"
  31 #include "libavutil/avassert.h"
  32 #include "dnn_backend_native_layer_pad.h"
  33 #include "dnn_backend_native_layer_maximum.h"
  34
  35 #include <tensorflow/c/c_api.h>
  36
  37 typedef struct TFModel{
  38     TF_Graph *graph;
  39     TF_Session *session;
  40     TF_Status *status;
  41     TF_Output input;
  42     TF_Tensor *input_tensor;
  43     TF_Output *outputs;
  44     TF_Tensor **output_tensors;
  45     uint32_t nb_output;
  46 } TFModel;
  47
  48 static void free_buffer(void *data, size_t length)
  49 {
  50     av_freep(&data);
  51 }
  52
  53 static TF_Buffer *read_graph(const char *model_filename)
  54 {
  55     TF_Buffer *graph_buf;
  56     unsigned char *graph_data = NULL;
  57     AVIOContext *model_file_context;
  58     long size, bytes_read;
  59
  60     if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
  61         return NULL;
  62     }
  63
  64     size = avio_size(model_file_context);
  65
  66     graph_data = av_malloc(size);
  67     if (!graph_data){
  68         avio_closep(&model_file_context);
  69         return NULL;
  70     }
  71     bytes_read = avio_read(model_file_context, graph_data, size);
  72     avio_closep(&model_file_context);
  73     if (bytes_read != size){
  74         av_freep(&graph_data);
  75         return NULL;
  76     }
  77
  78     graph_buf = TF_NewBuffer();
  79     graph_buf->data = (void *)graph_data;
  80     graph_buf->length = size;
  81     graph_buf->data_deallocator = free_buffer;
  82
  83     return graph_buf;
  84 }
  85
  86 static TF_Tensor *allocate_input_tensor(const DNNData *input)
  87 {
  88     TF_DataType dt;
  89     size_t size;
  90     int64_t input_dims[] = {1, input->height, input->width, input->channels};
  91     switch (input->dt) {
  92     case DNN_FLOAT:
  93         dt = TF_FLOAT;
  94         size = sizeof(float);
  95         break;
  96     case DNN_UINT8:
  97         dt = TF_UINT8;
  98         size = 1;
  99         break;
 100     default:
 101         av_assert0(!"should not reach here");
 102     }
 103
 104     return TF_AllocateTensor(dt, input_dims, 4,
 105                              input_dims[1] * input_dims[2] * input_dims[3] * size);
 106 }
 107
 108 static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
 109 {
 110     TFModel *tf_model = (TFModel *)model;
 111     TF_Status *status;
 112     int64_t dims[4];
 113
 114     TF_Output tf_output;
 115     tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 116     if (!tf_output.oper)
 117         return DNN_ERROR;
 118
 119     tf_output.index = 0;
 120     input->dt = TF_OperationOutputType(tf_output);
 121
 122     status = TF_NewStatus();
 123     TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status);
 124     if (TF_GetCode(status) != TF_OK){
 125         TF_DeleteStatus(status);
 126         return DNN_ERROR;
 127     }
 128     TF_DeleteStatus(status);
 129
 130     // currently only NHWC is supported
 131     av_assert0(dims[0] == 1);
 132     input->height = dims[1];
 133     input->width = dims[2];
 134     input->channels = dims[3];
 135
 136     return DNN_SUCCESS;
 137 }
 138
 139 static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
 140 {
 141     TFModel *tf_model = (TFModel *)model;
 142     TF_SessionOptions *sess_opts;
 143     const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
 144
 145     // Input operation
 146     tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 147     if (!tf_model->input.oper){
 148         return DNN_ERROR;
 149     }
 150     tf_model->input.index = 0;
 151     if (tf_model->input_tensor){
 152         TF_DeleteTensor(tf_model->input_tensor);
 153     }
 154     tf_model->input_tensor = allocate_input_tensor(input);
 155     if (!tf_model->input_tensor){
 156         return DNN_ERROR;
 157     }
 158     input->data = (float *)TF_TensorData(tf_model->input_tensor);
 159
 160     // Output operation
 161     if (nb_output == 0)
 162         return DNN_ERROR;
 163
 164     av_freep(&tf_model->outputs);
 165     tf_model->outputs = av_malloc_array(nb_output, sizeof(*tf_model->outputs));
 166     if (!tf_model->outputs)
 167         return DNN_ERROR;
 168     for (int i = 0; i < nb_output; ++i) {
 169         tf_model->outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
 170         if (!tf_model->outputs[i].oper){
 171             av_freep(&tf_model->outputs);
 172             return DNN_ERROR;
 173         }
 174         tf_model->outputs[i].index = 0;
 175     }
 176
 177     if (tf_model->output_tensors) {
 178         for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
 179             if (tf_model->output_tensors[i]) {
 180                 TF_DeleteTensor(tf_model->output_tensors[i]);
 181                 tf_model->output_tensors[i] = NULL;
 182             }
 183         }
 184     }
 185     av_freep(&tf_model->output_tensors);
 186     tf_model->output_tensors = av_mallocz_array(nb_output, sizeof(*tf_model->output_tensors));
 187     if (!tf_model->output_tensors) {
 188         av_freep(&tf_model->outputs);
 189         return DNN_ERROR;
 190     }
 191
 192     tf_model->nb_output = nb_output;
 193
 194     if (tf_model->session){
 195         TF_CloseSession(tf_model->session, tf_model->status);
 196         TF_DeleteSession(tf_model->session, tf_model->status);
 197     }
 198
 199     sess_opts = TF_NewSessionOptions();
 200     tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
 201     TF_DeleteSessionOptions(sess_opts);
 202     if (TF_GetCode(tf_model->status) != TF_OK)
 203     {
 204         return DNN_ERROR;
 205     }
 206
 207     // Run initialization operation with name "init" if it is present in graph
 208     if (init_op){
 209         TF_SessionRun(tf_model->session, NULL,
 210                       NULL, NULL, 0,
 211                       NULL, NULL, 0,
 212                       &init_op, 1, NULL, tf_model->status);
 213         if (TF_GetCode(tf_model->status) != TF_OK)
 214         {
 215             return DNN_ERROR;
 216         }
 217     }
 218
 219     return DNN_SUCCESS;
 220 }
 221
 222 static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
 223 {
 224     TF_Buffer *graph_def;
 225     TF_ImportGraphDefOptions *graph_opts;
 226
 227     graph_def = read_graph(model_filename);
 228     if (!graph_def){
 229         return DNN_ERROR;
 230     }
 231     tf_model->graph = TF_NewGraph();
 232     tf_model->status = TF_NewStatus();
 233     graph_opts = TF_NewImportGraphDefOptions();
 234     TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
 235     TF_DeleteImportGraphDefOptions(graph_opts);
 236     TF_DeleteBuffer(graph_def);
 237     if (TF_GetCode(tf_model->status) != TF_OK){
 238         TF_DeleteGraph(tf_model->graph);
 239         TF_DeleteStatus(tf_model->status);
 240         return DNN_ERROR;
 241     }
 242
 243     return DNN_SUCCESS;
 244 }
 245
 246 #define NAME_BUFFER_SIZE 256
 247
 248 static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
 249                                     ConvolutionalParams* params, const int layer)
 250 {
 251     TF_Operation *op;
 252     TF_OperationDescription *op_desc;
 253     TF_Output input;
 254     int64_t strides[] = {1, 1, 1, 1};
 255     TF_Tensor *tensor;
 256     int64_t dims[4];
 257     int dims_len;
 258     char name_buffer[NAME_BUFFER_SIZE];
 259     int32_t size;
 260
 261     size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
 262     input.index = 0;
 263
 264     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
 265     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 266     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 267     dims[0] = params->output_num;
 268     dims[1] = params->kernel_size;
 269     dims[2] = params->kernel_size;
 270     dims[3] = params->input_num;
 271     dims_len = 4;
 272     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
 273     memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
 274     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 275     if (TF_GetCode(tf_model->status) != TF_OK){
 276         return DNN_ERROR;
 277     }
 278     op = TF_FinishOperation(op_desc, tf_model->status);
 279     if (TF_GetCode(tf_model->status) != TF_OK){
 280         return DNN_ERROR;
 281     }
 282
 283     snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
 284     op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
 285     input.oper = op;
 286     TF_AddInput(op_desc, input);
 287     input.oper = transpose_op;
 288     TF_AddInput(op_desc, input);
 289     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 290     TF_SetAttrType(op_desc, "Tperm", TF_INT32);
 291     op = TF_FinishOperation(op_desc, tf_model->status);
 292     if (TF_GetCode(tf_model->status) != TF_OK){
 293         return DNN_ERROR;
 294     }
 295
 296     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
 297     op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
 298     input.oper = *cur_op;
 299     TF_AddInput(op_desc, input);
 300     input.oper = op;
 301     TF_AddInput(op_desc, input);
 302     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 303     TF_SetAttrIntList(op_desc, "strides", strides, 4);
 304     TF_SetAttrString(op_desc, "padding", "VALID", 5);
 305     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 306     if (TF_GetCode(tf_model->status) != TF_OK){
 307         return DNN_ERROR;
 308     }
 309
 310     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
 311     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 312     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 313     dims[0] = params->output_num;
 314     dims_len = 1;
 315     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
 316     memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
 317     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 318     if (TF_GetCode(tf_model->status) != TF_OK){
 319         return DNN_ERROR;
 320     }
 321     op = TF_FinishOperation(op_desc, tf_model->status);
 322     if (TF_GetCode(tf_model->status) != TF_OK){
 323         return DNN_ERROR;
 324     }
 325
 326     snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
 327     op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
 328     input.oper = *cur_op;
 329     TF_AddInput(op_desc, input);
 330     input.oper = op;
 331     TF_AddInput(op_desc, input);
 332     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 333     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 334     if (TF_GetCode(tf_model->status) != TF_OK){
 335         return DNN_ERROR;
 336     }
 337
 338     snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
 339     switch (params->activation){
 340     case RELU:
 341         op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
 342         break;
 343     case TANH:
 344         op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
 345         break;
 346     case SIGMOID:
 347         op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
 348         break;
 349     default:
 350         return DNN_ERROR;
 351     }
 352     input.oper = *cur_op;
 353     TF_AddInput(op_desc, input);
 354     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 355     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 356     if (TF_GetCode(tf_model->status) != TF_OK){
 357         return DNN_ERROR;
 358     }
 359
 360     return DNN_SUCCESS;
 361 }
 362
 363 static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
 364                                               DepthToSpaceParams *params, const int layer)
 365 {
 366     TF_OperationDescription *op_desc;
 367     TF_Output input;
 368     char name_buffer[NAME_BUFFER_SIZE];
 369
 370     snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
 371     op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
 372     input.oper = *cur_op;
 373     input.index = 0;
 374     TF_AddInput(op_desc, input);
 375     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 376     TF_SetAttrInt(op_desc, "block_size", params->block_size);
 377     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 378     if (TF_GetCode(tf_model->status) != TF_OK){
 379         return DNN_ERROR;
 380     }
 381
 382     return DNN_SUCCESS;
 383 }
 384
 385 static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
 386                                               LayerPadParams *params, const int layer)
 387 {
 388     TF_Operation *op;
 389     TF_Tensor *tensor;
 390     TF_OperationDescription *op_desc;
 391     TF_Output input;
 392     int32_t *pads;
 393     int64_t pads_shape[] = {4, 2};
 394
 395     char name_buffer[NAME_BUFFER_SIZE];
 396     snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
 397
 398     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 399     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 400     tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
 401     pads = (int32_t *)TF_TensorData(tensor);
 402     pads[0] = params->paddings[0][0];
 403     pads[1] = params->paddings[0][1];
 404     pads[2] = params->paddings[1][0];
 405     pads[3] = params->paddings[1][1];
 406     pads[4] = params->paddings[2][0];
 407     pads[5] = params->paddings[2][1];
 408     pads[6] = params->paddings[3][0];
 409     pads[7] = params->paddings[3][1];
 410     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 411     if (TF_GetCode(tf_model->status) != TF_OK){
 412         return DNN_ERROR;
 413     }
 414     op = TF_FinishOperation(op_desc, tf_model->status);
 415     if (TF_GetCode(tf_model->status) != TF_OK){
 416         return DNN_ERROR;
 417     }
 418
 419     op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
 420     input.oper = *cur_op;
 421     input.index = 0;
 422     TF_AddInput(op_desc, input);
 423     input.oper = op;
 424     TF_AddInput(op_desc, input);
 425     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 426     TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
 427     TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
 428     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 429     if (TF_GetCode(tf_model->status) != TF_OK){
 430         return DNN_ERROR;
 431     }
 432
 433     return DNN_SUCCESS;
 434 }
 435
 436 static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
 437                                        DnnLayerMaximumParams *params, const int layer)
 438 {
 439     TF_Operation *op;
 440     TF_Tensor *tensor;
 441     TF_OperationDescription *op_desc;
 442     TF_Output input;
 443     float *y;
 444
 445     char name_buffer[NAME_BUFFER_SIZE];
 446     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
 447
 448     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 449     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 450     tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
 451     y = (float *)TF_TensorData(tensor);
 452     *y = params->val.y;
 453     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 454     if (TF_GetCode(tf_model->status) != TF_OK){
 455         return DNN_ERROR;
 456     }
 457     op = TF_FinishOperation(op_desc, tf_model->status);
 458     if (TF_GetCode(tf_model->status) != TF_OK){
 459         return DNN_ERROR;
 460     }
 461
 462     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
 463     op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
 464     input.oper = *cur_op;
 465     input.index = 0;
 466     TF_AddInput(op_desc, input);
 467     input.oper = op;
 468     TF_AddInput(op_desc, input);
 469     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 470     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 471     if (TF_GetCode(tf_model->status) != TF_OK){
 472         return DNN_ERROR;
 473     }
 474
 475     return DNN_SUCCESS;
 476 }
 477
 478 static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
 479 {
 480     int32_t layer;
 481     TF_OperationDescription *op_desc;
 482     TF_Operation *op;
 483     TF_Operation *transpose_op;
 484     TF_Tensor *tensor;
 485     TF_Output input;
 486     int32_t *transpose_perm;
 487     int64_t transpose_perm_shape[] = {4};
 488     int64_t input_shape[] = {1, -1, -1, -1};
 489     DNNReturnType layer_add_res;
 490     DNNModel *native_model = NULL;
 491     ConvolutionalNetwork *conv_network;
 492
 493     native_model = ff_dnn_load_model_native(model_filename);
 494     if (!native_model){
 495         return DNN_ERROR;
 496     }
 497
 498     conv_network = (ConvolutionalNetwork *)native_model->model;
 499     tf_model->graph = TF_NewGraph();
 500     tf_model->status = TF_NewStatus();
 501
 502 #define CLEANUP_ON_ERROR(tf_model) \
 503     { \
 504         TF_DeleteGraph(tf_model->graph); \
 505         TF_DeleteStatus(tf_model->status); \
 506         return DNN_ERROR; \
 507     }
 508
 509     op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
 510     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 511     TF_SetAttrShape(op_desc, "shape", input_shape, 4);
 512     op = TF_FinishOperation(op_desc, tf_model->status);
 513     if (TF_GetCode(tf_model->status) != TF_OK){
 514         CLEANUP_ON_ERROR(tf_model);
 515     }
 516
 517     op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
 518     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 519     tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
 520     transpose_perm = (int32_t *)TF_TensorData(tensor);
 521     transpose_perm[0] = 1;
 522     transpose_perm[1] = 2;
 523     transpose_perm[2] = 3;
 524     transpose_perm[3] = 0;
 525     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 526     if (TF_GetCode(tf_model->status) != TF_OK){
 527         CLEANUP_ON_ERROR(tf_model);
 528     }
 529     transpose_op = TF_FinishOperation(op_desc, tf_model->status);
 530
 531     for (layer = 0; layer < conv_network->layers_num; ++layer){
 532         switch (conv_network->layers[layer].type){
 533         case DLT_INPUT:
 534             layer_add_res = DNN_SUCCESS;
 535             break;
 536         case DLT_CONV2D:
 537             layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
 538                                            (ConvolutionalParams *)conv_network->layers[layer].params, layer);
 539             break;
 540         case DLT_DEPTH_TO_SPACE:
 541             layer_add_res = add_depth_to_space_layer(tf_model, &op,
 542                                                      (DepthToSpaceParams *)conv_network->layers[layer].params, layer);
 543             break;
 544         case DLT_MIRROR_PAD:
 545             layer_add_res = add_pad_layer(tf_model, &op,
 546                                           (LayerPadParams *)conv_network->layers[layer].params, layer);
 547             break;
 548         case DLT_MAXIMUM:
 549             layer_add_res = add_maximum_layer(tf_model, &op,
 550                                           (DnnLayerMaximumParams *)conv_network->layers[layer].params, layer);
 551             break;
 552         default:
 553             CLEANUP_ON_ERROR(tf_model);
 554         }
 555
 556         if (layer_add_res != DNN_SUCCESS){
 557             CLEANUP_ON_ERROR(tf_model);
 558         }
 559     }
 560
 561     op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
 562     input.oper = op;
 563     input.index = 0;
 564     TF_AddInput(op_desc, input);
 565     TF_FinishOperation(op_desc, tf_model->status);
 566     if (TF_GetCode(tf_model->status) != TF_OK){
 567         CLEANUP_ON_ERROR(tf_model);
 568     }
 569
 570     ff_dnn_free_model_native(&native_model);
 571
 572     return DNN_SUCCESS;
 573 }
 574
 575 DNNModel *ff_dnn_load_model_tf(const char *model_filename, const char *options)
 576 {
 577     DNNModel *model = NULL;
 578     TFModel *tf_model = NULL;
 579
 580     model = av_malloc(sizeof(DNNModel));
 581     if (!model){
 582         return NULL;
 583     }
 584
 585     tf_model = av_mallocz(sizeof(TFModel));
 586     if (!tf_model){
 587         av_freep(&model);
 588         return NULL;
 589     }
 590
 591     if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
 592         if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
 593             av_freep(&tf_model);
 594             av_freep(&model);
 595
 596             return NULL;
 597         }
 598     }
 599
 600     model->model = (void *)tf_model;
 601     model->set_input_output = &set_input_output_tf;
 602     model->get_input = &get_input_tf;
 603     model->options = options;
 604
 605     return model;
 606 }
 607
 608
 609
 610 DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
 611 {
 612     TFModel *tf_model = (TFModel *)model->model;
 613     uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
 614     if (nb == 0)
 615         return DNN_ERROR;
 616
 617     av_assert0(tf_model->output_tensors);
 618     for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
 619         if (tf_model->output_tensors[i]) {
 620             TF_DeleteTensor(tf_model->output_tensors[i]);
 621             tf_model->output_tensors[i] = NULL;
 622         }
 623     }
 624
 625     TF_SessionRun(tf_model->session, NULL,
 626                   &tf_model->input, &tf_model->input_tensor, 1,
 627                   tf_model->outputs, tf_model->output_tensors, nb,
 628                   NULL, 0, NULL, tf_model->status);
 629
 630     if (TF_GetCode(tf_model->status) != TF_OK){
 631         return DNN_ERROR;
 632     }
 633
 634     for (uint32_t i = 0; i < nb; ++i) {
 635         outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
 636         outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
 637         outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
 638         outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
 639         outputs[i].dt = TF_TensorType(tf_model->output_tensors[i]);
 640     }
 641
 642     return DNN_SUCCESS;
 643 }
 644
 645 void ff_dnn_free_model_tf(DNNModel **model)
 646 {
 647     TFModel *tf_model;
 648
 649     if (*model){
 650         tf_model = (TFModel *)(*model)->model;
 651         if (tf_model->graph){
 652             TF_DeleteGraph(tf_model->graph);
 653         }
 654         if (tf_model->session){
 655             TF_CloseSession(tf_model->session, tf_model->status);
 656             TF_DeleteSession(tf_model->session, tf_model->status);
 657         }
 658         if (tf_model->status){
 659             TF_DeleteStatus(tf_model->status);
 660         }
 661         if (tf_model->input_tensor){
 662             TF_DeleteTensor(tf_model->input_tensor);
 663         }
 664         if (tf_model->output_tensors) {
 665             for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
 666                 if (tf_model->output_tensors[i]) {
 667                     TF_DeleteTensor(tf_model->output_tensors[i]);
 668                     tf_model->output_tensors[i] = NULL;
 669                 }
 670             }
 671         }
 672         av_freep(&tf_model->outputs);
 673         av_freep(&tf_model->output_tensors);
 674         av_freep(&tf_model);
 675         av_freep(model);
 676     }
 677 }