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 #include "dnn_io_proc.h"
  35
  36 #include <tensorflow/c/c_api.h>
  37
  38 typedef struct TFContext {
  39     const AVClass *class;
  40 } TFContext;
  41
  42 typedef struct TFModel{
  43     TFContext ctx;
  44     DNNModel *model;
  45     TF_Graph *graph;
  46     TF_Session *session;
  47     TF_Status *status;
  48 } TFModel;
  49
  50 static const AVClass dnn_tensorflow_class = {
  51     .class_name = "dnn_tensorflow",
  52     .item_name  = av_default_item_name,
  53     .option     = NULL,
  54     .version    = LIBAVUTIL_VERSION_INT,
  55     .category   = AV_CLASS_CATEGORY_FILTER,
  56 };
  57
  58 static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
  59                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame,
  60                                       int do_ioproc);
  61
  62 static void free_buffer(void *data, size_t length)
  63 {
  64     av_freep(&data);
  65 }
  66
  67 static TF_Buffer *read_graph(const char *model_filename)
  68 {
  69     TF_Buffer *graph_buf;
  70     unsigned char *graph_data = NULL;
  71     AVIOContext *model_file_context;
  72     long size, bytes_read;
  73
  74     if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
  75         return NULL;
  76     }
  77
  78     size = avio_size(model_file_context);
  79
  80     graph_data = av_malloc(size);
  81     if (!graph_data){
  82         avio_closep(&model_file_context);
  83         return NULL;
  84     }
  85     bytes_read = avio_read(model_file_context, graph_data, size);
  86     avio_closep(&model_file_context);
  87     if (bytes_read != size){
  88         av_freep(&graph_data);
  89         return NULL;
  90     }
  91
  92     graph_buf = TF_NewBuffer();
  93     graph_buf->data = (void *)graph_data;
  94     graph_buf->length = size;
  95     graph_buf->data_deallocator = free_buffer;
  96
  97     return graph_buf;
  98 }
  99
 100 static TF_Tensor *allocate_input_tensor(const DNNData *input)
 101 {
 102     TF_DataType dt;
 103     size_t size;
 104     int64_t input_dims[] = {1, input->height, input->width, input->channels};
 105     switch (input->dt) {
 106     case DNN_FLOAT:
 107         dt = TF_FLOAT;
 108         size = sizeof(float);
 109         break;
 110     case DNN_UINT8:
 111         dt = TF_UINT8;
 112         size = 1;
 113         break;
 114     default:
 115         av_assert0(!"should not reach here");
 116     }
 117
 118     return TF_AllocateTensor(dt, input_dims, 4,
 119                              input_dims[1] * input_dims[2] * input_dims[3] * size);
 120 }
 121
 122 static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
 123 {
 124     TFModel *tf_model = (TFModel *)model;
 125     TFContext *ctx = &tf_model->ctx;
 126     TF_Status *status;
 127     int64_t dims[4];
 128
 129     TF_Output tf_output;
 130     tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 131     if (!tf_output.oper) {
 132         av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
 133         return DNN_ERROR;
 134     }
 135
 136     tf_output.index = 0;
 137     input->dt = TF_OperationOutputType(tf_output);
 138
 139     status = TF_NewStatus();
 140     TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status);
 141     if (TF_GetCode(status) != TF_OK){
 142         TF_DeleteStatus(status);
 143         av_log(ctx, AV_LOG_ERROR, "Failed to get input tensor shape: number of dimension incorrect\n");
 144         return DNN_ERROR;
 145     }
 146     TF_DeleteStatus(status);
 147
 148     // currently only NHWC is supported
 149     av_assert0(dims[0] == 1);
 150     input->height = dims[1];
 151     input->width = dims[2];
 152     input->channels = dims[3];
 153
 154     return DNN_SUCCESS;
 155 }
 156
 157 static DNNReturnType get_output_tf(void *model, const char *input_name, int input_width, int input_height,
 158                                    const char *output_name, int *output_width, int *output_height)
 159 {
 160     DNNReturnType ret;
 161     TFModel *tf_model = (TFModel *)model;
 162     AVFrame *in_frame = av_frame_alloc();
 163     AVFrame *out_frame = av_frame_alloc();
 164     in_frame->width = input_width;
 165     in_frame->height = input_height;
 166
 167     ret = execute_model_tf(tf_model->model, input_name, in_frame, &output_name, 1, out_frame, 0);
 168     *output_width = out_frame->width;
 169     *output_height = out_frame->height;
 170
 171     av_frame_free(&out_frame);
 172     av_frame_free(&in_frame);
 173     return ret;
 174 }
 175
 176 static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
 177 {
 178     TFContext *ctx = &tf_model->ctx;
 179     TF_Buffer *graph_def;
 180     TF_ImportGraphDefOptions *graph_opts;
 181     TF_SessionOptions *sess_opts;
 182     const TF_Operation *init_op;
 183
 184     graph_def = read_graph(model_filename);
 185     if (!graph_def){
 186         av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
 187         return DNN_ERROR;
 188     }
 189     tf_model->graph = TF_NewGraph();
 190     tf_model->status = TF_NewStatus();
 191     graph_opts = TF_NewImportGraphDefOptions();
 192     TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
 193     TF_DeleteImportGraphDefOptions(graph_opts);
 194     TF_DeleteBuffer(graph_def);
 195     if (TF_GetCode(tf_model->status) != TF_OK){
 196         TF_DeleteGraph(tf_model->graph);
 197         TF_DeleteStatus(tf_model->status);
 198         av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
 199         return DNN_ERROR;
 200     }
 201
 202     init_op = TF_GraphOperationByName(tf_model->graph, "init");
 203     sess_opts = TF_NewSessionOptions();
 204     tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
 205     TF_DeleteSessionOptions(sess_opts);
 206     if (TF_GetCode(tf_model->status) != TF_OK)
 207     {
 208         av_log(ctx, AV_LOG_ERROR, "Failed to create new session with model graph\n");
 209         return DNN_ERROR;
 210     }
 211
 212     // Run initialization operation with name "init" if it is present in graph
 213     if (init_op){
 214         TF_SessionRun(tf_model->session, NULL,
 215                       NULL, NULL, 0,
 216                       NULL, NULL, 0,
 217                       &init_op, 1, NULL, tf_model->status);
 218         if (TF_GetCode(tf_model->status) != TF_OK)
 219         {
 220             av_log(ctx, AV_LOG_ERROR, "Failed to run session when initializing\n");
 221             return DNN_ERROR;
 222         }
 223     }
 224
 225     return DNN_SUCCESS;
 226 }
 227
 228 #define NAME_BUFFER_SIZE 256
 229
 230 static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
 231                                     ConvolutionalParams* params, const int layer)
 232 {
 233     TFContext *ctx = &tf_model->ctx;
 234     TF_Operation *op;
 235     TF_OperationDescription *op_desc;
 236     TF_Output input;
 237     int64_t strides[] = {1, 1, 1, 1};
 238     TF_Tensor *tensor;
 239     int64_t dims[4];
 240     int dims_len;
 241     char name_buffer[NAME_BUFFER_SIZE];
 242     int32_t size;
 243
 244     size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
 245     input.index = 0;
 246
 247     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
 248     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 249     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 250     dims[0] = params->output_num;
 251     dims[1] = params->kernel_size;
 252     dims[2] = params->kernel_size;
 253     dims[3] = params->input_num;
 254     dims_len = 4;
 255     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
 256     memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
 257     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 258     if (TF_GetCode(tf_model->status) != TF_OK){
 259         av_log(ctx, AV_LOG_ERROR, "Failed to set value for kernel of conv layer %d\n", layer);
 260         return DNN_ERROR;
 261     }
 262     op = TF_FinishOperation(op_desc, tf_model->status);
 263     if (TF_GetCode(tf_model->status) != TF_OK){
 264         av_log(ctx, AV_LOG_ERROR, "Failed to add kernel to conv layer %d\n", layer);
 265         return DNN_ERROR;
 266     }
 267
 268     snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
 269     op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
 270     input.oper = op;
 271     TF_AddInput(op_desc, input);
 272     input.oper = transpose_op;
 273     TF_AddInput(op_desc, input);
 274     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 275     TF_SetAttrType(op_desc, "Tperm", TF_INT32);
 276     op = TF_FinishOperation(op_desc, tf_model->status);
 277     if (TF_GetCode(tf_model->status) != TF_OK){
 278         av_log(ctx, AV_LOG_ERROR, "Failed to add transpose to conv layer %d\n", layer);
 279         return DNN_ERROR;
 280     }
 281
 282     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
 283     op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
 284     input.oper = *cur_op;
 285     TF_AddInput(op_desc, input);
 286     input.oper = op;
 287     TF_AddInput(op_desc, input);
 288     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 289     TF_SetAttrIntList(op_desc, "strides", strides, 4);
 290     TF_SetAttrString(op_desc, "padding", "VALID", 5);
 291     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 292     if (TF_GetCode(tf_model->status) != TF_OK){
 293         av_log(ctx, AV_LOG_ERROR, "Failed to add conv2d to conv layer %d\n", layer);
 294         return DNN_ERROR;
 295     }
 296
 297     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
 298     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 299     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 300     dims[0] = params->output_num;
 301     dims_len = 1;
 302     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
 303     memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
 304     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 305     if (TF_GetCode(tf_model->status) != TF_OK){
 306         av_log(ctx, AV_LOG_ERROR, "Failed to set value for conv_biases of conv layer %d\n", layer);
 307         return DNN_ERROR;
 308     }
 309     op = TF_FinishOperation(op_desc, tf_model->status);
 310     if (TF_GetCode(tf_model->status) != TF_OK){
 311         av_log(ctx, AV_LOG_ERROR, "Failed to add conv_biases to conv layer %d\n", layer);
 312         return DNN_ERROR;
 313     }
 314
 315     snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
 316     op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
 317     input.oper = *cur_op;
 318     TF_AddInput(op_desc, input);
 319     input.oper = op;
 320     TF_AddInput(op_desc, input);
 321     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 322     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 323     if (TF_GetCode(tf_model->status) != TF_OK){
 324         av_log(ctx, AV_LOG_ERROR, "Failed to add bias_add to conv layer %d\n", layer);
 325         return DNN_ERROR;
 326     }
 327
 328     snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
 329     switch (params->activation){
 330     case RELU:
 331         op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
 332         break;
 333     case TANH:
 334         op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
 335         break;
 336     case SIGMOID:
 337         op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
 338         break;
 339     default:
 340         av_log(ctx, AV_LOG_ERROR, "Unsupported convolutional activation function\n");
 341         return DNN_ERROR;
 342     }
 343     input.oper = *cur_op;
 344     TF_AddInput(op_desc, input);
 345     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 346     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 347     if (TF_GetCode(tf_model->status) != TF_OK){
 348         av_log(ctx, AV_LOG_ERROR, "Failed to add activation function to conv layer %d\n", layer);
 349         return DNN_ERROR;
 350     }
 351
 352     return DNN_SUCCESS;
 353 }
 354
 355 static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
 356                                               DepthToSpaceParams *params, const int layer)
 357 {
 358     TFContext *ctx = &tf_model->ctx;
 359     TF_OperationDescription *op_desc;
 360     TF_Output input;
 361     char name_buffer[NAME_BUFFER_SIZE];
 362
 363     snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
 364     op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
 365     input.oper = *cur_op;
 366     input.index = 0;
 367     TF_AddInput(op_desc, input);
 368     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 369     TF_SetAttrInt(op_desc, "block_size", params->block_size);
 370     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 371     if (TF_GetCode(tf_model->status) != TF_OK){
 372         av_log(ctx, AV_LOG_ERROR, "Failed to add depth_to_space to layer %d\n", layer);
 373         return DNN_ERROR;
 374     }
 375
 376     return DNN_SUCCESS;
 377 }
 378
 379 static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
 380                                               LayerPadParams *params, const int layer)
 381 {
 382     TFContext *ctx = &tf_model->ctx;
 383     TF_Operation *op;
 384     TF_Tensor *tensor;
 385     TF_OperationDescription *op_desc;
 386     TF_Output input;
 387     int32_t *pads;
 388     int64_t pads_shape[] = {4, 2};
 389
 390     char name_buffer[NAME_BUFFER_SIZE];
 391     snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
 392
 393     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 394     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 395     tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
 396     pads = (int32_t *)TF_TensorData(tensor);
 397     pads[0] = params->paddings[0][0];
 398     pads[1] = params->paddings[0][1];
 399     pads[2] = params->paddings[1][0];
 400     pads[3] = params->paddings[1][1];
 401     pads[4] = params->paddings[2][0];
 402     pads[5] = params->paddings[2][1];
 403     pads[6] = params->paddings[3][0];
 404     pads[7] = params->paddings[3][1];
 405     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 406     if (TF_GetCode(tf_model->status) != TF_OK){
 407         av_log(ctx, AV_LOG_ERROR, "Failed to set value for pad of layer %d\n", layer);
 408         return DNN_ERROR;
 409     }
 410     op = TF_FinishOperation(op_desc, tf_model->status);
 411     if (TF_GetCode(tf_model->status) != TF_OK){
 412         av_log(ctx, AV_LOG_ERROR, "Failed to add pad to layer %d\n", layer);
 413         return DNN_ERROR;
 414     }
 415
 416     op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
 417     input.oper = *cur_op;
 418     input.index = 0;
 419     TF_AddInput(op_desc, input);
 420     input.oper = op;
 421     TF_AddInput(op_desc, input);
 422     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 423     TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
 424     TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
 425     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 426     if (TF_GetCode(tf_model->status) != TF_OK){
 427         av_log(ctx, AV_LOG_ERROR, "Failed to add mirror_pad to layer %d\n", layer);
 428         return DNN_ERROR;
 429     }
 430
 431     return DNN_SUCCESS;
 432 }
 433
 434 static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
 435                                        DnnLayerMaximumParams *params, const int layer)
 436 {
 437     TFContext *ctx = &tf_model->ctx;
 438     TF_Operation *op;
 439     TF_Tensor *tensor;
 440     TF_OperationDescription *op_desc;
 441     TF_Output input;
 442     float *y;
 443
 444     char name_buffer[NAME_BUFFER_SIZE];
 445     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
 446
 447     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 448     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 449     tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
 450     y = (float *)TF_TensorData(tensor);
 451     *y = params->val.y;
 452     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 453     if (TF_GetCode(tf_model->status) != TF_OK){
 454         av_log(ctx, AV_LOG_ERROR, "Failed to set value for maximum/y of layer %d", layer);
 455         return DNN_ERROR;
 456     }
 457     op = TF_FinishOperation(op_desc, tf_model->status);
 458     if (TF_GetCode(tf_model->status) != TF_OK){
 459         av_log(ctx, AV_LOG_ERROR, "Failed to add maximum/y to layer %d\n", layer);
 460         return DNN_ERROR;
 461     }
 462
 463     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
 464     op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
 465     input.oper = *cur_op;
 466     input.index = 0;
 467     TF_AddInput(op_desc, input);
 468     input.oper = op;
 469     TF_AddInput(op_desc, input);
 470     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 471     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 472     if (TF_GetCode(tf_model->status) != TF_OK){
 473         av_log(ctx, AV_LOG_ERROR, "Failed to add maximum to layer %d\n", layer);
 474         return DNN_ERROR;
 475     }
 476
 477     return DNN_SUCCESS;
 478 }
 479
 480 static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
 481 {
 482     TFContext *ctx = &tf_model->ctx;
 483     int32_t layer;
 484     TF_OperationDescription *op_desc;
 485     TF_Operation *op;
 486     TF_Operation *transpose_op;
 487     TF_Tensor *tensor;
 488     TF_Output input;
 489     int32_t *transpose_perm;
 490     int64_t transpose_perm_shape[] = {4};
 491     int64_t input_shape[] = {1, -1, -1, -1};
 492     DNNReturnType layer_add_res;
 493     DNNModel *model = NULL;
 494     NativeModel *native_model;
 495
 496     model = ff_dnn_load_model_native(model_filename, NULL, NULL);
 497     if (!model){
 498         av_log(ctx, AV_LOG_ERROR, "Failed to load native model\n");
 499         return DNN_ERROR;
 500     }
 501
 502     native_model = (NativeModel *)model->model;
 503     tf_model->graph = TF_NewGraph();
 504     tf_model->status = TF_NewStatus();
 505
 506 #define CLEANUP_ON_ERROR(tf_model) \
 507     { \
 508         TF_DeleteGraph(tf_model->graph); \
 509         TF_DeleteStatus(tf_model->status); \
 510         av_log(ctx, AV_LOG_ERROR, "Failed to set value or add operator to layer\n"); \
 511         return DNN_ERROR; \
 512     }
 513
 514     op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
 515     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 516     TF_SetAttrShape(op_desc, "shape", input_shape, 4);
 517     op = TF_FinishOperation(op_desc, tf_model->status);
 518     if (TF_GetCode(tf_model->status) != TF_OK){
 519         CLEANUP_ON_ERROR(tf_model);
 520     }
 521
 522     op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
 523     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 524     tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
 525     transpose_perm = (int32_t *)TF_TensorData(tensor);
 526     transpose_perm[0] = 1;
 527     transpose_perm[1] = 2;
 528     transpose_perm[2] = 3;
 529     transpose_perm[3] = 0;
 530     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 531     if (TF_GetCode(tf_model->status) != TF_OK){
 532         CLEANUP_ON_ERROR(tf_model);
 533     }
 534     transpose_op = TF_FinishOperation(op_desc, tf_model->status);
 535
 536     for (layer = 0; layer < native_model->layers_num; ++layer){
 537         switch (native_model->layers[layer].type){
 538         case DLT_INPUT:
 539             layer_add_res = DNN_SUCCESS;
 540             break;
 541         case DLT_CONV2D:
 542             layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
 543                                            (ConvolutionalParams *)native_model->layers[layer].params, layer);
 544             break;
 545         case DLT_DEPTH_TO_SPACE:
 546             layer_add_res = add_depth_to_space_layer(tf_model, &op,
 547                                                      (DepthToSpaceParams *)native_model->layers[layer].params, layer);
 548             break;
 549         case DLT_MIRROR_PAD:
 550             layer_add_res = add_pad_layer(tf_model, &op,
 551                                           (LayerPadParams *)native_model->layers[layer].params, layer);
 552             break;
 553         case DLT_MAXIMUM:
 554             layer_add_res = add_maximum_layer(tf_model, &op,
 555                                           (DnnLayerMaximumParams *)native_model->layers[layer].params, layer);
 556             break;
 557         default:
 558             CLEANUP_ON_ERROR(tf_model);
 559         }
 560
 561         if (layer_add_res != DNN_SUCCESS){
 562             CLEANUP_ON_ERROR(tf_model);
 563         }
 564     }
 565
 566     op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
 567     input.oper = op;
 568     input.index = 0;
 569     TF_AddInput(op_desc, input);
 570     TF_FinishOperation(op_desc, tf_model->status);
 571     if (TF_GetCode(tf_model->status) != TF_OK){
 572         CLEANUP_ON_ERROR(tf_model);
 573     }
 574
 575     ff_dnn_free_model_native(&model);
 576
 577     return DNN_SUCCESS;
 578 }
 579
 580 DNNModel *ff_dnn_load_model_tf(const char *model_filename, const char *options, void *userdata)
 581 {
 582     DNNModel *model = NULL;
 583     TFModel *tf_model = NULL;
 584
 585     model = av_mallocz(sizeof(DNNModel));
 586     if (!model){
 587         return NULL;
 588     }
 589
 590     tf_model = av_mallocz(sizeof(TFModel));
 591     if (!tf_model){
 592         av_freep(&model);
 593         return NULL;
 594     }
 595     tf_model->ctx.class = &dnn_tensorflow_class;
 596     tf_model->model = model;
 597
 598     if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
 599         if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
 600             av_freep(&tf_model);
 601             av_freep(&model);
 602
 603             return NULL;
 604         }
 605     }
 606
 607     model->model = (void *)tf_model;
 608     model->get_input = &get_input_tf;
 609     model->get_output = &get_output_tf;
 610     model->options = options;
 611     model->userdata = userdata;
 612
 613     return model;
 614 }
 615
 616 static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
 617                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame,
 618                                       int do_ioproc)
 619 {
 620     TF_Output *tf_outputs;
 621     TFModel *tf_model = (TFModel *)model->model;
 622     TFContext *ctx = &tf_model->ctx;
 623     DNNData input, output;
 624     TF_Tensor **output_tensors;
 625     TF_Output tf_input;
 626     TF_Tensor *input_tensor;
 627
 628     if (get_input_tf(tf_model, &input, input_name) != DNN_SUCCESS)
 629         return DNN_ERROR;
 630     input.height = in_frame->height;
 631     input.width = in_frame->width;
 632
 633     tf_input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 634     if (!tf_input.oper){
 635         av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
 636         return DNN_ERROR;
 637     }
 638     tf_input.index = 0;
 639     input_tensor = allocate_input_tensor(&input);
 640     if (!input_tensor){
 641         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
 642         return DNN_ERROR;
 643     }
 644     input.data = (float *)TF_TensorData(input_tensor);
 645
 646     if (do_ioproc) {
 647         if (tf_model->model->pre_proc != NULL) {
 648             tf_model->model->pre_proc(in_frame, &input, tf_model->model->userdata);
 649         } else {
 650             proc_from_frame_to_dnn(in_frame, &input, ctx);
 651         }
 652     }
 653
 654     if (nb_output != 1) {
 655         // currently, the filter does not need multiple outputs,
 656         // so we just pending the support until we really need it.
 657         av_log(ctx, AV_LOG_ERROR, "do not support multiple outputs\n");
 658         return DNN_ERROR;
 659     }
 660
 661     tf_outputs = av_malloc_array(nb_output, sizeof(*tf_outputs));
 662     if (tf_outputs == NULL) {
 663         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *tf_outputs\n"); \
 664         return DNN_ERROR;
 665     }
 666
 667     output_tensors = av_mallocz_array(nb_output, sizeof(*output_tensors));
 668     if (!output_tensors) {
 669         av_freep(&tf_outputs);
 670         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n"); \
 671         return DNN_ERROR;
 672     }
 673
 674     for (int i = 0; i < nb_output; ++i) {
 675         tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
 676         if (!tf_outputs[i].oper) {
 677             av_freep(&tf_outputs);
 678             av_freep(&output_tensors);
 679             av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", output_names[i]); \
 680             return DNN_ERROR;
 681         }
 682         tf_outputs[i].index = 0;
 683     }
 684
 685     TF_SessionRun(tf_model->session, NULL,
 686                   &tf_input, &input_tensor, 1,
 687                   tf_outputs, output_tensors, nb_output,
 688                   NULL, 0, NULL, tf_model->status);
 689     if (TF_GetCode(tf_model->status) != TF_OK) {
 690         av_freep(&tf_outputs);
 691         av_freep(&output_tensors);
 692         av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
 693         return DNN_ERROR;
 694     }
 695
 696     for (uint32_t i = 0; i < nb_output; ++i) {
 697         output.height = TF_Dim(output_tensors[i], 1);
 698         output.width = TF_Dim(output_tensors[i], 2);
 699         output.channels = TF_Dim(output_tensors[i], 3);
 700         output.data = TF_TensorData(output_tensors[i]);
 701         output.dt = TF_TensorType(output_tensors[i]);
 702
 703         if (do_ioproc) {
 704             if (tf_model->model->post_proc != NULL) {
 705                 tf_model->model->post_proc(out_frame, &output, tf_model->model->userdata);
 706             } else {
 707                 proc_from_dnn_to_frame(out_frame, &output, ctx);
 708             }
 709         } else {
 710             out_frame->width = output.width;
 711             out_frame->height = output.height;
 712         }
 713     }
 714
 715     for (uint32_t i = 0; i < nb_output; ++i) {
 716         if (output_tensors[i]) {
 717             TF_DeleteTensor(output_tensors[i]);
 718         }
 719     }
 720     TF_DeleteTensor(input_tensor);
 721     av_freep(&output_tensors);
 722     av_freep(&tf_outputs);
 723     return DNN_SUCCESS;
 724 }
 725
 726 DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
 727                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame)
 728 {
 729     TFModel *tf_model = (TFModel *)model->model;
 730     TFContext *ctx = &tf_model->ctx;
 731
 732     if (!in_frame) {
 733         av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n");
 734         return DNN_ERROR;
 735     }
 736
 737     if (!out_frame) {
 738         av_log(ctx, AV_LOG_ERROR, "out frame is NULL when execute model.\n");
 739         return DNN_ERROR;
 740     }
 741
 742     return execute_model_tf(model, input_name, in_frame, output_names, nb_output, out_frame, 1);
 743 }
 744
 745 void ff_dnn_free_model_tf(DNNModel **model)
 746 {
 747     TFModel *tf_model;
 748
 749     if (*model){
 750         tf_model = (TFModel *)(*model)->model;
 751         if (tf_model->graph){
 752             TF_DeleteGraph(tf_model->graph);
 753         }
 754         if (tf_model->session){
 755             TF_CloseSession(tf_model->session, tf_model->status);
 756             TF_DeleteSession(tf_model->session, tf_model->status);
 757         }
 758         if (tf_model->status){
 759             TF_DeleteStatus(tf_model->status);
 760         }
 761         av_freep(&tf_model);
 762         av_freep(model);
 763     }
 764 }