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 "../internal.h"
  33 #include "dnn_backend_native_layer_pad.h"
  34 #include "dnn_backend_native_layer_maximum.h"
  35 #include "dnn_io_proc.h"
  36
  37 #include <tensorflow/c/c_api.h>
  38
  39 typedef struct TFOptions{
  40     char *sess_config;
  41 } TFOptions;
  42
  43 typedef struct TFContext {
  44     const AVClass *class;
  45     TFOptions options;
  46 } TFContext;
  47
  48 typedef struct TFModel{
  49     TFContext ctx;
  50     DNNModel *model;
  51     TF_Graph *graph;
  52     TF_Session *session;
  53     TF_Status *status;
  54 } TFModel;
  55
  56 #define OFFSET(x) offsetof(TFContext, x)
  57 #define FLAGS AV_OPT_FLAG_FILTERING_PARAM
  58 static const AVOption dnn_tensorflow_options[] = {
  59     { "sess_config", "config for SessionOptions", OFFSET(options.sess_config), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
  60     { NULL }
  61 };
  62
  63 AVFILTER_DEFINE_CLASS(dnn_tensorflow);
  64
  65 static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
  66                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame,
  67                                       int do_ioproc);
  68
  69 static void free_buffer(void *data, size_t length)
  70 {
  71     av_freep(&data);
  72 }
  73
  74 static TF_Buffer *read_graph(const char *model_filename)
  75 {
  76     TF_Buffer *graph_buf;
  77     unsigned char *graph_data = NULL;
  78     AVIOContext *model_file_context;
  79     long size, bytes_read;
  80
  81     if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
  82         return NULL;
  83     }
  84
  85     size = avio_size(model_file_context);
  86
  87     graph_data = av_malloc(size);
  88     if (!graph_data){
  89         avio_closep(&model_file_context);
  90         return NULL;
  91     }
  92     bytes_read = avio_read(model_file_context, graph_data, size);
  93     avio_closep(&model_file_context);
  94     if (bytes_read != size){
  95         av_freep(&graph_data);
  96         return NULL;
  97     }
  98
  99     graph_buf = TF_NewBuffer();
 100     graph_buf->data = graph_data;
 101     graph_buf->length = size;
 102     graph_buf->data_deallocator = free_buffer;
 103
 104     return graph_buf;
 105 }
 106
 107 static TF_Tensor *allocate_input_tensor(const DNNData *input)
 108 {
 109     TF_DataType dt;
 110     size_t size;
 111     int64_t input_dims[] = {1, input->height, input->width, input->channels};
 112     switch (input->dt) {
 113     case DNN_FLOAT:
 114         dt = TF_FLOAT;
 115         size = sizeof(float);
 116         break;
 117     case DNN_UINT8:
 118         dt = TF_UINT8;
 119         size = 1;
 120         break;
 121     default:
 122         av_assert0(!"should not reach here");
 123     }
 124
 125     return TF_AllocateTensor(dt, input_dims, 4,
 126                              input_dims[1] * input_dims[2] * input_dims[3] * size);
 127 }
 128
 129 static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
 130 {
 131     TFModel *tf_model = model;
 132     TFContext *ctx = &tf_model->ctx;
 133     TF_Status *status;
 134     int64_t dims[4];
 135
 136     TF_Output tf_output;
 137     tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 138     if (!tf_output.oper) {
 139         av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
 140         return DNN_ERROR;
 141     }
 142
 143     tf_output.index = 0;
 144     input->dt = TF_OperationOutputType(tf_output);
 145
 146     status = TF_NewStatus();
 147     TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status);
 148     if (TF_GetCode(status) != TF_OK){
 149         TF_DeleteStatus(status);
 150         av_log(ctx, AV_LOG_ERROR, "Failed to get input tensor shape: number of dimension incorrect\n");
 151         return DNN_ERROR;
 152     }
 153     TF_DeleteStatus(status);
 154
 155     // currently only NHWC is supported
 156     av_assert0(dims[0] == 1);
 157     input->height = dims[1];
 158     input->width = dims[2];
 159     input->channels = dims[3];
 160
 161     return DNN_SUCCESS;
 162 }
 163
 164 static DNNReturnType get_output_tf(void *model, const char *input_name, int input_width, int input_height,
 165                                    const char *output_name, int *output_width, int *output_height)
 166 {
 167     DNNReturnType ret;
 168     TFModel *tf_model = model;
 169     TFContext *ctx = &tf_model->ctx;
 170     AVFrame *in_frame = av_frame_alloc();
 171     AVFrame *out_frame = NULL;
 172
 173     if (!in_frame) {
 174         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
 175         return DNN_ERROR;
 176     }
 177
 178     out_frame = av_frame_alloc();
 179     if (!out_frame) {
 180         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n");
 181         av_frame_free(&in_frame);
 182         return DNN_ERROR;
 183     }
 184
 185     in_frame->width = input_width;
 186     in_frame->height = input_height;
 187
 188     ret = execute_model_tf(tf_model->model, input_name, in_frame, &output_name, 1, out_frame, 0);
 189     *output_width = out_frame->width;
 190     *output_height = out_frame->height;
 191
 192     av_frame_free(&out_frame);
 193     av_frame_free(&in_frame);
 194     return ret;
 195 }
 196
 197 static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
 198 {
 199     TFContext *ctx = &tf_model->ctx;
 200     TF_Buffer *graph_def;
 201     TF_ImportGraphDefOptions *graph_opts;
 202     TF_SessionOptions *sess_opts;
 203     const TF_Operation *init_op;
 204     uint8_t *sess_config = NULL;
 205     int sess_config_length = 0;
 206
 207     // prepare the sess config data
 208     if (tf_model->ctx.options.sess_config != NULL) {
 209         /*
 210         tf_model->ctx.options.sess_config is hex to present the serialized proto
 211         required by TF_SetConfig below, so we need to first generate the serialized
 212         proto in a python script, the following is a script example to generate
 213         serialized proto which specifies one GPU, we can change the script to add
 214         more options.
 215
 216         import tensorflow as tf
 217         gpu_options = tf.GPUOptions(visible_device_list='0')
 218         config = tf.ConfigProto(gpu_options=gpu_options)
 219         s = config.SerializeToString()
 220         b = ''.join("%02x" % int(ord(b)) for b in s[::-1])
 221         print('0x%s' % b)
 222
 223         the script output looks like: 0xab...cd, and then pass 0xab...cd to sess_config.
 224         */
 225         char tmp[3];
 226         tmp[2] = '\0';
 227
 228         if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
 229             av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
 230             return DNN_ERROR;
 231         }
 232
 233         sess_config_length = strlen(tf_model->ctx.options.sess_config);
 234         if (sess_config_length % 2 != 0) {
 235             av_log(ctx, AV_LOG_ERROR, "the length of sess_config is not even (%s), "
 236                                       "please re-generate the config.\n",
 237                                       tf_model->ctx.options.sess_config);
 238             return DNN_ERROR;
 239         }
 240
 241         sess_config_length -= 2; //ignore the first '0x'
 242         sess_config_length /= 2; //get the data length in byte
 243
 244         sess_config = av_malloc(sess_config_length);
 245         if (!sess_config) {
 246             av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
 247             return DNN_ERROR;
 248         }
 249
 250         for (int i = 0; i < sess_config_length; i++) {
 251             int index = 2 + (sess_config_length - 1 - i) * 2;
 252             tmp[0] = tf_model->ctx.options.sess_config[index];
 253             tmp[1] = tf_model->ctx.options.sess_config[index + 1];
 254             sess_config[i] = strtol(tmp, NULL, 16);
 255         }
 256     }
 257
 258     graph_def = read_graph(model_filename);
 259     if (!graph_def){
 260         av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
 261         av_freep(&sess_config);
 262         return DNN_ERROR;
 263     }
 264     tf_model->graph = TF_NewGraph();
 265     tf_model->status = TF_NewStatus();
 266     graph_opts = TF_NewImportGraphDefOptions();
 267     TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
 268     TF_DeleteImportGraphDefOptions(graph_opts);
 269     TF_DeleteBuffer(graph_def);
 270     if (TF_GetCode(tf_model->status) != TF_OK){
 271         TF_DeleteGraph(tf_model->graph);
 272         TF_DeleteStatus(tf_model->status);
 273         av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
 274         av_freep(&sess_config);
 275         return DNN_ERROR;
 276     }
 277
 278     init_op = TF_GraphOperationByName(tf_model->graph, "init");
 279     sess_opts = TF_NewSessionOptions();
 280
 281     if (sess_config) {
 282         TF_SetConfig(sess_opts, sess_config, sess_config_length,tf_model->status);
 283         av_freep(&sess_config);
 284         if (TF_GetCode(tf_model->status) != TF_OK) {
 285             av_log(ctx, AV_LOG_ERROR, "Failed to set config for sess options with %s\n",
 286                                       tf_model->ctx.options.sess_config);
 287             return DNN_ERROR;
 288         }
 289     }
 290
 291     tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
 292     TF_DeleteSessionOptions(sess_opts);
 293     if (TF_GetCode(tf_model->status) != TF_OK)
 294     {
 295         av_log(ctx, AV_LOG_ERROR, "Failed to create new session with model graph\n");
 296         return DNN_ERROR;
 297     }
 298
 299     // Run initialization operation with name "init" if it is present in graph
 300     if (init_op){
 301         TF_SessionRun(tf_model->session, NULL,
 302                       NULL, NULL, 0,
 303                       NULL, NULL, 0,
 304                       &init_op, 1, NULL, tf_model->status);
 305         if (TF_GetCode(tf_model->status) != TF_OK)
 306         {
 307             av_log(ctx, AV_LOG_ERROR, "Failed to run session when initializing\n");
 308             return DNN_ERROR;
 309         }
 310     }
 311
 312     return DNN_SUCCESS;
 313 }
 314
 315 #define NAME_BUFFER_SIZE 256
 316
 317 static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
 318                                     ConvolutionalParams* params, const int layer)
 319 {
 320     TFContext *ctx = &tf_model->ctx;
 321     TF_Operation *op;
 322     TF_OperationDescription *op_desc;
 323     TF_Output input;
 324     int64_t strides[] = {1, 1, 1, 1};
 325     TF_Tensor *tensor;
 326     int64_t dims[4];
 327     int dims_len;
 328     char name_buffer[NAME_BUFFER_SIZE];
 329     int32_t size;
 330
 331     size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
 332     input.index = 0;
 333
 334     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
 335     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 336     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 337     dims[0] = params->output_num;
 338     dims[1] = params->kernel_size;
 339     dims[2] = params->kernel_size;
 340     dims[3] = params->input_num;
 341     dims_len = 4;
 342     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
 343     memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
 344     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 345     if (TF_GetCode(tf_model->status) != TF_OK){
 346         av_log(ctx, AV_LOG_ERROR, "Failed to set value for kernel of conv layer %d\n", layer);
 347         return DNN_ERROR;
 348     }
 349     op = TF_FinishOperation(op_desc, tf_model->status);
 350     if (TF_GetCode(tf_model->status) != TF_OK){
 351         av_log(ctx, AV_LOG_ERROR, "Failed to add kernel to conv layer %d\n", layer);
 352         return DNN_ERROR;
 353     }
 354
 355     snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
 356     op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
 357     input.oper = op;
 358     TF_AddInput(op_desc, input);
 359     input.oper = transpose_op;
 360     TF_AddInput(op_desc, input);
 361     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 362     TF_SetAttrType(op_desc, "Tperm", TF_INT32);
 363     op = TF_FinishOperation(op_desc, tf_model->status);
 364     if (TF_GetCode(tf_model->status) != TF_OK){
 365         av_log(ctx, AV_LOG_ERROR, "Failed to add transpose to conv layer %d\n", layer);
 366         return DNN_ERROR;
 367     }
 368
 369     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
 370     op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
 371     input.oper = *cur_op;
 372     TF_AddInput(op_desc, input);
 373     input.oper = op;
 374     TF_AddInput(op_desc, input);
 375     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 376     TF_SetAttrIntList(op_desc, "strides", strides, 4);
 377     TF_SetAttrString(op_desc, "padding", "VALID", 5);
 378     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 379     if (TF_GetCode(tf_model->status) != TF_OK){
 380         av_log(ctx, AV_LOG_ERROR, "Failed to add conv2d to conv layer %d\n", layer);
 381         return DNN_ERROR;
 382     }
 383
 384     snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
 385     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 386     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 387     dims[0] = params->output_num;
 388     dims_len = 1;
 389     tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
 390     memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
 391     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 392     if (TF_GetCode(tf_model->status) != TF_OK){
 393         av_log(ctx, AV_LOG_ERROR, "Failed to set value for conv_biases of conv layer %d\n", layer);
 394         return DNN_ERROR;
 395     }
 396     op = TF_FinishOperation(op_desc, tf_model->status);
 397     if (TF_GetCode(tf_model->status) != TF_OK){
 398         av_log(ctx, AV_LOG_ERROR, "Failed to add conv_biases to conv layer %d\n", layer);
 399         return DNN_ERROR;
 400     }
 401
 402     snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
 403     op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
 404     input.oper = *cur_op;
 405     TF_AddInput(op_desc, input);
 406     input.oper = op;
 407     TF_AddInput(op_desc, input);
 408     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 409     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 410     if (TF_GetCode(tf_model->status) != TF_OK){
 411         av_log(ctx, AV_LOG_ERROR, "Failed to add bias_add to conv layer %d\n", layer);
 412         return DNN_ERROR;
 413     }
 414
 415     snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
 416     switch (params->activation){
 417     case RELU:
 418         op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
 419         break;
 420     case TANH:
 421         op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
 422         break;
 423     case SIGMOID:
 424         op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
 425         break;
 426     default:
 427         avpriv_report_missing_feature(ctx, "convolutional activation function %d", params->activation);
 428         return DNN_ERROR;
 429     }
 430     input.oper = *cur_op;
 431     TF_AddInput(op_desc, input);
 432     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 433     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 434     if (TF_GetCode(tf_model->status) != TF_OK){
 435         av_log(ctx, AV_LOG_ERROR, "Failed to add activation function to conv layer %d\n", layer);
 436         return DNN_ERROR;
 437     }
 438
 439     return DNN_SUCCESS;
 440 }
 441
 442 static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
 443                                               DepthToSpaceParams *params, const int layer)
 444 {
 445     TFContext *ctx = &tf_model->ctx;
 446     TF_OperationDescription *op_desc;
 447     TF_Output input;
 448     char name_buffer[NAME_BUFFER_SIZE];
 449
 450     snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
 451     op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
 452     input.oper = *cur_op;
 453     input.index = 0;
 454     TF_AddInput(op_desc, input);
 455     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 456     TF_SetAttrInt(op_desc, "block_size", params->block_size);
 457     *cur_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 depth_to_space to layer %d\n", layer);
 460         return DNN_ERROR;
 461     }
 462
 463     return DNN_SUCCESS;
 464 }
 465
 466 static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
 467                                               LayerPadParams *params, const int layer)
 468 {
 469     TFContext *ctx = &tf_model->ctx;
 470     TF_Operation *op;
 471     TF_Tensor *tensor;
 472     TF_OperationDescription *op_desc;
 473     TF_Output input;
 474     int32_t *pads;
 475     int64_t pads_shape[] = {4, 2};
 476
 477     char name_buffer[NAME_BUFFER_SIZE];
 478     snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
 479
 480     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 481     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 482     tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
 483     pads = (int32_t *)TF_TensorData(tensor);
 484     pads[0] = params->paddings[0][0];
 485     pads[1] = params->paddings[0][1];
 486     pads[2] = params->paddings[1][0];
 487     pads[3] = params->paddings[1][1];
 488     pads[4] = params->paddings[2][0];
 489     pads[5] = params->paddings[2][1];
 490     pads[6] = params->paddings[3][0];
 491     pads[7] = params->paddings[3][1];
 492     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 493     if (TF_GetCode(tf_model->status) != TF_OK){
 494         av_log(ctx, AV_LOG_ERROR, "Failed to set value for pad of layer %d\n", layer);
 495         return DNN_ERROR;
 496     }
 497     op = TF_FinishOperation(op_desc, tf_model->status);
 498     if (TF_GetCode(tf_model->status) != TF_OK){
 499         av_log(ctx, AV_LOG_ERROR, "Failed to add pad to layer %d\n", layer);
 500         return DNN_ERROR;
 501     }
 502
 503     op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
 504     input.oper = *cur_op;
 505     input.index = 0;
 506     TF_AddInput(op_desc, input);
 507     input.oper = op;
 508     TF_AddInput(op_desc, input);
 509     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 510     TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
 511     TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
 512     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 513     if (TF_GetCode(tf_model->status) != TF_OK){
 514         av_log(ctx, AV_LOG_ERROR, "Failed to add mirror_pad to layer %d\n", layer);
 515         return DNN_ERROR;
 516     }
 517
 518     return DNN_SUCCESS;
 519 }
 520
 521 static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
 522                                        DnnLayerMaximumParams *params, const int layer)
 523 {
 524     TFContext *ctx = &tf_model->ctx;
 525     TF_Operation *op;
 526     TF_Tensor *tensor;
 527     TF_OperationDescription *op_desc;
 528     TF_Output input;
 529     float *y;
 530
 531     char name_buffer[NAME_BUFFER_SIZE];
 532     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
 533
 534     op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
 535     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 536     tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
 537     y = (float *)TF_TensorData(tensor);
 538     *y = params->val.y;
 539     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 540     if (TF_GetCode(tf_model->status) != TF_OK){
 541         av_log(ctx, AV_LOG_ERROR, "Failed to set value for maximum/y of layer %d", layer);
 542         return DNN_ERROR;
 543     }
 544     op = TF_FinishOperation(op_desc, tf_model->status);
 545     if (TF_GetCode(tf_model->status) != TF_OK){
 546         av_log(ctx, AV_LOG_ERROR, "Failed to add maximum/y to layer %d\n", layer);
 547         return DNN_ERROR;
 548     }
 549
 550     snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
 551     op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
 552     input.oper = *cur_op;
 553     input.index = 0;
 554     TF_AddInput(op_desc, input);
 555     input.oper = op;
 556     TF_AddInput(op_desc, input);
 557     TF_SetAttrType(op_desc, "T", TF_FLOAT);
 558     *cur_op = TF_FinishOperation(op_desc, tf_model->status);
 559     if (TF_GetCode(tf_model->status) != TF_OK){
 560         av_log(ctx, AV_LOG_ERROR, "Failed to add maximum to layer %d\n", layer);
 561         return DNN_ERROR;
 562     }
 563
 564     return DNN_SUCCESS;
 565 }
 566
 567 static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
 568 {
 569     TFContext *ctx = &tf_model->ctx;
 570     int32_t layer;
 571     TF_OperationDescription *op_desc;
 572     TF_Operation *op;
 573     TF_Operation *transpose_op;
 574     TF_Tensor *tensor;
 575     TF_Output input;
 576     int32_t *transpose_perm;
 577     int64_t transpose_perm_shape[] = {4};
 578     int64_t input_shape[] = {1, -1, -1, -1};
 579     DNNReturnType layer_add_res;
 580     DNNModel *model = NULL;
 581     NativeModel *native_model;
 582
 583     model = ff_dnn_load_model_native(model_filename, NULL, NULL);
 584     if (!model){
 585         av_log(ctx, AV_LOG_ERROR, "Failed to load native model\n");
 586         return DNN_ERROR;
 587     }
 588
 589     native_model = model->model;
 590     tf_model->graph = TF_NewGraph();
 591     tf_model->status = TF_NewStatus();
 592
 593 #define CLEANUP_ON_ERROR(tf_model) \
 594     { \
 595         TF_DeleteGraph(tf_model->graph); \
 596         TF_DeleteStatus(tf_model->status); \
 597         av_log(ctx, AV_LOG_ERROR, "Failed to set value or add operator to layer\n"); \
 598         return DNN_ERROR; \
 599     }
 600
 601     op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
 602     TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
 603     TF_SetAttrShape(op_desc, "shape", input_shape, 4);
 604     op = TF_FinishOperation(op_desc, tf_model->status);
 605     if (TF_GetCode(tf_model->status) != TF_OK){
 606         CLEANUP_ON_ERROR(tf_model);
 607     }
 608
 609     op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
 610     TF_SetAttrType(op_desc, "dtype", TF_INT32);
 611     tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
 612     transpose_perm = (int32_t *)TF_TensorData(tensor);
 613     transpose_perm[0] = 1;
 614     transpose_perm[1] = 2;
 615     transpose_perm[2] = 3;
 616     transpose_perm[3] = 0;
 617     TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
 618     if (TF_GetCode(tf_model->status) != TF_OK){
 619         CLEANUP_ON_ERROR(tf_model);
 620     }
 621     transpose_op = TF_FinishOperation(op_desc, tf_model->status);
 622
 623     for (layer = 0; layer < native_model->layers_num; ++layer){
 624         switch (native_model->layers[layer].type){
 625         case DLT_INPUT:
 626             layer_add_res = DNN_SUCCESS;
 627             break;
 628         case DLT_CONV2D:
 629             layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
 630                                            (ConvolutionalParams *)native_model->layers[layer].params, layer);
 631             break;
 632         case DLT_DEPTH_TO_SPACE:
 633             layer_add_res = add_depth_to_space_layer(tf_model, &op,
 634                                                      (DepthToSpaceParams *)native_model->layers[layer].params, layer);
 635             break;
 636         case DLT_MIRROR_PAD:
 637             layer_add_res = add_pad_layer(tf_model, &op,
 638                                           (LayerPadParams *)native_model->layers[layer].params, layer);
 639             break;
 640         case DLT_MAXIMUM:
 641             layer_add_res = add_maximum_layer(tf_model, &op,
 642                                           (DnnLayerMaximumParams *)native_model->layers[layer].params, layer);
 643             break;
 644         default:
 645             CLEANUP_ON_ERROR(tf_model);
 646         }
 647
 648         if (layer_add_res != DNN_SUCCESS){
 649             CLEANUP_ON_ERROR(tf_model);
 650         }
 651     }
 652
 653     op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
 654     input.oper = op;
 655     input.index = 0;
 656     TF_AddInput(op_desc, input);
 657     TF_FinishOperation(op_desc, tf_model->status);
 658     if (TF_GetCode(tf_model->status) != TF_OK){
 659         CLEANUP_ON_ERROR(tf_model);
 660     }
 661
 662     ff_dnn_free_model_native(&model);
 663
 664     return DNN_SUCCESS;
 665 }
 666
 667 DNNModel *ff_dnn_load_model_tf(const char *model_filename, const char *options, AVFilterContext *filter_ctx)
 668 {
 669     DNNModel *model = NULL;
 670     TFModel *tf_model = NULL;
 671
 672     model = av_mallocz(sizeof(DNNModel));
 673     if (!model){
 674         return NULL;
 675     }
 676
 677     tf_model = av_mallocz(sizeof(TFModel));
 678     if (!tf_model){
 679         av_freep(&model);
 680         return NULL;
 681     }
 682     tf_model->ctx.class = &dnn_tensorflow_class;
 683     tf_model->model = model;
 684
 685     //parse options
 686     av_opt_set_defaults(&tf_model->ctx);
 687     if (av_opt_set_from_string(&tf_model->ctx, options, NULL, "=", "&") < 0) {
 688         av_log(&tf_model->ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
 689         av_freep(&tf_model);
 690         av_freep(&model);
 691         return NULL;
 692     }
 693
 694     if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
 695         if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
 696             av_freep(&tf_model);
 697             av_freep(&model);
 698
 699             return NULL;
 700         }
 701     }
 702
 703     model->model = tf_model;
 704     model->get_input = &get_input_tf;
 705     model->get_output = &get_output_tf;
 706     model->options = options;
 707     model->filter_ctx = filter_ctx;
 708
 709     return model;
 710 }
 711
 712 static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
 713                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame,
 714                                       int do_ioproc)
 715 {
 716     TF_Output *tf_outputs;
 717     TFModel *tf_model = model->model;
 718     TFContext *ctx = &tf_model->ctx;
 719     DNNData input, output;
 720     TF_Tensor **output_tensors;
 721     TF_Output tf_input;
 722     TF_Tensor *input_tensor;
 723
 724     if (get_input_tf(tf_model, &input, input_name) != DNN_SUCCESS)
 725         return DNN_ERROR;
 726     input.height = in_frame->height;
 727     input.width = in_frame->width;
 728
 729     tf_input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
 730     if (!tf_input.oper){
 731         av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
 732         return DNN_ERROR;
 733     }
 734     tf_input.index = 0;
 735     input_tensor = allocate_input_tensor(&input);
 736     if (!input_tensor){
 737         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
 738         return DNN_ERROR;
 739     }
 740     input.data = (float *)TF_TensorData(input_tensor);
 741
 742     if (do_ioproc) {
 743         if (tf_model->model->pre_proc != NULL) {
 744             tf_model->model->pre_proc(in_frame, &input, tf_model->model->filter_ctx);
 745         } else {
 746             ff_proc_from_frame_to_dnn(in_frame, &input, ctx);
 747         }
 748     }
 749
 750     if (nb_output != 1) {
 751         // currently, the filter does not need multiple outputs,
 752         // so we just pending the support until we really need it.
 753         avpriv_report_missing_feature(ctx, "multiple outputs");
 754         return DNN_ERROR;
 755     }
 756
 757     tf_outputs = av_malloc_array(nb_output, sizeof(*tf_outputs));
 758     if (tf_outputs == NULL) {
 759         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *tf_outputs\n"); \
 760         return DNN_ERROR;
 761     }
 762
 763     output_tensors = av_mallocz_array(nb_output, sizeof(*output_tensors));
 764     if (!output_tensors) {
 765         av_freep(&tf_outputs);
 766         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n"); \
 767         return DNN_ERROR;
 768     }
 769
 770     for (int i = 0; i < nb_output; ++i) {
 771         tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
 772         if (!tf_outputs[i].oper) {
 773             av_freep(&tf_outputs);
 774             av_freep(&output_tensors);
 775             av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", output_names[i]); \
 776             return DNN_ERROR;
 777         }
 778         tf_outputs[i].index = 0;
 779     }
 780
 781     TF_SessionRun(tf_model->session, NULL,
 782                   &tf_input, &input_tensor, 1,
 783                   tf_outputs, output_tensors, nb_output,
 784                   NULL, 0, NULL, tf_model->status);
 785     if (TF_GetCode(tf_model->status) != TF_OK) {
 786         av_freep(&tf_outputs);
 787         av_freep(&output_tensors);
 788         av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
 789         return DNN_ERROR;
 790     }
 791
 792     for (uint32_t i = 0; i < nb_output; ++i) {
 793         output.height = TF_Dim(output_tensors[i], 1);
 794         output.width = TF_Dim(output_tensors[i], 2);
 795         output.channels = TF_Dim(output_tensors[i], 3);
 796         output.data = TF_TensorData(output_tensors[i]);
 797         output.dt = TF_TensorType(output_tensors[i]);
 798
 799         if (do_ioproc) {
 800             if (tf_model->model->post_proc != NULL) {
 801                 tf_model->model->post_proc(out_frame, &output, tf_model->model->filter_ctx);
 802             } else {
 803                 ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
 804             }
 805         } else {
 806             out_frame->width = output.width;
 807             out_frame->height = output.height;
 808         }
 809     }
 810
 811     for (uint32_t i = 0; i < nb_output; ++i) {
 812         if (output_tensors[i]) {
 813             TF_DeleteTensor(output_tensors[i]);
 814         }
 815     }
 816     TF_DeleteTensor(input_tensor);
 817     av_freep(&output_tensors);
 818     av_freep(&tf_outputs);
 819     return DNN_SUCCESS;
 820 }
 821
 822 DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
 823                                       const char **output_names, uint32_t nb_output, AVFrame *out_frame)
 824 {
 825     TFModel *tf_model = model->model;
 826     TFContext *ctx = &tf_model->ctx;
 827
 828     if (!in_frame) {
 829         av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n");
 830         return DNN_ERROR;
 831     }
 832
 833     if (!out_frame) {
 834         av_log(ctx, AV_LOG_ERROR, "out frame is NULL when execute model.\n");
 835         return DNN_ERROR;
 836     }
 837
 838     return execute_model_tf(model, input_name, in_frame, output_names, nb_output, out_frame, 1);
 839 }
 840
 841 void ff_dnn_free_model_tf(DNNModel **model)
 842 {
 843     TFModel *tf_model;
 844
 845     if (*model){
 846         tf_model = (*model)->model;
 847         if (tf_model->graph){
 848             TF_DeleteGraph(tf_model->graph);
 849         }
 850         if (tf_model->session){
 851             TF_CloseSession(tf_model->session, tf_model->status);
 852             TF_DeleteSession(tf_model->session, tf_model->status);
 853         }
 854         if (tf_model->status){
 855             TF_DeleteStatus(tf_model->status);
 856         }
 857         av_freep(&tf_model);
 858         av_freep(model);
 859     }
 860 }