2 * Copyright (c) 2018 Sergey Lavrushkin
4 * This file is part of FFmpeg.
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.
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.
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
23 * DNN tensorflow backend implementation.
26 #include "dnn_backend_tf.h"
27 #include "dnn_backend_native.h"
28 #include "libavformat/avio.h"
30 #include <tensorflow/c/c_api.h>
32 typedef struct TFModel{
36 TF_Output input, output;
37 TF_Tensor *input_tensor;
41 static void free_buffer(void *data, size_t length)
46 static TF_Buffer *read_graph(const char *model_filename)
49 unsigned char *graph_data = NULL;
50 AVIOContext *model_file_context;
51 long size, bytes_read;
53 if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
57 size = avio_size(model_file_context);
59 graph_data = av_malloc(size);
61 avio_closep(&model_file_context);
64 bytes_read = avio_read(model_file_context, graph_data, size);
65 avio_closep(&model_file_context);
66 if (bytes_read != size){
67 av_freep(&graph_data);
71 graph_buf = TF_NewBuffer();
72 graph_buf->data = (void *)graph_data;
73 graph_buf->length = size;
74 graph_buf->data_deallocator = free_buffer;
79 static DNNReturnType set_input_output_tf(void *model, DNNData *input, DNNData *output)
81 TFModel *tf_model = (TFModel *)model;
82 int64_t input_dims[] = {1, input->height, input->width, input->channels};
83 TF_SessionOptions *sess_opts;
84 const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
85 TF_Tensor *output_tensor;
87 // Input operation should be named 'x'
88 tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
89 if (!tf_model->input.oper){
92 tf_model->input.index = 0;
93 if (tf_model->input_tensor){
94 TF_DeleteTensor(tf_model->input_tensor);
96 tf_model->input_tensor = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
97 input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
98 if (!tf_model->input_tensor){
101 input->data = (float *)TF_TensorData(tf_model->input_tensor);
103 // Output operation should be named 'y'
104 tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
105 if (!tf_model->output.oper){
108 tf_model->output.index = 0;
110 if (tf_model->session){
111 TF_CloseSession(tf_model->session, tf_model->status);
112 TF_DeleteSession(tf_model->session, tf_model->status);
115 sess_opts = TF_NewSessionOptions();
116 tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
117 TF_DeleteSessionOptions(sess_opts);
118 if (TF_GetCode(tf_model->status) != TF_OK)
123 // Run initialization operation with name "init" if it is present in graph
125 TF_SessionRun(tf_model->session, NULL,
128 &init_op, 1, NULL, tf_model->status);
129 if (TF_GetCode(tf_model->status) != TF_OK)
135 // Execute network to get output height, width and number of channels
136 TF_SessionRun(tf_model->session, NULL,
137 &tf_model->input, &tf_model->input_tensor, 1,
138 &tf_model->output, &output_tensor, 1,
139 NULL, 0, NULL, tf_model->status);
140 if (TF_GetCode(tf_model->status) != TF_OK){
144 output->height = TF_Dim(output_tensor, 1);
145 output->width = TF_Dim(output_tensor, 2);
146 output->channels = TF_Dim(output_tensor, 3);
147 output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
151 tf_model->output_data = output;
152 TF_DeleteTensor(output_tensor);
158 static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
160 TF_Buffer *graph_def;
161 TF_ImportGraphDefOptions *graph_opts;
163 graph_def = read_graph(model_filename);
167 tf_model->graph = TF_NewGraph();
168 tf_model->status = TF_NewStatus();
169 graph_opts = TF_NewImportGraphDefOptions();
170 TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
171 TF_DeleteImportGraphDefOptions(graph_opts);
172 TF_DeleteBuffer(graph_def);
173 if (TF_GetCode(tf_model->status) != TF_OK){
174 TF_DeleteGraph(tf_model->graph);
175 TF_DeleteStatus(tf_model->status);
182 #define NAME_BUFFER_SIZE 256
184 static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
185 ConvolutionalParams* params, const int layer)
188 TF_OperationDescription *op_desc;
190 int64_t strides[] = {1, 1, 1, 1};
194 char name_buffer[NAME_BUFFER_SIZE];
197 size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
200 snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
201 op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
202 TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
203 dims[0] = params->output_num;
204 dims[1] = params->kernel_size;
205 dims[2] = params->kernel_size;
206 dims[3] = params->input_num;
208 tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
209 memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
210 TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
211 if (TF_GetCode(tf_model->status) != TF_OK){
214 op = TF_FinishOperation(op_desc, tf_model->status);
215 if (TF_GetCode(tf_model->status) != TF_OK){
219 snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
220 op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
222 TF_AddInput(op_desc, input);
223 input.oper = transpose_op;
224 TF_AddInput(op_desc, input);
225 TF_SetAttrType(op_desc, "T", TF_FLOAT);
226 TF_SetAttrType(op_desc, "Tperm", TF_INT32);
227 op = TF_FinishOperation(op_desc, tf_model->status);
228 if (TF_GetCode(tf_model->status) != TF_OK){
232 snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
233 op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
234 input.oper = *cur_op;
235 TF_AddInput(op_desc, input);
237 TF_AddInput(op_desc, input);
238 TF_SetAttrType(op_desc, "T", TF_FLOAT);
239 TF_SetAttrIntList(op_desc, "strides", strides, 4);
240 TF_SetAttrString(op_desc, "padding", "VALID", 5);
241 *cur_op = TF_FinishOperation(op_desc, tf_model->status);
242 if (TF_GetCode(tf_model->status) != TF_OK){
246 snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
247 op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
248 TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
249 dims[0] = params->output_num;
251 tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
252 memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
253 TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
254 if (TF_GetCode(tf_model->status) != TF_OK){
257 op = TF_FinishOperation(op_desc, tf_model->status);
258 if (TF_GetCode(tf_model->status) != TF_OK){
262 snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
263 op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
264 input.oper = *cur_op;
265 TF_AddInput(op_desc, input);
267 TF_AddInput(op_desc, input);
268 TF_SetAttrType(op_desc, "T", TF_FLOAT);
269 *cur_op = TF_FinishOperation(op_desc, tf_model->status);
270 if (TF_GetCode(tf_model->status) != TF_OK){
274 snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
275 switch (params->activation){
277 op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
280 op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
283 op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
288 input.oper = *cur_op;
289 TF_AddInput(op_desc, input);
290 TF_SetAttrType(op_desc, "T", TF_FLOAT);
291 *cur_op = TF_FinishOperation(op_desc, tf_model->status);
292 if (TF_GetCode(tf_model->status) != TF_OK){
299 static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
300 DepthToSpaceParams *params, const int layer)
302 TF_OperationDescription *op_desc;
304 char name_buffer[NAME_BUFFER_SIZE];
306 snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
307 op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
308 input.oper = *cur_op;
310 TF_AddInput(op_desc, input);
311 TF_SetAttrType(op_desc, "T", TF_FLOAT);
312 TF_SetAttrInt(op_desc, "block_size", params->block_size);
313 *cur_op = TF_FinishOperation(op_desc, tf_model->status);
314 if (TF_GetCode(tf_model->status) != TF_OK){
321 static int calculate_pad(const ConvolutionalNetwork *conv_network)
323 ConvolutionalParams *params;
327 for (layer = 0; layer < conv_network->layers_num; ++layer){
328 if (conv_network->layers[layer].type == CONV){
329 params = (ConvolutionalParams *)conv_network->layers[layer].params;
330 pad += params->kernel_size >> 1;
337 static DNNReturnType add_pad_op(TFModel *tf_model, TF_Operation **cur_op, const int32_t pad)
341 TF_OperationDescription *op_desc;
344 int64_t pads_shape[] = {4, 2};
348 op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
349 TF_SetAttrType(op_desc, "dtype", TF_INT32);
350 tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
351 pads = (int32_t *)TF_TensorData(tensor);
352 pads[0] = 0; pads[1] = 0;
353 pads[2] = pad; pads[3] = pad;
354 pads[4] = pad; pads[5] = pad;
355 pads[6] = 0; pads[7] = 0;
356 TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
357 if (TF_GetCode(tf_model->status) != TF_OK){
360 op = TF_FinishOperation(op_desc, tf_model->status);
361 if (TF_GetCode(tf_model->status) != TF_OK){
365 op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
366 input.oper = *cur_op;
367 TF_AddInput(op_desc, input);
369 TF_AddInput(op_desc, input);
370 TF_SetAttrType(op_desc, "T", TF_FLOAT);
371 TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
372 TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
373 *cur_op = TF_FinishOperation(op_desc, tf_model->status);
374 if (TF_GetCode(tf_model->status) != TF_OK){
381 static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
384 TF_OperationDescription *op_desc;
386 TF_Operation *transpose_op;
389 int32_t *transpose_perm;
390 int64_t transpose_perm_shape[] = {4};
391 int64_t input_shape[] = {1, -1, -1, -1};
393 DNNReturnType layer_add_res;
394 DNNModel *native_model = NULL;
395 ConvolutionalNetwork *conv_network;
397 native_model = ff_dnn_load_model_native(model_filename);
402 conv_network = (ConvolutionalNetwork *)native_model->model;
403 pad = calculate_pad(conv_network);
404 tf_model->graph = TF_NewGraph();
405 tf_model->status = TF_NewStatus();
407 #define CLEANUP_ON_ERROR(tf_model) \
409 TF_DeleteGraph(tf_model->graph); \
410 TF_DeleteStatus(tf_model->status); \
414 op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
415 TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
416 TF_SetAttrShape(op_desc, "shape", input_shape, 4);
417 op = TF_FinishOperation(op_desc, tf_model->status);
418 if (TF_GetCode(tf_model->status) != TF_OK){
419 CLEANUP_ON_ERROR(tf_model);
422 if (add_pad_op(tf_model, &op, pad) != DNN_SUCCESS){
423 CLEANUP_ON_ERROR(tf_model);
426 op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
427 TF_SetAttrType(op_desc, "dtype", TF_INT32);
428 tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
429 transpose_perm = (int32_t *)TF_TensorData(tensor);
430 transpose_perm[0] = 1;
431 transpose_perm[1] = 2;
432 transpose_perm[2] = 3;
433 transpose_perm[3] = 0;
434 TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
435 if (TF_GetCode(tf_model->status) != TF_OK){
436 CLEANUP_ON_ERROR(tf_model);
438 transpose_op = TF_FinishOperation(op_desc, tf_model->status);
440 for (layer = 0; layer < conv_network->layers_num; ++layer){
441 switch (conv_network->layers[layer].type){
445 layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
446 (ConvolutionalParams *)conv_network->layers[layer].params, layer);
449 layer_add_res = add_depth_to_space_layer(tf_model, &op,
450 (DepthToSpaceParams *)conv_network->layers[layer].params, layer);
453 CLEANUP_ON_ERROR(tf_model);
456 if (layer_add_res != DNN_SUCCESS){
457 CLEANUP_ON_ERROR(tf_model);
461 op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
463 TF_AddInput(op_desc, input);
464 TF_FinishOperation(op_desc, tf_model->status);
465 if (TF_GetCode(tf_model->status) != TF_OK){
466 CLEANUP_ON_ERROR(tf_model);
469 ff_dnn_free_model_native(&native_model);
474 DNNModel *ff_dnn_load_model_tf(const char *model_filename)
476 DNNModel *model = NULL;
477 TFModel *tf_model = NULL;
479 model = av_malloc(sizeof(DNNModel));
484 tf_model = av_malloc(sizeof(TFModel));
489 tf_model->session = NULL;
490 tf_model->input_tensor = NULL;
491 tf_model->output_data = NULL;
493 if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
494 if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
502 model->model = (void *)tf_model;
503 model->set_input_output = &set_input_output_tf;
510 DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
512 TFModel *tf_model = (TFModel *)model->model;
513 TF_Tensor *output_tensor;
515 TF_SessionRun(tf_model->session, NULL,
516 &tf_model->input, &tf_model->input_tensor, 1,
517 &tf_model->output, &output_tensor, 1,
518 NULL, 0, NULL, tf_model->status);
520 if (TF_GetCode(tf_model->status) != TF_OK){
524 memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
525 tf_model->output_data->height * tf_model->output_data->width *
526 tf_model->output_data->channels * sizeof(float));
527 TF_DeleteTensor(output_tensor);
533 void ff_dnn_free_model_tf(DNNModel **model)
538 tf_model = (TFModel *)(*model)->model;
539 if (tf_model->graph){
540 TF_DeleteGraph(tf_model->graph);
542 if (tf_model->session){
543 TF_CloseSession(tf_model->session, tf_model->status);
544 TF_DeleteSession(tf_model->session, tf_model->status);
546 if (tf_model->status){
547 TF_DeleteStatus(tf_model->status);
549 if (tf_model->input_tensor){
550 TF_DeleteTensor(tf_model->input_tensor);
552 if (tf_model->output_data){
553 av_freep(&tf_model->output_data->data);