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authorSergey Lavrushkin <dualfal@gmail.com>2018-06-03 20:22:50 +0300
committerPedro Arthur <bygrandao@gmail.com>2018-06-05 10:16:50 -0300
commitd8c0bbb0aa45eed61b159c4842473fe27e77ac12 (patch)
treecc3f2b09aacf93ea7d5ce42adfd62a4fcc561fb1 /libavfilter/dnn_backend_tf.c
parentddf6ff9dc6e6441b68b83b3bf047dbc573cc5b7c (diff)
downloadffmpeg-d8c0bbb0aa45eed61b159c4842473fe27e77ac12.tar.gz
Adds TensorFlow backend for dnn inference module.
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Diffstat (limited to 'libavfilter/dnn_backend_tf.c')
-rw-r--r--libavfilter/dnn_backend_tf.c309
1 files changed, 309 insertions, 0 deletions
diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
new file mode 100644
index 0000000000..e46b1ad140
--- /dev/null
+++ b/libavfilter/dnn_backend_tf.c
@@ -0,0 +1,309 @@
+/*
+ * Copyright (c) 2018 Sergey Lavrushkin
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN tensorflow backend implementation.
+ */
+
+#include "dnn_backend_tf.h"
+#include "dnn_srcnn.h"
+#include "libavformat/avio.h"
+
+#include <tensorflow/c/c_api.h>
+
+typedef struct TFModel{
+ TF_Graph* graph;
+ TF_Session* session;
+ TF_Status* status;
+ TF_Output input, output;
+ TF_Tensor* input_tensor;
+ TF_Tensor* output_tensor;
+ const DNNData* input_data;
+ const DNNData* output_data;
+} TFModel;
+
+static void free_buffer(void* data, size_t length)
+{
+ av_freep(&data);
+}
+
+static TF_Buffer* read_graph(const char* model_filename)
+{
+ TF_Buffer* graph_buf;
+ unsigned char* graph_data = NULL;
+ AVIOContext* model_file_context;
+ long size, bytes_read;
+
+ if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
+ return NULL;
+ }
+
+ size = avio_size(model_file_context);
+
+ graph_data = av_malloc(size);
+ if (!graph_data){
+ avio_closep(&model_file_context);
+ return NULL;
+ }
+ bytes_read = avio_read(model_file_context, graph_data, size);
+ avio_closep(&model_file_context);
+ if (bytes_read != size){
+ av_freep(&graph_data);
+ return NULL;
+ }
+
+ graph_buf = TF_NewBuffer();
+ graph_buf->data = (void*)graph_data;
+ graph_buf->length = size;
+ graph_buf->data_deallocator = free_buffer;
+
+ return graph_buf;
+}
+
+static DNNReturnType set_input_output_tf(void* model, const DNNData* input, const DNNData* output)
+{
+ TFModel* tf_model = (TFModel*)model;
+ int64_t input_dims[] = {1, input->height, input->width, input->channels};
+ int64_t output_dims[] = {1, output->height, output->width, output->channels};
+ TF_SessionOptions* sess_opts;
+ const TF_Operation* init_op = TF_GraphOperationByName(tf_model->graph, "init");
+
+ // Input operation should be named 'x'
+ tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
+ if (!tf_model->input.oper){
+ return DNN_ERROR;
+ }
+ tf_model->input.index = 0;
+ if (tf_model->input_tensor){
+ TF_DeleteTensor(tf_model->input_tensor);
+ }
+ tf_model->input_tensor = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
+ input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
+ if (!tf_model->input_tensor){
+ return DNN_ERROR;
+ }
+
+ // Output operation should be named 'y'
+ tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
+ if (!tf_model->output.oper){
+ return DNN_ERROR;
+ }
+ tf_model->output.index = 0;
+ if (tf_model->output_tensor){
+ TF_DeleteTensor(tf_model->output_tensor);
+ }
+ tf_model->output_tensor = TF_AllocateTensor(TF_FLOAT, output_dims, 4,
+ output_dims[1] * output_dims[2] * output_dims[3] * sizeof(float));
+ if (!tf_model->output_tensor){
+ return DNN_ERROR;
+ }
+
+ tf_model->input_data = input;
+ tf_model->output_data = output;
+
+ if (tf_model->session){
+ TF_CloseSession(tf_model->session, tf_model->status);
+ TF_DeleteSession(tf_model->session, tf_model->status);
+ }
+
+ sess_opts = TF_NewSessionOptions();
+ tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
+ TF_DeleteSessionOptions(sess_opts);
+ if (TF_GetCode(tf_model->status) != TF_OK)
+ {
+ return DNN_ERROR;
+ }
+
+ // Run initialization operation with name "init" if it is present in graph
+ if (init_op){
+ TF_SessionRun(tf_model->session, NULL,
+ NULL, NULL, 0,
+ NULL, NULL, 0,
+ &init_op, 1, NULL, tf_model->status);
+ if (TF_GetCode(tf_model->status) != TF_OK)
+ {
+ return DNN_ERROR;
+ }
+ }
+
+ return DNN_SUCCESS;
+}
+
+DNNModel* ff_dnn_load_model_tf(const char* model_filename)
+{
+ DNNModel* model = NULL;
+ TFModel* tf_model = NULL;
+ TF_Buffer* graph_def;
+ TF_ImportGraphDefOptions* graph_opts;
+
+ model = av_malloc(sizeof(DNNModel));
+ if (!model){
+ return NULL;
+ }
+
+ tf_model = av_malloc(sizeof(TFModel));
+ if (!tf_model){
+ av_freep(&model);
+ return NULL;
+ }
+ tf_model->session = NULL;
+ tf_model->input_tensor = NULL;
+ tf_model->output_tensor = NULL;
+
+ graph_def = read_graph(model_filename);
+ if (!graph_def){
+ av_freep(&tf_model);
+ av_freep(&model);
+ return NULL;
+ }
+ tf_model->graph = TF_NewGraph();
+ tf_model->status = TF_NewStatus();
+ graph_opts = TF_NewImportGraphDefOptions();
+ TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
+ TF_DeleteImportGraphDefOptions(graph_opts);
+ TF_DeleteBuffer(graph_def);
+ if (TF_GetCode(tf_model->status) != TF_OK){
+ TF_DeleteGraph(tf_model->graph);
+ TF_DeleteStatus(tf_model->status);
+ av_freep(&tf_model);
+ av_freep(&model);
+ return NULL;
+ }
+
+ model->model = (void*)tf_model;
+ model->set_input_output = &set_input_output_tf;
+
+ return model;
+}
+
+DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
+{
+ DNNModel* model = NULL;
+ TFModel* tf_model = NULL;
+ TF_Buffer* graph_def;
+ unsigned char* graph_data = NULL;
+ TF_ImportGraphDefOptions* graph_opts;
+
+ graph_def = TF_NewBuffer();
+ switch (model_type){
+ case DNN_SRCNN:
+ graph_data = av_malloc(srcnn_tf_size);
+ if (!graph_data){
+ TF_DeleteBuffer(graph_def);
+ return NULL;
+ }
+ memcpy(graph_data, srcnn_tf_model, srcnn_tf_size);
+ graph_def->data = (void*)graph_data;
+ graph_def->length = srcnn_tf_size;
+ graph_def->data_deallocator = free_buffer;
+ break;
+ default:
+ TF_DeleteBuffer(graph_def);
+ return NULL;
+ }
+
+ model = av_malloc(sizeof(DNNModel));
+ if (!model){
+ TF_DeleteBuffer(graph_def);
+ return NULL;
+ }
+
+ tf_model = av_malloc(sizeof(TFModel));
+ if (!tf_model){
+ TF_DeleteBuffer(graph_def);
+ av_freep(&model);
+ return NULL;
+ }
+ tf_model->session = NULL;
+ tf_model->input_tensor = NULL;
+ tf_model->output_tensor = NULL;
+
+ tf_model->graph = TF_NewGraph();
+ tf_model->status = TF_NewStatus();
+ graph_opts = TF_NewImportGraphDefOptions();
+ TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
+ TF_DeleteImportGraphDefOptions(graph_opts);
+ TF_DeleteBuffer(graph_def);
+ if (TF_GetCode(tf_model->status) != TF_OK){
+ TF_DeleteGraph(tf_model->graph);
+ TF_DeleteStatus(tf_model->status);
+ av_freep(&tf_model);
+ av_freep(&model);
+ return NULL;
+ }
+
+ model->model = (void*)tf_model;
+ model->set_input_output = &set_input_output_tf;
+
+ return model;
+}
+
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model)
+{
+ TFModel* tf_model = (TFModel*)model->model;
+
+ memcpy(TF_TensorData(tf_model->input_tensor), tf_model->input_data->data,
+ tf_model->input_data->height * tf_model->input_data->width *
+ tf_model->input_data->channels * sizeof(float));
+
+ TF_SessionRun(tf_model->session, NULL,
+ &tf_model->input, &tf_model->input_tensor, 1,
+ &tf_model->output, &tf_model->output_tensor, 1,
+ NULL, 0, NULL, tf_model->status);
+
+ if (TF_GetCode(tf_model->status) != TF_OK){
+ return DNN_ERROR;
+ }
+ else{
+ memcpy(tf_model->output_data->data, TF_TensorData(tf_model->output_tensor),
+ tf_model->output_data->height * tf_model->output_data->width *
+ tf_model->output_data->channels * sizeof(float));
+
+ return DNN_SUCCESS;
+ }
+}
+
+void ff_dnn_free_model_tf(DNNModel** model)
+{
+ TFModel* tf_model;
+
+ if (*model){
+ tf_model = (TFModel*)(*model)->model;
+ if (tf_model->graph){
+ TF_DeleteGraph(tf_model->graph);
+ }
+ if (tf_model->session){
+ TF_CloseSession(tf_model->session, tf_model->status);
+ TF_DeleteSession(tf_model->session, tf_model->status);
+ }
+ if (tf_model->status){
+ TF_DeleteStatus(tf_model->status);
+ }
+ if (tf_model->input_tensor){
+ TF_DeleteTensor(tf_model->input_tensor);
+ }
+ if (tf_model->output_tensor){
+ TF_DeleteTensor(tf_model->output_tensor);
+ }
+ av_freep(&tf_model);
+ av_freep(model);
+ }
+}
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