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dnn_backend_tf.c: add option sess_config for tf backend 

TensorFlow C library accepts config for session options to
set different parameters for the inference. This patch exports
this interface.

The config is a serialized tensorflow.ConfigProto proto, so we need
two steps to use it:
1. generate the serialized proto with python (see script example below)
the output looks like: 0xab...cd
where 0xcd is the least significant byte and 0xab is the most significant byte.

2. pass the python script output into ffmpeg with
dnn_processing=options=sess_config=0xab...cd

The following script is an example to specify one GPU. If the system contains
3 GPU cards, the visible_device_list could be '0', '1', '2', '0,1' etc.
'0' does not mean physical GPU card 0, we need to try and see.
And we can also add more opitions here to generate more serialized proto.

script example to generate serialized proto which specifies one GPU:
import tensorflow as tf
gpu_options = tf.GPUOptions(visible_device_list='0')
config = tf.ConfigProto(gpu_options=gpu_options)
s = config.SerializeToString()
b = ''.join("%02x" % int(ord(b)) for b in s[::-1])
print('0x%s' % b)
This commit is contained in:
Guo, Yejun 2020-10-12 15:52:26 +08:00
parent 4ccb68dc67
commit c4a3dbe726
1 changed files with 88 additions and 6 deletions
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94
libavfilter/dnn/dnn_backend_tf.c
View File

@ -29,14 +29,20 @@
#include "dnn_backend_native_layer_depth2space.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
#include "../internal.h"
#include "dnn_backend_native_layer_pad.h"
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_io_proc.h"
#include <tensorflow/c/c_api.h>
typedef struct TFOptions{
char *sess_config;
} TFOptions;
typedef struct TFContext {
const AVClass *class;
TFOptions options;
} TFContext;
typedef struct TFModel{
@ -47,14 +53,15 @@ typedef struct TFModel{
TF_Status *status;
} TFModel;
static const AVClass dnn_tensorflow_class = {
.class_name = "dnn_tensorflow",
.item_name = av_default_item_name,
.option = NULL,
.version = LIBAVUTIL_VERSION_INT,
.category = AV_CLASS_CATEGORY_FILTER,
#define OFFSET(x) offsetof(TFContext, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_tensorflow_options[] = {
{ "sess_config", "config for SessionOptions", OFFSET(options.sess_config), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_tensorflow);
static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
const char **output_names, uint32_t nb_output, AVFrame *out_frame,
int do_ioproc);
@ -194,10 +201,64 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_ImportGraphDefOptions *graph_opts;
TF_SessionOptions *sess_opts;
const TF_Operation *init_op;
uint8_t *sess_config = NULL;
int sess_config_length = 0;
// prepare the sess config data
if (tf_model->ctx.options.sess_config != NULL) {
/*
tf_model->ctx.options.sess_config is hex to present the serialized proto
required by TF_SetConfig below, so we need to first generate the serialized
proto in a python script, the following is a script example to generate
serialized proto which specifies one GPU, we can change the script to add
more options.
import tensorflow as tf
gpu_options = tf.GPUOptions(visible_device_list='0')
config = tf.ConfigProto(gpu_options=gpu_options)
s = config.SerializeToString()
b = ''.join("%02x" % int(ord(b)) for b in s[::-1])
print('0x%s' % b)
the script output looks like: 0xab...cd, and then pass 0xab...cd to sess_config.
*/
char tmp[3];
tmp[2] = '\0';
if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
return DNN_ERROR;
}
sess_config_length = strlen(tf_model->ctx.options.sess_config);
if (sess_config_length % 2 != 0) {
av_log(ctx, AV_LOG_ERROR, "the length of sess_config is not even (%s), "
"please re-generate the config.\n",
tf_model->ctx.options.sess_config);
return DNN_ERROR;
}
sess_config_length -= 2; //ignore the first '0x'
sess_config_length /= 2; //get the data length in byte
sess_config = av_malloc(sess_config_length);
if (!sess_config) {
av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
return DNN_ERROR;
}
for (int i = 0; i < sess_config_length; i++) {
int index = 2 + (sess_config_length - 1 - i) * 2;
tmp[0] = tf_model->ctx.options.sess_config[index];
tmp[1] = tf_model->ctx.options.sess_config[index + 1];
sess_config[i] = strtol(tmp, NULL, 16);
}
}
graph_def = read_graph(model_filename);
if (!graph_def){
av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
av_freep(&sess_config);
return DNN_ERROR;
}
tf_model->graph = TF_NewGraph();
@ -210,11 +271,23 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
av_freep(&sess_config);
return DNN_ERROR;
}
init_op = TF_GraphOperationByName(tf_model->graph, "init");
sess_opts = TF_NewSessionOptions();
if (sess_config) {
TF_SetConfig(sess_opts, sess_config, sess_config_length,tf_model->status);
av_freep(&sess_config);
if (TF_GetCode(tf_model->status) != TF_OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set config for sess options with %s\n",
tf_model->ctx.options.sess_config);
return DNN_ERROR;
}
}
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)
@ -609,6 +682,15 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, const char *options,
tf_model->ctx.class = &dnn_tensorflow_class;
tf_model->model = model;
//parse options
av_opt_set_defaults(&tf_model->ctx);
if (av_opt_set_from_string(&tf_model->ctx, options, NULL, "=", "&") < 0) {
av_log(&tf_model->ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
av_freep(&tf_model);
av_freep(&model);
return NULL;
}
if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
av_freep(&tf_model);