avfilter/dnn/dnn_backend_tf: simplify the code with ff_hex_to_data

please use tools/python/tf_sess_config.py to get the sess_config after that.
note the byte order of session config is in normal order.
bump the MICRO version for the config change.

Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
pull/362/head
Limin Wang 4 years ago committed by Guo, Yejun
parent 4e8d22478b
commit f183d6555e
  1. 42
      libavfilter/dnn/dnn_backend_tf.c
  2. 2
      libavfilter/version.h
  3. 45
      tools/python/tf_sess_config.py

@ -28,6 +28,7 @@
#include "dnn_backend_native_layer_conv2d.h" #include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layer_depth2space.h" #include "dnn_backend_native_layer_depth2space.h"
#include "libavformat/avio.h" #include "libavformat/avio.h"
#include "libavformat/internal.h"
#include "libavutil/avassert.h" #include "libavutil/avassert.h"
#include "../internal.h" #include "../internal.h"
#include "dnn_backend_native_layer_pad.h" #include "dnn_backend_native_layer_pad.h"
@ -206,53 +207,26 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
// prepare the sess config data // prepare the sess config data
if (tf_model->ctx.options.sess_config != NULL) { if (tf_model->ctx.options.sess_config != NULL) {
const char *config;
/* /*
tf_model->ctx.options.sess_config is hex to present the serialized proto 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 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 proto in a python script, tools/python/tf_sess_config.py is a script example
serialized proto which specifies one GPU, we can change the script to add to generate the configs of sess_config.
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) { if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n"); av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
return DNN_ERROR; return DNN_ERROR;
} }
config = tf_model->ctx.options.sess_config + 2;
sess_config_length = ff_hex_to_data(NULL, config);
sess_config_length = strlen(tf_model->ctx.options.sess_config); sess_config = av_mallocz(sess_config_length + AV_INPUT_BUFFER_PADDING_SIZE);
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) { if (!sess_config) {
av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n"); av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
return DNN_ERROR; return DNN_ERROR;
} }
ff_hex_to_data(sess_config, config);
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); graph_def = read_graph(model_filename);

@ -31,7 +31,7 @@
#define LIBAVFILTER_VERSION_MAJOR 8 #define LIBAVFILTER_VERSION_MAJOR 8
#define LIBAVFILTER_VERSION_MINOR 0 #define LIBAVFILTER_VERSION_MINOR 0
#define LIBAVFILTER_VERSION_MICRO 100 #define LIBAVFILTER_VERSION_MICRO 101
#define LIBAVFILTER_VERSION_INT AV_VERSION_INT(LIBAVFILTER_VERSION_MAJOR, \ #define LIBAVFILTER_VERSION_INT AV_VERSION_INT(LIBAVFILTER_VERSION_MAJOR, \

@ -0,0 +1,45 @@
# Copyright (c) 2021
#
# 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
# ==============================================================================
# verified with Python 3.6.8 on CentOS 7.2
import tensorflow as tf
visible_device_list = '0' # use , separator for more GPUs like '0, 1'
per_process_gpu_memory_fraction = 0.9 # avoid out of memory
intra_op_parallelism_threads = 2 # default in tensorflow
inter_op_parallelism_threads = 5 # default in tensorflow
gpu_options = tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction = per_process_gpu_memory_fraction,
visible_device_list = visible_device_list,
allow_growth = True)
config = tf.compat.v1.ConfigProto(
allow_soft_placement = True,
log_device_placement = False,
intra_op_parallelism_threads = intra_op_parallelism_threads,
inter_op_parallelism_threads = inter_op_parallelism_threads,
gpu_options = gpu_options)
s = config.SerializeToString()
# print(list(map(hex, s))) # print by json if need
print('a serialized protobuf string for TF_SetConfig, note the byte order is in normal order.')
b = ''.join(format(b,'02x') for b in s)
print('0x%s' % b) # print by hex format
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