#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layer_depth2space.h"
#include "libavformat/avio.h"
+#include "libavformat/internal.h"
#include "libavutil/avassert.h"
#include "../internal.h"
#include "dnn_backend_native_layer_pad.h"
// prepare the sess config data
if (tf_model->ctx.options.sess_config != NULL) {
+ const char *config;
/*
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.
+ proto in a python script, tools/python/tf_sess_config.py is a script example
+ to generate the configs of 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;
}
+ 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);
- 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);
+ sess_config = av_mallocz(sess_config_length + AV_INPUT_BUFFER_PADDING_SIZE);
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);
- }
+ ff_hex_to_data(sess_config, config);
}
graph_def = read_graph(model_filename);
input.data = (float *)TF_TensorData(input_tensor);
if (do_ioproc) {
- if (tf_model->model->pre_proc != NULL) {
- tf_model->model->pre_proc(in_frame, &input, tf_model->model->filter_ctx);
+ if (tf_model->model->frame_pre_proc != NULL) {
+ tf_model->model->frame_pre_proc(in_frame, &input, tf_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(in_frame, &input, tf_model->model->func_type, ctx);
}
if (nb_output != 1) {
// currently, the filter does not need multiple outputs,
// so we just pending the support until we really need it.
+ TF_DeleteTensor(input_tensor);
avpriv_report_missing_feature(ctx, "multiple outputs");
return DNN_ERROR;
}
tf_outputs = av_malloc_array(nb_output, sizeof(*tf_outputs));
if (tf_outputs == NULL) {
+ TF_DeleteTensor(input_tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *tf_outputs\n"); \
return DNN_ERROR;
}
output_tensors = av_mallocz_array(nb_output, sizeof(*output_tensors));
if (!output_tensors) {
+ TF_DeleteTensor(input_tensor);
av_freep(&tf_outputs);
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n"); \
return DNN_ERROR;
for (int i = 0; i < nb_output; ++i) {
tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
if (!tf_outputs[i].oper) {
+ TF_DeleteTensor(input_tensor);
av_freep(&tf_outputs);
av_freep(&output_tensors);
av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", output_names[i]); \
tf_outputs, output_tensors, nb_output,
NULL, 0, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK) {
+ TF_DeleteTensor(input_tensor);
av_freep(&tf_outputs);
av_freep(&output_tensors);
av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
output.dt = TF_TensorType(output_tensors[i]);
if (do_ioproc) {
- if (tf_model->model->post_proc != NULL) {
- tf_model->model->post_proc(out_frame, &output, tf_model->model->filter_ctx);
+ if (tf_model->model->frame_post_proc != NULL) {
+ tf_model->model->frame_post_proc(out_frame, &output, tf_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
}