This commit adds handling for cases where an error may occur, clearing
the allocated memory resources.
Signed-off-by: Shubhanshu Saxena <shubhanshu....@gmail.com>
---
libavfilter/dnn/dnn_backend_tf.c | 100 +++++++++++++++++++++++--------
1 file changed, 74 insertions(+), 26 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index 5d34da5db1..31746deef4 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -114,14 +114,18 @@ static tf_infer_request* tf_create_inference_request(void)
static DNNReturnType extract_inference_from_task(TaskItem *task, Queue
*inference_queue)
{
+ TFModel *tf_model = task->model;
+ TFContext *ctx = &tf_model->ctx;
InferenceItem *inference = av_malloc(sizeof(*inference));
if (!inference) {
+ av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for
InferenceItem\n");
return DNN_ERROR;
}
task->inference_todo = 1;
task->inference_done = 0;
inference->task = task;
if (ff_queue_push_back(inference_queue, inference) < 0) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to push back inference_queue.\n");
av_freep(&inference);
return DNN_ERROR;
}
@@ -232,14 +236,15 @@ static DNNReturnType get_output_tf(void *model, const
char *input_name, int inpu
if (!in_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input
frame\n");
- return DNN_ERROR;
+ ret = DNN_ERROR;
+ goto final;
}
out_frame = av_frame_alloc();
if (!out_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output
frame\n");
- av_frame_free(&in_frame);
- return DNN_ERROR;
+ ret = DNN_ERROR;
+ goto final;
}
in_frame->width = input_width;
@@ -256,19 +261,22 @@ static DNNReturnType get_output_tf(void *model, const
char *input_name, int inpu
if (extract_inference_from_task(&task, tf_model->inference_queue) !=
DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
- return DNN_ERROR;
+ ret = DNN_ERROR;
+ goto final;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ ret = DNN_ERROR;
+ goto final;
}
ret = execute_model_tf(request, tf_model->inference_queue);
*output_width = out_frame->width;
*output_height = out_frame->height;
+final:
av_frame_free(&out_frame);
av_frame_free(&in_frame);
return ret;
@@ -788,18 +796,13 @@ DNNModel *ff_dnn_load_model_tf(const char
*model_filename, DNNFunctionType func_
//parse options
av_opt_set_defaults(&ctx);
if (av_opt_set_from_string(&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;
+ av_log(&ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n",
options);
+ goto err;
}
if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
- av_freep(&tf_model);
- av_freep(&model);
-
- return NULL;
+ goto err;
}
}
@@ -808,14 +811,34 @@ DNNModel *ff_dnn_load_model_tf(const char
*model_filename, DNNFunctionType func_
}
tf_model->request_queue = ff_safe_queue_create();
+ if (!tf_model->request_queue) {
+ goto err;
+ }
for (int i = 0; i < ctx->options.nireq; i++) {
RequestItem *item = av_mallocz(sizeof(*item));
+ if (!item) {
+ goto err;
+ }
item->infer_request = tf_create_inference_request();
- ff_safe_queue_push_back(tf_model->request_queue, item);
+ if (!item->infer_request) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
TensorFlow inference request\n");
+ av_freep(&item);
+ goto err;
+ }
+
+ if (ff_safe_queue_push_back(tf_model->request_queue, item) < 0) {
+ av_freep(&item->infer_request);
+ av_freep(&item);
+ goto err;
+ }
}
tf_model->inference_queue = ff_queue_create();
+ if (!tf_model->inference_queue) {
+ goto err;
+ }
+
model->model = tf_model;
model->get_input = &get_input_tf;
model->get_output = &get_output_tf;
@@ -824,6 +847,9 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename,
DNNFunctionType func_
model->func_type = func_type;
return model;
+err:
+ ff_dnn_free_model_tf(&model);
+ return NULL;
}
static DNNReturnType fill_model_input_tf(TFModel *tf_model, RequestItem
*request) {
@@ -838,24 +864,31 @@ static DNNReturnType fill_model_input_tf(TFModel
*tf_model, RequestItem *request
task = inference->task;
request->inference = inference;
- if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS)
- return DNN_ERROR;
+ if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS) {
+ goto err;
+ }
infer_request = request->infer_request;
input.height = task->in_frame->height;
input.width = task->in_frame->width;
infer_request->tf_input = av_malloc(sizeof(TF_Output));
+ if (!infer_request->tf_input) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input
tensor\n");
+ goto err;
+ }
+
infer_request->tf_input->oper = TF_GraphOperationByName(tf_model->graph,
task->input_name);
if (!infer_request->tf_input->oper){
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n",
task->input_name);
- return DNN_ERROR;
+ goto err;
}
infer_request->tf_input->index = 0;
+
infer_request->input_tensor = allocate_input_tensor(&input);
if (!infer_request->input_tensor){
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input
tensor\n");
- return DNN_ERROR;
+ goto err;
}
input.data = (float *)TF_TensorData(infer_request->input_tensor);
@@ -880,27 +913,35 @@ static DNNReturnType fill_model_input_tf(TFModel
*tf_model, RequestItem *request
infer_request->tf_outputs = av_malloc_array(task->nb_output,
sizeof(TF_Output));
if (infer_request->tf_outputs == NULL) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
*tf_outputs\n");
- return DNN_ERROR;
+ goto err;
}
infer_request->output_tensors = av_mallocz_array(task->nb_output,
sizeof(*infer_request->output_tensors));
if (!infer_request->output_tensors) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output
tensor\n");
- return DNN_ERROR;
+ goto err;
}
-
for (int i = 0; i < task->nb_output; ++i) {
infer_request->tf_outputs[i].oper =
TF_GraphOperationByName(tf_model->graph, task->output_names[i]);
if (!infer_request->tf_outputs[i].oper) {
av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in
model\n", task->output_names[i]);
- return DNN_ERROR;
+ goto err;
}
infer_request->tf_outputs[i].index = 0;
}
return DNN_SUCCESS;
+err:
+ for (uint32_t i = 0; i < task->nb_output; ++i) {
+ if (infer_request->output_tensors[i]) {
+ TF_DeleteTensor(infer_request->output_tensors[i]);
+ }
+ }
+ tf_free_request(infer_request);
+ return DNN_ERROR;
}
+
static void infer_completion_callback(void *args) {
RequestItem *request = args;
InferenceItem *inference = request->inference;
@@ -912,9 +953,8 @@ static void infer_completion_callback(void *args) {
outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
if (!outputs) {
- tf_free_request(infer_request);
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
- return;
+ goto final;
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
@@ -952,7 +992,7 @@ static void infer_completion_callback(void *args) {
}
}
av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this
kind of dnn filter now\n");
- return;
+ goto final;
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
if (infer_request->output_tensors[i]) {
@@ -960,9 +1000,13 @@ static void infer_completion_callback(void *args) {
}
}
task->inference_done++;
+final:
tf_free_request(infer_request);
av_freep(&outputs);
- ff_safe_queue_push_back(tf_model->request_queue, request);
+
+ if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
+ }
}
static DNNReturnType execute_model_tf(RequestItem *request, Queue
*inference_queue)
@@ -974,6 +1018,10 @@ static DNNReturnType execute_model_tf(RequestItem
*request, Queue *inference_que
TaskItem *task;
inference = ff_queue_peek_front(inference_queue);
+ if (!inference) {
+ av_log(NULL, AV_LOG_ERROR, "Failed to get inference item\n");
+ return DNN_ERROR;
+ }
task = inference->task;
tf_model = task->model;
ctx = &tf_model->ctx;
--
2.25.1
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