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Code Issues Releases Activity

dnn_backend_native.c: refine code for fail case

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This commit is contained in:
Guo Yejun 2020-06-10 10:59:19 +08:00 committed by Guo, Yejun
parent c0974355c7
commit fc932195ab
1 changed files with 39 additions and 45 deletions
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84
libavfilter/dnn/dnn_backend_native.c
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@ -126,26 +126,23 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
int32_t layer;
DNNLayerType layer_type;
model = av_malloc(sizeof(DNNModel));
if (!model){
return NULL;
}
if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
av_freep(&model);
return NULL;
}
file_size = avio_size(model_file_context);
model = av_mallocz(sizeof(DNNModel));
if (!model){
goto fail;
}
/**
* check file header with string and version
*/
size = sizeof(header_expected);
buf = av_malloc(size);
if (!buf) {
avio_closep(&model_file_context);
av_freep(&model);
return NULL;
goto fail;
}
// size - 1 to skip the ending '\0' which is not saved in file
@ -153,18 +150,14 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
dnn_size = size - 1;
if (strncmp(buf, header_expected, size) != 0) {
av_freep(&buf);
avio_closep(&model_file_context);
av_freep(&model);
return NULL;
goto fail;
}
av_freep(&buf);
version = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (version != major_version_expected) {
avio_closep(&model_file_context);
av_freep(&model);
return NULL;
goto fail;
}
// currently no need to check minor version
@ -174,9 +167,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
network = av_mallocz(sizeof(ConvolutionalNetwork));
if (!network){
avio_closep(&model_file_context);
av_freep(&model);
return NULL;
goto fail;
}
model->model = (void *)network;
@ -188,16 +179,12 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
network->layers = av_mallocz(network->layers_num * sizeof(Layer));
if (!network->layers){
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
return NULL;
goto fail;
}
network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
if (!network->operands){
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
return NULL;
goto fail;
}
for (layer = 0; layer < network->layers_num; ++layer){
@ -205,17 +192,13 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
dnn_size += 4;
if (layer_type >= DLT_COUNT) {
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
return NULL;
goto fail;
}
network->layers[layer].type = layer_type;
parsed_size = layer_funcs[layer_type].pf_load(&network->layers[layer], model_file_context, file_size);
if (!parsed_size) {
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
return NULL;
goto fail;
}
dnn_size += parsed_size;
}
@ -258,6 +241,11 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
model->get_input = &get_input_native;
return model;
fail:
ff_dnn_free_model_native(&model);
avio_closep(&model_file_context);
return NULL;
}
DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
@ -314,23 +302,29 @@ void ff_dnn_free_model_native(DNNModel **model)
if (*model)
{
network = (ConvolutionalNetwork *)(*model)->model;
for (layer = 0; layer < network->layers_num; ++layer){
if (network->layers[layer].type == DLT_CONV2D){
conv_params = (ConvolutionalParams *)network->layers[layer].params;
av_freep(&conv_params->kernel);
av_freep(&conv_params->biases);
if ((*model)->model) {
network = (ConvolutionalNetwork *)(*model)->model;
if (network->layers) {
for (layer = 0; layer < network->layers_num; ++layer){
if (network->layers[layer].type == DLT_CONV2D){
conv_params = (ConvolutionalParams *)network->layers[layer].params;
av_freep(&conv_params->kernel);
av_freep(&conv_params->biases);
}
av_freep(&network->layers[layer].params);
}
av_freep(&network->layers);
}
av_freep(&network->layers[layer].params);
if (network->operands) {
for (uint32_t operand = 0; operand < network->operands_num; ++operand)
av_freep(&network->operands[operand].data);
av_freep(&network->operands);
}
av_freep(&network->output_indexes);
av_freep(&network);
}
av_freep(&network->layers);
for (uint32_t operand = 0; operand < network->operands_num; ++operand)
av_freep(&network->operands[operand].data);
av_freep(&network->operands);
av_freep(&network->output_indexes);
av_freep(&network);
av_freep(model);
}
}