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dnn_backend_native.c: refine code for fail case
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c0974355c7
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@ -126,26 +126,23 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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int32_t layer;
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DNNLayerType layer_type;
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model = av_malloc(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
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av_freep(&model);
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return NULL;
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}
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file_size = avio_size(model_file_context);
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model = av_mallocz(sizeof(DNNModel));
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if (!model){
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goto fail;
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}
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/**
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* check file header with string and version
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*/
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size = sizeof(header_expected);
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buf = av_malloc(size);
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if (!buf) {
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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goto fail;
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}
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// size - 1 to skip the ending '\0' which is not saved in file
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@ -153,18 +150,14 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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dnn_size = size - 1;
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if (strncmp(buf, header_expected, size) != 0) {
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av_freep(&buf);
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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goto fail;
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}
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av_freep(&buf);
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version = (int32_t)avio_rl32(model_file_context);
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dnn_size += 4;
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if (version != major_version_expected) {
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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goto fail;
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}
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// currently no need to check minor version
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@ -174,9 +167,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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network = av_mallocz(sizeof(ConvolutionalNetwork));
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if (!network){
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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goto fail;
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}
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model->model = (void *)network;
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@ -188,16 +179,12 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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network->layers = av_mallocz(network->layers_num * sizeof(Layer));
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if (!network->layers){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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goto fail;
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}
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network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
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if (!network->operands){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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goto fail;
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}
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for (layer = 0; layer < network->layers_num; ++layer){
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@ -205,17 +192,13 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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dnn_size += 4;
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if (layer_type >= DLT_COUNT) {
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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goto fail;
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}
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network->layers[layer].type = layer_type;
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parsed_size = layer_funcs[layer_type].pf_load(&network->layers[layer], model_file_context, file_size);
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if (!parsed_size) {
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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goto fail;
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}
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dnn_size += parsed_size;
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}
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@ -258,6 +241,11 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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model->get_input = &get_input_native;
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return model;
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fail:
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ff_dnn_free_model_native(&model);
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avio_closep(&model_file_context);
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return NULL;
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}
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DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
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@ -314,7 +302,9 @@ void ff_dnn_free_model_native(DNNModel **model)
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if (*model)
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{
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if ((*model)->model) {
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network = (ConvolutionalNetwork *)(*model)->model;
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if (network->layers) {
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for (layer = 0; layer < network->layers_num; ++layer){
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if (network->layers[layer].type == DLT_CONV2D){
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conv_params = (ConvolutionalParams *)network->layers[layer].params;
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@ -324,13 +314,17 @@ void ff_dnn_free_model_native(DNNModel **model)
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av_freep(&network->layers[layer].params);
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}
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av_freep(&network->layers);
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}
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if (network->operands) {
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for (uint32_t operand = 0; operand < network->operands_num; ++operand)
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av_freep(&network->operands[operand].data);
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av_freep(&network->operands);
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}
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av_freep(&network->output_indexes);
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av_freep(&network);
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}
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av_freep(model);
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}
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}
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