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avfilter/dnn_backend_torch: Simplify memory allocation

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Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
This commit is contained in:
Zhao Zhili 2024-05-08 00:08:16 +08:00 • committed by Guo Yejun
parent abfefbb33b
commit a1fea7e11b
1 changed files with 12 additions and 19 deletions
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31
libavfilter/dnn/dnn_backend_torch.cpp
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@ -37,8 +37,8 @@ extern "C" {
}
typedef struct THModel {
DNNModel model;
DnnContext *ctx;
DNNModel *model;
torch::jit::Module *jit_model;
SafeQueue *request_queue;
Queue *task_queue;
@ -141,7 +141,7 @@ static void dnn_free_model_th(DNNModel **model)
ff_queue_destroy(th_model->task_queue);
delete th_model->jit_model;
av_freep(&th_model);
av_freep(model);
*model = NULL;
}
static int get_input_th(void *model, DNNData *input, const char *input_name)
@ -195,19 +195,19 @@ static int fill_model_input_th(THModel *th_model, THRequestItem *request)
infer_request->input_tensor = new torch::Tensor();
infer_request->output = new torch::Tensor();
switch (th_model->model->func_type) {
switch (th_model->model.func_type) {
case DFT_PROCESS_FRAME:
input.scale = 255;
if (task->do_ioproc) {
if (th_model->model->frame_pre_proc != NULL) {
th_model->model->frame_pre_proc(task->in_frame, &input, th_model->model->filter_ctx);
if (th_model->model.frame_pre_proc != NULL) {
th_model->model.frame_pre_proc(task->in_frame, &input, th_model->model.filter_ctx);
} else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
}
}
break;
default:
avpriv_report_missing_feature(NULL, "model function type %d", th_model->model->func_type);
avpriv_report_missing_feature(NULL, "model function type %d", th_model->model.func_type);
break;
}
*infer_request->input_tensor = torch::from_blob(input.data,
@ -282,13 +282,13 @@ static void infer_completion_callback(void *args) {
goto err;
}
switch (th_model->model->func_type) {
switch (th_model->model.func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
outputs.scale = 255;
outputs.data = output->data_ptr();
if (th_model->model->frame_post_proc != NULL) {
th_model->model->frame_post_proc(task->out_frame, &outputs, th_model->model->filter_ctx);
if (th_model->model.frame_post_proc != NULL) {
th_model->model.frame_post_proc(task->out_frame, &outputs, th_model->model.filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, &outputs, th_model->ctx);
}
@ -298,7 +298,7 @@ static void infer_completion_callback(void *args) {
}
break;
default:
avpriv_report_missing_feature(th_model->ctx, "model function type %d", th_model->model->func_type);
avpriv_report_missing_feature(th_model->ctx, "model function type %d", th_model->model.func_type);
goto err;
}
task->inference_done++;
@ -417,17 +417,10 @@ static DNNModel *dnn_load_model_th(DnnContext *ctx, DNNFunctionType func_type, A
THRequestItem *item = NULL;
const char *device_name = ctx->device ? ctx->device : "cpu";
model = (DNNModel *)av_mallocz(sizeof(DNNModel));
if (!model) {
return NULL;
}
th_model = (THModel *)av_mallocz(sizeof(THModel));
if (!th_model) {
av_freep(&model);
if (!th_model)
return NULL;
}
th_model->model = model;
model = &th_model->model;
model->model = th_model;
th_model->ctx = ctx;