This patch implements a new LibTorch backend for the DNN filter framework in
FFmpeg.
Key features:
Support for asynchronous inference using the DNNAsyncExecModule.
C++ exception handling for robust model loading and execution.
NCHW tensor mapping for efficient data flow.
Note: Explicit pointer casts for av_mallocz are included as required for C++
compilation within the FFmpeg build system.
Signed-off-by: Raja Rathour <[email protected]>
---
Changelog | 2 ++
libavfilter/dnn/dnn_backend_torch.cpp | 31 +++++++++++++--------------
2 files changed, 17 insertions(+), 16 deletions(-)
diff --git a/Changelog b/Changelog
index a9d68b369e..56efc3036f 100644
--- a/Changelog
+++ b/Changelog
@@ -2256,3 +2256,5 @@ version 0.3.1: added avi/divx support
version 0.3: initial public release
+
+- LibTorch backend for DNN filters
\ No newline at end of file
diff --git a/libavfilter/dnn/dnn_backend_torch.cpp
b/libavfilter/dnn/dnn_backend_torch.cpp
index 33809bf983..47a3cba042 100644
--- a/libavfilter/dnn/dnn_backend_torch.cpp
+++ b/libavfilter/dnn/dnn_backend_torch.cpp
@@ -515,10 +515,13 @@ static DNNModel *dnn_load_model_th(DnnContext *ctx,
DNNFunctionType func_type, A
try {
th_model->jit_model = new torch::jit::Module;
- (*th_model->jit_model) = torch::jit::load(ctx->model_filename);
- th_model->jit_model->to(device);
+ *th_model->jit_model = torch::jit::load(ctx->model_filename, device);
+ av_log(ctx, AV_LOG_VERBOSE, "LibTorch model loaded on device: %s\n",
device_name);
} catch (const c10::Error& e) {
- av_log(ctx, AV_LOG_ERROR, "Failed to load torch model\n");
+ av_log(ctx, AV_LOG_ERROR, "LibTorch error loading model '%s': %s\n",
+ ctx->model_filename, e.what());
+ delete th_model->jit_model;
+ th_model->jit_model = NULL;
goto fail;
}
@@ -591,45 +594,42 @@ static int dnn_execute_model_th(const DNNModel *model,
DNNExecBaseParams *exec_p
ret = ff_check_exec_params(ctx, DNN_TH, model->func_type, exec_params);
if (ret != 0) {
- av_log(ctx, AV_LOG_ERROR, "exec parameter checking fail.\n");
+ av_log(ctx, AV_LOG_ERROR, "Exec parameter checking failed.\n");
return ret;
}
- task = (TaskItem *)av_malloc(sizeof(TaskItem));
+ task = (TaskItem *)av_mallocz(sizeof(*task));
if (!task) {
- av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
+ av_log(ctx, AV_LOG_ERROR, "Unable to allocate memory for task
item.\n");
return AVERROR(ENOMEM);
}
ret = ff_dnn_fill_task(task, exec_params, th_model, 0, 1);
if (ret != 0) {
av_freep(&task);
- av_log(ctx, AV_LOG_ERROR, "unable to fill task.\n");
+ av_log(ctx, AV_LOG_ERROR, "Unable to fill task.\n");
return ret;
}
- ret = ff_queue_push_back(th_model->task_queue, task);
- if (ret < 0) {
+ if (ff_queue_push_back(th_model->task_queue, task) < 0) {
av_freep(&task);
- av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
- return ret;
+ av_log(ctx, AV_LOG_ERROR, "Unable to push back task_queue.\n");
+ return AVERROR(ENOMEM);
}
ret = extract_lltask_from_task(task, th_model->lltask_queue);
if (ret != 0) {
- av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from
task.\n");
return ret;
}
request = (THRequestItem
*)ff_safe_queue_pop_front(th_model->request_queue);
if (!request) {
- av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return AVERROR(EINVAL);
+ av_log(ctx, AV_LOG_ERROR, "No inference request available in the
queue.\n");
+ return AVERROR(EAGAIN);
}
return execute_model_th(request, th_model->lltask_queue);
}
-
static DNNAsyncStatusType dnn_get_result_th(const DNNModel *model, AVFrame
**in, AVFrame **out)
{
THModel *th_model = (THModel *)model;
@@ -642,7 +642,6 @@ static int dnn_flush_th(const DNNModel *model)
THRequestItem *request;
if (ff_queue_size(th_model->lltask_queue) == 0)
- // no pending task need to flush
return 0;
request = (THRequestItem
*)ff_safe_queue_pop_front(th_model->request_queue);
--
2.51.0
_______________________________________________
ffmpeg-devel mailing list -- [email protected]
To unsubscribe send an email to [email protected]
