Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) T4
• DeepStream Version 5.0.1
• TensorRT Version 7.2.1
• CuDNN Version 8.0.4
• NVIDIA GPU Driver Version (valid for GPU only) 450.80.02
• CONTAINER : Modified from the deepstream 5.0.1 20.09 base
We have a Python based deepstream 5 pipeline running 1 primary detector and several secondary ones. In one of the secondary models what we are interested is the embedding in the last layer. Sometimes, although very rarely, the tensor is not there. We access the data at the end of the pipeline in a gstreamer appsink.
Is this known/expected?
If not, are there any pointers to things we can investigate? it doesnt seem to print any log
Since it happens very seldomly it’s not a prioritized issue for us, but we’ll try to have something reproducible to share.
I.e. the following path
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer)) l_frame = batch_meta.frame_meta_list frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data) l_obj = frame_meta.obj_meta_list obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data) obj_meta.obj_user_meta_list
ends with obj_meta.user_meta_list being
None, even though
class_id = obj_meta.class_id confidence = obj_meta.confidence rect = obj_meta.rect_params obj_label = obj_meta.obj_label bbox = np.asarray( [rect.left, rect.top, rect.left + rect.width, rect.top+rect.height])
All gives perfectly sensible results.
the pipeline is something like:
gst-launch-1.0 rtspsrc location=rtsp://xxxxxxxxx1 ! rtph265depay ! nvv4l2decoder ! nvstreammux name=mux batch-size=1 batched-push-timeout=400000 live-source=true width=1280 height=768 ! nvinfer config-file-path=./config_infer_primary_yoloV4.txt ! queue ! nvinfer config-file-path=config_infer_secondary_1.txt ! queue ! nvinfer config-file-path=config_infer_secondary_2.txt ! appsink wait-on-eos=false drop=true max-buffers=1 enable-last-sample=false eos=false sync=0 async=0 emit-signals=true
And the config file for the affected model is
[property] gpu-id=0 net-scale-factor=0.0039215697906911373 model-engine-file=model.engine batch-size=4 network-mode=0 network-type=1 process-mode=2 model-color-format=0 gie-unique-id=2 operate-on-gie-id=1 operate-on-class-ids=0 output-tensor-meta=1
The model is custom, but we’ve not had to implement any custom layers.