Wrong values in model output using gRPC

Accelerated Computing Intelligent Video Analytics DeepStream SDK
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#1

Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.1
• JetPack Version (valid for Jetson only)
• TensorRT Version 8.2.5
• NVIDIA GPU Driver Version (valid for GPU only) 511.65
• Issue Type( questions, new requirements, bugs) bug
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)

I was testing the deepstream python example deepstream-ssd-parser inside docker nvcr.io/nvidia/deepstream:6.1-triton.

When using the triton server inside the container everything works fine. But as soon as I am switching to an external triton server nvcr.io/nvidia/tritonserver:22.06-py3 the results from the model contain errors. Specifically the first 4 entries in every model output layer are wrong. To show them I printed every output inside make_nodi() from ssd_parser.py.

print("Confidence", pyds.get_detections(score_layer.buffer, index))

For the changes in dstest_ssd_nopostprocess.txt, I replaced the model_repo entry with

      grpc {
        url: "0.0.0.0:8001"
      }

The output is as follows

Confidence -1.4424493044793344e+17
Confidence 4.5596850730665223e-41
Confidence 6.191364830612113e+26
Confidence 4.559825202912955e-41
Confidence 0.5849516987800598
Confidence 0.4417745769023895
Confidence 0.41973045468330383
Frame Number=0 Number of Objects=7 Vehicle_count=0 Person_count=0
Confidence -1.4435625600024576e+17
Confidence 4.5596850730665223e-41
Confidence 6.191364830612113e+26
Confidence 4.559825202912955e-41
Confidence 0.6589468121528625
Confidence 0.5667120814323425
Confidence 0.45494136214256287
Frame Number=1 Number of Objects=7 Vehicle_count=0 Person_count=0
Confidence -1.4436422745954714e+17
Confidence 4.5596850730665223e-41
Confidence 6.191364830612113e+26
Confidence 4.559825202912955e-41
Confidence 0.664429783821106
Confidence 0.5177415609359741
Frame Number=2 Number of Objects=6 Vehicle_count=0 Person_count=0

I am not quite sure what causes this or how it can be avoided. The same behavior also presents when testing with a custom pytorch model.

2 Likes
#3

I have the same issue that the first four entries of every output layer are wrong. Does anyone have any suggestions how to fix this bug?

#4

What seems to be related is that every few frames not only the first 4 but a large number of other random entries are not correct, meaning extremely large or small.

Same result using driver version: 510.73.05

#5

Hi @fabian.vogel @sf-zg
We are checking this… thanks!

#6

Using deepstream:6.1-triton to access tritonserver:22.06-py3 by gRPC, the above bug can reproduce locally, it will be fixed in next release soon.

#7

Thank you @fanzh for the update. Do you have a rough time frame for the next release?

#8

No， Please pay attention to deepstream forum.

#9
#10