Tensorrt error on second and subsequent execute_async calls

[kb1ooo]

Hello,

I'm using a small modification of predict_tensorrt.py from the WIDERFACE example (on my own data), which works fine on my x64 linux PC. However, I'm having an issue on jetson xavier. After the first execute_async call, I'm getting the below error on subsequent calls:

python predict_tensorrt.py
Engine info:
	max batch size:  1
	device memory_size:  2417152
before memcpy_htod_async
after memcpy_htod_async
before memcpy_dtod_async
after memcpy_dtod_async
after stream.synchronize()
before memcpy_htod_async
after memcpy_htod_async
[TensorRT] ERROR: 1: [reformat.cu::NCHWToNCQHW4::826] Error Code 1: Cuda Runtime (invalid resource handle)
before memcpy_dtod_async
after memcpy_dtod_async
after stream.synchronize()
before memcpy_htod_async
after memcpy_htod_async
[TensorRT] ERROR: 1: [reformat.cu::NCHWToNCQHW4::826] Error Code 1: Cuda Runtime (invalid resource handle)
before memcpy_dtod_async
after memcpy_dtod_async
after stream.synchronize()

Code from lfd/model/lfd.py

        print("before memcpy_htod_async")
        [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in input_buffers]
        print("after memcpy_htod_async")
        tensorrt_engine_context.execute_async(batch_size=1, bindings=bindings, stream_handle=stream.handle)
        print("before memcpy_dtod_async")
        [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in output_buffers]
        print("after memcpy_dtod_async")
        stream.synchronize()
        print("after stream.synchronize()")

Any insight helpful.

Thanks,
Marc

[YonghaoHe]

@kb1ooo have you solved the problem? I guess that the lib versions matter?