I am using TensorRT engine for inference using Jetson TX2
with engine.create_execution_context() as context: context.debug_sync = False # Transfer input data to the GPU. cuda.memcpy_htod_async(d_input_1, h_input_1, stream) # Run inference. print('load profiler') context.profiler = trt.Profiler() print('execute') context.execute(batch_size=1, bindings=[int(d_input_1), int(d_output)]) print('Transfer predictions back from the GPU.') # Transfer predictions back from the GPU. cuda.memcpy_dtoh_async(h_output, d_output, stream) # Synchronize the stream stream.synchronize() # Return the host output. print(h_output.shape) out = h_output.reshape((1,-1)) return out
context.execute(batch_size=1, bindings=[int(d_input_1), int(d_output)]) it logs the model layers all of them which makes the console not suitable for debugging. I tried to disable it but I couldn’t.
I think the problem is in this method
engine.create_execution_context() as documented in the api this return IExecutionContext with
debug_sync set to
True . I don’t know if this is a bug or not.
context.debug_sync = False but it didn’t stop logging the execution layers.
how I could disable logging the execution layers on the terminal?
TensorRT Version: 18.104.22.168
GPU Type: Jetson TX2
CUDA Version: 10.2.89
CUDNN Version: 22.214.171.124
Operating System + Version: jetpack 4.4.1
TensorFlow Version (if applicable): 2.3.1+nv20.10