Hi there, I’m trying to run LPRNet with TensorRT in python on Jetson NX. The Jetpack version is 4.4.0. I downloaded the model from ngc and converted the .etlt model to .engine model with the following command:
tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 us_lprnet_baseline18_deployable.etlt -t fp16 -e lpr_us_onnx_b16.engine
Bellow is the script I used for inferencing:
import os import time import cv2 #import matplotlib.pyplot as plt import numpy as np import pycuda.autoinit import pycuda.driver as cuda import tensorrt as trt from PIL import Image class HostDeviceMem(object): def __init__(self, host_mem, device_mem): self.host = host_mem self.device = device_mem def __str__(self): return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) def __repr__(self): return self.__str__() def load_engine(trt_runtime, engine_path): with open(engine_path, "rb") as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) return engine # Allocates all buffers required for an engine, i.e. host/device inputs/outputs. def allocate_buffers(engine): inputs =  outputs =  bindings =  stream = cuda.Stream() for binding in engine: size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size dtype = trt.nptype(engine.get_binding_dtype(binding)) # Allocate host and device buffers host_mem = cuda.pagelocked_empty(size, dtype) device_mem = cuda.mem_alloc(host_mem.nbytes) # Append the device buffer to device bindings. bindings.append(int(device_mem)) # Append to the appropriate list. if engine.binding_is_input(binding): inputs.append(HostDeviceMem(host_mem, device_mem)) else: outputs.append(HostDeviceMem(host_mem, device_mem)) return inputs, outputs, bindings, stream def do_inference(context, bindings, inputs, outputs, stream, batch_size=1): # Transfer input data to the GPU. [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] # Run inference. context.execute_async( batch_size=batch_size, bindings=bindings, stream_handle=stream.handle ) # Transfer predictions back from the GPU. [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] # Synchronize the stream stream.synchronize() # Return only the host outputs. return [out.host for out in outputs] # TensorRT logger singleton TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_engine_path = "lpr_us_onnx_b16.engine" trt_runtime = trt.Runtime(TRT_LOGGER) trt_engine = load_engine(trt_runtime, trt_engine_path) # Execution context is needed for inference context = trt_engine.create_execution_context() # This allocates memory for network inputs/outputs on both CPU and GPU inputs, outputs, bindings, stream = allocate_buffers(trt_engine) image = cv2.imread("license_plate_us.png") image = cv2.resize(image, (96, 48)) np.copyto(inputs.host, image.ravel()) outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream) print(outputs)
However I got the following error when allocating the buffer:
pycuda._driver.MemoryError: cuMemHostAlloc failed: out of memory
When I looked into that issue, I found out that
engine.get_binding_shape(binding) gives me [-1,3,48,96] which is a negative value, I’m not sure if the conversion is wrong or it’s how this model works.
Any ideas why this is happening? Is there any sample code that I can follow? Thanks in advance!