[TensorRT] ERROR: …/rtSafe/cuda/reformat.cu (925) - Cuda Error in NCHWToNCHHW2: 400 (invalid resource handle)

I have converted YOLOV3 model to onnx and from onnx to TRT model .

Now I am trying to run inference on received image using socketio . Here is my code:

import os
import time
import argparse
import numpy as np
import cv2
import pycuda.autoinit # This is needed for initializing CUDA driver
import socketio
import base64
from utils.yolo_classes import get_cls_dict
from utils.camera import add_camera_args, Camera
from utils.display import open_window, set_display, show_fps
from utils.visualization import BBoxVisualization
from utils.yolo_with_plugins import TrtYOLO

global conf_th

conf_th = 0.3
Loading Model

global trt_yolo

trt_yolo = TrtYOLO(“yolov3-custom-416”, (416, 416), 3)

print (“trt_yolo ==>”, trt_yolo )


inputShape = (300,300)

Shinobi Plugin Variables
shinobiPLuginName = “NoMask”
shinobiPluginKey = “NoMask123123”
shinobiHost = ‘

Socker IO Connection with Reconnection
sio = socketio.Client(reconnection=True,reconnection_delay=1,ssl_verify = False)

#Socket IO Connection Event , Built in Reconneciton Logic
def connect():
print(‘connection established :’)

#Socket IO Re Connection Event
def reconnect():
print (“Reconnection established :”)

#Socket IO Disconnect Event
def disconnect():
print(‘disconnected from server’)

def yolo_detection(img_np,trt_yolo,recvdImg,height, width,shinobiId,shonibiKe):
frame = img_np
trt_yolo = trt_yolo
print (“trt_yolo_YOLODETECTION”, trt_yolo)
(h, w) = frame.shape[:2]
#shinobiIdSend = sId
#shonibiKeSend = ske
recvdImg = recvdImg
boxes, confs, clss = trt_yolo.detect(frame, conf_th)
print ("boxes ", boxes)
print (“confs”, confs)
print (“clss” , clss)

#f event ! , Frame will be recived in this fucntion
def f(data):
# print (“on_f”)
#print (“Data”,data)
# print (“type(data)”,type(data))
# print (“data[ke]”,data.get(“ke”))
# print (“data[f]”,data.get(“f”))
# print (“data[id]”,data.get(“id”))
shinobiId = data.get(“id”)
shonibiKe = data.get(“ke”)
#print (“data[frame]”,data.get(“frame”))
recvdImg = data.get(“frame”)
#print (“Type of Image”, type(recvdImg))
#print (“Length of Image”,len(recvdImg))
nparr = np.fromstring(recvdImg, np.uint8)
print (“trt_yolo ON F!! ==>”, trt_yolo )
img_np = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
#img_np = cv2.resize(img_np,inputShape,interpolation = cv2.INTER_AREA)
#print (“Image Recieved !!!”)

It will wait for socket io events!!



Could you share a complete error log with us first?

Hi @AastaLLL :

Here is the Error Log !

Blockquote[TensorRT] WARNING: Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.

Blockquote[TensorRT] ERROR: …/rtSafe/cuda/reformat.cu (925) - Cuda Error in NCHWToNCHHW2: 400 (invalid resource handle)

Blockquote [TensorRT] ERROR: FAILED_EXECUTION: std::exception


Based on the log, do you generate the TensorRT plan file from the same platform and the same software version?
Please note that the TensoRT engine is not portable. You will need to generate the file from the same environment.


HI @AastaLLL,

Yes I am using the same platform and the same environment. [JETSON NANO]
After conversion of the model I have checked it with local images. And the model seems to be working OKAY.

The issue I am facing is with the socketio connection in combination with model.

If you see the code which I have shared , I am initially loading the model and every time I recieve the frame from websocket then I am trying to do inference on the received image . At that point of time I am getting this error.

If i run on local image and / or RTSP feeds the model is working OKAY.

Is there any mistake I am doing while loading the model in the above code ?trt_mask_plugin.txt (3.1 KB)

I have attached my python code file.

Here is the complete details of board:

NVIDIA Jetson Nano (Developer Kit Version)
L4T 32.4.4 [ JetPack UNKNOWN ]
Ubuntu 18.04.5 LTS
Kernel Version: 4.9.140-tegra
CUDA 10.2.89
CUDA Architecture: 5.3
OpenCV version: 4.1.1
OpenCV Cuda: YES
Vision Works:
VPI: 0.4.4

hi @AastaLLL ,

If I am not wrong I am getting this error because I am not handling async events . Can you please help me with how can I handle async received images and do inference on them ?


Not sure if I understand your problem correctly.
It seems that you try to run the inference as a kind of callback function from the internet.

Then, a common error is that the CUDA context is refreshed and mixed up with other applications.
Please store the CUDA context before leaving the yolo_detection function and restore it when back.

A similar example can be found in this topic: