Getting CUDNN_STATUS_EXECUTION_FAILED error on Jetson Nano with OpenCV dnn module - nvidia-jetson

Question

I was following one of the online tutorials but I was getting this error:
Traceback (most recent call last):
File “ssd_object_detection.py”, line 20, in
detections = net.forward()
cv2.error: OpenCV(4.3.0) /home/blah/opencv/modules/dnn/src/layers/…/cuda4dnn/primitives/…/csl/cudnn/convolution.hpp:461: error: (-217:Gpu API call) CUDNN_STATUS_EXECUTION_FAILED in function ‘convolve_with_bias_activation’
It's a python script and I use Opencv dnn module with a pre-trained model
This is my configuration:
Jetson Nano device
Ubuntu 18.04
/usr/local/cuda/bin/nvcc --version
nvcc: NVIDIA ® Cuda compiler driver
Copyright © 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_21:14:42_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
– NVIDIA CUDA: YES (ver 10.2, CUFFT CUBLAS FAST_MATH)
– NVIDIA GPU arch: 53
– NVIDIA PTX archs:
– cuDNN: YES (ver 8.0)
– NVIDIA CUDA: YES (ver 10.2, CUFFT CUBLAS FAST_MATH)
– NVIDIA GPU arch: 53
– cuDNN: YES (ver 8.0)
opencv 4.3.0 built from source with OPENCV_DNN_CUDA=ON, CUDNN_VERSION=‘8.0’, WITH_CUDA=ON, WITH_CUDNN=ON, and many other settings enabled
Python 3.7.7
This is a snippet of the code I am trying to run (it completes successfully if I don’t use the GPU). It fails at the line detections = net.forward()
CLASSES = [“background”, “aeroplane”]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
net = cv2.dnn.readNetFromCaffe(args[“prototxt”], args[“model”])
print("[INFO] setting preferable backend and target to CUDA…")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
print("[INFO] accessing video stream…")
vs = cv2.VideoCapture(args[“input”] if args[“input”] else 0)
writer = None
fps = FPS().start()
while True:
(grabbed, frame) = vs.read()
frame = imutils.resize(frame, width=400)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
....