if __name__ == '__main__':
torch.set_grad_enabled(False)
cfg = None
net = None
if args.trained_model is not None:
cfg = cfg_plate
net = BaseModel(cfg=cfg, phase='test')
else:
print("Don't support network!")
exit(0)
net = load_model(net, args.trained_model, args.cpu)
net.eval()
print('Finished loading model!')
print(net)
from torchscope import scope
scope(net, input_size=(3, 480, 850))
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
image_paths = get_image_path(args.image_path)
_t = {'pre': Timer(), 'forward_pass': Timer(), 'misc': Timer()}
# testing begin
for path in image_paths:
_t['pre'].tic()
# path = "/home/can/AI_Camera/License_Plate/LP_Detection/data/val/images/40000/61539302914508AF4442_B.jpg_out-full_1.jpg"
img_raw = cv2.imread(path, cv2.IMREAD_COLOR)
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_weather",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_blur",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_tilt",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_db",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_fn",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_rotate",
# "/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_np",
"/home/can/AI_Camera/Dataset/License_Plate/CCPD2019/ccpd_challenge"
]
print("loading model")
# Initialize model
model = BaseModel(cfg=cfg_plate)
checkpoint = torch.load(checkpoint_path, map_location='cuda')
model.load_state_dict(checkpoint['state_dict'])
del checkpoint
model.eval()
model.to(device)
for i in np.linspace(0.5, 0.9, 8):
print("############################")
print("threshold: " + str(i))
for index, path in enumerate(img_dir):
print("**************************")
print(path)
val_dataset = ChaLocDataLoader([path], imgSize=320)
valid_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=256,
shuffle=False,
num_workers=6,
collate_fn=detection_collate,
