if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LPR Demo')
parser.add_argument("-image", help='image path', default='data/ccpd_weather/吉BTW976.jpg', type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage))
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(torch.load('weights/Final_STN_model.pth', map_location=lambda storage, loc: storage))
STN.eval()
print("Successful to build network!")
since = time.time()
image = cv2.imread(args.image)
im = cv2.resize(image, (94, 24), interpolation=cv2.INTER_CUBIC)
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5)*0.0078125
data = torch.from_numpy(im).float().unsqueeze(0).to(device) # torch.Size([1, 3, 24, 94])
transfer = STN(data)
preds = lprnet(transfer)
preds = preds.cpu().detach().numpy() # (1, 68, 18)
labels, pred_labels = decode(preds, CHARS)
print("model inference in {:2.3f} seconds".format(time.time() - since))
type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(
torch.load(
'/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/LPRNet/saving_ckpt/lprnet_epoch_001200_model.ckpt'
)['net_state_dict'])
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(
torch.load(
'/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/LPRNet/saving_ckpt/stn_epoch_001200_model.ckpt'
)['net_state_dict'])
STN.eval()
print("Successful to build network!")
since = time.time()
image = cv2.imread(args.image)
im = cv2.resize(image, (94, 24), interpolation=cv2.INTER_CUBIC)
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5) * 0.0078125
data = torch.from_numpy(im).float().unsqueeze(0).to(
device) # torch.Size([1, 3, 24, 94])
transfer = STN(data)
preds = lprnet(transfer)
preds = preds.cpu().detach().numpy() # (1, 68, 18)
parser = argparse.ArgumentParser(description='LPR Demo')
parser.add_argument("-image", help='image path', default='data/ccpd_weather/吉BTW976.jpg', type=str)
parser.add_argument("-lprnet_model", help='lprnet model', default='weights/Final_LPRNet_model.pth', type=str)
parser.add_argument("-stn_model", help='stn model', default='weights/Final_STN_model.pth', type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(torch.load(args.lprnet_model, map_location=lambda storage, loc: storage))
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(torch.load(args.stn_model, map_location=lambda storage, loc: storage))
STN.eval()
print("Successful to build network!")
since = time.time()
image = cv2.imread(args.image)
im = cv2.resize(image, (94, 24), interpolation=cv2.INTER_CUBIC)
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5)*0.0078125
data = torch.from_numpy(im).float().unsqueeze(0).to(device) # torch.Size([1, 3, 24, 94])
transfer = STN(data)
preds = lprnet(transfer)
preds = preds.cpu().detach().numpy() # (1, 68, 18)
labels, pred_labels = decode(preds, CHARS)
print("model inference in {:2.3f} seconds".format(time.time() - since))
parser.add_argument('--batch_size', default=24 * 4, help='batch size')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=args.dropout_rate)
lprnet.to(device)
lprnet.load_state_dict(
torch.load('LPRNet/weights/LPRNet_model_Init.pth',
map_location=lambda storage, loc: storage))
# lprnet.load_state_dict(torch.load('/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/saving_ckpt/lprnet_Iter_007200_model.ckpt')['net_state_dict'])
print("LPRNet loaded")
STN = STNet()
STN.to(device)
STN.load_state_dict(
torch.load('LPRNet/weights/STN_model_Init.pth',
map_location=lambda storage, loc: storage))
# STN.load_state_dict(torch.load('/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/saving_ckpt/stn_Iter_007200_model.ckpt')['net_state_dict'])
print("STN loaded")
dataset = LPRDataLoader([args.img_dirs_train], args.img_size)
dataloader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
collate_fn=collate_fn)
print('training dataset loaded with length : {}'.format(len(dataset)))
# print('validation dataset loaded with length : {}'.format(len(dataset['val'])))
# define optimizer & loss
optimizer = torch.optim.Adam([{