构建LPRNet，基于CCPD数据集进行车牌检测和识别_License-plate-recognition.zip

# -*- coding: utf-8 -*- # /usr/bin/env/python3 ''' Pytorch implementation for LPRNet. Author: aiboy.wei@outlook.com . ''' from load_data import CHARS, LPRDataLoader from model.LPRNet import build_lprnet from torch.autograd import Variable from torch.utils.data import * from torch import optim import torch.nn as nn import numpy as np import argparse import torch import time import os import matplotlib.pyplot as plt def sparse_tuple_for_ctc(T_length, lengths): ''' get sparse tuple for ctc loss ''' input_lengths = [] target_lengths = [] for ch in lengths: input_lengths.append(T_length) target_lengths.append(ch) return tuple(input_lengths), tuple(target_lengths) def adjust_learning_rate(optimizer, cur_epoch, base_lr, lr_schedule): """ Sets the learning rate """ lr = 0 for i, e in enumerate(lr_schedule): if cur_epoch < e: lr = base_lr * (0.1 ** i) break if lr == 0: lr = base_lr for param_group in optimizer.param_groups: param_group['lr'] = lr return lr def get_parser(): parser = argparse.ArgumentParser(description='parameters to train net') parser.add_argument('--max_epoch', default=15, help='epoch to train the network') parser.add_argument('--img_size', default=(94, 24), help='the image size') parser.add_argument('--train_img_dirs', default="train", help='the train images path') parser.add_argument('--test_img_dirs', default="test", help='the test images path') parser.add_argument('--dropout_rate', default=0.5, help='dropout rate.') parser.add_argument('--learning_rate', default=0.1, help='base value of learning rate.') parser.add_argument('--lpr_max_len', default=8, help='license plate number max length.') parser.add_argument('--train_batch_size', default=128, type=int, help='training batch size.') parser.add_argument('--test_batch_size', default=120, type=int, help='testing batch size.') parser.add_argument('--phase_train', default=True, type=bool, help='train or test phase flag.') parser.add_argument('--num_workers', default=8, type=int, help='Number of workers used in dataloading') parser.add_argument('--cuda', default=False, type=bool, help='Use cuda to train model') parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining') parser.add_argument('--interval', default=2000, type=int, help='interval for evaluate') parser.add_argument('--momentum', default=0.9, type=float, help='momentum') parser.add_argument('--weight_decay', default=2e-5, type=float, help='Weight decay for SGD') parser.add_argument('--lr_schedule', default=[4, 8, 12, 14, 16], help='schedule for learning rate.') parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models') parser.add_argument('--pretrained_model', default='./weights/Final_LPRNet_model.pth', help='pretrained base model') # parser.add_argument('--pretrained_model', default='', help='pretrained base model') args = parser.parse_args() return args def collate_fn(batch): ''' User-defined Batch output ''' imgs = [] labels = [] lengths = [] for _, sample in enumerate(batch): img, label, length = sample imgs.append(torch.from_numpy(img)) labels.extend(label) lengths.append(length) labels = np.asarray(labels).flatten().astype(np.int) return (torch.stack(imgs, 0), torch.from_numpy(labels), lengths) def train(): args = get_parser() T_length = 18 # args.lpr_max_len epoch = 0 + args.resume_epoch loss_val = 0 if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) # build network lprnet = build_lprnet(lpr_max_len=args.lpr_max_len, phase=args.phase_train, class_num=len(CHARS), dropout_rate=args.dropout_rate) device = torch.device("cuda:0" if args.cuda else "cpu") lprnet.to(device) print("Successful to build network!") # load pretrained model if args.pretrained_model: lprnet.load_state_dict(torch.load(args.pretrained_model,map_location=torch.device('cpu'))) print("load pretrained model successful!") else: def xavier(param): nn.init.xavier_uniform(param) def weights_init(m): for key in m.state_dict(): if key.split('.')[-1] == 'weight': if 'conv' in key: nn.init.kaiming_normal_(m.state_dict()[key], mode='fan_out') if 'bn' in key: m.state_dict()[key][...] = xavier(1) elif key.split('.')[-1] == 'bias': m.state_dict()[key][...] = 0.01 lprnet.backbone.apply(weights_init) lprnet.container.apply(weights_init) print("initial net weights successful!") # define optimizer optimizer = optim.RMSprop(lprnet.parameters(), lr=args.learning_rate, alpha = 0.9, eps=1e-08, momentum=args.momentum, weight_decay=args.weight_decay) # prepare dataset train_img_dirs = os.path.expanduser(args.train_img_dirs) test_img_dirs = os.path.expanduser(args.test_img_dirs) train_dataset = LPRDataLoader(train_img_dirs.split(','), args.img_size, args.lpr_max_len) test_dataset = LPRDataLoader(test_img_dirs.split(','), args.img_size, args.lpr_max_len) epoch_size = len(train_dataset) // args.train_batch_size max_iter = args.max_epoch * epoch_size ctc_loss = nn.CTCLoss(blank=len(CHARS)-1, reduction='mean') # reduction: 'none' | 'mean' | 'sum' if args.resume_epoch > 0: start_iter = args.resume_epoch * epoch_size else: start_iter = 0 Tp = 0 Tn_1 = 0 Tn_2 = 0 Train_Accuracy=[] Test_Accuracy=[] Train_loss=[] for iteration in range(start_iter, max_iter): if iteration % epoch_size == 0: # create batch iterator batch_iterator = iter(DataLoader(train_dataset, args.train_batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=collate_fn)) loss_val = 0 epoch += 1 start_time = time.time() # load train data images, labels, lengths = next(batch_iterator) # get ctc parameters input_lengths, target_lengths = sparse_tuple_for_ctc(T_length, lengths) # update lr lr = adjust_learning_rate(optimizer, epoch, args.learning_rate, args.lr_schedule) targets = [] start=0 for length in lengths: label = labels[start:start+length] targets.append(label) start += length targets = np.array([el.numpy() for el in targets]) if args.cuda: images = Variable(images, requires_grad=False).cuda() labels = Variable(labels, requires_grad=False).cuda() else: images = Variable(images, requires_grad=False) labels = Variable(labels, requires_grad=False) # forward logits = lprnet(images) log_probs = logits.permute(2, 0, 1) # for ctc loss: T x N x C log_probs = log_probs.log_softmax(2).requires_grad_() # backprop optimizer.zero_grad() loss = ctc_loss(log_probs, labels, input_lengths=input_lengths, target_lengths=target_lengths) if loss.item() == np.inf: continue loss.backward() optimizer.step() loss_val += loss.item() end_time = time.time() # calculate accuracy prebs = logits.cpu().detach().numpy() preb_labels = list() for i in range(prebs.shape[0]): preb = prebs[i, :, :] preb_label = list() for j in range(preb.shape[1]): preb_label.append(np.argmax(preb[:, j], axis=0)) no_repeat_blank_label = list() pre_c = preb_label[0] if pre_c != len(CHARS) - 1: no_repeat_bla