def __init__(self):
self.config=get_config()
self.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
self.model = crnn.get_crnn(self.config).to(self.device)
checkpoint = torch.load(self.config.checkpoint)
if 'state_dict' in checkpoint.keys():
self.model.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint)
self.model.eval()
def main():
config, args = parse_arg()
model = crnn.get_crnn(config)
criterion = torch.nn.CTCLoss()
# get device
if torch.cuda.is_available():
device = torch.device("cuda:{}".format(config.GPUID))
else:
device = torch.device("cpu:0")
model = model.to(device)
val_dataset = get_dataset(config)(config, is_train=False)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=config.TEST.BATCH_SIZE_PER_GPU,
shuffle=config.TEST.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
acc = validate(config, val_loader, val_dataset, converter, model,
criterion, device)
def main():
# load config
config = parse_arg()
# create output folder
output_dict = utils.create_log_folder(config, phase='train')
# cudnn
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
# writer dict
writer_dict = {
'writer': SummaryWriter(log_dir=output_dict['tb_dir']),
'train_global_steps': 0,
'valid_global_steps': 0,
}
# construct face related neural networks
model = crnn.get_crnn(config)
# get device
if torch.cuda.is_available():
device = torch.device("cuda:{}".format(config.GPUID))
else:
device = torch.device("cpu:0")
model = model.to(device)
# define loss function
criterion = torch.nn.CTCLoss()
optimizer = utils.get_optimizer(config, model)
last_epoch = config.TRAIN.BEGIN_EPOCH
if config.TRAIN.RESUME.IS_RESUME:
model_state_file = config.TRAIN.RESUME.FILE
if model_state_file == '':
print(" => no checkpoint found")
checkpoint = torch.load(model_state_file, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'])
last_epoch = checkpoint['epoch']
if isinstance(config.TRAIN.LR_STEP, list):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP,
config.TRAIN.LR_FACTOR, last_epoch-1
)
else:
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, config.TRAIN.LR_STEP,
config.TRAIN.LR_FACTOR, last_epoch - 1
)
train_dataset = get_dataset(config)(config, is_train=True)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
val_dataset = get_dataset(config)(config, is_train=False)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=config.TEST.BATCH_SIZE_PER_GPU,
shuffle=config.TEST.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
best_acc = 0.5
converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
function.train(config, train_loader, train_dataset, converter, model, criterion, optimizer, device, epoch, writer_dict, output_dict)
lr_scheduler.step()
acc = function.validate(config, val_loader, val_dataset, converter, model, criterion, device, epoch, writer_dict, output_dict)
is_best = acc > best_acc
best_acc = max(acc, best_acc)
print("is best:", is_best)
print("best acc is:", best_acc)
# save checkpoint
torch.save(
{
"state_dict": model.state_dict(),
"epoch": epoch + 1,
"best_acc": best_acc,
}, os.path.join(output_dict['chs_dir'], "checkpoint_{}_acc_{:.4f}.pth".format(epoch, acc))
)
writer_dict['writer'].close()
alphabets_char_file = [
char.strip()
for char in open(config_crnn.DATASET.CHAR_FILE).readlines()[1:]
]
alphabets_char_file = ''.join(alphabets_char_file)
config_crnn.DATASET.ALPHABETS = alphabets_char_file
else:
config_crnn.DATASET.ALPHABETS = alphabets.alphabet
# alphabets_char_file = [char.split('\n')[0] for char in open(args.alphabet_path).readlines()[1:]]
# alphabets_char_file = "".join(alphabets_char_file)
# config_crnn.DATASET.ALPHABETS = alphabets_char_file
config_crnn.MODEL.NUM_CLASSES = len(config_crnn.DATASET.ALPHABETS)
ocr_rec_model = CRNN_model.get_crnn(config_crnn).to(0)
print("loading pretrained rec model from {0}".format(
args.ocr_recognition_model_path))
checkpoint = torch.load(args.ocr_recognition_model_path)
if "state_dict" in checkpoint.keys():
ocr_rec_model.load_state_dict(checkpoint["state_dict"])
else:
ocr_rec_model.load_state_dict(checkpoint)
f = open(args.wts_save_path, 'w')
f.write("{}\n".format(len(ocr_rec_model.state_dict().keys())))
for k, v in ocr_rec_model.state_dict().items():
print('key: ', k)
print('value: ', v.shape)
vr = v.reshape(-1).cpu().numpy()
f.write("{} {}".format(k, len(vr)))
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
print('results: {0}'.format(sim_pred))
if __name__ == '__main__':
config, args = parse_arg()
device = torch.device(
'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
model = crnn.get_crnn(config).to(device)
print('loading pretrained model from {0}'.format(args.checkpoint))
checkpoint = torch.load(args.checkpoint)
if 'state_dict' in checkpoint.keys():
model.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint)
started = time.time()
img = cv2.imread(args.image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
recognition(config, img, model, converter, device)
def main():
# load config
config = parse_arg()
# create output folder
output_dict = utils.create_log_folder(config, phase='train')
# cudnn
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
# writer dict
writer_dict = {
'writer': SummaryWriter(log_dir=output_dict['tb_dir']),
'train_global_steps': 0,
'valid_global_steps': 0,
}
# construct face related neural networks
model = crnn.get_crnn(config)
#
# checkpoint = torch.load('/data/yolov5/CRNN_Chinese_Characters_Rec/output/OWN/crnn/2020-09-15-22-13/checkpoints/checkpoint_98_acc_1.0983.pth')
# if 'state_dict' in checkpoint.keys():
# model.load_state_dict(checkpoint['state_dict'])
# else:
# model.load_state_dict(checkpoint)
# get device
if torch.cuda.is_available():
device = torch.device("cuda:{}".format(config.GPUID))
else:
device = torch.device("cpu:0")
model = model.to(device)
# define loss function
# criterion = torch.nn.CTCLoss()
criterion = CTCLoss()
last_epoch = config.TRAIN.BEGIN_EPOCH
optimizer = utils.get_optimizer(config, model)
if isinstance(config.TRAIN.LR_STEP, list):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR,
last_epoch - 1)
else:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
config.TRAIN.LR_STEP,
config.TRAIN.LR_FACTOR,
last_epoch - 1)
if config.TRAIN.FINETUNE.IS_FINETUNE:
model_state_file = config.TRAIN.FINETUNE.FINETUNE_CHECKPOINIT
if model_state_file == '':
print(" => no checkpoint found")
checkpoint = torch.load(model_state_file, map_location='cpu')
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
from collections import OrderedDict
model_dict = OrderedDict()
for k, v in checkpoint.items():
if 'cnn' in k:
model_dict[k[4:]] = v
model.cnn.load_state_dict(model_dict)
if config.TRAIN.FINETUNE.FREEZE:
for p in model.cnn.parameters():
p.requires_grad = False
elif config.TRAIN.RESUME.IS_RESUME:
model_state_file = config.TRAIN.RESUME.FILE
if model_state_file == '':
print(" => no checkpoint found")
checkpoint = torch.load(model_state_file, map_location='cpu')
if 'state_dict' in checkpoint.keys():
model.load_state_dict(checkpoint['state_dict'])
last_epoch = checkpoint['epoch']
# optimizer.load_state_dict(checkpoint['optimizer'])
# lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
else:
model.load_state_dict(checkpoint)
model_info(model)
train_dataset = get_dataset(config)(config, is_train=True)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
val_dataset = get_dataset(config)(config, is_train=False)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=config.TEST.BATCH_SIZE_PER_GPU,
shuffle=config.TEST.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
best_acc = 0.5
converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
function.train(config, train_loader, train_dataset, converter, model,
criterion, optimizer, device, epoch, writer_dict,
output_dict)
lr_scheduler.step()
acc = function.validate(config, val_loader, val_dataset, converter,
model, criterion, device, epoch, writer_dict,
output_dict)
is_best = acc > best_acc
best_acc = max(acc, best_acc)
print("is best:", is_best)
print("best acc is:", best_acc)
# save checkpoint
torch.save(
{
"state_dict": model.state_dict(),
"epoch": epoch + 1,
# "optimizer": optimizer.state_dict(),
# "lr_scheduler": lr_scheduler.state_dict(),
"best_acc": best_acc,
},
os.path.join(output_dict['chs_dir'],
"checkpoint_{}_acc_{:.4f}.pth".format(epoch, acc)))
writer_dict['writer'].close()
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
# 写入结果
with open('/home/lab/zts/datasets/warped_100_images_results.txt', 'a') as f:
f.write(image_name + ' ' + sim_pred + '\n')
f.close()
print('results: {0}'.format(sim_pred))
if __name__ == '__main__':
# 参数解析
config, args = parse_args()
# 加载模型
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
model = crnn.get_crnn(config, len(alphabets.alphabet)).to(device)
print('loading pre trained model from {0}'.format(args.checkpoint))
checkpoint = torch.load(args.checkpoint)
if 'state_dict' in checkpoint.keys():
model.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint)
# time
started = time.time()
# 标签转换
converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
# 处理文件夹
image_folder = args.image_folder
for image_name in os.listdir(image_folder):
image_path = os.path.join(image_folder, image_name)
img = cv2.imread(image_path)
