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()
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()