def main():
# [*] args, loggers and tensorboard
args = parse_args()
reset_config(config, args)
logger, _, tb_log_dir = create_logger(config, 'DAG', 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
}
# [*] gpus parallel and model prepare
# prepare pretrained model -- download from google drive
# auto-download train model from GoogleDrive
if not os.path.exists('./pretrain'):
os.makedirs('./pretrain')
if config.DAG.TRAIN.ALIGN:
print('====> train object-aware version <====')
model = models.__dict__[config.DAG.TRAIN.MODEL](
align=True).cuda() # build model
else:
print(
'====> Default: train without object-aware, also prepare for DAGPlus <===='
)
model = models.__dict__[config.DAG.TRAIN.MODEL](
align=False).cuda() # build model
print(model)
model = load_pretrain(model, './pretrain/{0}'.format(
config.DAG.TRAIN.PRETRAIN)) # load pretrain
# get optimizer
if not config.DAG.TRAIN.START_EPOCH == config.DAG.TRAIN.UNFIX_EPOCH: # 10
optimizer, lr_scheduler = build_opt_lr(config, model,
config.DAG.TRAIN.START_EPOCH)
else:
optimizer, lr_scheduler = build_opt_lr(config, model, 0)
# check trainable again
print('==========double check trainable==========')
trainable_params = check_trainable(model, logger)
if config.DAG.TRAIN.RESUME and config.DAG.TRAIN.START_EPOCH != 0: # resume
model, optimizer, args.start_epoch, arch = restore_from(
model, optimizer, config.DAG.TRAIN.RESUME)
# parallel
gpus = [int(i) for i in config.GPUS.split(',')]
gpu_num = len(gpus)
logger.info('GPU NUM: {:2d}'.format(len(gpus)))
device = torch.device(
'cuda:{}'.format(gpus[0]) if torch.cuda.is_available() else 'cpu')
model = torch.nn.DataParallel(model, device_ids=gpus).to(device)
logger.info(lr_scheduler)
logger.info('model prepare done')
# [*] train
for epoch in range(config.DAG.TRAIN.START_EPOCH,
config.DAG.TRAIN.END_EPOCH):
# build dataloader, benefit to tracking
train_set = DAGDataset(config)
train_loader = DataLoader(train_set,
batch_size=config.DAG.TRAIN.BATCH * gpu_num,
num_workers=config.WORKERS,
pin_memory=True,
sampler=None,
drop_last=True)
# check if it's time to train backbone
if epoch == config.DAG.TRAIN.UNFIX_EPOCH:
logger.info('training backbone')
optimizer, lr_scheduler = build_opt_lr(config, model.module, epoch)
print('==========double check trainable==========')
check_trainable(model, logger) # print trainable params info
lr_scheduler.step(epoch)
curLR = lr_scheduler.get_cur_lr()
model, writer_dict = DAG_train(train_loader,
model,
optimizer,
epoch + 1,
curLR,
config,
writer_dict,
logger,
device=device)
# save model
save_model(model,
epoch,
optimizer,
config.DAG.TRAIN.MODEL,
config,
isbest=False)
writer_dict['writer'].close()
def main():
# [*] args, loggers and tensorboard
args = parse_args()
reset_config(config, args)
logger, _, tb_log_dir = create_logger(config, 'SIAMRPN', 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
writer_dict = {
'writer': SummaryWriter(logdir=tb_log_dir),
'train_global_steps': 0,
}
# [*] gpus parallel and model prepare
# prepare pretrained model -- download from google drive
if not os.path.exists('./pretrain'):
os.makedirs('./pretrain')
# auto-download train model from GoogleDrive
try:
DRIVEID = pretrain_zoo()
if not os.path.exists('./pretrain/{}'.format(config.SIAMRPN.TRAIN.PRETRAIN)):
os.system(
'wget --no-check-certificate \'https://drive.google.com/uc?export=download&id={0}\' -O ./pretrain/{1}'
.format(DRIVEID[config.SIAMRPN.TRAIN.MODEL], config.SIAMRPN.TRAIN.PRETRAIN))
except:
print('auto-download pretrained model fail, please download it and put it in pretrain directory')
# define model
anchor_nums = len(config.SIAMRPN.TRAIN.ANCHORS_RATIOS) * len(config.SIAMRPN.TRAIN.ANCHORS_SCALES)
model = models.__dict__[config.SIAMRPN.TRAIN.MODEL](anchors_nums=anchor_nums, cls_type=config.SIAMRPN.TRAIN.CLS_TYPE) # build model
print(model)
model = load_pretrain(model, './pretrain/{0}'.format(config.SIAMRPN.TRAIN.PRETRAIN)) # load pretrain
trainable_params = check_trainable(model, logger) # print trainable params info
optimizer = get_optimizer(config, trainable_params) # optimizer
lr_scheduler = lr_decay(config, optimizer) # learning rate decay scheduler
if config.SIAMRPN.TRAIN.RESUME : # resume
logger.info("resume from {}".format(config.SIAMRPN.TRAIN.RESUME))
assert os.path.isfile(config.SIAMRPN.TRAIN.RESUME), \
'{} is not a valid file.'.format(config.SIAMRPN.TRAIN.RESUME)
model, optimizer, config.SIAMRPN.TRAIN.START_EPOCH = \
restore_from(model, optimizer, config.SIAMRPN.TRAIN.RESUME)
model.features.unfix((config.SIAMRPN.TRAIN.START_EPOCH - 1) / config.SIAMRPN.TRAIN.END_EPOCH)
# parallel
gpus = [int(i) for i in config.GPUS.split(',')]
gpu_num = len(gpus)
logger.info('GPU NUM: {:2d}'.format(len(gpus)))
#model=model.cuda()
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
logger.info('model prepare done')
# [*] train
for epoch in range(config.SIAMRPN.TRAIN.START_EPOCH, config.SIAMRPN.TRAIN.END_EPOCH):
# build dataloader, benefit to tracking
train_set = SiamRPNDataset(config)
model.module.anchors = train_set.anchorsPairs
model.module.cfg=config
train_loader = DataLoader(train_set, batch_size=config.SIAMRPN.TRAIN.BATCH * gpu_num, num_workers=config.WORKERS,
pin_memory=True, sampler=None)
if config.SIAMRPN.TRAIN.LR_POLICY == 'log':
curLR = lr_scheduler[epoch]
for param_group in optimizer.param_groups:
param_group['lr'] = curLR
else:
lr_scheduler.step()
curLR = get_lr(optimizer)
model, writer_dict = siamrpn_train(train_loader, model, optimizer, epoch + 1, curLR, config, writer_dict,
logger, cls_type = config.SIAMRPN.TRAIN.CLS_TYPE)
# save model
save_model(model, epoch, optimizer, config.SIAMRPN.TRAIN.MODEL, config, isbest=False)
writer_dict['writer'].close()
def main():
# [*] args, loggers and tensorboard
args = parse_args()
reset_config(config, args)
logger, _, tb_log_dir = create_logger(config, 'SIAMFC', 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
}
# [*] gpus parallel and model prepare
# prepare
model = models.__dict__[config.SIAMFC.TRAIN.MODEL]() # build model
model = load_pretrain(model, config.SIAMFC.TRAIN.PRETRAIN) # load pretrain
trainable_params = check_trainable(model,
logger) # print trainable params info
optimizer = get_optimizer(config, trainable_params) # optimizer
lr_scheduler = lr_decay(config, optimizer) # learning rate decay scheduler
if config.SIAMFC.TRAIN.RESUME and config.SIAMFC.TRAIN.START_EPOCH != 0: # resume
model.features.unfix((config.SIAMFC.TRAIN.START_EPOCH - 1) /
config.SIAMFC.TRAIN.END_EPOCH)
model, optimizer, args.start_epoch, arch = restore_from(
model, optimizer, config.SIAMFC.TRAIN.RESUME)
# parallel
gpus = [int(i) for i in config.GPUS.split(',')]
gpu_num = len(gpus)
logger.info('GPU NUM: {:2d}'.format(len(gpus)))
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
logger.info('model prepare done')
# [*] train
for epoch in range(config.SIAMFC.TRAIN.START_EPOCH,
config.SIAMFC.TRAIN.END_EPOCH):
# build dataloader, benefit to tracking
train_set = SiamFCDataset(config)
train_loader = DataLoader(train_set,
batch_size=config.SIAMFC.TRAIN.BATCH *
gpu_num,
num_workers=config.WORKERS,
pin_memory=True,
sampler=None)
if config.SIAMFC.TRAIN.LR_POLICY == 'log':
curLR = lr_scheduler[epoch]
for param_group in optimizer.param_groups:
param_group['lr'] = curLR
else:
lr_scheduler.step()
model, writer_dict = siamfc_train(train_loader, model, optimizer,
epoch + 1, curLR, config,
writer_dict, logger)
# save model
save_model(model,
epoch,
optimizer,
config.SIAMFC.TRAIN.MODEL,
config,
isbest=False)
writer_dict['writer'].close()
