def train(cfg, args, model, device, distributed):
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = comm.get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
if cfg.MODEL.BACKBONE.CONV_BODY is "DLA-34-DCN":
ckpt = cfg.MODEL.WEIGHT if args.ckpt is None else args.ckpt
extra_checkpoint_data = checkpointer.load(ckpt)
arguments.update(extra_checkpoint_data)
elif args.ckpt is not None:
extra_checkpoint_data = checkpointer.load(args.ckpt)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(cfg, distributed, model, data_loader, optimizer, scheduler,
checkpointer, device, checkpoint_period, arguments)
def train(cfg, model, device, distributed):
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = comm.get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
cfg,
distributed,
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments
)