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add last epoch argument #64

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add last epoch argument #64

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15 changes: 8 additions & 7 deletions train_ssd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,8 @@
parser.add_argument('--pretrained_ssd', help='Pre-trained base model')
parser.add_argument('--resume', default=None, type=str,
help='Checkpoint state_dict file to resume training from')
parser.add_argument('--last-epoch', default=-1, type=int, help='Epoch to start from, when resuming the model.')


# Scheduler
parser.add_argument('--scheduler', default="multi-step", type=str,
Expand Down Expand Up @@ -240,8 +242,7 @@ def test(loader, net, criterion, device):
logging.info("Build network.")
net = create_net(num_classes)
min_loss = -10000.0
last_epoch = -1


base_net_lr = args.base_net_lr if args.base_net_lr is not None else args.lr
extra_layers_lr = args.extra_layers_lr if args.extra_layers_lr is not None else args.lr
if args.freeze_base_net:
Expand Down Expand Up @@ -302,18 +303,18 @@ def test(loader, net, criterion, device):
if args.scheduler == 'multi-step':
logging.info("Uses MultiStepLR scheduler.")
milestones = [int(v.strip()) for v in args.milestones.split(",")]
scheduler = MultiStepLR(optimizer, milestones=milestones,
gamma=0.1, last_epoch=last_epoch)
scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.1)

elif args.scheduler == 'cosine':
logging.info("Uses CosineAnnealingLR scheduler.")
scheduler = CosineAnnealingLR(optimizer, args.t_max, last_epoch=last_epoch)
scheduler = CosineAnnealingLR(optimizer, args.t_max)
else:
logging.fatal(f"Unsupported Scheduler: {args.scheduler}.")
parser.print_help(sys.stderr)
sys.exit(1)

logging.info(f"Start training from epoch {last_epoch + 1}.")
for epoch in range(last_epoch + 1, args.num_epochs):
logging.info(f"Start training from epoch {args.last_epoch + 1}.")
for epoch in range(args.last_epoch + 1, args.num_epochs):
scheduler.step()
train(train_loader, net, criterion, optimizer,
device=DEVICE, debug_steps=args.debug_steps, epoch=epoch)
Expand Down