Skip to content

Further enhance Classification Reference #4444

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 11 commits into from
Sep 21, 2021
Merged

Further enhance Classification Reference #4444

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
2867310
Adding ExponentialLR and LinearLR
datumbox Sep 18, 2021
a5b2789
Merge branch 'main' into references/classification_improvements
datumbox Sep 18, 2021
ae7d7e9
Fix arg type of --lr-warmup-decay
datumbox Sep 18, 2021
5308098
Adding support of Zero gamma BN and SGD with nesterov.
datumbox Sep 19, 2021
12357aa
Fix --lr-warmup-decay for video_classification.
datumbox Sep 19, 2021
e51ddb0
Update bn_reinit
datumbox Sep 19, 2021
1b2dc95
Fix pre-existing bug on num_classes of model
datumbox Sep 20, 2021
29615b7
Remove zero gamma.
datumbox Sep 20, 2021
cb7905c
Use fstrings.
datumbox Sep 21, 2021
d1b7046
Merge branch 'main' into references/classification_improvements
datumbox Sep 21, 2021
a6ba4bc
Merge branch 'main' into references/classification_improvements
datumbox Sep 21, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 21 additions & 7 deletions references/classification/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -186,17 +186,19 @@ def main(args):
sampler=test_sampler, num_workers=args.workers, pin_memory=True)

print("Creating model")
model = torchvision.models.__dict__[args.model](pretrained=args.pretrained)
model = torchvision.models.__dict__[args.model](pretrained=args.pretrained, num_classes=num_classes)
model.to(device)

if args.distributed and args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

criterion = nn.CrossEntropyLoss(label_smoothing=args.label_smoothing)

opt_name = args.opt.lower()
if opt_name == 'sgd':
if opt_name.startswith("sgd"):
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Why this change? Are there other options starting with sgd?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It's a nasty workaround to support nesterov. Instead of adding another flat args parameter that is useful only when SGD is selected, the user can request for sgd_nesterov.

optimizer = torch.optim.SGD(
model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay,
nesterov="nesterov" in opt_name)
elif opt_name == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay, eps=0.0316, alpha=0.9)
Expand All @@ -214,15 +216,25 @@ def main(args):
elif args.lr_scheduler == 'cosineannealinglr':
main_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.epochs - args.lr_warmup_epochs)
elif args.lr_scheduler == 'exponentiallr':
main_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.lr_gamma)
else:
raise RuntimeError("Invalid lr scheduler '{}'. Only StepLR and CosineAnnealingLR "
raise RuntimeError("Invalid lr scheduler '{}'. Only StepLR, CosineAnnealingLR and ExponentialLR "
"are supported.".format(args.lr_scheduler))

if args.lr_warmup_epochs > 0:
if args.lr_warmup_method == 'linear':
warmup_lr_scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=args.lr_warmup_decay,
total_iters=args.lr_warmup_epochs)
elif args.lr_warmup_method == 'constant':
warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer, factor=args.lr_warmup_decay,
total_iters=args.lr_warmup_epochs)
else:
raise RuntimeError(f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant "
"are supported.")
lr_scheduler = torch.optim.lr_scheduler.SequentialLR(
optimizer,
schedulers=[torch.optim.lr_scheduler.ConstantLR(optimizer, factor=args.lr_warmup_decay,
total_iters=args.lr_warmup_epochs), main_lr_scheduler],
schedulers=[warmup_lr_scheduler, main_lr_scheduler],
milestones=[args.lr_warmup_epochs]
)
else:
Expand Down Expand Up @@ -307,7 +319,9 @@ def get_args_parser(add_help=True):
parser.add_argument('--cutmix-alpha', default=0.0, type=float, help='cutmix alpha (default: 0.0)')
parser.add_argument('--lr-scheduler', default="steplr", help='the lr scheduler (default: steplr)')
parser.add_argument('--lr-warmup-epochs', default=0, type=int, help='the number of epochs to warmup (default: 0)')
parser.add_argument('--lr-warmup-decay', default=0.01, type=int, help='the decay for lr')
parser.add_argument('--lr-warmup-method', default="constant", type=str,
help='the warmup method (default: constant)')
parser.add_argument('--lr-warmup-decay', default=0.01, type=float, help='the decay for lr')
parser.add_argument('--lr-step-size', default=30, type=int, help='decrease lr every step-size epochs')
parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')
parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
Expand Down
2 changes: 1 addition & 1 deletion references/segmentation/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -220,7 +220,7 @@ def get_args_parser(add_help=True):
dest='weight_decay')
parser.add_argument('--lr-warmup-epochs', default=0, type=int, help='the number of epochs to warmup (default: 0)')
parser.add_argument('--lr-warmup-method', default="linear", type=str, help='the warmup method (default: linear)')
parser.add_argument('--lr-warmup-decay', default=0.01, type=int, help='the decay for lr')
parser.add_argument('--lr-warmup-decay', default=0.01, type=float, help='the decay for lr')
parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
parser.add_argument('--output-dir', default='.', help='path where to save')
parser.add_argument('--resume', default='', help='resume from checkpoint')
Expand Down
2 changes: 1 addition & 1 deletion references/video_classification/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -296,7 +296,7 @@ def parse_args():
parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')
parser.add_argument('--lr-warmup-epochs', default=10, type=int, help='the number of epochs to warmup (default: 10)')
parser.add_argument('--lr-warmup-method', default="linear", type=str, help='the warmup method (default: linear)')
parser.add_argument('--lr-warmup-decay', default=0.001, type=int, help='the decay for lr')
parser.add_argument('--lr-warmup-decay', default=0.001, type=float, help='the decay for lr')
parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
parser.add_argument('--output-dir', default='.', help='path where to save')
parser.add_argument('--resume', default='', help='resume from checkpoint')
Expand Down