Further enhance Classification Reference (#4444)

* Adding ExponentialLR and LinearLR

* Fix arg type of --lr-warmup-decay

* Adding support of Zero gamma BN and SGD with nesterov.

* Fix --lr-warmup-decay for video_classification.

* Update bn_reinit

* Fix pre-existing bug on num_classes of model

* Remove zero gamma.

* Use fstrings.

references/classification/train.py

@@ -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"):
         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)
@@ -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:
@@ -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')

references/segmentation/train.py

@@ -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')

references/video_classification/train.py

@@ -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')