Update training script and loader factory to allow use of scheduler updates, repeat augment, and bce loss

timm/data/loader.py

@@ -11,7 +11,7 @@ import numpy as np
 
 from .transforms_factory import create_transform
 from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .distributed_sampler import OrderedDistributedSampler
+from .distributed_sampler import OrderedDistributedSampler, RepeatAugSampler
 from .random_erasing import RandomErasing
 from .mixup import FastCollateMixup
 
@@ -142,6 +142,7 @@ def create_loader(
         vflip=0.,
         color_jitter=0.4,
         auto_augment=None,
+        num_aug_repeats=0,
         num_aug_splits=0,
         interpolation='bilinear',
         mean=IMAGENET_DEFAULT_MEAN,
@@ -186,11 +187,16 @@ def create_loader(
     sampler = None
     if distributed and not isinstance(dataset, torch.utils.data.IterableDataset):
         if is_training:
-            sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+            if num_aug_repeats:
+                sampler = RepeatAugSampler(dataset, num_repeats=num_aug_repeats)
+            else:
+                sampler = torch.utils.data.distributed.DistributedSampler(dataset)
         else:
             # This will add extra duplicate entries to result in equal num
             # of samples per-process, will slightly alter validation results
             sampler = OrderedDistributedSampler(dataset)
+    else:
+        assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"
 
     if collate_fn is None:
         collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate

timm/loss/__init__.py

@@ -1,3 +1,4 @@
+from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
+from .binary_cross_entropy import DenseBinaryCrossEntropy
 from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
 from .jsd import JsdCrossEntropy
-from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel

train.py

@@ -32,7 +32,7 @@ from timm.data import create_dataset, create_loader, resolve_data_config, Mixup,
 from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint,\
     convert_splitbn_model, model_parameters
 from timm.utils import *
-from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
+from timm.loss import *
 from timm.optim import create_optimizer_v2, optimizer_kwargs
 from timm.scheduler import create_scheduler
 from timm.utils import ApexScaler, NativeScaler
@@ -140,8 +140,12 @@ parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                     help='learning rate noise std-dev (default: 1.0)')
 parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                     help='learning rate cycle len multiplier (default: 1.0)')
+parser.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
+                    help='amount to decay each learning rate cycle (default: 0.5)')
 parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
-                    help='learning rate cycle limit')
+                    help='learning rate cycle limit, cycles enabled if > 1')
+parser.add_argument('--lr-k-decay', type=float, default=1.0,
+                    help='learning rate k-decay for cosine/poly (default: 1.0)')
 parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                     help='warmup learning rate (default: 0.0001)')
 parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
@@ -178,10 +182,14 @@ parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                     help='Color jitter factor (default: 0.4)')
 parser.add_argument('--aa', type=str, default=None, metavar='NAME',
                     help='Use AutoAugment policy. "v0" or "original". (default: None)'),
+parser.add_argument('--aug-repeat', type=int, default=0,
+                    help='Number of augmentation repetitions (distributed training only) (default: 0)')
 parser.add_argument('--aug-splits', type=int, default=0,
                     help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
-parser.add_argument('--jsd', action='store_true', default=False,
+parser.add_argument('--jsd-loss', action='store_true', default=False,
                     help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
+parser.add_argument('--bce-loss', action='store_true', default=False,
+                    help='Enable BCE loss w/ Mixup/CutMix use.')
 parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                     help='Random erase prob (default: 0.)')
 parser.add_argument('--remode', type=str, default='const',
@@ -516,6 +524,7 @@ def main():
         vflip=args.vflip,
         color_jitter=args.color_jitter,
         auto_augment=args.aa,
+        num_aug_repeats=args.aug_repeats,
         num_aug_splits=num_aug_splits,
         interpolation=train_interpolation,
         mean=data_config['mean'],
@@ -543,16 +552,23 @@ def main():
     )
 
     # setup loss function
-    if args.jsd:
+    if args.jsd_loss:
         assert num_aug_splits > 1  # JSD only valid with aug splits set
-        train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
+        train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing)
     elif mixup_active:
-        # smoothing is handled with mixup target transform
+        # smoothing is handled with mixup target transform which outputs sparse, soft targets
-        train_loss_fn = SoftTargetCrossEntropy().cuda()
+        if args.bce_loss:
+            train_loss_fn = nn.BCEWithLogitsLoss()
+        else:
+            train_loss_fn = SoftTargetCrossEntropy()
     elif args.smoothing:
-        train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
+        if args.bce_loss:
+            train_loss_fn = DenseBinaryCrossEntropy(smoothing=args.smoothing)
+        else:
+            train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
     else:
-        train_loss_fn = nn.CrossEntropyLoss().cuda()
+        train_loss_fn = nn.CrossEntropyLoss()
+    train_loss_fn = train_loss_fn.cuda()
     validate_loss_fn = nn.CrossEntropyLoss().cuda()
 
     # setup checkpoint saver and eval metric tracking
@@ -692,7 +708,7 @@ def train_one_epoch(
             if args.local_rank == 0:
                 _logger.info(
                     'Train: {} [{:>4d}/{} ({:>3.0f}%)]  '
-                    'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  '
+                    'Loss: {loss.val:#.4g} ({loss.avg:#.3g})  '
                     'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  '
                     '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                     'LR: {lr:.3e}  '