use utils namespace instead of function/classnames

This fixes buggy behavior introduced by
#1266.

Related to #1273.

Author: kaczmarj

train.py

@@ -31,9 +31,7 @@
 from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
 from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint,\
     convert_splitbn_model, model_parameters
-from timm.utils import setup_default_logging, random_seed, set_jit_fuser, ModelEmaV2,\
-    get_outdir, CheckpointSaver, distribute_bn, update_summary, accuracy, AverageMeter,\
-    dispatch_clip_grad, reduce_tensor
+from timm import utils
 from timm.loss import JsdCrossEntropy, BinaryCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy,\
     LabelSmoothingCrossEntropy
 from timm.optim import create_optimizer_v2, optimizer_kwargs
@@ -346,7 +344,7 @@ def _parse_args():
 
 
 def main():
-    setup_default_logging()
+    utils.setup_default_logging()
     args, args_text = _parse_args()
 
     if args.log_wandb:
@@ -391,10 +389,10 @@ def main():
         _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
                         "Install NVIDA apex or upgrade to PyTorch 1.6")
 
-    random_seed(args.seed, args.rank)
+    utils.random_seed(args.seed, args.rank)
 
     if args.fuser:
-        set_jit_fuser(args.fuser)
+        utils.set_jit_fuser(args.fuser)
 
     model = create_model(
         args.model,
@@ -492,7 +490,7 @@ def main():
     model_ema = None
     if args.model_ema:
         # Important to create EMA model after cuda(), DP wrapper, and AMP but before DDP wrapper
-        model_ema = ModelEmaV2(
+        model_ema = utils.ModelEmaV2(
             model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None)
         if args.resume:
             load_checkpoint(model_ema.module, args.resume, use_ema=True)
@@ -640,9 +638,9 @@ def main():
                 safe_model_name(args.model),
                 str(data_config['input_size'][-1])
             ])
-        output_dir = get_outdir(args.output if args.output else './output/train', exp_name)
+        output_dir = utils.get_outdir(args.output if args.output else './output/train', exp_name)
         decreasing = True if eval_metric == 'loss' else False
-        saver = CheckpointSaver(
+        saver = utils.CheckpointSaver(
             model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,
             checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing, max_history=args.checkpoint_hist)
         with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
@@ -661,13 +659,13 @@ def main():
             if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                 if args.local_rank == 0:
                     _logger.info("Distributing BatchNorm running means and vars")
-                distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
+                utils.distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
 
             eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)
 
             if model_ema is not None and not args.model_ema_force_cpu:
                 if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
-                    distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
+                    utils.distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
                 ema_eval_metrics = validate(
                     model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)')
                 eval_metrics = ema_eval_metrics
@@ -677,7 +675,7 @@ def main():
                 lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
 
             if output_dir is not None:
-                update_summary(
+                utils.update_summary(
                     epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
                     write_header=best_metric is None, log_wandb=args.log_wandb and has_wandb)
 
@@ -704,9 +702,9 @@ def train_one_epoch(
             mixup_fn.mixup_enabled = False
 
     second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
-    batch_time_m = AverageMeter()
-    data_time_m = AverageMeter()
-    losses_m = AverageMeter()
+    batch_time_m = utils.AverageMeter()
+    data_time_m = utils.AverageMeter()
+    losses_m = utils.AverageMeter()
 
     model.train()
 
@@ -740,7 +738,7 @@ def train_one_epoch(
         else:
             loss.backward(create_graph=second_order)
             if args.clip_grad is not None:
-                dispatch_clip_grad(
+                utils.dispatch_clip_grad(
                     model_parameters(model, exclude_head='agc' in args.clip_mode),
                     value=args.clip_grad, mode=args.clip_mode)
             optimizer.step()
@@ -756,7 +754,7 @@ def train_one_epoch(
             lr = sum(lrl) / len(lrl)
 
             if args.distributed:
-                reduced_loss = reduce_tensor(loss.data, args.world_size)
+                reduced_loss = utils.reduce_tensor(loss.data, args.world_size)
                 losses_m.update(reduced_loss.item(), input.size(0))
 
             if args.local_rank == 0:
@@ -801,10 +799,10 @@ def train_one_epoch(
 
 
 def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
-    batch_time_m = AverageMeter()
-    losses_m = AverageMeter()
-    top1_m = AverageMeter()
-    top5_m = AverageMeter()
+    batch_time_m = utils.AverageMeter()
+    losses_m = utils.AverageMeter()
+    top1_m = utils.AverageMeter()
+    top5_m = utils.AverageMeter()
 
     model.eval()
 
@@ -831,12 +829,12 @@ def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix='')
                 target = target[0:target.size(0):reduce_factor]
 
             loss = loss_fn(output, target)
-            acc1, acc5 = accuracy(output, target, topk=(1, 5))
+            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
 
             if args.distributed:
-                reduced_loss = reduce_tensor(loss.data, args.world_size)
-                acc1 = reduce_tensor(acc1, args.world_size)
-                acc5 = reduce_tensor(acc5, args.world_size)
+                reduced_loss = utils.reduce_tensor(loss.data, args.world_size)
+                acc1 = utils.reduce_tensor(acc1, args.world_size)
+                acc5 = utils.reduce_tensor(acc5, args.world_size)
             else:
                 reduced_loss = loss.data