Merge pull request #62 from rwightman/reduce-bn

Distribute BatchNorm stats

timm/data/random_erasing.py

@@ -7,12 +7,10 @@ def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='
     # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
     # paths, flip the order so normal is run on CPU if this becomes a problem
     # Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
-    # will revert back to doing normal_() on GPU when it's in next release
     if per_pixel:
-        return torch.empty(
-            patch_size, dtype=dtype).normal_().to(device=device)
+        return torch.empty(patch_size, dtype=dtype, device=device).normal_()
     elif rand_color:
-        return torch.empty((patch_size[0], 1, 1), dtype=dtype).normal_().to(device=device)
+        return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
     else:
         return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
 

timm/utils.py

@@ -21,11 +21,15 @@ except ImportError:
 from torch import distributed as dist
 
 
-def get_state_dict(model):
+def unwrap_model(model):
     if isinstance(model, ModelEma):
-        return get_state_dict(model.ema)
+        return unwrap_model(model.ema)
     else:
-        return model.module.state_dict() if hasattr(model, 'module') else model.state_dict()
+        return model.module if hasattr(model, 'module') else model
+
+
+def get_state_dict(model):
+    return unwrap_model(model).state_dict()
 
 
 class CheckpointSaver:
@@ -206,6 +210,19 @@ def reduce_tensor(tensor, n):
     return rt
 
 
+def distribute_bn(model, world_size, reduce=False):
+    # ensure every node has the same running bn stats
+    for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
+        if ('running_mean' in bn_name) or ('running_var' in bn_name):
+            if reduce:
+                # average bn stats across whole group
+                torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
+                bn_buf /= float(world_size)
+            else:
+                # broadcast bn stats from rank 0 to whole group
+                torch.distributed.broadcast(bn_buf, 0)
+
+
 class ModelEma:
     """ Model Exponential Moving Average
     Keep a moving average of everything in the model state_dict (parameters and buffers).

train.py

@@ -55,6 +55,8 @@ parser.add_argument('--gp', default='avg', type=str, metavar='POOL',
                     help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")')
 parser.add_argument('--img-size', type=int, default=None, metavar='N',
                     help='Image patch size (default: None => model default)')
+parser.add_argument('--crop-pct', default=None, type=float,
+                    metavar='N', help='Input image center crop percent (for validation only)')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                     help='Override mean pixel value of dataset')
 parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
@@ -121,6 +123,10 @@ parser.add_argument('--bn-momentum', type=float, default=None,
                     help='BatchNorm momentum override (if not None)')
 parser.add_argument('--bn-eps', type=float, default=None,
                     help='BatchNorm epsilon override (if not None)')
+parser.add_argument('--sync-bn', action='store_true',
+                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
+parser.add_argument('--dist-bn', type=str, default='',
+                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
 # Model Exponential Moving Average
 parser.add_argument('--model-ema', action='store_true', default=False,
                     help='Enable tracking moving average of model weights')
@@ -143,8 +149,6 @@ parser.add_argument('--save-images', action='store_true', default=False,
                     help='save images of input bathes every log interval for debugging')
 parser.add_argument('--amp', action='store_true', default=False,
                     help='use NVIDIA amp for mixed precision training')
-parser.add_argument('--sync-bn', action='store_true',
-                    help='enabling apex sync BN.')
 parser.add_argument('--no-prefetcher', action='store_true', default=False,
                     help='disable fast prefetcher')
 parser.add_argument('--output', default='', type=str, metavar='PATH',
@@ -256,7 +260,7 @@ def main():
             if args.local_rank == 0:
                 logging.info('Restoring NVIDIA AMP state from checkpoint')
             amp.load_state_dict(resume_state['amp'])
-    resume_state = None  # clear it
+    del resume_state
 
     model_ema = None
     if args.model_ema:
@@ -347,6 +351,7 @@ def main():
         std=data_config['std'],
         num_workers=args.workers,
         distributed=args.distributed,
+        crop_pct=data_config['crop_pct'],
     )
 
     if args.mixup > 0.:
@@ -388,9 +393,17 @@ def main():
                 lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                 use_amp=use_amp, model_ema=model_ema)
 
+            if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
+                if args.local_rank == 0:
+                    logging.info("Distributing BatchNorm running means and vars")
+                distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
+
             eval_metrics = validate(model, loader_eval, validate_loss_fn, args)
 
             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')
+
                 ema_eval_metrics = validate(
                     model_ema.ema, loader_eval, validate_loss_fn, args, log_suffix=' (EMA)')
                 eval_metrics = ema_eval_metrics