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Change reduce_bn to distribute_bn, add ability to choose between broadcast and reduce (mean). Add crop_pct arg to allow selecting validation crop while training.

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Ross Wightman 2019-12-19 22:56:54 -08:00
parent 3bff2b21dc
commit a435ea1327
2 changed files with 19 additions and 11 deletions
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11
timm/utils.py
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@ -210,12 +210,17 @@ def reduce_tensor(tensor, n):
return rt return rt
def reduce_bn(model, world_size): def distribute_bn(model, world_size, reduce=False):
# ensure every node has the same running bn stats # ensure every node has the same running bn stats
for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True): 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 ('running_mean' in bn_name) or ('running_var' in bn_name):
torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM) if reduce:
bn_buf /= float(world_size) # 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: class ModelEma:

19
train.py
View File

@ -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")') help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")')
parser.add_argument('--img-size', type=int, default=None, metavar='N', parser.add_argument('--img-size', type=int, default=None, metavar='N',
help='Image patch size (default: None => model default)') 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', parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override mean pixel value of dataset') help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', 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)') help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None, parser.add_argument('--bn-eps', type=float, default=None,
help='BatchNorm epsilon override (if not 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 # Model Exponential Moving Average
parser.add_argument('--model-ema', action='store_true', default=False, parser.add_argument('--model-ema', action='store_true', default=False,
help='Enable tracking moving average of model weights') help='Enable tracking moving average of model weights')
@ -143,10 +149,6 @@ parser.add_argument('--save-images', action='store_true', default=False,
help='save images of input bathes every log interval for debugging') help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False, parser.add_argument('--amp', action='store_true', default=False,
help='use NVIDIA amp for mixed precision training') help='use NVIDIA amp for mixed precision training')
parser.add_argument('--sync-bn', action='store_true',
help='enabling apex sync BN.')
parser.add_argument('--reduce-bn', action='store_true',
help='average BN running stats across all distributed nodes between train and validation.')
parser.add_argument('--no-prefetcher', action='store_true', default=False, parser.add_argument('--no-prefetcher', action='store_true', default=False,
help='disable fast prefetcher') help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH', parser.add_argument('--output', default='', type=str, metavar='PATH',
@ -349,6 +351,7 @@ def main():
std=data_config['std'], std=data_config['std'],
num_workers=args.workers, num_workers=args.workers,
distributed=args.distributed, distributed=args.distributed,
crop_pct=data_config['crop_pct'],
) )
if args.mixup > 0.: if args.mixup > 0.:
@ -390,16 +393,16 @@ def main():
lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir, lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
use_amp=use_amp, model_ema=model_ema) use_amp=use_amp, model_ema=model_ema)
if args.distributed and args.reduce_bn: if args.distributed and args.dist_bn and args.dist_bn in ('broadcast', 'reduce'):
if args.local_rank == 0: if args.local_rank == 0:
logging.info("Averaging bn running means and vars") logging.info("Distributing BatchNorm running means and vars")
reduce_bn(model, args.world_size) distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
eval_metrics = validate(model, loader_eval, validate_loss_fn, args) eval_metrics = validate(model, loader_eval, validate_loss_fn, args)
if model_ema is not None and not args.model_ema_force_cpu: if model_ema is not None and not args.model_ema_force_cpu:
if args.distributed and args.reduce_bn: if args.distributed and args.reduce_bn:
reduce_bn(model_ema, args.world_size) distribute_bn(model_ema, args.world_size)
ema_eval_metrics = validate( ema_eval_metrics = validate(
model_ema.ema, loader_eval, validate_loss_fn, args, log_suffix=' (EMA)') model_ema.ema, loader_eval, validate_loss_fn, args, log_suffix=' (EMA)')