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Minor updates to optical flow ref for consistency #5654

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Merged
merged 6 commits into from
Apr 20, 2022
Merged

Minor updates to optical flow ref for consistency #5654

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37901e8
Minor updates to optical flow ref for consistency
NicolasHug Mar 21, 2022
84e0a70
Actually put back name
NicolasHug Mar 21, 2022
8e7a637
Merge branch 'main' into optical_flow_minor_updates_consistency
NicolasHug Mar 22, 2022
8bad400
linting
NicolasHug Mar 22, 2022
4ffe27c
Merge branch 'main' into optical_flow_minor_updates_consistency
NicolasHug Apr 19, 2022
30696f4
Merge branch 'main' into optical_flow_minor_updates_consistency
NicolasHug Apr 20, 2022
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24 changes: 11 additions & 13 deletions references/optical_flow/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@ def _evaluate(model, args, val_dataset, *, padder_mode, num_flow_updates=None, b
sampler=sampler,
batch_size=batch_size,
pin_memory=True,
num_workers=args.num_workers,
num_workers=args.workers,
)

num_flow_updates = num_flow_updates or args.num_flow_updates
Expand Down Expand Up @@ -269,17 +269,17 @@ def main(args):
sampler=sampler,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.num_workers,
num_workers=args.workers,
)

logger = utils.MetricLogger()

done = False
for current_epoch in range(args.start_epoch, args.epochs):
print(f"EPOCH {current_epoch}")
for epoch in range(args.start_epoch, args.epochs):
print(f"EPOCH {epoch}")
if args.distributed:
# needed on distributed mode, otherwise the data loading order would be the same for all epochs
sampler.set_epoch(current_epoch)
sampler.set_epoch(epoch)

train_one_epoch(
model=model,
Expand All @@ -291,20 +291,20 @@ def main(args):
)

# Note: we don't sync the SmoothedValues across processes, so the printed metrics are just those of rank 0
print(f"Epoch {current_epoch} done. ", logger)
print(f"Epoch {epoch} done. ", logger)

if not args.distributed or args.rank == 0:
checkpoint = {
"model": model_without_ddp.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"epoch": current_epoch,
"epoch": epoch,
"args": args,
}
torch.save(checkpoint, Path(args.output_dir) / f"{args.name}_{current_epoch}.pth")
torch.save(checkpoint, Path(args.output_dir) / f"{args.name}_{epoch}.pth")
torch.save(checkpoint, Path(args.output_dir) / f"{args.name}.pth")

if current_epoch % args.val_freq == 0 or done:
if epoch % args.val_freq == 0 or done:
evaluate(model, args)
model.train()
if args.freeze_batch_norm:
Expand All @@ -319,16 +319,14 @@ def get_args_parser(add_help=True):
type=str,
help="The name of the experiment - determines the name of the files where weights are saved.",
)
parser.add_argument(
"--output-dir", default="checkpoints", type=str, help="Output dir where checkpoints will be stored."
)
parser.add_argument("--output-dir", default=".", type=str, help="Output dir where checkpoints will be stored.")
parser.add_argument(
"--resume",
type=str,
help="A path to previously saved weights. Used to re-start training from, or evaluate a pre-saved model.",
)

parser.add_argument("--num-workers", type=int, default=12, help="Number of workers for the data loading part.")
parser.add_argument("--workers", type=int, default=12, help="Number of workers for the data loading part.")

parser.add_argument(
"--train-dataset",
Expand Down