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Fix: ZeroDivisionError in SwinUNETR Auto3DSeg training when validation fold contains classes with no samples. #427

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Fix: ZeroDivisionError in SwinUNETR Auto3DSeg training when validation fold contains classes with no samples. #427

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a3deff0
fix: prevent ZeroDivisionError in SwinUNETR training when validation …
daxellwells May 3, 2026
9fa7e04
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 3, 2026
b344c3c
fix: guard avg_metric division against zero count when all classes a…
daxellwells May 3, 2026
a5b53c1
Merge branch 'main' of https://github.com/daxellwells/research-contri…
daxellwells May 3, 2026
d4a7af5
fix: skip NaN metrics in MLflow/TensorBoard logging and promote missi…
daxellwells May 3, 2026
812f557
fix: skip early stopping and progress.yaml write when avg_metric is NaN
daxellwells May 3, 2026
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67 changes: 37 additions & 30 deletions auto3dseg/algorithm_templates/swinunetr/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -640,28 +640,30 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
metric = metric.tolist()
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
for _c in range(metric_dim):
logger.debug(f"Evaluation metric - class {_c + 1}: {metric[2 * _c] / metric[2 * _c + 1]}")
try:
writer.add_scalar(
f"val_class/acc_{class_names[_c]}", metric[2 * _c] / metric[2 * _c + 1], epoch
)
mlflow.log_metric(
f"val_class/acc_{class_names[_c]}", metric[2 * _c] / metric[2 * _c + 1], step=epoch
)
except BaseException:
writer.add_scalar(f"val_class/acc_{_c}", metric[2 * _c] / metric[2 * _c + 1], epoch)
mlflow.log_metric(
f"val_class/acc_{_c}", metric[2 * _c] / metric[2 * _c + 1], step=epoch
)
class_metric = metric[2 * _c] / metric[2 * _c + 1] if metric[2 * _c + 1] != 0 else float('nan')
if metric[2 * _c +1] == 0:
logger.warning(f"Class {_c + 1} has no samples in validation fold; logging as NaN.")
logger.debug(f"Evaluation metric - class {_c + 1}: {class_metric}")
if not math.isnan(class_metric):
try:
writer.add_scalar(f"val_class/acc_{class_names[_c]}", class_metric, epoch)
mlflow.log_metric(f"val_class/acc_{class_names[_c]}", class_metric, step=epoch)
except BaseException:
writer.add_scalar(f"val_class/acc_{_c}", class_metric, epoch)
mlflow.log_metric(f"val_class/acc_{_c}", class_metric, step=epoch)

avg_metric = 0
count = 0
for _c in range(metric_dim):
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
avg_metric = avg_metric / float(metric_dim)
if metric[2 * _c + 1] != 0:
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
count +=1
avg_metric = avg_metric / float(count) if count > 0 else float('nan')
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logger.debug(f"Avg_metric: {avg_metric}")

writer.add_scalar("val/acc", avg_metric, epoch)
mlflow.log_metric("val/acc", avg_metric, step=epoch)
if not math.isnan(avg_metric):
writer.add_scalar("val/acc", avg_metric, epoch)
mlflow.log_metric("val/acc", avg_metric, step=epoch)

if avg_metric > best_metric:
best_metric = avg_metric
Expand Down Expand Up @@ -694,7 +696,7 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
)
)

if es:
if es and not math.isnan(avg_metric):
early_stopping(val_acc=avg_metric)
stop_train = torch.tensor(early_stopping.early_stop).to(device)

Expand Down Expand Up @@ -800,26 +802,31 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
metric = metric.tolist()
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
for _c in range(metric_dim):
logger.debug(
f"Evaluation metric at original resolution - class {_c + 1}: {metric[2 * _c] / metric[2 * _c + 1]}"
)
class_metric = metric[2 * _c] / metric[2 * _c + 1] if metric[2 * _c + 1] != 0 else float('nan')
if metric[2 * _c + 1] == 0:
logger.warning(f"Class {_c + 1} has no samples in validation fold; logging as NaN.")
logger.debug(f"Evaluation metric at original resolution - class {_c + 1}: {class_metric}")

avg_metric = 0
count = 0
for _c in range(metric_dim):
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
avg_metric = avg_metric / float(metric_dim)
if metric[2 * _c + 1] != 0:
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
count += 1
avg_metric = avg_metric / float(count) if count > 0 else float('nan')
logger.debug(f"Avg_metric at original resolution: {avg_metric}")

with open(os.path.join(ckpt_path, "progress.yaml"), "r") as out_file:
progress = yaml.safe_load(out_file)

dict_file = {}
dict_file["best_avg_dice_score"] = float(avg_metric)
dict_file["best_avg_dice_score_epoch"] = int(progress[-1]["best_avg_dice_score_epoch"])
dict_file["best_avg_dice_score_iteration"] = int(progress[-1]["best_avg_dice_score_iteration"])
dict_file["inverted_best_validation"] = True
with open(os.path.join(ckpt_path, "progress.yaml"), "a") as out_file:
yaml.dump([dict_file], stream=out_file)
if not math.isnan(avg_metric):
dict_file = {}
dict_file["best_avg_dice_score"] = float(avg_metric)
dict_file["best_avg_dice_score_epoch"] = int(progress[-1]["best_avg_dice_score_epoch"])
dict_file["best_avg_dice_score_iteration"] = int(progress[-1]["best_avg_dice_score_iteration"])
dict_file["inverted_best_validation"] = True
with open(os.path.join(ckpt_path, "progress.yaml"), "a") as out_file:
yaml.dump([dict_file], stream=out_file)

if torch.cuda.device_count() > 1:
dist.barrier()
Expand Down
3 changes: 2 additions & 1 deletion auto3dseg/configs/metadata.json
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
{
"version": "0.0.8",
"version": "0.0.9",
"changelog": {
"0.0.9": "Fix ZeroDivisionError in swinunetr training script for missing classes in validation fold.",
"0.0.8": "Update swin unetr pretrained weights link",
"0.0.7": "Add support for MLFlow experiment name.",
"0.0.6": "Move metadata.json under 'configs' to be consistent with bundles.",
Expand Down
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