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rename csv path var name to match train function #16

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rename csv path var name to match train function #16

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10 changes: 5 additions & 5 deletions timm/data/loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,7 +229,7 @@ def create_loader(
worker_seeding: str = 'all',
tf_preprocessing: bool = False,
balance_classes: bool = False,
dataset_csv_path: Optional[str] = None
samples_csv_path: Optional[str] = None
):
"""

Expand Down Expand Up @@ -274,7 +274,7 @@ def create_loader(
worker_seeding: Control worker random seeding at init.
tf_preprocessing: Use TF 1.0 inference preprocessing for testing model ports.
balance_classes: Sample classes with uniform probability
dataset_csv_path: Path to dataset csv, used for class balancing
samples_csv_path: Path to dataset csv, used for class balancing

Returns:
DataLoader
Expand Down Expand Up @@ -333,9 +333,9 @@ def create_loader(
else:
assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"
if balance_classes:
assert dataset_csv_path, "Provide csv with labels to use balance_classes."
dataset_csv = pd.read_csv(dataset_csv_path)
all_labels = dataset_csv["label"].values
assert samples_csv_path, "Provide csv with labels to use balance_classes."
samples_csv = pd.read_csv(samples_csv_path)
all_labels = samples_csv["label"].values
unique, counts = np.unique(all_labels, return_counts=True)
unique_counts = {v: c for v, c in zip(unique, counts)}
label_weights = np.array([1 / unique_counts[num] for num in all_labels])
Expand Down