tensorflow/models · error · FileNotFoundError

Root directory does not exist: {root_dir}

Error message

Root directory does not exist: {root_dir}

What it means

Error "Root directory does not exist: {root_dir}" thrown in tensorflow/models.

Source

Thrown at official/projects/waste_identification_ml/data_generation/auto_labeler_pipeline/segmentation.py:159

# ── Dataset discovery and validation ──────────────────────────────────────────


def discover_dataset_directories(root_dir: str) -> list[tuple[str, str]]:
  """Returns the list of dataset subdirectories directly under ``root_dir``.

  Args:
      root_dir: Path to the root directory containing dataset subfolders.

  Returns:
      A sorted list of ``(dataset_name, dataset_path)`` tuples.

  Raises:
      FileNotFoundError: If ``root_dir`` does not exist.
      ValueError: If ``root_dir`` contains no subdirectories.
  """
  if not os.path.isdir(root_dir):
    raise FileNotFoundError(f"Root directory does not exist: {root_dir}")

  dataset_entries = sorted(
      [entry for entry in os.scandir(root_dir) if entry.is_dir()],
      key=lambda entry: entry.name,
  )

  if not dataset_entries:
    raise ValueError(f"No dataset subfolders found under: {root_dir}")

  return [(entry.name, entry.path) for entry in dataset_entries]


def validate_dataset_paths(
    dataset_directories: list[tuple[str, str]], train_val_folder_name: str
) -> list[tuple[str, str]]:
  """Validates that each dataset has the expected input layout.

  Args:

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/waste_identification_ml/data_generation/auto_labeler_pipeline/segmentation.py:159 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/49ce8636f0060046. Report an issue: GitHub.