huggingface/pytorch-image-models · error · ValueError

batch_sizes must contain at least one value.

Error message

batch_sizes must contain at least one value.

What it means

batch_sizes (the list of batch sizes the scheduler alternates/schedules among) was empty, leaving the sampler with no valid batch size to emit; caught during constructor validation.

Source

Thrown at timm/data/scheduled_sampler.py:65

            self,
            sampler: Sampler,
            batch_sizes: Sequence[int],
            choice_weights: Optional[Sequence[float]] = None,
            seed: int = 0,
            drop_last: bool = True,
            shuffle_schedule: bool = True,
            num_batches: Optional[int] = None,
            choice_schedule: str = 'constant',
            schedule_epochs: Optional[int] = None,
            schedule_spread: float = 0.65,
            schedule_random_mix: float = 0.1,
    ) -> None:
        if not hasattr(sampler, '__len__'):
            raise TypeError('ScheduledBatchSampler requires a sampler with a length.')
        if len(sampler) <= 0:
            raise ValueError('ScheduledBatchSampler requires a non-empty sampler.')
        if not batch_sizes:
            raise ValueError('batch_sizes must contain at least one value.')
        if any(int(batch_size) != batch_size or batch_size <= 0 for batch_size in batch_sizes):
            raise ValueError('All scheduled batch sizes must be positive integers.')
        if num_batches is not None and (int(num_batches) != num_batches or num_batches <= 0):
            raise ValueError('num_batches must be a positive integer when specified.')
        if choice_schedule not in ('constant', 'progressive'):
            raise ValueError("choice_schedule must be 'constant' or 'progressive'.")
        if choice_schedule == 'progressive':
            if len(batch_sizes) < 2:
                raise ValueError('A progressive schedule requires at least two choices.')
            if schedule_epochs is None or int(schedule_epochs) != schedule_epochs or schedule_epochs <= 0:
                raise ValueError('schedule_epochs must be a positive integer for a progressive schedule.')
            if schedule_spread < 0:
                raise ValueError('schedule_spread must be non-negative.')
            if not 0 <= schedule_random_mix <= 1:
                raise ValueError('schedule_random_mix must be between 0 and 1.')

        self.sampler = sampler
        self.batch_sizes = tuple(int(batch_size) for batch_size in batch_sizes)

View on GitHub (pinned to 9a5261e31b)

Solutions

1. Provide at least one positive batch size, e.g. batch_sizes=[256].
2. Fix the config loader to default to a sensible single-size list like [batch_size].

Example fix

# before
ScheduledBatchSampler(sampler, batch_sizes=[])

# after
ScheduledBatchSampler(sampler, batch_sizes=[256])

batch_sizes = batch_sizes or [config['batch_size']]
assert len(batch_sizes) >= 1

When it happens

Trigger: Calling ScheduledBatchSampler(sampler, batch_sizes=[]) or batch_sizes=None after a config layer turned a missing value into an empty list.

Common situations: YAML/JSON config where the batch_sizes key is empty or omitted and defaults to []; programmatically generated lists that end up empty for small models.

Related errors

• All scheduled batch sizes must be positive integers.
• No full scheduled batch fits the sampler; reduce the batch s
• ScheduledBatchSampler requires a sampler with a length.
• ScheduledBatchSampler requires a non-empty sampler.
• num_batches must be a positive integer when specified.

AI-assisted analysis of huggingface/pytorch-image-models@9a5261e31b (2026-08-27). Data as JSON: /api/errors/6c27ea4c75a96c31. Report an issue: GitHub.