WZMIAOMIAO/deep-learning-for-image-processing · error · ValueError

sampler should be an instance of torch.utils.data.Sampler, b

Error message

sampler should be an instance of torch.utils.data.Sampler, but got sampler={}

What it means

GroupedBatchSampler's __init__ requires sampler to be an instance of torch.utils.data.Sampler because it iterates the sampler to build batches per group. Passing a list, range, or DataLoader instead of a Sampler raises ValueError with the repr of the object.

Source

Thrown at pytorch_object_detection/mask_rcnn/train_utils/group_by_aspect_ratio.py:39


class GroupedBatchSampler(BatchSampler):
    """
    Wraps another sampler to yield a mini-batch of indices.
    It enforces that the batch only contain elements from the same group.
    It also tries to provide mini-batches which follows an ordering which is
    as close as possible to the ordering from the original sampler.
    Arguments:
        sampler (Sampler): Base sampler.
        group_ids (list[int]): If the sampler produces indices in range [0, N),
            `group_ids` must be a list of `N` ints which contains the group id of each sample.
            The group ids must be a continuous set of integers starting from
            0, i.e. they must be in the range [0, num_groups).
        batch_size (int): Size of mini-batch.
    """
    def __init__(self, sampler, group_ids, batch_size):
        if not isinstance(sampler, Sampler):
            raise ValueError(
                "sampler should be an instance of "
                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
            )
        self.sampler = sampler
        self.group_ids = group_ids
        self.batch_size = batch_size

    def __iter__(self):
        buffer_per_group = defaultdict(list)
        samples_per_group = defaultdict(list)

        num_batches = 0
        for idx in self.sampler:
            group_id = self.group_ids[idx]
            buffer_per_group[group_id].append(idx)
            samples_per_group[group_id].append(idx)
            if len(buffer_per_group[group_id]) == self.batch_size:
                yield buffer_per_group[group_id]

View on GitHub (pinned to 1ec3fe6f37)

Solutions

  1. Pass a torch.utils.data.Sampler subclass instance, e.g. torch.utils.data.RandomSampler(dataset) or a custom aspect-ratio sampler
  2. Wrap any iterable in Sampler: class ListSampler(Sampler): def __init__(self, lst): self.lst = lst; def __iter__(self): return iter(self.lst); def __len__(self): return len(self.lst)
  3. Ensure the DataLoader wiring is sampler=GroupedBatchSampler(sampler, group_ids, batch_size), not batch_sampler with wrong args

Example fix

// before
sampler = GroupedBatchSampler(dataset, group_ids, batch_size=4)  # dataset is not a Sampler
// after
base = torch.utils.data.RandomSampler(dataset)
sampler = GroupedBatchSampler(base, group_ids, batch_size=4)
Defensive patterns

Strategy: type-guard

Validate before calling

from torch.utils.data import Sampler
assert isinstance(sampler, Sampler), 'GroupedBatchSampler requires a torch Sampler instance'
gbs = GroupedBatchSampler(sampler, group_ids, batch_size)

Type guard

def is_torch_sampler(s):
    from torch.utils.data import Sampler
    return isinstance(s, Sampler)

Try / catch

try:
    gbs = GroupedBatchSampler(sampler, group_ids, batch_size)
except ValueError as e:
    if 'instance of' in str(e):
        sampler = torch.utils.data.RandomSampler(dataset)
        gbs = GroupedBatchSampler(sampler, group_ids, batch_size)
    else:
        raise

Prevention

When it happens

Trigger: Instantiating GroupedBatchSampler(batch_size=..., group_ids=...) with a plain list, range object, or dataset as sampler — commonly when using batch_sampler=GroupedBatchSampler(dataset, ...) instead of a sampler instance.

Common situations: Passing a DataLoader where a sampler is expected; confusing torch's BatchSampler composition order; using a non-torch sampler implementation.

Understand the failure class

Background: Invalid argument type errors: "must be of type string", "expected X, got Y", and ERR_INVALID_ARG_TYPE explained — this error's family across 15 libraries.

Related errors

AI-assisted analysis of WZMIAOMIAO/deep-learning-for-image-processing@1ec3fe6f37 (2026-08-30). Data as JSON: /api/errors/6d341dc6f4e1e7c0. Report an issue: GitHub.