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96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import logging
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import random
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from torch.utils.data import Dataset, Sampler
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logger = logging.getLogger(__name__)
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class BucketSampler(Sampler):
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r"""
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PyTorch Sampler that groups 3D data by height, width and frames.
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Args:
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data_source (`VideoDataset`):
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A PyTorch dataset object that is an instance of `VideoDataset`.
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batch_size (`int`, defaults to `8`):
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The batch size to use for training.
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shuffle (`bool`, defaults to `True`):
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Whether or not to shuffle the dataset order and each batch before
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dispatching to the dataloader.
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drop_last (`bool`, defaults to `False`):
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Whether or not to drop incomplete buckets of data after completely iterating over all data
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in the dataset. If set to True, only batches that have `batch_size` number of entries will
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be yielded. If set to False, it is guaranteed that all data in the dataset will be processed
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and batches that do not have `batch_size` number of entries will also be yielded.
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"""
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def __init__(
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self,
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data_source: Dataset,
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batch_size: int = 8,
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shuffle: bool = True,
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drop_last: bool = False,
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) -> None:
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self.data_source = data_source
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self.batch_size = batch_size
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self.shuffle = shuffle
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self.drop_last = drop_last
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self.buckets = {resolution: [] for resolution in data_source.video_resolution_buckets}
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self._raised_warning_for_drop_last = False
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def __len__(self):
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if self.drop_last and not self._raised_warning_for_drop_last:
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self._raised_warning_for_drop_last = True
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logger.warning(
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"Calculating the length for bucket sampler is not possible when `drop_last` is set to True. This may cause problems when setting the number of epochs used for training."
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)
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return (len(self.data_source) + self.batch_size - 1) // self.batch_size
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def __iter__(self):
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# Buckets hold samples between yields. They must be recreated for
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# every epoch: with ``drop_last=True`` an incomplete bucket is
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# intentionally discarded and must not be mixed with samples from the
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# next call to ``__iter__``.
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self.buckets = {resolution: [] for resolution in self.data_source.video_resolution_buckets}
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# Shuffling individual buckets is not sufficient because the dataset
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# is otherwise read in index order. A global permutation changes the
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# order in which buckets are filled while retaining homogeneous
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# batches, which gives each epoch a genuinely different sample order.
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indices = list(range(len(self.data_source)))
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if self.shuffle:
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random.shuffle(indices)
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for index in indices:
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data = self.data_source[index]
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video_metadata = data["video_metadata"]
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f, h, w = (
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video_metadata["num_frames"],
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video_metadata["height"],
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video_metadata["width"],
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)
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self.buckets[(f, h, w)].append(data)
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if len(self.buckets[(f, h, w)]) == self.batch_size:
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if self.shuffle:
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random.shuffle(self.buckets[(f, h, w)])
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yield self.buckets[(f, h, w)]
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del self.buckets[(f, h, w)]
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self.buckets[(f, h, w)] = []
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if self.drop_last:
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return
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for fhw, bucket in list(self.buckets.items()):
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if len(bucket) == 0:
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continue
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if self.shuffle:
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random.shuffle(bucket)
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yield bucket
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del self.buckets[fhw]
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self.buckets[fhw] = []
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