172 lines
6.1 KiB
Python
172 lines
6.1 KiB
Python
import math
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from typing import cast, Iterator, List, Optional, Sized, Union
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import torch
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import torch.distributed as dist
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from torch.utils.data import Sampler
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from torchvision.datasets.video_utils import VideoClips
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class DistributedSampler(Sampler):
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"""
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Extension of DistributedSampler, as discussed in
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https://github.com/pytorch/pytorch/issues/23430
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Example:
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dataset: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
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num_replicas: 4
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shuffle: False
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when group_size = 1
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RANK | shard_dataset
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=========================
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rank_0 | [0, 4, 8, 12]
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rank_1 | [1, 5, 9, 13]
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rank_2 | [2, 6, 10, 0]
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rank_3 | [3, 7, 11, 1]
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when group_size = 2
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RANK | shard_dataset
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=========================
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rank_0 | [0, 1, 8, 9]
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rank_1 | [2, 3, 10, 11]
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rank_2 | [4, 5, 12, 13]
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rank_3 | [6, 7, 0, 1]
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"""
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def __init__(
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self,
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dataset: Sized,
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num_replicas: Optional[int] = None,
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rank: Optional[int] = None,
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shuffle: bool = False,
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group_size: int = 1,
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) -> None:
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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if len(dataset) % group_size != 0:
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raise ValueError(
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f"dataset length must be a multiplier of group size dataset length: {len(dataset)}, group size: {group_size}"
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)
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self.dataset = dataset
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self.group_size = group_size
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self.num_replicas = num_replicas
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self.rank = rank
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self.epoch = 0
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dataset_group_length = len(dataset) // group_size
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self.num_group_samples = int(math.ceil(dataset_group_length * 1.0 / self.num_replicas))
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self.num_samples = self.num_group_samples * group_size
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self.total_size = self.num_samples * self.num_replicas
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self.shuffle = shuffle
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def __iter__(self) -> Iterator[int]:
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices: Union[torch.Tensor, List[int]]
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if self.shuffle:
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indices = torch.randperm(len(self.dataset), generator=g).tolist()
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else:
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indices = list(range(len(self.dataset)))
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# add extra samples to make it evenly divisible
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indices += indices[: (self.total_size - len(indices))]
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assert len(indices) == self.total_size
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total_group_size = self.total_size // self.group_size
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indices = torch.reshape(torch.LongTensor(indices), (total_group_size, self.group_size))
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# subsample
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indices = indices[self.rank : total_group_size : self.num_replicas, :]
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indices = torch.reshape(indices, (-1,)).tolist()
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assert len(indices) == self.num_samples
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if isinstance(self.dataset, Sampler):
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orig_indices = list(iter(self.dataset))
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indices = [orig_indices[i] for i in indices]
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return iter(indices)
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def __len__(self) -> int:
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return self.num_samples
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def set_epoch(self, epoch: int) -> None:
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self.epoch = epoch
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class UniformClipSampler(Sampler):
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"""
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Sample `num_video_clips_per_video` clips for each video, equally spaced.
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When number of unique clips in the video is fewer than num_video_clips_per_video,
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repeat the clips until `num_video_clips_per_video` clips are collected
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Args:
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video_clips (VideoClips): video clips to sample from
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num_clips_per_video (int): number of clips to be sampled per video
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"""
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def __init__(self, video_clips: VideoClips, num_clips_per_video: int) -> None:
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if not isinstance(video_clips, VideoClips):
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raise TypeError(f"Expected video_clips to be an instance of VideoClips, got {type(video_clips)}")
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self.video_clips = video_clips
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self.num_clips_per_video = num_clips_per_video
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def __iter__(self) -> Iterator[int]:
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idxs = []
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s = 0
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# select num_clips_per_video for each video, uniformly spaced
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for c in self.video_clips.clips:
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length = len(c)
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if length == 0:
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# corner case where video decoding fails
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continue
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sampled = torch.linspace(s, s + length - 1, steps=self.num_clips_per_video).floor().to(torch.int64)
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s += length
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idxs.append(sampled)
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return iter(cast(List[int], torch.cat(idxs).tolist()))
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def __len__(self) -> int:
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return sum(self.num_clips_per_video for c in self.video_clips.clips if len(c) > 0)
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class RandomClipSampler(Sampler):
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"""
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Samples at most `max_video_clips_per_video` clips for each video randomly
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Args:
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video_clips (VideoClips): video clips to sample from
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max_clips_per_video (int): maximum number of clips to be sampled per video
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"""
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def __init__(self, video_clips: VideoClips, max_clips_per_video: int) -> None:
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if not isinstance(video_clips, VideoClips):
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raise TypeError(f"Expected video_clips to be an instance of VideoClips, got {type(video_clips)}")
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self.video_clips = video_clips
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self.max_clips_per_video = max_clips_per_video
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def __iter__(self) -> Iterator[int]:
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idxs = []
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s = 0
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# select at most max_clips_per_video for each video, randomly
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for c in self.video_clips.clips:
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length = len(c)
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size = min(length, self.max_clips_per_video)
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sampled = torch.randperm(length)[:size] + s
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s += length
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idxs.append(sampled)
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idxs_ = torch.cat(idxs)
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# shuffle all clips randomly
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perm = torch.randperm(len(idxs_))
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return iter(idxs_[perm].tolist())
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def __len__(self) -> int:
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return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips)
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