import math
import random
import torch
import torch.distributed as dist
import torchvision.datasets.video_utils
from torch.utils.data import Sampler
class DistributedSampler(Sampler):
"""
Extension of DistributedSampler, as discussed in
https://github.com/pytorch/pytorch/issues/23430
"""
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
if self.shuffle:
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset)))
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
if isinstance(self.dataset, Sampler):
orig_indices = list(iter(self.dataset))
indices = [orig_indices[i] for i in indices]
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class UniformClipSampler(torch.utils.data.Sampler):
"""
Samples at most `max_video_clips_per_video` clips for each video, equally spaced
Arguments:
video_clips (VideoClips): video clips to sample from
max_clips_per_video (int): maximum number of clips to be sampled per video
"""
def __init__(self, video_clips, max_clips_per_video):
if not isinstance(video_clips, torchvision.datasets.video_utils.VideoClips):
raise TypeError("Expected video_clips to be an instance of VideoClips, "
"got {}".format(type(video_clips)))
self.video_clips = video_clips
self.max_clips_per_video = max_clips_per_video
def __iter__(self):
idxs = []
s = 0
# select at most max_clips_per_video for each video, uniformly spaced
for c in self.video_clips.clips:
length = len(c)
step = max(length // self.max_clips_per_video, 1)
sampled = torch.arange(length)[::step] + s
s += length
idxs.append(sampled)
idxs = torch.cat(idxs).tolist()
return iter(idxs)
def __len__(self):
return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips)
class RandomClipSampler(torch.utils.data.Sampler):
"""
Samples at most `max_video_clips_per_video` clips for each video randomly
Arguments:
video_clips (VideoClips): video clips to sample from
max_clips_per_video (int): maximum number of clips to be sampled per video
"""
def __init__(self, video_clips, max_clips_per_video):
if not isinstance(video_clips, torchvision.datasets.video_utils.VideoClips):
raise TypeError("Expected video_clips to be an instance of VideoClips, "
"got {}".format(type(video_clips)))
self.video_clips = video_clips
self.max_clips_per_video = max_clips_per_video
def __iter__(self):
idxs = []
s = 0
# select at most max_clips_per_video for each video, randomly
for c in self.video_clips.clips:
length = len(c)
size = min(length, self.max_clips_per_video)
sampled = torch.randperm(length)[:size] + s
s += length
idxs.append(sampled)
idxs = torch.cat(idxs)
# shuffle all clips randomly
perm = torch.randperm(len(idxs))
idxs = idxs[perm].tolist()
return iter(idxs)
def __len__(self):
return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips)
# return 100
class ConcatSampler(torch.utils.data.Sampler):
def __init__(self, samplers):
# to be used with concatdataset, if you need different sampler for each dataset in the concat
# order of samplers must be same as order of corresponding datasets in concatdataset
assert type(samplers) is list and len(samplers) > 0, 'must pass in list of 1 or more samplers'
self.samplers = samplers
self.cum_lens = torch.LongTensor([len(s.video_clips) for s in samplers]).cumsum(0)
def __iter__(self):
idxs = list(iter(self.samplers[0]))
for s, l in zip(self.samplers[1:], self.cum_lens):
idxs.extend(map(lambda i: (l+i).item(), iter(s)))
random.shuffle(idxs)
return iter(idxs)
def __len__(self):
return sum(len(_) for _ in self.samplers)
