import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
[docs]class DistributedSampler(_DistributedSampler):
"""DistributedSampler inheriting from
``torch.utils.data.DistributedSampler``.
In pytorch of lower versions, there is no ``shuffle`` argument. This child
class will port one to DistributedSampler.
"""
def __init__(self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
seed=0):
super().__init__(
dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
# for the compatibility from PyTorch 1.3+
self.seed = seed if seed is not None else 0
def __iter__(self):
# deterministically shuffle based on epoch
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# 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
return iter(indices)
[docs]class DistributedPowerSampler(_DistributedSampler):
"""DistributedPowerSampler inheriting from
``torch.utils.data.DistributedSampler``.
Samples are sampled with the probability that is proportional to the power
of label frequency (freq ^ power). The sampler only applies to single class
recognition dataset.
The default value of power is 1, which is equivalent to bootstrap sampling
from the entire dataset.
"""
def __init__(self, dataset, num_replicas=None, rank=None, power=1, seed=0):
super().__init__(dataset, num_replicas=num_replicas, rank=rank)
self.power = power
self.seed = seed if seed is not None else 0
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch + self.seed)
video_infos_by_class = self.dataset.video_infos_by_class
num_classes = self.dataset.num_classes
# For simplicity, discontinuous labels are not permitted
assert set(video_infos_by_class) == set(range(num_classes))
counts = [len(video_infos_by_class[i]) for i in range(num_classes)]
counts = [cnt**self.power for cnt in counts]
indices = torch.multinomial(
torch.Tensor(counts),
self.total_size,
replacement=True,
generator=g)
indices = indices.data.numpy().tolist()
assert len(indices) == self.total_size
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)