# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
try:
import torch
from torch.utils.data import Sampler
except ImportError: # pragma: no cover
torch = None
Sampler = object
from ....utils import require_not_none
@require_not_none(torch)
class MarsDistributedSampler(Sampler):
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
import torch.distributed as dist
super().__init__(dataset)
if num_replicas is None: # pragma: no cover
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None: # pragma: no cover
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 generate_indices(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: # pragma: no cover
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
self.dataset.prefetch(indices)
return indices
def __iter__(self):
return iter(self.generate_indices())
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
[文档]class MarsRandomSampler(Sampler):
[文档] def __init__(self, data_source, replacement=False, num_samples=None):
super().__init__(data_source)
self.data_source = data_source
self.replacement = replacement
self._num_samples = num_samples
if not isinstance(self.replacement, bool): # pragma: no cover
raise ValueError("replacement should be a boolean value, but got "
f"replacement={self.replacement}")
if self._num_samples is not None and not replacement: # pragma: no cover
raise ValueError("With replacement=False, num_samples should not be specified, "
"since a random permute will be performed.")
if not isinstance(self.num_samples, int) or self.num_samples <= 0: # pragma: no cover
raise ValueError("num_samples should be a positive integer "
f"value, but got num_samples={self.num_samples}")
@property
def num_samples(self):
# dataset size might change at runtime
if self._num_samples is None:
return len(self.data_source)
else: # pragma: no cover
return self._num_samples
def __iter__(self):
n = len(self.data_source)
if self.replacement: # pragma: no cover
indices = torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64).tolist()
self.data_source.prefetch(indices)
return iter(indices)
else:
indices = torch.randperm(n).tolist()
self.data_source.prefetch(indices)
return iter(indices)
def __len__(self):
return self.num_samples