File size: 8,321 Bytes
52a9452 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 |
import math
import bisect
import imgaug
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data import Sampler, ConcatDataset, BatchSampler
from concern.config import Configurable, State
def default_worker_init_fn(worker_id):
np.random.seed(worker_id)
imgaug.seed(worker_id)
class DataLoader(Configurable, torch.utils.data.DataLoader):
dataset = State()
batch_size = State(default=256)
num_workers = State(default=10)
is_train = State(default=True)
collect_fn = State(default=None)
drop_last = State(default=True)
shuffle = State()
def __init__(self, **kwargs):
self.load_all(**kwargs)
if self.collect_fn is None:
self.collect_fn = torch.utils.data.dataloader.default_collate
cmd = kwargs.get('cmd', {})
self.is_train = cmd['is_train']
if 'batch_size' in cmd:
self.batch_size = cmd['batch_size']
if self.shuffle is None:
self.shuffle = self.is_train
self.num_workers = cmd.get('num_workers', self.num_workers)
if cmd.get('distributed'):
sampler = DistributedSampler(
self.dataset, shuffle=self.shuffle,
num_replicas=cmd['num_gpus'])
batch_sampler = BatchSampler(
sampler, self.batch_size//cmd['num_gpus'], False)
torch.utils.data.DataLoader.__init__(
self, self.dataset, batch_sampler=batch_sampler,
num_workers=self.num_workers, pin_memory=False,
drop_last=self.drop_last, collate_fn=self.collect_fn,
worker_init_fn=default_worker_init_fn)
else:
torch.utils.data.DataLoader.__init__(
self, self.dataset,
batch_size=self.batch_size, num_workers=self.num_workers,
drop_last=self.drop_last, shuffle=self.shuffle,
pin_memory=True, collate_fn=self.collect_fn,
worker_init_fn=default_worker_init_fn)
self.collect_fn = str(self.collect_fn)
class SuccessiveRandomSampler(Sampler):
'''Random Sampler that yields sorted data in successive ranges.
Args:
dataset: Dataset used for sampling.
'''
def __init__(self, dataset):
self.dataset = dataset
self.epoch = 0
def __iter__(self):
if self.shuffle:
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset)).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
offset = self.num_samples * self.rank
indices = indices[offset: offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return len(self.dataset)
def set_epoch(self, epoch):
self.epoch = epoch
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
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):
if self.shuffle:
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset)).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
offset = self.num_samples * self.rank
indices = indices[offset: offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class InfiniteOrderedSampler(Sampler):
def __init__(self, data_source, limit_size):
self.data_source = data_source
self.limit_size = limit_size
def __iter__(self):
n = len(self.data_source)
def wrapper():
cnt = 0
while cnt < self.limit_size:
if cnt % n == 0:
idx = torch.randperm(n).tolist()
yield idx[cnt % n]
cnt += 1
return wrapper()
def __len__(self):
return self.limit_size
class InfiniteDataLoader(Configurable, torch.utils.data.DataLoader):
dataset = State()
batch_size = State(default=256)
num_workers = State(default=10)
limit_size = State(default=2 ** 31)
def __init__(self, **kwargs):
self.load_all(**kwargs)
cmd = kwargs['cmd']
if 'batch_size' in cmd:
self.batch_size = cmd['batch_size']
sampler = InfiniteOrderedSampler(self.dataset, self.limit_size)
torch.utils.data.DataLoader.__init__(
self, self.dataset,
batch_size=self.batch_size, num_workers=self.num_workers,
sampler=sampler, worker_init_fn=default_worker_init_fn,
)
class RandomSampleSampler(Sampler):
def __init__(self, data_source, weights=None, size=2 ** 31):
self.data_source = data_source
if weights is None:
self.probabilities = np.full(len(data_source), 1 / len(data_source))
else:
self.probabilities = np.array(weights) / np.sum(weights)
self.cum_prob = np.cumsum(self.probabilities)
self.size = size
def __iter__(self):
def wrapper():
for i in range(self.size):
yield bisect.bisect(self.cum_prob, torch.rand(1)[0], hi=len(self.data_source) - 1)
return wrapper()
def __len__(self):
return self.size
class RandomSampleDataLoader(Configurable, torch.utils.data.DataLoader):
datasets = State()
weights = State()
batch_size = State(default=256)
num_workers = State(default=10)
size = State(default=2 ** 31)
def __init__(self, **kwargs):
self.load_all(**kwargs)
cmd = kwargs['cmd']
if 'batch_size' in cmd:
self.batch_size = cmd['batch_size']
probs = []
for dataset, weight in zip(self.datasets, self.weights):
probs.append(np.full(len(dataset), weight / len(dataset)))
dataset = ConcatDataset(self.datasets)
probs = np.concatenate(probs)
assert(len(dataset) == len(probs))
sampler = RandomSampleSampler(dataset, probs, self.size)
torch.utils.data.DataLoader.__init__(
self, dataset,
batch_size=self.batch_size, num_workers=self.num_workers,
sampler=sampler, worker_init_fn=default_worker_init_fn,
)
|