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import numbers
import os
import queue as Queue
import threading
from functools import partial
from typing import Iterable
import mxnet as mx
import numpy as np
import torch
from torch import distributed
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.datasets import ImageFolder
from utils.utils_distributed_sampler import DistributedSampler
from utils.utils_distributed_sampler import get_dist_info
from utils.utils_distributed_sampler import worker_init_fn
def get_dataloader(
root_dir,
local_rank,
batch_size,
dali=False,
seed=2048,
num_workers=2,
) -> Iterable:
rec = os.path.join(root_dir, "train.rec")
idx = os.path.join(root_dir, "train.idx")
train_set = None
# Synthetic
if root_dir == "synthetic":
train_set = SyntheticDataset()
dali = False
# Mxnet RecordIO
elif os.path.exists(rec) and os.path.exists(idx):
train_set = MXFaceDataset(root_dir=root_dir, local_rank=local_rank)
# Image Folder
else:
transform = transforms.Compose(
[
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
train_set = ImageFolder(root_dir, transform)
# DALI
if dali:
return dali_data_iter(batch_size=batch_size, rec_file=rec, idx_file=idx, num_threads=2, local_rank=local_rank)
rank, world_size = get_dist_info()
train_sampler = DistributedSampler(train_set, num_replicas=world_size, rank=rank, shuffle=True, seed=seed)
if seed is None:
init_fn = None
else:
init_fn = partial(worker_init_fn, num_workers=num_workers, rank=rank, seed=seed)
train_loader = DataLoaderX(
local_rank=local_rank,
dataset=train_set,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True,
drop_last=True,
worker_init_fn=init_fn,
)
return train_loader
class BackgroundGenerator(threading.Thread):
def __init__(self, generator, local_rank, max_prefetch=6):
super(BackgroundGenerator, self).__init__()
self.queue = Queue.Queue(max_prefetch)
self.generator = generator
self.local_rank = local_rank
self.daemon = True
self.start()
def run(self):
torch.cuda.set_device(self.local_rank)
for item in self.generator:
self.queue.put(item)
self.queue.put(None)
def next(self):
next_item = self.queue.get()
if next_item is None:
raise StopIteration
return next_item
def __next__(self):
return self.next()
def __iter__(self):
return self
class DataLoaderX(DataLoader):
def __init__(self, local_rank, **kwargs):
super(DataLoaderX, self).__init__(**kwargs)
self.stream = torch.cuda.Stream(local_rank)
self.local_rank = local_rank
def __iter__(self):
self.iter = super(DataLoaderX, self).__iter__()
self.iter = BackgroundGenerator(self.iter, self.local_rank)
self.preload()
return self
def preload(self):
self.batch = next(self.iter, None)
if self.batch is None:
return None
with torch.cuda.stream(self.stream):
for k in range(len(self.batch)):
self.batch[k] = self.batch[k].to(device=self.local_rank, non_blocking=True)
def __next__(self):
torch.cuda.current_stream().wait_stream(self.stream)
batch = self.batch
if batch is None:
raise StopIteration
self.preload()
return batch
class MXFaceDataset(Dataset):
def __init__(self, root_dir, local_rank):
super(MXFaceDataset, self).__init__()
self.transform = transforms.Compose(
[
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
self.root_dir = root_dir
self.local_rank = local_rank
path_imgrec = os.path.join(root_dir, "train.rec")
path_imgidx = os.path.join(root_dir, "train.idx")
self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, "r")
s = self.imgrec.read_idx(0)
header, _ = mx.recordio.unpack(s)
if header.flag > 0:
self.header0 = (int(header.label[0]), int(header.label[1]))
self.imgidx = np.array(range(1, int(header.label[0])))
else:
self.imgidx = np.array(list(self.imgrec.keys))
def __getitem__(self, index):
idx = self.imgidx[index]
s = self.imgrec.read_idx(idx)
header, img = mx.recordio.unpack(s)
label = header.label
if not isinstance(label, numbers.Number):
label = label[0]
label = torch.tensor(label, dtype=torch.long)
sample = mx.image.imdecode(img).asnumpy()
if self.transform is not None:
sample = self.transform(sample)
return sample, label
def __len__(self):
return len(self.imgidx)
class SyntheticDataset(Dataset):
def __init__(self):
super(SyntheticDataset, self).__init__()
img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
img = np.transpose(img, (2, 0, 1))
img = torch.from_numpy(img).squeeze(0).float()
img = ((img / 255) - 0.5) / 0.5
self.img = img
self.label = 1
def __getitem__(self, index):
return self.img, self.label
def __len__(self):
return 1000000
def dali_data_iter(
batch_size: int,
rec_file: str,
idx_file: str,
num_threads: int,
initial_fill=32768,
random_shuffle=True,
prefetch_queue_depth=1,
local_rank=0,
name="reader",
mean=(127.5, 127.5, 127.5),
std=(127.5, 127.5, 127.5),
):
"""
Parameters:
----------
initial_fill: int
Size of the buffer that is used for shuffling. If random_shuffle is False, this parameter is ignored.
"""
rank: int = distributed.get_rank()
world_size: int = distributed.get_world_size()
import nvidia.dali.fn as fn
import nvidia.dali.types as types
from nvidia.dali.pipeline import Pipeline
from nvidia.dali.plugin.pytorch import DALIClassificationIterator
pipe = Pipeline(
batch_size=batch_size,
num_threads=num_threads,
device_id=local_rank,
prefetch_queue_depth=prefetch_queue_depth,
)
condition_flip = fn.random.coin_flip(probability=0.5)
with pipe:
jpegs, labels = fn.readers.mxnet(
path=rec_file,
index_path=idx_file,
initial_fill=initial_fill,
num_shards=world_size,
shard_id=rank,
random_shuffle=random_shuffle,
pad_last_batch=False,
name=name,
)
images = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB)
images = fn.crop_mirror_normalize(images, dtype=types.FLOAT, mean=mean, std=std, mirror=condition_flip)
pipe.set_outputs(images, labels)
pipe.build()
return DALIWarper(
DALIClassificationIterator(
pipelines=[pipe],
reader_name=name,
)
)
@torch.no_grad()
class DALIWarper(object):
def __init__(self, dali_iter):
self.iter = dali_iter
def __next__(self):
data_dict = self.iter.__next__()[0]
tensor_data = data_dict["data"].cuda()
tensor_label: torch.Tensor = data_dict["label"].cuda().long()
tensor_label.squeeze_()
return tensor_data, tensor_label
def __iter__(self):
return self
def reset(self):
self.iter.reset()
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