/
dataloaders.py
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/
dataloaders.py
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import torch
import torchvision
from torchvision import transforms
from typing import List
import os
import time
import warnings
CIFAR_MEAN = [125.307, 122.961, 113.8575]
CIFAR_STD = [51.5865, 50.847, 51.255]
def cifar10_dataloaders(config):
"""
Vanilla cifar10 dataloader. No augmentations
"""
torch.manual_seed(config.seed)
train_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.ConvertImageDtype(torch.float32),
transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)
) if config.normalize else lambda x:x
])
val_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.ConvertImageDtype(torch.float32),
transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)
) if config.normalize else lambda x:x
])
train = torchvision.datasets.CIFAR10(root='./data', train=True,
transform=train_transform, download=True)
train_loader = torch.utils.data.DataLoader(train,
batch_size=config.train_batch_size,
shuffle=True)
test = torchvision.datasets.CIFAR10(root='./data', train=False,
transform=val_transform, download=True)
test_loader = torch.utils.data.DataLoader(test,
batch_size=config.test_batch_size,
shuffle=True) ## shuffling
## to get random
## neighborhood for LC
return train_loader, test_loader
def cifar10_dataloaders_ffcv(config,
train_path='./data/cifar10_train.beton',
test_path='./data/cifar10_test.beton',
precision='fp32',
os_cache=True,
num_workers=2
):
"""
Create ffcv dataloaders if ffcv is available
"""
try:
from ffcv.fields import IntField, RGBImageField
from ffcv.fields.decoders import IntDecoder, SimpleRGBImageDecoder
from ffcv.fields.rgb_image import CenterCropRGBImageDecoder, \
RandomResizedCropRGBImageDecoder
from ffcv.loader import Loader, OrderOption
from ffcv.pipeline.operation import Operation
from ffcv.transforms import RandomHorizontalFlip, Cutout, \
RandomTranslate, Convert, ToDevice, ToTensor, ToTorchImage
from ffcv.transforms.common import Squeeze
from ffcv.writer import DatasetWriter
except:
warnings.warn("cant import ffcv. falling back to legacy dataloader")
return cifar10_dataloaders(config)
paths = {
'train': train_path,
'test': test_path
}
### create ffcv datasets if not exists
if not os.path.exists(train_path):
print(f'{train_path} not found. Creating FFCV dataset...')
datasets = {
'train': torchvision.datasets.CIFAR10('./data', train=True, download=True),
'test': torchvision.datasets.CIFAR10('./data', train=False, download=True)
}
for (name, ds) in datasets.items():
path = paths[name]
writer = DatasetWriter(path, {
'image': RGBImageField(),
'label': IntField()
})
writer.from_indexed_dataset(ds)
loaders = {}
for name in ['train', 'test']:
label_pipeline: List[Operation] = [IntDecoder(), ToTensor(),
ToDevice(torch.device('cuda:0')), Squeeze()]
image_pipeline: List[Operation] = [SimpleRGBImageDecoder()]
### add augmentations for train
if name == 'train' and config.use_aug:
print('Using training augmentations')
image_pipeline.extend([
RandomHorizontalFlip(),
RandomTranslate(padding=2, fill=tuple(map(int, CIFAR_MEAN))),
Cutout(4, tuple(map(int, CIFAR_MEAN))),
])
image_pipeline.extend([
ToTensor(),
ToDevice(torch.device('cuda:0'), non_blocking=True),
ToTorchImage(),
Convert(torch.float16) if precision == 'fp16' else Convert(torch.float32),
])
if config.normalize:
image_pipeline.extend([
torchvision.transforms.Normalize(CIFAR_MEAN, CIFAR_STD)
])
ordering = OrderOption.RANDOM # if name == 'train' else OrderOption.SEQUENTIAL
loaders[name] = Loader(paths[name],
batch_size=getattr(config,f'{name}_batch_size'),
num_workers=num_workers,
order=ordering, drop_last=(name == 'train'), os_cache=os_cache,
pipelines={'image': image_pipeline, 'label': label_pipeline})
return loaders['train'], loaders['test']
def get_LC_samples(dloader,config):
"""
Selects a set of samples for LC computation. TODO: allow subsampling classwise
"""
samples = []
labels = []
size = 0
for x,y in dloader:
samples.append(x)
labels.append(y)
size += x.shape[0]
if size >= config.approx_n: break
## concat and keep LC_batch_size
samples = torch.concatenate(samples,axis=0)[:config.approx_n]
labels = torch.concatenate(labels,axis=0)[:config.approx_n]
return samples, labels