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dataloaders.py
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dataloaders.py
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'''
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import os
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import ConcatDataset
from timm.data.transforms_factory import create_transform
# mean and std fr https://github.com/pytorch/examples/blob/master/imagenet/main.py
imagenet_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# fr https://github.com/kakaobrain/fast-autoaugment
_IMAGENET_PCA = {
'eigval': [0.2175, 0.0188, 0.0045],
'eigvec': [
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
]
}
imagenet_size=224
imagenet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(imagenet_size),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
transforms.ToTensor(),
imagenet_normalize,
])
imagenet_test_transform = transforms.Compose([
transforms.Resize(imagenet_size+32),
transforms.CenterCrop(imagenet_size),
transforms.ToTensor(),
imagenet_normalize,
])
imagenet_train_transform_timm = create_transform(224, is_training=True,)
imagenet_test_transform_timm = create_transform(224, is_training=False,)
class ClassifierLoader:
def __init__(self,
root='/data/imagenet',
batch_size=128,
dataset=datasets.ImageNet,
transform={'train':imagenet_train_transform_timm, 'test':imagenet_test_transform_timm},
device=None,
dataset_name="imagenet",
shuffle_test=False,
corruption=None):
super(ClassifierLoader, self).__init__()
self.test = None
self.train = None
self._build(root,
batch_size,
dataset,
transform,
device,
dataset_name,
shuffle_test,
corruption)
def _build(self,
root,
batch_size,
dataset,
transform,
device,
dataset_name,
shuffle_test,
corruption):
DataLoader = torch.utils.data.DataLoader
workers = torch.cuda.device_count() * 4
if "cuda" in str(device):
print("num_workers: ", workers)
kwargs = {'num_workers': workers, 'pin_memory': True}
else:
kwargs = {}
if dataset_name == "svhn" or dataset_name == "svhn-core":
x_train = dataset(root=root,
split='train',
download=True,
transform=transform['train'])
if dataset_name == "svhn":
x_extra = dataset(root=root,
split='extra',
download=True,
transform=transform['train'])
x_train = ConcatDataset([x_train, x_extra])
x_test = dataset(root=root,
split='test',
download=True,
transform=transform['test'])
elif dataset_name == "imagenet":
x_train = dataset(root=root,
split='train',
transform=transform['train'])
if corruption is None:
x_test = dataset(root=root,
split='val',
transform=transform['test'])
else:
root = os.path.join(root, corruption)
corrupt_test = []
for i in range(1, 6):
folder = os.path.join(root, str(i))
x_test = datasets.ImageFolder(root=folder,
transform=transform['test'])
corrupt_test.append(x_test)
x_test = ConcatDataset(corrupt_test)
elif dataset_name == "speech_commands":
x_train = dataset(root=root,
split='train',
transform=transform['train'])
x_val = dataset(root=root,
split='valid',
transform=transform['test'])
x_test = dataset(root=root,
split='test',
transform=transform['test'])
self.val = DataLoader(x_val,
shuffle=False,
batch_size=batch_size,
**kwargs)
#self.train = DataLoader(x_train,
# shuffle=True,
# batch_size=batch_size,
# **kwargs)
#self.test = DataLoader(x_test,
# shuffle=False,
# batch_size=batch_size,
# **kwargs)
#return
else:
x_train = dataset(root=root,
train=True,
download=True,
transform=transform['train'])
x_test = dataset(root=root,
train=False,
download=True,
transform=transform['test'])
self.train = DataLoader(x_train,
shuffle=True,
batch_size=batch_size,
**kwargs)
self.test = DataLoader(x_test,
shuffle=shuffle_test,
batch_size=batch_size,
**kwargs)