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datasets.py
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datasets.py
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import os
from torchvision import datasets, transforms
import torch
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.data import create_transform
def build_dataset(is_train, args):
transform = build_transform(is_train, args)
print("Transform = ")
if isinstance(transform, tuple):
for trans in transform:
print(" - - - - - - - - - - ")
for t in trans.transforms:
print(t)
else:
for t in transform.transforms:
print(t)
print("---------------------------")
if args.data_set == 'cifar10':
dataset = datasets.CIFAR10(args.data_path, train=is_train, download=True, transform=transform)
nb_classes = 10
elif args.data_set == 'cifar100':
dataset = datasets.CIFAR100(args.data_path, train=is_train, download=True, transform=transform)
nb_classes = 100
elif args.data_set == 'IMNET':
root = os.path.join(args.data_path, 'train' if is_train else 'val')
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = args.nb_classes
elif args.data_set == "flowers":
if is_train:
train_dataset = datasets.Flowers102(root=args.data_path,
split='train',
download=True,
transform=transform)
val_dataset = datasets.Flowers102(root=args.data_path,
split='val',
download=True,
transform=transform)
dataset = torch.utils.data.ConcatDataset([train_dataset, val_dataset])
else:
dataset = datasets.Flowers102(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 102
elif args.data_set == "pets":
if is_train:
dataset = datasets.OxfordIIITPet(root=args.data_path,
split='trainval',
download=True,
transform=transform)
else:
dataset = datasets.OxfordIIITPet(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 37
elif args.data_set == "stl10":
if is_train:
dataset = datasets.STL10(root=args.data_path,
split='train',
download=True,
transform=transform)
else:
dataset = datasets.STL10(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 10
elif args.data_set == "food101":
if is_train:
dataset = datasets.Food101(root=args.data_path,
split='train',
download=True,
transform=transform)
else:
dataset = datasets.Food101(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 101
elif args.data_set == "dtd":
if is_train:
train_dataset = datasets.DTD(root=args.data_path,
split='train',
download=True,
transform=transform)
val_dataset = datasets.DTD(root=args.data_path,
split='val',
download=True,
transform=transform)
dataset = torch.utils.data.ConcatDataset([train_dataset, val_dataset])
else:
dataset = datasets.DTD(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 47
elif args.data_set == "svhn":
if is_train:
dataset = datasets.SVHN(root=args.data_path,
split='train',
download=True,
transform=transform)
else:
dataset = datasets.SVHN(root=args.data_path,
split='test',
download=True,
transform=transform)
nb_classes = 10
elif args.data_set == "caltech101":
dataset = datasets.Caltech101(root=args.data_path, download=True, transform=transform)
trainset, testset = torch.utils.data.random_split(dataset, [0.7, 0.3],torch.Generator().manual_seed(0))
dataset = trainset if is_train else testset
nb_classes = 101
elif args.data_set == "eurosat":
dataset = datasets.ImageFolder(root=os.path.join(args.data_path, '2750'), transform=transform)
trainset, testset = torch.utils.data.random_split(dataset, [0.7, 0.3],torch.Generator().manual_seed(0))
dataset = trainset if is_train else testset
nb_classes = 10
elif args.data_set == "image_folder":
root = args.data_path if is_train else args.eval_data_path
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = args.nb_classes
assert len(dataset.class_to_idx) == nb_classes
else:
raise NotImplementedError()
print("Number of the class = %d" % nb_classes)
return dataset, nb_classes
def build_transform(is_train, args):
resize_im = args.input_size > 32
if args.imagenet_default_mean_and_std:
mean, std = (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
else:
mean, std = (IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_MEAN)
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=args.input_size,
is_training=True,
no_aug = args.no_aug,
hflip=args.hflip,
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation=args.train_interpolation,
re_prob=args.reprob,
re_mode=args.remode,
re_count=args.recount,
mean=mean,
std=std,
)
if not resize_im and not args.no_aug:
transform.transforms[0] = transforms.RandomCrop(
args.input_size, padding=4)
return transform
t = []
if resize_im:
# warping (no cropping) when evaluated at 384 or larger
if args.input_size >= 384:
t.append(
transforms.Resize((args.input_size, args.input_size),
interpolation=transforms.InterpolationMode.BICUBIC),
)
print(f"Warping {args.input_size} size input images...")
else:
if args.crop_pct is None:
args.crop_pct = 224 / 256
size = int(args.input_size / args.crop_pct)
t.append(
# to maintain same ratio w.r.t. 224 images
transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),
)
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
t.append(transforms.Normalize(mean, std))
return transforms.Compose(t)