/
dataloader.py
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/
dataloader.py
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from torchvision import datasets, transforms
import torch
from dataset.caltech import Caltech101
from dataset.camvid import CamVid
from dataset.nyu import NYUv2, NYUv2Depth
from utils import ext_transforms
def get_dataloader(args):
if args.dataset.lower()=='mnist':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_root, train=False, download=True,
transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
elif args.dataset.lower()=='cifar10':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.data_root, train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
elif args.dataset.lower()=='cifar100':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(args.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(args.data_root, train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
elif args.dataset.lower()=='caltech101':
train_loader = torch.utils.data.DataLoader(
Caltech101(args.data_root, train=True, download=args.download,
transform=transforms.Compose([
transforms.Resize(128),
transforms.RandomCrop(128),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
Caltech101(args.data_root, train=False, download=args.download,
transform=transforms.Compose([
transforms.Resize(128),
transforms.CenterCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])),
batch_size=args.test_batch_size, shuffle=False, num_workers=2)
elif args.dataset.lower()=='imagenet':
train_loader = None # not required
test_loader = torch.utils.data.DataLoader(
datasets.ImageNet(args.data_root, split='val', download=True,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])),
batch_size=args.batch_size, shuffle=True, num_workers=4) # shuffle for visualization
############ Segmentation
elif args.dataset.lower()=='camvid':
train_loader = torch.utils.data.DataLoader(
CamVid(args.data_root, split='train',
transform=ext_transforms.ExtCompose([
ext_transforms.ExtResize(256),
ext_transforms.ExtRandomCrop(128, pad_if_needed=True),
ext_transforms.ExtRandomHorizontalFlip(),
ext_transforms.ExtToTensor(),
ext_transforms.ExtNormalize((0.5,), (0.5,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
CamVid(args.data_root, split='test',
transform=ext_transforms.ExtCompose([
ext_transforms.ExtResize(256),
ext_transforms.ExtToTensor(),
ext_transforms.ExtNormalize((0.5,), (0.5,))
])),
batch_size=args.test_batch_size, shuffle=False, num_workers=2)
elif args.dataset.lower() in ['nyuv2']:
train_loader = torch.utils.data.DataLoader(
NYUv2(args.data_root, split='train',
transform=ext_transforms.ExtCompose([
ext_transforms.ExtResize(256),
ext_transforms.ExtRandomCrop(128, pad_if_needed=True),
ext_transforms.ExtRandomHorizontalFlip(),
ext_transforms.ExtToTensor(),
ext_transforms.ExtNormalize((0.5,), (0.5,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
NYUv2(args.data_root, split='test',
transform=ext_transforms.ExtCompose([
ext_transforms.ExtResize(256),
ext_transforms.ExtToTensor(),
ext_transforms.ExtNormalize((0.5,), (0.5,))
])),
batch_size=args.test_batch_size, shuffle=False, num_workers=2)
return train_loader, test_loader