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import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, random_split
from torch.utils.data.sampler import SubsetRandomSampler
from utils import *
from .atlas import Atlas
from .brat import Brat
from .ddti import DDTI
from .isic import ISIC2016
from .kits import KITS
from .lidc import LIDC
from .lnq import LNQ
from .pendal import Pendal
from .refuge import REFUGE
from .segrap import SegRap
from .stare import STARE
from .toothfairy import ToothFairy
from .wbc import WBC
def get_dataloader(args):
transform_train = transforms.Compose([
transforms.Resize((args.image_size,args.image_size)),
transforms.ToTensor(),
])
transform_train_seg = transforms.Compose([
transforms.Resize((args.out_size,args.out_size)),
transforms.ToTensor(),
])
transform_test = transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
transforms.ToTensor(),
])
transform_test_seg = transforms.Compose([
transforms.Resize((args.out_size,args.out_size)),
transforms.ToTensor(),
])
if args.dataset == 'isic':
'''isic data'''
isic_train_dataset = ISIC2016(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
isic_test_dataset = ISIC2016(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')
nice_train_loader = DataLoader(isic_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(isic_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'decathlon':
nice_train_loader, nice_test_loader, transform_train, transform_val, train_list, val_list = get_decath_loader(args)
elif args.dataset == 'REFUGE':
'''REFUGE data'''
refuge_train_dataset = REFUGE(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
refuge_test_dataset = REFUGE(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')
nice_train_loader = DataLoader(refuge_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(refuge_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'LIDC':
'''LIDC data'''
# dataset = LIDC(data_path = args.data_path)
dataset = MyLIDC(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.2 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'DDTI':
'''DDTI data'''
refuge_train_dataset = DDTI(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
refuge_test_dataset = DDTI(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')
nice_train_loader = DataLoader(refuge_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(refuge_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'Brat':
'''Brat data'''
dataset = Brat(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'STARE':
'''STARE data'''
# dataset = LIDC(data_path = args.data_path)
dataset = STARE(args, data_path = args.data_path, transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.2 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'kits':
'''kits data'''
dataset = KITS(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'WBC':
'''WBC data'''
dataset = WBC(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'segrap':
'''segrap data'''
dataset = SegRap(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'toothfairy':
'''toothfairy data'''
dataset = ToothFairy(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'atlas':
'''atlas data'''
dataset = Atlas(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'pendal':
'''pendal data'''
dataset = Pendal(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
elif args.dataset == 'lnq':
'''lnq data'''
dataset = LNQ(args, data_path = args.data_path,transform = transform_train, transform_msk= transform_train_seg)
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(0.3 * dataset_size))
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[split:])
test_sampler = SubsetRandomSampler(indices[:split])
nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
'''end'''
else:
print("the dataset is not supported now!!!")
return nice_train_loader, nice_test_loader |