def get_loader(dataset, dataroot,
origin_size, image_size, batch_size, workers,
split='train', shuffle=True, seed=None):
if dataset =="trans":
from datasets.trans import trans as commonDataset
import transforms.pix2pix as transforms
elif dataset == 'folder':
from torchvision.datasets.folder import ImageFolder as commonDataset
import torchvision.transforms as transforms
elif dataset == 'pix2pix':
from datasets.pix2pix import pix2pix as commonDataset
import transforms.pix2pix as transforms
if dataset != "folder":
# for training set
if split == "train":
transform = transforms.Compose([
transforms.Resize(origin_size),
transforms.RandomCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5))])
dataset = commonDataset(root=dataroot, transform=transform, seed=seed)
# for validating set
else:
transform = transforms.Compose([
transforms.Resize(origin_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5))])
dataset = commonDataset(root=dataroot, transform=transform, seed=seed)
else:
# for training set
if split == "train":
transform = transforms.Compose([
transforms.Resize(origin_size),
transforms.RandomCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5))])
dataset = commonDataset(root=dataroot, transform=transform, seed=seed)
# for validating set
else:
transform = transforms.Compose([
transforms.Resize(origin_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5))])
dataset = commonDataset(root=dataroot, transform=transform, seed=seed)
assert dataset
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=workers)
return dataloader
def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None):
#import pdb; pdb.set_trace()
if datasetName == 'pix2pix':
from datasets.pix2pix import pix2pix as commonDataset
import transforms.pix2pix as transforms
elif datasetName == 'folder':
from datasets.folder import ImageFolder as commonDataset
import torchvision.transforms as transforms
elif datasetName == 'classification':
from datasets.classification import classification as commonDataset
import torchvision.transforms as transforms
elif datasetName == 'pix2pix_val':
from datasets.pix2pix_val import pix2pix_val as commonDataset
import torchvision.transforms as transforms
elif datasetName == 'pix2pix_val2':
from datasets.pix2pix_val2 import pix2pix_val as commonDataset
# import torchvision.transforms as transforms
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.RandomCrop(imageSize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
else:
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize=64,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None,
pre="",
label_file=""):
if datasetName == 'my_loader':
from datasets.my_loader import my_loader as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.RandomCrop(imageSize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed,
pre=pre,
label_file=label_file)
else:
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed,
pre=pre,
label_file=label_file)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize=64,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None):
#import pdb; pdb.set_trace()
if datasetName == 'pix2pix':
# from datasets.pix2pix import pix2pix as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix import pix2pix as commonDataset
import transforms.pix2pix as transforms
elif datasetName == 'pix2pix_val':
# from datasets.pix2pix_val import pix2pix_val as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_val import pix2pix_val as commonDataset
import transforms.pix2pix as transforms
if datasetName == 'pix2pix_class':
# from datasets.pix2pix import pix2pix as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_class import pix2pix as commonDataset
import transforms.pix2pix as transforms
if split == 'no_change':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
elif split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.RandomCrop(imageSize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
else:
dataset = commonDataset(
root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
# transforms.CenterCrop(imageSize), # DID-MDN 数据集
transforms.CenterCrop(imageSize), # IDC_GAN 数据集
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
