def getLoader(datasetName,
dataroot,
batchSize,
workers,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None):
if datasetName == 'pix2pix':
from datasets.pix2pix import pix2pix as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
else:
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
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):
#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 == 'train':
dataset = commonDataset(
root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
#transforms.RandomCrop(imageSize),
#transforms.CenterCrop(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=0,
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
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
else:
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
