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import random
import torch.utils.data
import torchvision.transforms as transforms
from data.base_data_loader import BaseDataLoader
from data.image_folder import ImageFolder
# pip install future --upgrade
from builtins import object
from pdb import set_trace as st
class PairedData(object):
def __init__(self, data_loader_A, data_loader_B, max_dataset_size, flip):
self.data_loader_A = data_loader_A
self.data_loader_B = data_loader_B
self.stop_A = False
self.stop_B = False
self.max_dataset_size = max_dataset_size
self.flip = flip
def __iter__(self):
self.stop_A = False
self.stop_B = False
self.data_loader_A_iter = iter(self.data_loader_A)
self.data_loader_B_iter = iter(self.data_loader_B)
self.iter = 0
return self
def __next__(self):
A, A_paths = None, None
B, B_paths = None, None
try:
A, A_paths = next(self.data_loader_A_iter)
except StopIteration:
if A is None or A_paths is None:
self.stop_A = True
self.data_loader_A_iter = iter(self.data_loader_A)
A, A_paths = next(self.data_loader_A_iter)
try:
B, B_paths = next(self.data_loader_B_iter)
except StopIteration:
if B is None or B_paths is None:
self.stop_B = True
self.data_loader_B_iter = iter(self.data_loader_B)
B, B_paths = next(self.data_loader_B_iter)
if (self.stop_A and self.stop_B) or self.iter > self.max_dataset_size:
self.stop_A = False
self.stop_B = False
raise StopIteration()
else:
self.iter += 1
if self.flip and random.random() < 0.5:
idx = [i for i in range(A.size(3) - 1, -1, -1)]
idx = torch.LongTensor(idx)
A = A.index_select(3, idx)
B = B.index_select(3, idx)
return {'A': A, 'A_paths': A_paths,
'B': B, 'B_paths': B_paths}
class UnalignedDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transformations = [transforms.Scale(opt.loadSize),
transforms.RandomCrop(opt.fineSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
transform = transforms.Compose(transformations)
# Dataset A
dataset_A = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'A',
transform=transform, return_paths=True)
data_loader_A = torch.utils.data.DataLoader(
dataset_A,
batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads))
# Dataset B
dataset_B = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'B',
transform=transform, return_paths=True)
data_loader_B = torch.utils.data.DataLoader(
dataset_B,
batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads))
self.dataset_A = dataset_A
self.dataset_B = dataset_B
flip = opt.isTrain and not opt.no_flip
self.paired_data = PairedData(data_loader_A, data_loader_B,
self.opt.max_dataset_size, flip)
def name(self):
return 'UnalignedDataLoader'
def load_data(self):
return self.paired_data
def __len__(self):
return min(max(len(self.dataset_A), len(self.dataset_B)), self.opt.max_dataset_size)
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