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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
from pdb import set_trace as st
from builtins import object
class PairedData(object):
def __init__(self, data_loader, fineSize):
self.data_loader = data_loader
self.fineSize = fineSize
# st()
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
return self
def __next__(self):
# st()
AB, AB_paths = next(self.data_loader_iter)
# st()
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w + w_offset:w + w_offset + self.fineSize]
return {'A': A, 'A_paths': AB_paths, 'B': B, 'B_paths': AB_paths}
class AlignedDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
self.fineSize = opt.fineSize
transform = transforms.Compose([
# TODO: Scale
#transforms.Scale((opt.loadSize * 2, opt.loadSize)),
#transforms.CenterCrop(opt.fineSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=opt.dataroot + '/' + opt.phase,
transform=transform, return_paths=True)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads))
self.dataset = dataset
self.paired_data = PairedData(data_loader, opt.fineSize)
def name(self):
return 'AlignedDataLoader'
def load_data(self):
return self.paired_data
def __len__(self):
return len(self.dataset)
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