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unaligned_data_loader_opp.py
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unaligned_data_loader_opp.py
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import torch.utils.data
import torchnet as tnt
from builtins import object
import torchvision.transforms as transforms
from datasets_opp import Dataset
class PairedData(object):
def __init__(self, data_loader_A, data_loader_B, max_dataset_size):
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
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
return {'S': A, 'S_label': A_paths,
'T': B, 'T_label': B_paths}
class UnalignedDataLoader():
def initialize(self, source, target, batch_size1, batch_size2):
"""
transform = transforms.Compose([
transforms.ToTensor()
])
"""
dataset_source = Dataset(source['imgs'], source['labels'])
dataset_target = Dataset(target['imgs'], target['labels'])
data_loader_s = torch.utils.data.DataLoader(
dataset_source,
batch_size=batch_size1,
shuffle=True,
num_workers=0)
data_loader_t = torch.utils.data.DataLoader(
dataset_target,
batch_size=batch_size2,
shuffle=True,
num_workers=0)
self.dataset_s = dataset_source
self.dataset_t = dataset_target
self.paired_data = PairedData(data_loader_s, data_loader_t,
float("inf"))
def name(self):
return 'UnalignedDataLoader'
def load_data(self):
return self.paired_data
def __len__(self):
return min(max(len(self.dataset_s), len(self.dataset_t)), float("inf"))