def _create_dataset(self):
self._dataset = DatasetFactory.get_by_name(self._opt.dataset_mode, self._opt, self._is_for_train)
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.batch_size,
shuffle=not self._opt.serial_batches,
num_workers=int(self._num_threds),
drop_last=True)
def _create_dataset(self):
self._dataset = DatasetFactory.get_by_name(self._opt.dataset_mode, self._opt, self._is_for_train)
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.batch_size,
shuffle=not self._opt.serial_batches,
num_workers=int(self._num_threds),
drop_last=True)
def _create_datasets(self):
self.datasets = OrderedDict()
for i, dataset_name in enumerate(self._opt.dataset_names):
task = self._opt.tasks[i]
self.datasets[task] = DatasetFactory.get_by_name(
dataset_name, self._opt, self.train_mode, self.transform)
self.cumulative_sizes = self.cumsum([
dataset for (k, dataset) in self.datasets.items()
]) # number of instances, cumulative sizes
def _create_dataset(self):
self._dataset = DatasetFactory.get_by_name(self._opt.dataset_mode,
self._opt,
self._is_for_train)
if self._is_for_train:
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.train_batch_size,
## TODO
shuffle=self._is_for_train,
# shuffle=False,
num_workers=int(self._num_threds),
drop_last=True)
else:
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.test_batch_size,
shuffle=self._is_for_train,
num_workers=int(self._num_threds),
drop_last=True)
def _create_dataset(self):
self._dataset = DatasetFactory.get_by_name(self._opt.dataset_mode,
self._opt, self._mode)
if hasattr(self._dataset, 'collate_fn'):
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.batch_size,
collate_fn=self._dataset.collate_fn,
shuffle=True,
#shuffle=not self._opt.serial_batches and self._mode == 'train',
num_workers=int(self._num_threds),
drop_last=True)
else:
self._dataloader = torch.utils.data.DataLoader(
self._dataset,
batch_size=self._opt.batch_size,
shuffle=True,
#shuffle=not self._opt.serial_batches and self._mode == 'train',
num_workers=int(self._num_threds),
drop_last=True)