def get_valloader(self, dataset=None):
dataset = 'val' if dataset is None else dataset
if self.configer.get('val.loader', default=None) in [None, 'default']:
dataset = DefaultLoader(root_dir=self.configer.get(
'data', 'data_dir'),
dataset=dataset,
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
sampler = None
if self.configer.get('network.distributed'):
sampler = torch.utils.data.distributed.DistributedSampler(
dataset)
valloader = data.DataLoader(
dataset,
sampler=sampler,
batch_size=self.configer.get('val', 'batch_size'),
shuffle=False,
num_workers=self.configer.get('data', 'workers'),
pin_memory=True,
collate_fn=lambda *args: collate(
*args,
trans_dict=self.configer.get('val', 'data_transformer')))
return valloader
else:
Log.error('{} val loader is invalid.'.format(
self.configer.get('val', 'loader')))
exit(1)
def get_trainloader(self):
if self.configer.get('train.loader',
default=None) in [None, 'default']:
dataset = DefaultLoader(root_dir=self.configer.get(
'data', 'data_dir'),
dataset='train',
aug_transform=self.aug_train_transform,
img_transform=self.img_transform,
configer=self.configer)
sampler = None
if self.configer.get('network.distributed'):
sampler = torch.utils.data.distributed.DistributedSampler(
dataset)
trainloader = data.DataLoader(
dataset,
sampler=sampler,
batch_size=self.configer.get('train', 'batch_size'),
shuffle=(sampler is None),
num_workers=self.configer.get('data', 'workers'),
pin_memory=True,
drop_last=self.configer.get('data', 'drop_last'),
collate_fn=lambda *args: collate(
*args,
trans_dict=self.configer.get('train', 'data_transformer')))
return trainloader
else:
Log.error('{} train loader is invalid.'.format(
self.configer.get('train', 'loader')))
exit(1)
def get_valloader(self, dataset=None):
dataset = 'val' if dataset is None else dataset
if not self.configer.exists('val', 'loader') or self.configer.get('val', 'loader') == 'default':
valloader = data.DataLoader(
DefaultLoader(root_dir=self.configer.get('data', 'data_dir'), dataset=dataset,
aug_transform=self.aug_val_transform,
img_transform=self.img_transform, configer=self.configer),
batch_size=self.configer.get('val', 'batch_size'), shuffle=False,
num_workers=self.configer.get('data', 'workers'), pin_memory=True,
collate_fn=lambda *args: collate(
*args, trans_dict=self.configer.get('val', 'data_transformer')
)
)
return valloader
else:
Log.error('{} val loader is invalid.'.format(self.configer.get('val', 'loader')))
exit(1)
def get_trainloader(self):
if not self.configer.exists('train', 'loader') or self.configer.get('train', 'loader') == 'default':
trainloader = data.DataLoader(
DefaultLoader(root_dir=self.configer.get('data', 'data_dir'), dataset='train',
aug_transform=self.aug_train_transform,
img_transform=self.img_transform, configer=self.configer),
batch_size=self.configer.get('train', 'batch_size'), shuffle=True,
num_workers=self.configer.get('data', 'workers'), pin_memory=True,
drop_last=self.configer.get('data', 'drop_last'),
collate_fn=lambda *args: collate(
*args, trans_dict=self.configer.get('train', 'data_transformer')
)
)
return trainloader
else:
Log.error('{} train loader is invalid.'.format(self.configer.get('train', 'loader')))
exit(1)