def get_valloader(self):
if self.configer.get('dataset', default=None) in [None, 'default']:
dataset = DefaultDataset(root_dir=self.configer.get(
'data', 'data_dir'),
dataset='val',
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
else:
Log.error('{} dataset is invalid.'.format(
self.configer.get('dataset')))
exit(1)
valloader = data.DataLoader(
dataset,
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
def get_trainloader(self):
if self.configer.get('dataset', default=None) in [None, 'default']:
dataset = DefaultDataset(root_dir=self.configer.get(
'data', 'data_dir'),
dataset='train',
aug_transform=self.aug_train_transform,
img_transform=self.img_transform,
configer=self.configer)
else:
Log.error('{} dataset is invalid.'.format(
self.configer.get('dataset')))
exit(1)
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