def get_valloader(self, dataset=None):
dataset = 'val' if dataset is None else dataset
if self.configer.get('dataset', default=None) == 'default_cpm':
dataset = DefaultCPMDataset(root_dir=self.configer.get('data', 'data_dir'), dataset=dataset,
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('dataset', default=None) == 'default_openpose':
dataset = DefaultOpenPoseDataset(root_dir=self.configer.get('data', 'data_dir'), dataset=dataset,
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_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,
label_transform=self.label_transform,
configer=self.configer)
elif self.configer.get('dataset', default=None) == 'cityscapes':
dataset = CityscapesDataset(root_dir=self.configer.get(
'data', 'data_dir'),
dataset='val',
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
label_transform=self.label_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
def get_valloader(self, dataset=None):
dataset = 'val' if dataset is None else dataset
if self.configer.get('dataset') == 'default_pix2pix':
dataset = DefaultPix2pixDataset(root_dir=self.configer.get('data', 'data_dir'), dataset=dataset,
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('dataset') == 'default_cyclegan':
dataset = DefaultCycleGANDataset(root_dir=self.configer.get('data', 'data_dir'), dataset=dataset,
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('dataset') == 'default_facegan':
dataset = DefaultFaceGANDataset(root_dir=self.configer.get('data', 'data_dir'),
dataset=dataset, tag=self.configer.get('data', 'tag'),
aug_transform=self.aug_val_transform,
img_transform=self.img_transform,
configer=self.configer)
else:
Log.error('{} val loader is invalid.'.format(self.configer.get('val', 'loader')))
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) == 'default_pix2pix':
dataset = DefaultPix2pixDataset(root_dir=self.configer.get('data', 'data_dir'), dataset='train',
aug_transform=self.aug_train_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('dataset') == 'default_cyclegan':
dataset = DefaultCycleGANDataset(root_dir=self.configer.get('data', 'data_dir'), dataset='train',
aug_transform=self.aug_train_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('dataset') == 'default_facegan':
dataset = DefaultFaceGANDataset(root_dir=self.configer.get('data', 'data_dir'),
dataset='train', tag=self.configer.get('data', 'tag'),
aug_transform=self.aug_train_transform,
img_transform=self.img_transform,
configer=self.configer)
else:
Log.error('{} train loader is invalid.'.format(self.configer.get('train', 'loader')))
exit(1)
trainloader = data.DataLoader(
dataset,
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
def get_testloader(self, test_dir=None, list_path=None, json_path=None):
if self.configer.get('test.dataset',
default=None) in [None, 'default']:
test_dir = test_dir if test_dir is not None else self.configer.get(
'test', 'test_dir')
dataset = DefaultDataset(test_dir=test_dir,
aug_transform=self.aug_test_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('test.dataset') == 'list':
list_path = list_path if list_path is not None else self.configer.get(
'test', 'list_path')
dataset = ListDataset(root_dir=self.configer.get(
'test', 'root_dir'),
list_path=list_path,
aug_transform=self.aug_test_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('test.dataset') == 'json':
json_path = json_path if json_path is not None else self.configer.get(
'test', 'json_path')
dataset = JsonDataset(root_dir=self.configer.get(
'test', 'root_dir'),
json_path=json_path,
aug_transform=self.aug_test_transform,
img_transform=self.img_transform,
configer=self.configer)
elif self.configer.get('test.dataset') == 'facegan':
json_path = json_path if json_path is not None else self.configer.get(
'test', 'json_path')
dataset = FaceGANDataset(root_dir=self.configer.get(
'test', 'root_dir'),
json_path=json_path,
aug_transform=self.aug_test_transform,
img_transform=self.img_transform,
configer=self.configer)
else:
Log.error('{} test dataset is invalid.'.format(
self.configer.get('test.dataset')))
exit(1)
testloader = data.DataLoader(
dataset,
batch_size=self.configer.get('test.batch_size',
default=torch.cuda.device_count()),
shuffle=False,
num_workers=self.configer.get('data', 'workers'),
pin_memory=True,
collate_fn=lambda *args: collate(*args,
trans_dict=self.configer.get(
'test', 'data_transformer')))
return testloader