def get_data_loader(self, data_file, aug): #parameters that would change on train/val set
transform = self.trans_loader.get_composed_transform(aug)
dataset = SimpleDataset(data_file, transform)
data_loader_params = dict(batch_size = self.batch_size, shuffle = True, num_workers = 4, pin_memory = True)
data_loader = torch.utils.data.DataLoader(dataset, **data_loader_params)
return data_loader
def get_data_loader(self, data_file, aug):
transform = self.trans_loader.get_composed_transform(aug)
dataset = SimpleDataset(data_file, transform)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
return data_loader
def get_data_loader(self, data_path, load_set, aug):
transform = self.trans_loader.get_composed_transform(aug)
dataset = SimpleDataset(data_path, load_set, transform)
data_loader_params = dict(batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=True)
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader_dct(self, data_file, aug, filter_size):
transform = self.trans_loader.get_composed_transform_dct(
aug, filter_size)
dataset = SimpleDataset(data_file, transform, dct_status=True)
data_loader_params = dict(batch_size=self.batch_size,
shuffle=False,
num_workers=16,
pin_memory=True)
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(self, data_file,
aug): #parameters that would change on train/val set
transform = self.trans_loader.get_composed_transform(aug)
## Add transform for jigsaw puzzle
self.transform_patch_jigsaw = None
self.transform_jigsaw = None
if self.jigsaw:
if aug:
self.transform_jigsaw = transforms.Compose([
# transforms.Resize(256),
# transforms.CenterCrop(225),
## follow paper setting:
# transforms.Resize(255),
# transforms.CenterCrop(240),
## setting of my experiment before 0515
transforms.RandomResizedCrop(255, scale=(0.5, 1.0)),
transforms.RandomHorizontalFlip()
])
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std =[0.229, 0.224, 0.225])])
else:
self.transform_jigsaw = transforms.Compose([
# transforms.Resize(256),
# transforms.CenterCrop(225),])
# transforms.RandomResizedCrop(225,scale=(0.5, 1.0))])
transforms.Resize(255)
])
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std =[0.229, 0.224, 0.225])])
self.transform_patch_jigsaw = transforms.Compose([
transforms.RandomCrop(64),
# transforms.Resize((75, 75), Image.BILINEAR),
transforms.Lambda(self.rgb_jittering),
transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std =[0.229, 0.224, 0.225])
])
dataset = SimpleDataset(data_file, transform, jigsaw=self.jigsaw, \
transform_jigsaw=self.transform_jigsaw, transform_patch_jigsaw=self.transform_patch_jigsaw, \
rotation=self.rotation, isAircraft=self.isAircraft, grey=self.grey, return_name=self.return_name)
data_loader_params = dict(batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=NUM_WORKERS,
pin_memory=True,
drop_last=self.drop_last)
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(self, data_file, aug):
transform = self.trans_loader.get_simple_transform(aug=aug)
dataset = SimpleDataset(data_file, transform)
data_loader_params = dict(
batch_size=self.batch_size,
shuffle=True,
num_workers=0,
pin_memory=False
) # pin_memory for fast load to GPU, but i don't need it
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(self, data_file):
# transform = transforms.ToTensor()
transform = self.trans_loader.get_simple_transform(aug=self.aug)
dataset = SimpleDataset(data_file,
transform=transform,
return_path=True)
data_loader_params = dict(
batch_size=self.batch_size,
shuffle=True,
num_workers=12,
pin_memory=True
) # not sure if should be True when input to a TensorFlow model
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(self, data_file,
aug): #parameters that would change on train/val set
transform = self.trans_loader.get_composed_transform(aug)
dataset = SimpleDataset(data_file, transform)
if sys.platform == "win32": # Note: windows system doesn't support num_workers multiple threads
data_loader_params = dict(batch_size=self.batch_size,
shuffle=True,
pin_memory=True)
elif sys.platform == "linux":
data_loader_params = dict(batch_size=self.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
else:
assert False, "Unknown OS!"
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(
self,
data_file,
aug,
lang_dir=None,
normalize=True,
to_pil=False): #parameters that would change on train/val set
if lang_dir is not None:
raise NotImplementedError
transform = self.trans_loader.get_composed_transform(
aug, normalize=normalize, to_pil=to_pil)
dataset = SimpleDataset(data_file, transform)
data_loader_params = dict(batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=self.pin_memory)
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(
self,
data_file,
aug,
shuffle=True,
num_workers=12,
return_path=False): #parameters that would change on train/val set
transform = self.trans_loader.get_composed_transform(aug)
dataset = SimpleDataset(data_file, transform, return_path=return_path)
data_loader_params = dict(batch_size=self.batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=True)
########### DEBUG ###########
# data_loader_params['num_workers'] = 0 # set to 0 when debugging
########### DEBUG ###########
data_loader = torch.utils.data.DataLoader(dataset,
**data_loader_params)
return data_loader
def get_data_loader(self, root='./filelists/tabula_muris', mode='train'): #parameters that would change on train/val set
dataset = SimpleDataset(root=root, mode=mode, min_samples=self.batch_size)
data_loader_params = dict(batch_size = self.batch_size, shuffle = True, num_workers = 4, pin_memory = True)
data_loader = torch.utils.data.DataLoader(dataset, **data_loader_params)
return data_loader
