def __init__(self, path=None, shuffle=True):
DatasetGroup.__init__(self, 'svhn', path=path)
self.train_on_extra = False # disabled
self.image_shape = (32, 32, 3)
self.label_shape = ()
self.shuffle = shuffle
self._load_datasets()
def __init__(self, path=None, shuffle=True):
DatasetGroup.__init__(self, 'mnist', path)
self.image_shape = (28, 28, 1)
self.label_shape = ()
self.shuffle = shuffle
# self.download()
self._load_datasets()
def __init__(self, path=None, shuffle=True):
data_path = "D:/Dataset/Domain_Adaptation/"
DatasetGroup.__init__(self, 'DAGM-10-to-4', data_path)
self.image_shape = (256, 256, 1)
self.label_shape = ()
self.shuffle = shuffle
# self.download()
self._load_datasets()
def __init__(self, path=None, shuffle=True):
data_path = "D:/Dataset/Domain_Adaptation/"
DatasetGroup.__init__(self, 'SVHN-to-MNIST', data_path)
self.image_shape = (32, 32, 3)
self.label_shape = ()
self.shuffle = shuffle
# self.download()
self._load_datasets()
def __init__(self, path=None, shuffle=True):
data_path = "D:/Dataset/Domain_Adaptation/"
DatasetGroup.__init__(self, 'MLCC_1st_to_2nd', data_path)
self.image_shape = (160, 160, 3)
self.label_shape = ()
self.shuffle = shuffle
# self.download()
self._load_datasets()
def __init__(self, path=None, shuffle=True, download=False):
DatasetGroup.__init__(self, 'vda2017s', path=path, download=False)
self.image_shape = (384, 216, 3)
self.label_shape = ()
self.shuffle = shuffle
self.base_path = os.path.join(path, 'train')
for split in self.file_names.keys():
with open(os.path.join(self.base_path,
self.file_names[split])) as f:
img_file_names, labels = zip(
*[line.split() for line in f.readlines()])
full_file_names = [
os.path.join(self.base_path, x) for x in img_file_names
]
dataset = FilenameDataset(full_file_names, list(map(int, labels)),
'png')
setattr(self, split, dataset)
def __init__(self, path=None, shuffle=True, download=False):
DatasetGroup.__init__(self, 'vda2017coco', path=path, download=False)
self.image_shape = (None, None, 3)
self.label_shape = ()
self.shuffle = shuffle
self.base_path = os.path.join(path, 'validation')
for split in self.file_names.keys():
with open(os.path.join(self.base_path,
self.file_names[split])) as f:
img_file_names, labels = zip(
*[line.split() for line in f.readlines()])
full_file_names = [
os.path.join(self.base_path, x) for x in img_file_names
]
int_label_list = list(map(int, labels))
self.num_classes = max(self.num_classes,
np.max(int_label_list) + 1)
dataset = FilenameDataset(full_file_names, int_label_list, 'jpeg')
setattr(self, split, dataset)
logging.info('detected %d classes in input data' % self.num_classes)
def __init__(self, path=None, shuffle=True):
DatasetGroup.__init__(self, 'product', path=path)
self.image_shape = (256, 256, 3)
self.label_shape = ()
self.shuffle = shuffle
self._load_datasets()
def __init__(self, path=None, shuffle=True, download=True):
DatasetGroup.__init__(self, 'usps', path=path, download=download)
self.image_shape = (16, 16, 1)
self.label_shape = ()
self.shuffle = shuffle
self._load_datasets()
def __init__(self, path=None, shuffle=True):
DatasetGroup.__init__(self, 'Caltech', path=path)
self.image_shape = (224, 224, 3)
self.label_shape = ()
self.shuffle = shuffle
self._load_datasets()
