def __init__(self,
data_dir,
batch_size,
mode,
num_parallel_batches=1,
repeat_num=5,
padding=8,
fp16=False,
drop_remainder=False):
"""Init Cifar10TF."""
self.data_dir = FileOps.download_dataset(data_dir)
self.batch_size = batch_size
self.mode = mode
self.num_parallel_batches = num_parallel_batches
self.repeat_num = repeat_num
self.dtype = tf.float16 if fp16 is True else tf.float32
self.num_channels = 3
self.height = 32
self.width = 32
self.single_data_bytes = self.height * self.width * self.num_channels + 1
self.num_images_train = 50000
self.num_images_valid = 10000
self.drop_remainder = drop_remainder
self.single_data_size = [self.num_channels, self.height, self.width]
self.padding = padding
def __init__(self, **kwargs):
"""Init Cifar10."""
super(Imagenet, self).__init__(**kwargs)
self.data_path = FileOps.download_dataset(self.args.data_path)
self.fp16 = self.args.fp16
self.num_parallel_batches = self.args.num_parallel_batches
self.image_size = self.args.image_size
self.drop_remainder = self.args.drop_last
if self.data_path == 'null' or not self.data_path:
self.data_path = None
self.num_parallel_calls = self.args.num_parallel_calls
def __init__(self, **kwargs):
super(Cityscapes, self).__init__(**kwargs)
config = obj2config(getattr(self.config, self.mode))
config.update(self.args)
self.args = config
self.root_dir = self.args['root_dir']
self.image_size = self.args.Rescale.size
self.list_file = self.args.list_file
self.batch_size = self.args.get('batch_size', 1)
self.num_parallel_batches = self.args.get('num_parallel_batches', 1)
self.drop_remainder = self.args.get('drop_remainder', False)
self.transforms = self._init_transforms()
self.root_dir = FileOps.download_dataset(self.root_dir)
self._init_data_files()
def __init__(self,
data_dir,
batch_size,
mode,
num_parallel_batches=1,
repeat_num=5,
padding=8,
fp16=False,
drop_remainder=False):
"""Init CocoTF."""
self.data_dir = FileOps.download_dataset(data_dir)
self.batch_size = batch_size
self.mode = mode
self.num_parallel_batches = num_parallel_batches
self.repeat_num = repeat_num
self.dtype = tf.float16 if fp16 is True else tf.float32
self.drop_remainder = drop_remainder
self._include_mask = False
self._dataset_fn = tf.data.TFRecordDataset
def __init__(self, **kwargs):
"""Init Cifar10."""
super(Cifar10, self).__init__(**kwargs)
self.data_path = FileOps.download_dataset(self.args.data_path)
self.num_parallel_batches = self.args.num_parallel_batches
self.train_portion = self.args.train_portion
self.dtype = tf.float16 if self.args.fp16 is True else tf.float32
self.num_channels = 3
self.height = 32
self.width = 32
self.single_data_bytes = self.height * self.width * self.num_channels + 1
self.num_images = self.args.num_images
if self.train_portion != 1:
if self.mode == 'train':
self.num_images = int(self.num_images * self.train_portion)
elif self.mode == 'val':
self.num_images = int(self.args.num_images_train *
(1 - self.train_portion))
self.drop_remainder = self.args.drop_last
self.single_data_size = [self.num_channels, self.height, self.width]
if self.mode == 'train':
self.padding = self.args.padding
