help='When this flag is provided, flip augmentation is performed.')
parser.add_argument(
'--crop_augment',
choices=['center', 'avgpool', 'five'],
default=None,
help='When this flag is provided, crop augmentation is performed.'
'`avgpool` means the full image at the precrop size is used and '
'the augmentation is performed by the average pooling. `center` means'
'only the center crop is used and `five` means the four corner and '
'center crops are used. When not provided, by default the image is '
'resized to network input size.')
parser.add_argument(
'--aggregator',
choices=AGGREGATORS.keys(),
default=None,
help='The type of aggregation used to combine the different embeddings '
'after augmentation.')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='Don\'t be so verbose.')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
def flip_augment(image, fid, pid):
""" Returns both the original and the horizontal flip of an image. """
parser.add_argument(
'--flip_augment', action='store_true', default=False,
help='When this flag is provided, flip augmentation is performed.')
parser.add_argument(
'--crop_augment', choices=['center', 'avgpool', 'five'], default=None,
help='When this flag is provided, crop augmentation is performed.'
'`avgpool` means the full image at the precrop size is used and '
'the augmentation is performed by the average pooling. `center` means'
'only the center crop is used and `five` means the four corner and '
'center crops are used. When not provided, by default the image is '
'resized to network input size.')
parser.add_argument(
'--aggregator', choices=AGGREGATORS.keys(), default=None,
help='The type of aggregation used to combine the different embeddings '
'after augmentation.')
parser.add_argument(
'--quiet', action='store_true', default=False,
help='Don\'t be so verbose.')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
def flip_augment(image, fid, pid):
""" Returns both the original and the horizontal flip of an image. """
images = tf.stack([image, tf.reverse(image, [1])])
return images, [fid]*2, [pid]*2