def update_argparser(parser):
datasets.update_argparser(parser)
parser.add_argument(
'--noise-sigma',
help='Scale for image super-resolution',
default=25,
type=float)
parser.add_argument(
'--train-patch-size',
help='Number of pixels in height or width of patches',
default=43,
type=int)
parser.add_argument(
'--eval-patch-size',
help='Number of pixels in height or width of patches',
default=43,
type=int)
parser.add_argument(
'--train-flist',
help='GCS location to write checkpoints and export models',
type=str,
required=True)
parser.add_argument(
'--eval-flist',
help='GCS location to write checkpoints and export models',
type=str,
required=True)
parser.set_defaults(
num_channels=NUM_CHANNELS,
train_batch_size=16,
eval_batch_size=1,
shuffle_buffer_size=800,
)
def update_argparser(parser):
datasets.update_argparser(parser)
parser.add_argument('--scale',
help='Scale factor for image super-resolution.',
default=2,
type=int)
parser.add_argument(
'--lr_patch_size',
help='Number of pixels in height or width of LR patches.',
default=48,
type=int)
parser.add_argument(
'--ignored_boundary_size',
help='Number of ignored boundary pixels of LR patches.',
default=2,
type=int)
parser.add_argument(
'--num_patches',
help='Number of sampling patches per image for training.',
default=100,
type=int)
parser.set_defaults(
train_batch_size=16,
eval_batch_size=1,
image_mean=0.5,
)
def update_argparser(parser):
datasets.update_argparser(parser)
parser.set_defaults(
num_classes=NUM_CLASSES,
train_batch_size=128,
eval_batch_size=512,
shuffle_buffer_size=50000,
)
def update_argparser(parser):
datasets.update_argparser(parser)
parser.add_argument('--scale',
help='Scale for image super-resolution',
default=2,
type=int)
parser.add_argument(
'--lr-patch-size',
help='Number of pixels in height or width of LR patches',
default=48,
type=int)
parser.set_defaults(
num_channels=NUM_CHANNELS,
train_batch_size=16,
eval_batch_size=1,
shuffle_buffer_size=800,
)