from pathlib import Path
from VSR.DataLoader.Dataset import load_datasets, Dataset
from VSR.DataLoader.Loader import QuickLoader
from VSR.Models import get_model, list_supported_models
from VSR.Util.Config import Config
from VSR.Framework.Callbacks import (
save_image, to_rgb, to_gray, lr_decay
)
try:
from .custom_api import *
except ImportError:
from custom_api import *
tf.flags.DEFINE_enum('model', None, list_supported_models(), help="specify a model to use")
tf.flags.DEFINE_enum('output_color', 'RGB', ('RGB', 'L', 'GRAY', 'Y'), help="specify output color format")
tf.flags.DEFINE_integer('epochs', 50, lower_bound=1, help="training epochs")
tf.flags.DEFINE_integer('steps_per_epoch', 200, lower_bound=1, help="specify steps in every epoch training")
tf.flags.DEFINE_integer('threads', 1, lower_bound=1, help="number of threads to use while reading data")
tf.flags.DEFINE_integer('output_index', -1, help="specify access index of output array")
tf.flags.DEFINE_string('c', None, help="specify a configure file")
tf.flags.DEFINE_string('p', None, help="specify a parameter file, otherwise will use the file in ./parameters")
tf.flags.DEFINE_string('test', None, help="specify another dataset for testing")
tf.flags.DEFINE_string('infer', None, help="specify a file, a path or a dataset for inferring")
tf.flags.DEFINE_string('save_dir', '../Results', help="specify a folder to save checkpoint and output images")
tf.flags.DEFINE_string('data_config', '../Data/datasets.yaml', help="path to data config file")
tf.flags.DEFINE_string('dataset', 'none', help="specify a dataset alias for training")
tf.flags.DEFINE_string('memory_limit', None, help="limit the memory usage. i.e. '4GB', '1024MB'")
tf.flags.DEFINE_string('comment', None, help="append a postfix string to save dir")
tf.flags.DEFINE_multi_string('add_custom_callbacks', None, help="")
from VSR.Util.Config import Config
from VSR.Framework.Callbacks import save_image, to_rgb, to_gray, lr_decay
# tricky import for intellisense
try:
from .custom_api import *
except ImportError:
from custom_api import *
# Import models in development
try:
from Exp import *
except ImportError as ex:
pass
tf.flags.DEFINE_enum('model',
'srcnn',
list_supported_models(),
help="specify a model to use")
tf.flags.DEFINE_enum('output_color',
'RGB', ('RGB', 'L', 'GRAY', 'Y'),
help="specify output color format")
tf.flags.DEFINE_integer('epochs', 50, lower_bound=1, help="training epochs")
tf.flags.DEFINE_integer('steps_per_epoch',
200,
lower_bound=1,
help="specify steps in every epoch training")
tf.flags.DEFINE_integer('threads',
1,
lower_bound=1,
help="number of threads to use while reading data")
tf.flags.DEFINE_integer('output_index',
-1,