tra_set, val_set = load_dataset()
observations = args.observations
input_size = (args.input_x, args.input_y, observations)
framebatch = args.framebatch
output_size = (args.input_x, args.input_y)
downscale = args.downscale
tra_kwag = {'inputs': tra_set, 'framebatch': framebatch}
val_kwag = {'inputs': val_set, 'framebatch': framebatch}
if args.downscale == 'mix':
tra_provider = DataProvider.SpecialSuperResolutionProvider(
stride=(1, 1),
input_size=input_size,
output_size=output_size,
batchsize=args.batchsize,
shuffle=True)
val_provider = DataProvider.SpecialSuperResolutionProvider(
stride=(1, 1),
input_size=input_size,
output_size=output_size,
batchsize=-1,
shuffle=False)
else:
tra_provider = DataProvider.SuperResolutionProvider(
stride=(1, 1),
input_size=input_size,
output_size=output_size,
batchsize=args.batchsize,
shuffle=True)
'std': args.std,
'norm_tar': True
}
test_kwag = {
'inputs': test_set,
'framebatch': framebatch,
'mean': args.mean,
'std': args.std,
'norm_tar': True
}
tra_provider = DataProvider.Provider(stride=stride,
input_size=input_size,
output_size=output_size,
prediction_gap=prediction_gap,
batchsize=batchsize,
pad=pad,
pad_value=pad_value,
shuffle=True)
val_provider = DataProvider.Provider(stride=(4, 4),
input_size=input_size,
output_size=output_size,
prediction_gap=prediction_gap,
batchsize=-1,
pad=pad,
pad_value=pad_value,
shuffle=False)
test_provider = DataProvider.Provider(stride=stride,
input_size=input_size,
default=80,
help="spatial length of input of y axis")
return parser.parse_args()
args = get_arguments()
test_set = (np.load(args.datadir + 'milan_tra.npy') - args.mean) / args.std
print('test set:', test_set.shape)
observations = args.observations
input_size = (args.input_x, args.input_y, observations)
output_size = (args.input_x, args.input_y)
downscale = args.downscale
test_provider = DataProvider.MoverProvider(length=observations)
test_val = {
'inputs':test_set_y,
'targets':test_set_y,
'special': True if downscale == 'mix' else False,
'keepdims':True}
if args.downscale == 2:
from up2 import zipper
if args.downscale == 4 or 'mix':
from up4 import zipper
if args.downscale == 10:
from up10 import zipper