def get_colorize_data(
sz: int,
bs: int,
crappy_path: Path,
good_path: Path,
random_seed: int = None,
keep_pct: float = 1.0,
num_workers: int = 8,
stats: tuple = imagenet_stats,
xtra_tfms=[],
) -> ImageDataBunch:
src = (ImageImageList.from_folder(crappy_path,
convert_mode='RGB').use_partial_data(
sample_pct=keep_pct,
seed=random_seed).split_by_rand_pct(
0.1, seed=random_seed))
data = (src.label_from_func(
lambda x: good_path / x.relative_to(crappy_path)).transform(
get_transforms(max_zoom=1.2,
max_lighting=0.5,
max_warp=0.25,
xtra_tfms=xtra_tfms),
size=sz,
tfm_y=True,
).databunch(bs=bs, num_workers=num_workers,
no_check=True).normalize(stats, do_y=True))
data.c = 3
return data
def get_DIV2k_data(pLow, pFull, bs: int, sz: int):
"""Given the path of low resolution images with a proper suffix
returns a databunch
"""
suffixes = {
"dataset/DIV2K_train_LR_x8": "x8",
"dataset/DIV2K_train_LR_difficult": "x4d",
"dataset/DIV2K_train_LR_mild": "x4m"
}
lowResSuffix = suffixes[str(pLow)]
src = ImageImageList.from_folder(pLow, presort=True).split_by_idxs(
train_idx=list(range(0, 800)), valid_idx=list(range(800, 900)))
data = (src.label_from_func(
lambda x: pFull / (x.name).replace(lowResSuffix, '')).transform(
get_transforms(max_rotate=30,
max_zoom=3.,
max_lighting=.4,
max_warp=.4,
p_affine=.85),
size=sz,
tfm_y=True,
).databunch(bs=bs, num_workers=8,
no_check=True).normalize(imagenet_stats, do_y=True))
data.c = 3
return data
def get_dummy_databunch(bs: int, sz: int):
"""Returns sz databunch
"""
path = Path('./dataset/dummy/')
src = ImageImageList.from_folder(path).split_none()
data = (src.label_from_func(
lambda x: path / (x.name.replace(".jpg", ".png"))).transform(
size=sz,
tfm_y=True).databunch(bs=bs, num_workers=1,
no_check=True).normalize(imagenet_stats,
do_y=True))
data.c = 3
return data
def get_DIV2k_data_QF(pLow, pFull, bs: int, sz: int):
"""Given the path of low resolution images
returns a databunch
"""
src = ImageImageList.from_folder(pLow, presort=True).split_by_idxs(
train_idx=list(range(0, 800)), valid_idx=list(range(800, 900)))
data = (src.label_from_func(
lambda x: pFull / (x.name.replace(".jpg", ".png"))).transform(
get_transforms(max_zoom=2.), size=sz,
tfm_y=True).databunch(bs=bs, num_workers=8,
no_check=True).normalize(imagenet_stats,
do_y=True))
data.c = 3
return data
