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import fastai | |
from fastai import * | |
from fastai.core import * | |
from fastai.vision.transform import get_transforms | |
from fastai.vision.data import ImageImageList, ImageDataBunch, imagenet_stats | |
from .augs import noisify | |
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_dummy_databunch() -> ImageDataBunch: | |
path = Path('./dummy/') | |
return get_colorize_data( | |
sz=1, bs=1, crappy_path=path, good_path=path, keep_pct=0.001 | |
) | |