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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 | |
| ) | |