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author | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2022-09-27 23:18:20 +0200 |
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committer | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2022-09-27 23:18:20 +0200 |
commit | 723cf87846bf7297326fc82973a1e148af317638 (patch) | |
tree | 0bd49e42a3ca8c2c9e16472355b7f262cef27e05 /text_recognizer/data/transforms/load_transform.py | |
parent | 2dcbd13eb311d0a4b31e086a53d2afc55d50e0b5 (diff) |
Remove transforms
Diffstat (limited to 'text_recognizer/data/transforms/load_transform.py')
-rw-r--r-- | text_recognizer/data/transforms/load_transform.py | 46 |
1 files changed, 0 insertions, 46 deletions
diff --git a/text_recognizer/data/transforms/load_transform.py b/text_recognizer/data/transforms/load_transform.py deleted file mode 100644 index e8c57bc..0000000 --- a/text_recognizer/data/transforms/load_transform.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Load a config of transforms.""" -from pathlib import Path -from typing import Callable - -import torchvision.transforms as T -from hydra.utils import instantiate -from loguru import logger as log -from omegaconf import DictConfig, OmegaConf - -TRANSFORM_DIRNAME = ( - Path(__file__).resolve().parents[3] / "training" / "conf" / "datamodule" -) - - -def _load_config(filepath: str) -> DictConfig: - log.debug(f"Loading transforms from config: {filepath}") - path = TRANSFORM_DIRNAME / Path(filepath) - with open(path) as f: - cfgs = OmegaConf.load(f) - return cfgs - - -def _load_transform(transform: DictConfig) -> Callable: - """Loads a transform.""" - if "ColorJitter" in transform._target_: - return T.ColorJitter(brightness=list(transform.brightness)) - if transform.get("interpolation"): - transform.interpolation = getattr( - T.functional.InterpolationMode, transform.interpolation - ) - return instantiate(transform, _recursive_=False) - - -def load_transform_from_file(filepath: str) -> T.Compose: - """Loads transforms from a config.""" - cfgs = _load_config(filepath) - transform = load_transform(cfgs) - return transform - - -def load_transform(cfgs: DictConfig) -> T.Compose: - transforms = [] - for cfg in cfgs.values(): - transform = _load_transform(cfg) - transforms.append(transform) - return T.Compose(transforms) |