core.modules.normalization._load_utils

Copyright (c) Meta, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Functions

_load_check_duplicates(→ dict[str, torch.nn.Module])	Attempt to load a single file with normalizers/element references and check config for duplicate targets.
_load_from_config(→ dict[str, torch.nn.Module])	Load or fit normalizers or element references from config

Module Contents

core.modules.normalization._load_utils._load_check_duplicates(config: dict, name: str) → dict[str, torch.nn.Module]

Attempt to load a single file with normalizers/element references and check config for duplicate targets.

Parameters:

• config – configuration dictionary

• name – Name of module to use for logging

Returns:

dictionary of normalizer or element reference modules

core.modules.normalization._load_utils._load_from_config(config: dict, name: str, fit_fun: Callable[[list[str], torch.utils.data.Dataset, Any, Ellipsis], dict[str, torch.nn.Module]], create_fun: Callable[[str | pathlib.Path], torch.nn.Module], dataset: torch.utils.data.Dataset, checkpoint_dir: str | pathlib.Path | None = None, **fit_kwargs) → dict[str, torch.nn.Module]

Load or fit normalizers or element references from config

If a fit is done, a fitted key with value true is added to the config to avoid re-fitting once a checkpoint has been saved.

Parameters:

• config – configuration dictionary

• name – Name of module to use for logging

• fit_fun – Function to fit modules

• create_fun – Function to create a module from file

• checkpoint_dir – directory to save modules. If not given, modules won’t be saved.

Returns:

dictionary of normalizer or element reference modules