core.modules.normalization._load_utils

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

Contents