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"""Normalization callback utils.""" |
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import importlib |
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from lightning.pytorch import Callback |
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from omegaconf import DictConfig |
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from anomalib.utils.normalization import NormalizationMethod |
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from anomalib.utils.types import NORMALIZATION |
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from .min_max_normalization import _MinMaxNormalizationCallback |
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def get_normalization_callback( |
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normalization_method: NORMALIZATION = NormalizationMethod.MIN_MAX, |
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) -> Callback | None: |
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"""Return normalization object. |
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normalization_method is an instance of ``Callback``, it is returned as is. |
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if normalization_method is of type ``NormalizationMethod``, then a new class is created based on the type of |
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normalization_method. |
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Otherwise it expects a dictionary containing class_path and init_args. |
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normalization_method: |
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class_path: MinMaxNormalizer |
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init_args: |
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- |
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- |
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Example: |
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>>> normalizer = get_normalization_callback(NormalizationMethod.MIN_MAX) |
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or |
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>>> normalizer = get_normalization_callback("min_max") |
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or |
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>>> normalizer = get_normalization_callback({"class_path": "MinMaxNormalizationCallback", "init_args": {}}) |
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or |
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>>> normalizer = get_normalization_callback(MinMaxNormalizationCallback()) |
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""" |
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normalizer: Callback | None |
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if isinstance(normalization_method, NormalizationMethod | str): |
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normalizer = _get_normalizer_from_method(NormalizationMethod(normalization_method)) |
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elif isinstance(normalization_method, Callback): |
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normalizer = normalization_method |
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elif isinstance(normalization_method, DictConfig): |
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normalizer = _parse_normalizer_config(normalization_method) |
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else: |
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msg = f"Unknown normalizer type {normalization_method}" |
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raise TypeError(msg) |
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return normalizer |
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def _get_normalizer_from_method(normalization_method: NormalizationMethod | str) -> Callback | None: |
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if normalization_method == NormalizationMethod.NONE: |
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normalizer = None |
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elif normalization_method == NormalizationMethod.MIN_MAX: |
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normalizer = _MinMaxNormalizationCallback() |
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else: |
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msg = f"Unknown normalization method {normalization_method}" |
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raise ValueError(msg) |
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return normalizer |
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def _parse_normalizer_config(normalization_method: DictConfig) -> Callback: |
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class_path = normalization_method.class_path |
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init_args = normalization_method.init_args |
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if len(class_path.split(".")) == 1: |
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module_path = "anomalib.utils.callbacks.normalization" |
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else: |
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module_path = ".".join(class_path.split(".")[:-1]) |
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class_path = class_path.split(".")[-1] |
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module = importlib.import_module(module_path) |
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class_ = getattr(module, class_path) |
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return class_(**init_args) |
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