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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Metric configurations for TF Model Garden."""
from collections.abc import Mapping
import dataclasses
from typing import Any
import tensorflow_models as tfm
@dataclasses.dataclass(kw_only=True)
class SlicedMetricConfig(tfm.core.config_definitions.base_config.Config):
"""Sliced metric configuration.
Attributes:
slicing_feature: The feature whose values to slice the metric on. Required.
slicing_spec: A mapping from the name of the slice to the value to slice on.
The name will be displayed on TB. Required.
slicing_feature_dtype: Optional dtype to cast the slicing feature and the
values to slice on.
"""
slicing_feature: str | None = None
slicing_spec: Mapping[str, int] | None = None
slicing_feature_dtype: str | None = None
def __post_init__(
self, default_params: dict[str, Any], restrictions: list[str]
):
if not restrictions:
restrictions = ['slicing_feature != None', 'slicing_spec != None']
super().__post_init__(
default_params=default_params, restrictions=restrictions
)
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