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"""Module that tracks the min and max values of the observations in each batch."""
# Copyright (C) 2020 Intel Corporation
#
# 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.
from typing import Tuple
import torch
from torch import Tensor
from torchmetrics import Metric
class MinMax(Metric):
"""Track the min and max values of the observations in each batch."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.add_state("min", torch.tensor(float("inf")), persistent=True) # pylint: disable=not-callable
self.add_state("max", torch.tensor(float("-inf")), persistent=True) # pylint: disable=not-callable
self.min = torch.tensor(float("inf")) # pylint: disable=not-callable
self.max = torch.tensor(float("-inf")) # pylint: disable=not-callable
# pylint: disable=arguments-differ
def update(self, predictions: Tensor) -> None: # type: ignore
"""Update the min and max values."""
self.max = torch.max(self.max, torch.max(predictions))
self.min = torch.min(self.min, torch.min(predictions))
def compute(self) -> Tuple[Tensor, Tensor]:
"""Return min and max values."""
return self.min, self.max
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