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mae.py
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mae.py
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# Copyright The PyTorch Lightning team.
#
# 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.utilities.checks import _check_same_shape
def _mean_absolute_error_update(preds: Tensor, target: Tensor) -> Tuple[Tensor, int]:
"""Updates and returns variables required to compute Mean Absolute Error.
Checks for same shape of input tensors.
Args:
preds: Predicted tensor
target: Ground truth tensor
"""
_check_same_shape(preds, target)
sum_abs_error = torch.sum(torch.abs(preds - target))
n_obs = target.numel()
return sum_abs_error, n_obs
def _mean_absolute_error_compute(sum_abs_error: Tensor, n_obs: int) -> Tensor:
"""Computes Mean Absolute Error.
Args:
sum_abs_error: Sum of absolute value of errors over all observations
n_obs: Number of predictions or observations
Example:
>>> preds = torch.tensor([0., 1, 2, 3])
>>> target = torch.tensor([0., 1, 2, 2])
>>> sum_abs_error, n_obs = _mean_absolute_error_update(preds, target)
>>> _mean_absolute_error_compute(sum_abs_error, n_obs)
tensor(0.2500)
"""
return sum_abs_error / n_obs
def mean_absolute_error(preds: Tensor, target: Tensor) -> Tensor:
"""Computes mean absolute error.
Args:
preds: estimated labels
target: ground truth labels
Return:
Tensor with MAE
Example:
>>> from torchmetrics.functional import mean_absolute_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_absolute_error(x, y)
tensor(0.2500)
"""
sum_abs_error, n_obs = _mean_absolute_error_update(preds, target)
return _mean_absolute_error_compute(sum_abs_error, n_obs)