/
log_mse.py
80 lines (62 loc) · 2.83 KB
/
log_mse.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
# 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 Any, Dict, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.regression.log_mse import _mean_squared_log_error_compute, _mean_squared_log_error_update
from torchmetrics.metric import Metric
class MeanSquaredLogError(Metric):
r"""Computes `mean squared logarithmic error`_ (MSLE):
.. math:: \text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
Args:
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics import MeanSquaredLogError
>>> target = torch.tensor([2.5, 5, 4, 8])
>>> preds = torch.tensor([3, 5, 2.5, 7])
>>> mean_squared_log_error = MeanSquaredLogError()
>>> mean_squared_log_error(preds, target)
tensor(0.0397)
.. note::
Half precision is only support on GPU for this metric
"""
is_differentiable = True
higher_is_better = False
sum_squared_log_error: Tensor
total: Tensor
def __init__(
self,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(compute_on_step=compute_on_step, **kwargs)
self.add_state("sum_squared_log_error", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
sum_squared_log_error, n_obs = _mean_squared_log_error_update(preds, target)
self.sum_squared_log_error += sum_squared_log_error
self.total += n_obs
def compute(self) -> Tensor:
"""Compute mean squared logarithmic error over state."""
return _mean_squared_log_error_compute(self.sum_squared_log_error, self.total)