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tv.py
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tv.py
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# Copyright The 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, List, Optional, Sequence, Union
import torch
from torch import Tensor, tensor
from typing_extensions import Literal
from torchmetrics.functional.image.tv import _total_variation_compute, _total_variation_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["TotalVariation.plot"]
class TotalVariation(Metric):
"""Compute Total Variation loss (`TV`_).
As input to ``forward`` and ``update`` the metric accepts the following input
- ``img`` (:class:`~torch.Tensor`): A tensor of shape ``(N, C, H, W)`` consisting of images
As output of `forward` and `compute` the metric returns the following output
- ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average TV value
over sample else returns tensor of shape ``(N,)`` with TV values per sample
Args:
reduction: a method to reduce metric score over samples
- ``'mean'``: takes the mean over samples
- ``'sum'``: takes the sum over samples
- ``None`` or ``'none'``: return the score per sample
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``reduction`` is not one of ``'sum'``, ``'mean'``, ``'none'`` or ``None``
Example:
>>> import torch
>>> from torchmetrics.image import TotalVariation
>>> _ = torch.manual_seed(42)
>>> tv = TotalVariation()
>>> img = torch.rand(5, 3, 28, 28)
>>> tv(img)
tensor(7546.8018)
"""
full_state_update: bool = False
is_differentiable: bool = True
higher_is_better: bool = False
plot_lower_bound: float = 0.0
num_elements: Tensor
score_list: List[Tensor]
score: Tensor
def __init__(self, reduction: Optional[Literal["mean", "sum", "none"]] = "sum", **kwargs: Any) -> None:
super().__init__(**kwargs)
if reduction is not None and reduction not in ("sum", "mean", "none"):
raise ValueError("Expected argument `reduction` to either be 'sum', 'mean', 'none' or None")
self.reduction = reduction
self.add_state("score_list", default=[], dist_reduce_fx="cat")
self.add_state("score", default=tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state("num_elements", default=tensor(0, dtype=torch.int), dist_reduce_fx="sum")
def update(self, img: Tensor) -> None:
"""Update current score with batch of input images."""
score, num_elements = _total_variation_update(img)
if self.reduction is None or self.reduction == "none":
self.score_list.append(score)
else:
self.score += score.sum()
self.num_elements += num_elements
def compute(self) -> Tensor:
"""Compute final total variation."""
score = dim_zero_cat(self.score_list) if self.reduction is None or self.reduction == "none" else self.score
return _total_variation_compute(score, self.num_elements, self.reduction)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import TotalVariation
>>> metric = TotalVariation()
>>> metric.update(torch.rand(5, 3, 28, 28))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import TotalVariation
>>> metric = TotalVariation()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(5, 3, 28, 28)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)