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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 Optional, Union
from torch import Tensor
from typing_extensions import Literal
def _total_variation_update(img: Tensor) -> tuple[Tensor, int]:
"""Compute total variation statistics on current batch."""
if img.ndim != 4:
raise RuntimeError(f"Expected input `img` to be an 4D tensor, but got {img.shape}")
diff1 = img[..., 1:, :] - img[..., :-1, :]
diff2 = img[..., :, 1:] - img[..., :, :-1]
res1 = diff1.abs().sum([1, 2, 3])
res2 = diff2.abs().sum([1, 2, 3])
score = res1 + res2
return score, img.shape[0]
def _total_variation_compute(
score: Tensor, num_elements: Union[int, Tensor], reduction: Optional[Literal["mean", "sum", "none"]]
) -> Tensor:
"""Compute final total variation score."""
if reduction == "mean":
return score.sum() / num_elements
if reduction == "sum":
return score.sum()
if reduction is None or reduction == "none":
return score
raise ValueError("Expected argument `reduction` to either be 'sum', 'mean', 'none' or None")
def total_variation(img: Tensor, reduction: Optional[Literal["mean", "sum", "none"]] = "sum") -> Tensor:
"""Compute total variation loss.
Args:
img: A `Tensor` of shape `(N, C, H, W)` consisting of images
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
Returns:
A loss scalar value containing the total variation
Raises:
ValueError:
If ``reduction`` is not one of ``'sum'``, ``'mean'``, ``'none'`` or ``None``
RuntimeError:
If ``img`` is not 4D tensor
Example:
>>> from torch import rand
>>> from torchmetrics.functional.image import total_variation
>>> img = rand(5, 3, 28, 28)
>>> total_variation(img)
tensor(7546.8018)
"""
# code adapted from:
# from kornia.losses import total_variation as kornia_total_variation
score, num_elements = _total_variation_update(img)
return _total_variation_compute(score, num_elements, reduction)
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