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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.
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.utilities.checks import _check_same_shape
from torchmetrics.utilities.distributed import reduce
def _ergas_update(preds: Tensor, target: Tensor) -> tuple[Tensor, Tensor]:
"""Update and returns variables required to compute Erreur Relative Globale Adimensionnelle de Synthèse.
Args:
preds: Predicted tensor
target: Ground truth tensor
"""
if preds.dtype != target.dtype:
raise TypeError(
"Expected `preds` and `target` to have the same data type."
f" Got preds: {preds.dtype} and target: {target.dtype}."
)
_check_same_shape(preds, target)
if len(preds.shape) != 4:
raise ValueError(
f"Expected `preds` and `target` to have BxCxHxW shape. Got preds: {preds.shape} and target: {target.shape}."
)
return preds, target
def _ergas_compute(
preds: Tensor,
target: Tensor,
ratio: float = 4,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
) -> Tensor:
"""Erreur Relative Globale Adimensionnelle de Synthèse.
Args:
preds: estimated image
target: ground truth image
ratio: ratio of high resolution to low resolution
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'`` or ``None``: no reduction will be applied
Example:
>>> from torch import rand
>>> preds = rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> preds, target = _ergas_update(preds, target)
>>> torch.round(_ergas_compute(preds, target))
tensor(10.)
"""
b, c, h, w = preds.shape
preds = preds.reshape(b, c, h * w)
target = target.reshape(b, c, h * w)
diff = preds - target
sum_squared_error = torch.sum(diff * diff, dim=2)
rmse_per_band = torch.sqrt(sum_squared_error / (h * w))
mean_target = torch.mean(target, dim=2)
ergas_score = 100 / ratio * torch.sqrt(torch.sum((rmse_per_band / mean_target) ** 2, dim=1) / c)
return reduce(ergas_score, reduction)
def error_relative_global_dimensionless_synthesis(
preds: Tensor,
target: Tensor,
ratio: float = 4,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
) -> Tensor:
"""Calculates `Error relative global dimensionless synthesis`_ (ERGAS) metric.
Args:
preds: estimated image
target: ground truth image
ratio: ratio of high resolution to low resolution
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'`` or ``None``: no reduction will be applied
Return:
Tensor with RelativeG score
Raises:
TypeError:
If ``preds`` and ``target`` don't have the same data type.
ValueError:
If ``preds`` and ``target`` don't have ``BxCxHxW shape``.
Example:
>>> from torch import rand
>>> from torchmetrics.functional.image import error_relative_global_dimensionless_synthesis
>>> preds = rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> error_relative_global_dimensionless_synthesis(preds, target)
tensor(9.6193)
"""
preds, target = _ergas_update(preds, target)
return _ergas_compute(preds, target, ratio, reduction)
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