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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 _sam_update(preds: Tensor, target: Tensor) -> tuple[Tensor, Tensor]:
"""Update and returns variables required to compute Spectral Angle Mapper.
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}."
)
if (preds.shape[1] <= 1) or (target.shape[1] <= 1):
raise ValueError(
"Expected channel dimension of `preds` and `target` to be larger than 1."
f" Got preds: {preds.shape[1]} and target: {target.shape[1]}."
)
return preds, target
def _sam_compute(
preds: Tensor,
target: Tensor,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
) -> Tensor:
"""Compute Spectral Angle Mapper.
Args:
preds: estimated image
target: ground truth image
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, 3, 16, 16])
>>> target = rand([16, 3, 16, 16])
>>> preds, target = _sam_update(preds, target)
>>> _sam_compute(preds, target)
tensor(0.5914)
"""
dot_product = (preds * target).sum(dim=1)
preds_norm = preds.norm(dim=1)
target_norm = target.norm(dim=1)
sam_score = torch.clamp(dot_product / (preds_norm * target_norm), -1, 1).acos()
return reduce(sam_score, reduction)
def spectral_angle_mapper(
preds: Tensor,
target: Tensor,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
) -> Tensor:
"""Universal Spectral Angle Mapper.
Args:
preds: estimated image
target: ground truth image
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 Spectral Angle Mapper 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 spectral_angle_mapper
>>> preds = rand([16, 3, 16, 16],)
>>> target = rand([16, 3, 16, 16])
>>> spectral_angle_mapper(preds, target)
tensor(0.5914)
References:
[1] Roberta H. Yuhas, Alexander F. H. Goetz and Joe W. Boardman, "Discrimination among semi-arid
landscape endmembers using the Spectral Angle Mapper (SAM) algorithm" in PL, Summaries of the Third Annual JPL
Airborne Geoscience Workshop, vol. 1, June 1, 1992.
"""
preds, target = _sam_update(preds, target)
return _sam_compute(preds, target, reduction)
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