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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, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.image.scc import _scc_per_channel_compute as _scc_compute
from torchmetrics.functional.image.scc import _scc_update
from torchmetrics.metric import Metric
class SpatialCorrelationCoefficient(Metric):
"""Compute Spatial Correlation Coefficient (SCC_).
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)`` or ``(N,H,W)``.
- ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)`` or ``(N,H,W)``.
As output of `forward` and `compute` the metric returns the following output
- ``scc`` (:class:`~torch.Tensor`): Tensor with scc score
Args:
hp_filter: High-pass filter tensor. default: tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]).
window_size: Local window size integer. default: 8.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torch import randn
>>> from torchmetrics.image import SpatialCorrelationCoefficient as SCC
>>> preds = randn([32, 3, 64, 64])
>>> target = randn([32, 3, 64, 64])
>>> scc = SCC()
>>> scc(preds, target)
tensor(0.0023)
"""
is_differentiable = True
higher_is_better = True
full_state_update = False
scc_score: Tensor
total: Tensor
def __init__(self, high_pass_filter: Optional[Tensor] = None, window_size: int = 8, **kwargs: Any) -> None:
super().__init__(**kwargs)
if high_pass_filter is None:
high_pass_filter = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
self.hp_filter = high_pass_filter
self.ws = window_size
self.add_state("scc_score", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0.0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
preds, target, hp_filter = _scc_update(preds, target, self.hp_filter, self.ws)
scc_per_channel = [
_scc_compute(preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, self.ws)
for i in range(preds.size(1))
]
self.scc_score += torch.sum(torch.mean(torch.cat(scc_per_channel, dim=1), dim=[1, 2, 3]))
self.total += preds.size(0)
def compute(self) -> Tensor:
"""Compute the VIF score based on inputs passed in to ``update`` previously."""
return self.scc_score / self.total
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