| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| 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 |
|
|
