From c3649ca2c2ad08e24fc7fc5e397cc21b5f0640d8 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Wed, 29 Nov 2023 13:50:22 +0000
Subject: [PATCH 01/21] SpatialCorrelationCoefficient functionality and module
 added.

---
 src/torchmetrics/functional/image/__init__.py |   2 +
 src/torchmetrics/functional/image/scc.py      | 118 ++++++++++++++++++
 src/torchmetrics/image/__init__.py            |   2 +
 src/torchmetrics/image/scc.py                 |  40 ++++++
 4 files changed, 162 insertions(+)
 create mode 100644 src/torchmetrics/functional/image/scc.py
 create mode 100644 src/torchmetrics/image/scc.py

diff --git a/src/torchmetrics/functional/image/__init__.py b/src/torchmetrics/functional/image/__init__.py
index 329b33b66fe..41c4090a542 100644
--- a/src/torchmetrics/functional/image/__init__.py
+++ b/src/torchmetrics/functional/image/__init__.py
@@ -21,6 +21,7 @@
 from torchmetrics.functional.image.rase import relative_average_spectral_error
 from torchmetrics.functional.image.rmse_sw import root_mean_squared_error_using_sliding_window
 from torchmetrics.functional.image.sam import spectral_angle_mapper
+from torchmetrics.functional.image.scc import spatial_correlation_coefficient
 from torchmetrics.functional.image.ssim import (
     multiscale_structural_similarity_index_measure,
     structural_similarity_index_measure,
@@ -45,4 +46,5 @@
     "visual_information_fidelity",
     "learned_perceptual_image_patch_similarity",
     "perceptual_path_length",
+    "spatial_correlation_coefficient"
 ]
diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
new file mode 100644
index 00000000000..6c40df40047
--- /dev/null
+++ b/src/torchmetrics/functional/image/scc.py
@@ -0,0 +1,118 @@
+
+from typing import Union, Tuple, Optional
+from typing_extensions import Literal
+
+import torch
+from torch import Tensor, tensor
+from torch.nn.functional import conv2d
+
+from torchmetrics.utilities.checks import _check_same_shape
+from torchmetrics.utilities.distributed import reduce
+
+def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int) -> Tuple[Tensor, Tensor, Tensor]:
+    """Update and returns variables required to compute Spatial Correlation Coefficient.
+
+    Args:
+        preds: Predicted tensor
+        target: Ground truth tensor
+        hp_filter: High-pass filter tensor
+        window_size: Local window size integer
+    """
+    if preds.dtype != target.dtype:
+        target = target.to(preds.dtype)
+    _check_same_shape(preds, target)
+    if len(preds.shape) not in (3, 4):
+        raise ValueError(
+            "Expected `preds` and `target` to have batch of colored images with BxCxHxW shape"
+            "  or batch of grayscale images of BxHxW shape."
+            f" Got preds: {preds.shape} and target: {target.shape}."
+        )
+    
+    if len(preds.shape) == 3:
+        preds = preds.unsqueeze(1)
+        target = target.unsqueeze(1)
+
+    if not window_size > 0:
+        raise ValueError(
+            f"Expected `window_size` to be a positive integer. Got {window_size}."
+        )
+
+    if window_size > preds.size(2) or window_size > preds.size(3):
+        raise ValueError(
+            f"Expected `window_size` to be less than or equal to the size of the image."
+            f" Got window_size: {window_size} and image size: {preds.size(2)}x{preds.size(3)}."
+        )
+    
+    preds = preds.to(torch.float32)
+    target = target.to(torch.float32)
+    hp_filter = hp_filter[None, None, :].to(dtype=preds.dtype, device=preds.device)
+    return preds, target, hp_filter
+
+def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
+    if isinstance(pad, int):
+        pad = (pad, pad, pad, pad)
+    assert len(pad) == 4
+
+    left_pad = input[:, :, :, 0:pad[0]].flip(dims=[3])
+    right_pad = input[:, :, :, -pad[1]:].flip(dims=[3])
+    padded = torch.cat([left_pad, input, right_pad], dim=3)
+
+    top_pad = padded[:, :, 0:pad[2], :].flip(dims=[2])
+    bottom_pad = padded[:, :, -pad[3]:, :].flip(dims=[2])
+    padded = torch.cat([top_pad, padded, bottom_pad], dim=2)
+    return padded
+
+def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
+    left_padding = int(torch.floor(tensor((kernel.size(3)-1)/2)).item())
+    right_padding = int(torch.ceil(tensor((kernel.size(3)-1)/2)).item())
+    top_padding = int(torch.floor(tensor((kernel.size(2)-1)/2)).item())
+    bottom_padding = int(torch.ceil(tensor((kernel.size(2)-1)/2)).item())
+
+    padded = _symmetric_reflect_pad_2d(input, pad=(left_padding, right_padding, top_padding, bottom_padding))
+    kernel = kernel.flip([2, 3])
+    out = conv2d(padded, kernel, stride=1, padding=0)
+    return out
+
+def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
+    output = _signal_convolve_2d(input, kernel)
+    output += _signal_convolve_2d(input, kernel)
+    return output
+
+def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor):
+    preds_mean = conv2d(preds, window, stride=1, padding='same')
+    target_mean = conv2d(target, window, stride=1, padding='same')
+
+    preds_var = conv2d(preds**2, window, stride=1, padding='same') - preds_mean**2
+    target_var = conv2d(target**2, window, stride=1, padding='same') - target_mean**2
+    target_preds_cov = conv2d(target*preds, window, stride=1, padding='same') - target_mean*preds_mean
+
+    return preds_var, target_var, target_preds_cov
+
+def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int):
+    dtype = preds.dtype
+    device = preds.device
+
+    window = torch.ones(size=(1, 1, window_size, window_size), dtype=dtype, device=device)/( window_size**2 )
+    
+    preds_hp = _hp_2d_laplacian(preds, hp_filter)
+    target_hp = _hp_2d_laplacian(target, hp_filter)
+
+    preds_var, target_var, target_preds_cov = _local_variance_covariance(preds_hp, target_hp, window)
+
+    preds_var[preds_var<0] = 0
+    target_var[target_var<0] = 0
+
+    den = torch.sqrt(target_var) * torch.sqrt(preds_var)
+    idx = (den==0)
+    den[den == 0] = 1
+    scc = target_preds_cov / den
+    scc[idx] = 0
+    return scc
+
+def spatial_correlation_coefficient(preds: Tensor, target: Tensor, 
+                                    hp_filter: Tensor = tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]),
+                                    window_size: int = 8):
+    preds, target, hp_filter = _scc_update(preds, target, hp_filter, window_size)
+
+    per_channel = [_scc_per_channel_compute(preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, window_size) for i in range(preds.size(1))]
+    return reduce(torch.cat(per_channel, dim=1), reduction='elementwise_mean')
diff --git a/src/torchmetrics/image/__init__.py b/src/torchmetrics/image/__init__.py
index 1defa78bbf5..623fb32af23 100644
--- a/src/torchmetrics/image/__init__.py
+++ b/src/torchmetrics/image/__init__.py
@@ -23,6 +23,7 @@
 from torchmetrics.image.tv import TotalVariation
 from torchmetrics.image.uqi import UniversalImageQualityIndex
 from torchmetrics.image.vif import VisualInformationFidelity
+from torchmetrics.image.scc import SpatialCorrelationCoefficient
 from torchmetrics.utilities.imports import (
     _TORCH_FIDELITY_AVAILABLE,
     _TORCHVISION_AVAILABLE,
@@ -42,6 +43,7 @@
     "UniversalImageQualityIndex",
     "VisualInformationFidelity",
     "TotalVariation",
+    "SpatialCorrelationCoefficient",
 ]
 
 if _TORCH_FIDELITY_AVAILABLE:
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
new file mode 100644
index 00000000000..7fe6e5d0754
--- /dev/null
+++ b/src/torchmetrics/image/scc.py
@@ -0,0 +1,40 @@
+from typing import Any, Optional
+from typing_extensions import Literal
+
+import torch
+from torch import Tensor, tensor
+
+from torchmetrics.functional.image.scc import _scc_per_channel_compute as _scc_compute, _scc_update
+from torchmetrics.metric import Metric
+
+class SpatialCorrelationCoefficient(Metric):
+    is_differentiable = True
+    higher_is_better = True
+    full_state_update = False
+
+    scc_score: Tensor
+    total: Tensor
+
+    def __init__(self, high_pass_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]), 
+                 window_size: int = 11, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        
+        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
\ No newline at end of file

From be3da8724f5b67d4fadc723013871a8654a7986e Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Wed, 29 Nov 2023 13:57:01 +0000
Subject: [PATCH 02/21] tests for spatial correlation coefficient (scc) added.

---
 tests/unittests/image/test_scc.py | 60 +++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 tests/unittests/image/test_scc.py

diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
new file mode 100644
index 00000000000..322a58b5bfc
--- /dev/null
+++ b/tests/unittests/image/test_scc.py
@@ -0,0 +1,60 @@
+from collections import namedtuple
+
+import numpy as np
+import pytest
+import torch
+
+from torchmetrics.functional.image import spatial_correlation_coefficient
+from torchmetrics.image import SpatialCorrelationCoefficient
+from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests.helpers import seed_all
+from unittests.helpers.testers import MetricTester
+
+from sewar.full_ref import scc as sewar_scc
+
+seed_all(42)
+
+Input = namedtuple('Input', ["preds", "target"])
+_inputs = [
+    Input(
+        preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
+        target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128)
+    )
+    for channels in [1, 3]
+]
+
+def _reference_scc(preds, target):
+    """Reference implementation of scc from sewar"""
+    preds = torch.movedim(preds, 1, -1)
+    target = torch.movedim(target, 1, -1)
+    preds = preds.cpu().numpy()
+    target = target.cpu().numpy()
+    hp_filter = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
+    window_size = 8
+    scc = [sewar_scc(GT=target[batch], P=preds[batch], win=hp_filter, ws=window_size) for batch in range(preds.shape[0])]
+    return np.mean(scc)
+
+@pytest.mark.parametrize(
+    "preds, target", 
+    [(i.preds, i.target) for i in _inputs]
+)
+class TestSpatialCorrelationCoefficient(MetricTester):
+    atol = 1e-3
+
+    @pytest.mark.parametrize("ddp", [True, False])
+    def test_scc(self, preds, target, ddp):
+        self.run_class_metric_test(
+            ddp,
+            preds,
+            target,
+            metric_class=SpatialCorrelationCoefficient,
+            reference_metric=_reference_scc
+        )
+
+    def test_scc_functional(self, preds, target):
+        self.run_functional_metric_test(
+            preds,
+            target,
+            metric_functional=spatial_correlation_coefficient,
+            reference_metric=_reference_scc,
+        )
\ No newline at end of file

From d678f2aee84a31459595f80a975bf14d1aa7ad03 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Wed, 29 Nov 2023 14:32:51 +0000
Subject: [PATCH 03/21] documentation for spatial correlation coefficient (scc)
 updated.

---
 .../image/spatial_correlation_coefficient.rst | 21 ++++++++
 docs/source/links.rst                         |  1 +
 src/torchmetrics/functional/image/scc.py      | 49 ++++++++++++++++++-
 src/torchmetrics/image/scc.py                 | 31 +++++++++++-
 4 files changed, 98 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/image/spatial_correlation_coefficient.rst

diff --git a/docs/source/image/spatial_correlation_coefficient.rst b/docs/source/image/spatial_correlation_coefficient.rst
new file mode 100644
index 00000000000..d08e2c4a99e
--- /dev/null
+++ b/docs/source/image/spatial_correlation_coefficient.rst
@@ -0,0 +1,21 @@
+.. customcarditem::
+   :header: Spatial Correlation Coefficient (SCC)
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
+   :tags: Image
+
+.. include:: ../links.rst
+
+#################################
+Spatial Correlation Coefficient (SCC)
+#################################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.image.SpatialCorrelationCoefficient
+    :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.image.spatial_correlation_coefficient
diff --git a/docs/source/links.rst b/docs/source/links.rst
index 4ee095dd659..9ff27f4c95a 100644
--- a/docs/source/links.rst
+++ b/docs/source/links.rst
@@ -167,3 +167,4 @@
 .. _FLORES-101: https://arxiv.org/abs/2106.03193
 .. _FLORES-200: https://arxiv.org/abs/2207.04672
 .. _averaging curve objects: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
+.. _SCC: https://www.tandfonline.com/doi/abs/10.1080/014311698215973
diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 6c40df40047..bf82d2665fa 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -1,6 +1,5 @@
 
-from typing import Union, Tuple, Optional
-from typing_extensions import Literal
+from typing import Union, Tuple
 
 import torch
 from torch import Tensor, tensor
@@ -17,6 +16,17 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
         target: Ground truth tensor
         hp_filter: High-pass filter tensor
         window_size: Local window size integer
+
+    Return:
+        Tuple of (preds, target, hp_filter) tensors
+
+    Raises:
+        ValueError:
+            If ``preds`` and ``target`` have different number of channels
+            If ``preds`` and ``target`` have different shapes
+            If ``preds`` and ``target`` have invalid shapes
+            If ``window_size`` is not a positive integer
+            If ``window_size`` is greater than the size of the image
     """
     if preds.dtype != target.dtype:
         target = target.to(preds.dtype)
@@ -89,9 +99,22 @@ def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor):
     return preds_var, target_var, target_preds_cov
 
 def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int):
+    """Computes per channel Spatial Correlation Coefficient.
+
+    Args:
+        preds: estimated image of Bx1xHxW shape.
+        target: ground truth image of Bx1xHxW shape.
+        hp_filter: 2D high-pass filter.
+        window_size: size of window for local mean calculation.
+    Return:
+        Tensor with Spatial Correlation Coefficient score"""
+    
     dtype = preds.dtype
     device = preds.device
 
+    # This code is inspired by
+    # https://github.com/andrewekhalel/sewar/blob/master/sewar/full_ref.py#L187.
+
     window = torch.ones(size=(1, 1, window_size, window_size), dtype=dtype, device=device)/( window_size**2 )
     
     preds_hp = _hp_2d_laplacian(preds, hp_filter)
@@ -112,6 +135,28 @@ def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, w
 def spatial_correlation_coefficient(preds: Tensor, target: Tensor, 
                                     hp_filter: Tensor = tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]),
                                     window_size: int = 8):
+    """Compute Spatial Correlation Coefficient (SCC_).
+
+    Args:
+        preds: predicted images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
+        target: ground truth images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
+        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
+
+    Return:
+        Tensor with scc score
+
+    Example:
+        >>> import torch
+        >>> from torchmetrics.functional.image import spatial_correlation_coefficient as scc
+        >>> _ = torch.manual_seed(42)
+        >>> x = torch.randn(5, 3, 16, 16)
+        >>> scc(x, x)
+        tensor(1.)
+        >>> x = torch.randn(5, 16, 16)
+        >>> scc(x, x)
+        tensor(1.)
+    """
     preds, target, hp_filter = _scc_update(preds, target, hp_filter, window_size)
 
     per_channel = [_scc_per_channel_compute(preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, window_size) for i in range(preds.size(1))]
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 7fe6e5d0754..1f7366c4f14 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -1,5 +1,4 @@
-from typing import Any, Optional
-from typing_extensions import Literal
+from typing import Any
 
 import torch
 from torch import Tensor, tensor
@@ -8,6 +7,34 @@
 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:
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> from torchmetrics.image import SpatialCorrelationCoefficient as SCC
+        >>> preds = torch.randn([32, 3, 64, 64])
+        >>> target = torch.randn([32, 3, 64, 64])
+        >>> scc = SCC()
+        >>> scc(preds, target)
+        tensor(0.0022)
+
+    """
+
     is_differentiable = True
     higher_is_better = True
     full_state_update = False

From 952233bf0b1dd0a3664a4223b8861a1a1d73a9ef Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 29 Nov 2023 14:52:53 +0000
Subject: [PATCH 04/21] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/functional/image/__init__.py |  2 +-
 src/torchmetrics/functional/image/scc.py      | 86 +++++++++++--------
 src/torchmetrics/image/__init__.py            |  2 +-
 src/torchmetrics/image/scc.py                 | 26 +++---
 tests/unittests/image/test_scc.py             | 30 +++----
 5 files changed, 81 insertions(+), 65 deletions(-)

diff --git a/src/torchmetrics/functional/image/__init__.py b/src/torchmetrics/functional/image/__init__.py
index 41c4090a542..0e713683b28 100644
--- a/src/torchmetrics/functional/image/__init__.py
+++ b/src/torchmetrics/functional/image/__init__.py
@@ -46,5 +46,5 @@
     "visual_information_fidelity",
     "learned_perceptual_image_patch_similarity",
     "perceptual_path_length",
-    "spatial_correlation_coefficient"
+    "spatial_correlation_coefficient",
 ]
diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index bf82d2665fa..b5a87ee1f8d 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -1,5 +1,4 @@
-
-from typing import Union, Tuple
+from typing import Tuple, Union
 
 import torch
 from torch import Tensor, tensor
@@ -8,6 +7,7 @@
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.distributed import reduce
 
+
 def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int) -> Tuple[Tensor, Tensor, Tensor]:
     """Update and returns variables required to compute Spatial Correlation Coefficient.
 
@@ -27,6 +27,7 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
             If ``preds`` and ``target`` have invalid shapes
             If ``window_size`` is not a positive integer
             If ``window_size`` is greater than the size of the image
+
     """
     if preds.dtype != target.dtype:
         target = target.to(preds.dtype)
@@ -37,67 +38,68 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
             "  or batch of grayscale images of BxHxW shape."
             f" Got preds: {preds.shape} and target: {target.shape}."
         )
-    
+
     if len(preds.shape) == 3:
         preds = preds.unsqueeze(1)
         target = target.unsqueeze(1)
 
     if not window_size > 0:
-        raise ValueError(
-            f"Expected `window_size` to be a positive integer. Got {window_size}."
-        )
+        raise ValueError(f"Expected `window_size` to be a positive integer. Got {window_size}.")
 
     if window_size > preds.size(2) or window_size > preds.size(3):
         raise ValueError(
             f"Expected `window_size` to be less than or equal to the size of the image."
             f" Got window_size: {window_size} and image size: {preds.size(2)}x{preds.size(3)}."
         )
-    
+
     preds = preds.to(torch.float32)
     target = target.to(torch.float32)
     hp_filter = hp_filter[None, None, :].to(dtype=preds.dtype, device=preds.device)
     return preds, target, hp_filter
 
+
 def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
     if isinstance(pad, int):
         pad = (pad, pad, pad, pad)
     assert len(pad) == 4
 
-    left_pad = input[:, :, :, 0:pad[0]].flip(dims=[3])
-    right_pad = input[:, :, :, -pad[1]:].flip(dims=[3])
+    left_pad = input[:, :, :, 0 : pad[0]].flip(dims=[3])
+    right_pad = input[:, :, :, -pad[1] :].flip(dims=[3])
     padded = torch.cat([left_pad, input, right_pad], dim=3)
 
-    top_pad = padded[:, :, 0:pad[2], :].flip(dims=[2])
-    bottom_pad = padded[:, :, -pad[3]:, :].flip(dims=[2])
-    padded = torch.cat([top_pad, padded, bottom_pad], dim=2)
-    return padded
+    top_pad = padded[:, :, 0 : pad[2], :].flip(dims=[2])
+    bottom_pad = padded[:, :, -pad[3] :, :].flip(dims=[2])
+    return torch.cat([top_pad, padded, bottom_pad], dim=2)
+
 
 def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
-    left_padding = int(torch.floor(tensor((kernel.size(3)-1)/2)).item())
-    right_padding = int(torch.ceil(tensor((kernel.size(3)-1)/2)).item())
-    top_padding = int(torch.floor(tensor((kernel.size(2)-1)/2)).item())
-    bottom_padding = int(torch.ceil(tensor((kernel.size(2)-1)/2)).item())
+    left_padding = int(torch.floor(tensor((kernel.size(3) - 1) / 2)).item())
+    right_padding = int(torch.ceil(tensor((kernel.size(3) - 1) / 2)).item())
+    top_padding = int(torch.floor(tensor((kernel.size(2) - 1) / 2)).item())
+    bottom_padding = int(torch.ceil(tensor((kernel.size(2) - 1) / 2)).item())
 
     padded = _symmetric_reflect_pad_2d(input, pad=(left_padding, right_padding, top_padding, bottom_padding))
     kernel = kernel.flip([2, 3])
-    out = conv2d(padded, kernel, stride=1, padding=0)
-    return out
+    return conv2d(padded, kernel, stride=1, padding=0)
+
 
 def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
     output = _signal_convolve_2d(input, kernel)
     output += _signal_convolve_2d(input, kernel)
     return output
 
+
 def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor):
-    preds_mean = conv2d(preds, window, stride=1, padding='same')
-    target_mean = conv2d(target, window, stride=1, padding='same')
+    preds_mean = conv2d(preds, window, stride=1, padding="same")
+    target_mean = conv2d(target, window, stride=1, padding="same")
 
-    preds_var = conv2d(preds**2, window, stride=1, padding='same') - preds_mean**2
-    target_var = conv2d(target**2, window, stride=1, padding='same') - target_mean**2
-    target_preds_cov = conv2d(target*preds, window, stride=1, padding='same') - target_mean*preds_mean
+    preds_var = conv2d(preds**2, window, stride=1, padding="same") - preds_mean**2
+    target_var = conv2d(target**2, window, stride=1, padding="same") - target_mean**2
+    target_preds_cov = conv2d(target * preds, window, stride=1, padding="same") - target_mean * preds_mean
 
     return preds_var, target_var, target_preds_cov
 
+
 def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int):
     """Computes per channel Spatial Correlation Coefficient.
 
@@ -106,35 +108,41 @@ def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, w
         target: ground truth image of Bx1xHxW shape.
         hp_filter: 2D high-pass filter.
         window_size: size of window for local mean calculation.
+
     Return:
-        Tensor with Spatial Correlation Coefficient score"""
-    
+        Tensor with Spatial Correlation Coefficient score
+
+    """
     dtype = preds.dtype
     device = preds.device
 
     # This code is inspired by
     # https://github.com/andrewekhalel/sewar/blob/master/sewar/full_ref.py#L187.
 
-    window = torch.ones(size=(1, 1, window_size, window_size), dtype=dtype, device=device)/( window_size**2 )
-    
+    window = torch.ones(size=(1, 1, window_size, window_size), dtype=dtype, device=device) / (window_size**2)
+
     preds_hp = _hp_2d_laplacian(preds, hp_filter)
     target_hp = _hp_2d_laplacian(target, hp_filter)
 
     preds_var, target_var, target_preds_cov = _local_variance_covariance(preds_hp, target_hp, window)
 
-    preds_var[preds_var<0] = 0
-    target_var[target_var<0] = 0
+    preds_var[preds_var < 0] = 0
+    target_var[target_var < 0] = 0
 
     den = torch.sqrt(target_var) * torch.sqrt(preds_var)
-    idx = (den==0)
+    idx = den == 0
     den[den == 0] = 1
     scc = target_preds_cov / den
     scc[idx] = 0
     return scc
 
-def spatial_correlation_coefficient(preds: Tensor, target: Tensor, 
-                                    hp_filter: Tensor = tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]),
-                                    window_size: int = 8):
+
+def spatial_correlation_coefficient(
+    preds: Tensor,
+    target: Tensor,
+    hp_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
+    window_size: int = 8,
+):
     """Compute Spatial Correlation Coefficient (SCC_).
 
     Args:
@@ -156,8 +164,14 @@ def spatial_correlation_coefficient(preds: Tensor, target: Tensor,
         >>> x = torch.randn(5, 16, 16)
         >>> scc(x, x)
         tensor(1.)
+
     """
     preds, target, hp_filter = _scc_update(preds, target, hp_filter, window_size)
 
-    per_channel = [_scc_per_channel_compute(preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, window_size) for i in range(preds.size(1))]
-    return reduce(torch.cat(per_channel, dim=1), reduction='elementwise_mean')
+    per_channel = [
+        _scc_per_channel_compute(
+            preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, window_size
+        )
+        for i in range(preds.size(1))
+    ]
+    return reduce(torch.cat(per_channel, dim=1), reduction="elementwise_mean")
diff --git a/src/torchmetrics/image/__init__.py b/src/torchmetrics/image/__init__.py
index 623fb32af23..503ed7ad82a 100644
--- a/src/torchmetrics/image/__init__.py
+++ b/src/torchmetrics/image/__init__.py
@@ -19,11 +19,11 @@
 from torchmetrics.image.rase import RelativeAverageSpectralError
 from torchmetrics.image.rmse_sw import RootMeanSquaredErrorUsingSlidingWindow
 from torchmetrics.image.sam import SpectralAngleMapper
+from torchmetrics.image.scc import SpatialCorrelationCoefficient
 from torchmetrics.image.ssim import MultiScaleStructuralSimilarityIndexMeasure, StructuralSimilarityIndexMeasure
 from torchmetrics.image.tv import TotalVariation
 from torchmetrics.image.uqi import UniversalImageQualityIndex
 from torchmetrics.image.vif import VisualInformationFidelity
-from torchmetrics.image.scc import SpatialCorrelationCoefficient
 from torchmetrics.utilities.imports import (
     _TORCH_FIDELITY_AVAILABLE,
     _TORCHVISION_AVAILABLE,
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 1f7366c4f14..64a660009b9 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -3,9 +3,11 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.image.scc import _scc_per_channel_compute as _scc_compute, _scc_update
+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_).
 
@@ -42,26 +44,30 @@ class SpatialCorrelationCoefficient(Metric):
     scc_score: Tensor
     total: Tensor
 
-    def __init__(self, high_pass_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]), 
-                 window_size: int = 11, **kwargs: Any) -> None:
+    def __init__(
+        self,
+        high_pass_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
+        window_size: int = 11,
+        **kwargs: Any
+    ) -> None:
         super().__init__(**kwargs)
-        
+
         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.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
\ No newline at end of file
+        return self.scc_score / self.total
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 322a58b5bfc..61e065b844c 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -3,52 +3,48 @@
 import numpy as np
 import pytest
 import torch
-
+from sewar.full_ref import scc as sewar_scc
 from torchmetrics.functional.image import spatial_correlation_coefficient
 from torchmetrics.image import SpatialCorrelationCoefficient
+
 from unittests import BATCH_SIZE, NUM_BATCHES
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
-from sewar.full_ref import scc as sewar_scc
-
 seed_all(42)
 
-Input = namedtuple('Input', ["preds", "target"])
+Input = namedtuple("Input", ["preds", "target"])
 _inputs = [
     Input(
         preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
-        target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128)
+        target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
     )
     for channels in [1, 3]
 ]
 
+
 def _reference_scc(preds, target):
-    """Reference implementation of scc from sewar"""
+    """Reference implementation of scc from sewar."""
     preds = torch.movedim(preds, 1, -1)
     target = torch.movedim(target, 1, -1)
     preds = preds.cpu().numpy()
     target = target.cpu().numpy()
     hp_filter = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
     window_size = 8
-    scc = [sewar_scc(GT=target[batch], P=preds[batch], win=hp_filter, ws=window_size) for batch in range(preds.shape[0])]
+    scc = [
+        sewar_scc(GT=target[batch], P=preds[batch], win=hp_filter, ws=window_size) for batch in range(preds.shape[0])
+    ]
     return np.mean(scc)
 
-@pytest.mark.parametrize(
-    "preds, target", 
-    [(i.preds, i.target) for i in _inputs]
-)
+
+@pytest.mark.parametrize("preds, target", [(i.preds, i.target) for i in _inputs])
 class TestSpatialCorrelationCoefficient(MetricTester):
     atol = 1e-3
 
     @pytest.mark.parametrize("ddp", [True, False])
     def test_scc(self, preds, target, ddp):
         self.run_class_metric_test(
-            ddp,
-            preds,
-            target,
-            metric_class=SpatialCorrelationCoefficient,
-            reference_metric=_reference_scc
+            ddp, preds, target, metric_class=SpatialCorrelationCoefficient, reference_metric=_reference_scc
         )
 
     def test_scc_functional(self, preds, target):
@@ -57,4 +53,4 @@ def test_scc_functional(self, preds, target):
             target,
             metric_functional=spatial_correlation_coefficient,
             reference_metric=_reference_scc,
-        )
\ No newline at end of file
+        )

From 48164a4d0eac395cab0fa6f02809e81377f7b003 Mon Sep 17 00:00:00 2001
From: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Date: Thu, 30 Nov 2023 13:17:34 +0100
Subject: [PATCH 05/21] Apply suggestions from code review

Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
---
 .../image/spatial_correlation_coefficient.rst |  4 +--
 src/torchmetrics/functional/image/scc.py      | 32 +++++++++++++------
 src/torchmetrics/image/scc.py                 | 13 ++++++++
 tests/unittests/image/test_scc.py             | 13 ++++++++
 4 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/docs/source/image/spatial_correlation_coefficient.rst b/docs/source/image/spatial_correlation_coefficient.rst
index d08e2c4a99e..02ed96fd107 100644
--- a/docs/source/image/spatial_correlation_coefficient.rst
+++ b/docs/source/image/spatial_correlation_coefficient.rst
@@ -5,9 +5,9 @@
 
 .. include:: ../links.rst
 
-#################################
+#####################################
 Spatial Correlation Coefficient (SCC)
-#################################
+#####################################
 
 Module Interface
 ________________
diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index b5a87ee1f8d..cdc48ed9cf1 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -1,3 +1,16 @@
+# 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 Tuple, Union
 
 import torch
@@ -32,7 +45,7 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
     if preds.dtype != target.dtype:
         target = target.to(preds.dtype)
     _check_same_shape(preds, target)
-    if len(preds.shape) not in (3, 4):
+    if preds.ndim not in (3, 4):
         raise ValueError(
             "Expected `preds` and `target` to have batch of colored images with BxCxHxW shape"
             "  or batch of grayscale images of BxHxW shape."
@@ -61,7 +74,8 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
 def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
     if isinstance(pad, int):
         pad = (pad, pad, pad, pad)
-    assert len(pad) == 4
+    if len(pad) != 4:
+        raise ValueError(f"Expected padding to have length 4, but got {len(pad)}")
 
     left_pad = input[:, :, :, 0 : pad[0]].flip(dims=[3])
     right_pad = input[:, :, :, -pad[1] :].flip(dims=[3])
@@ -73,10 +87,10 @@ def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -
 
 
 def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
-    left_padding = int(torch.floor(tensor((kernel.size(3) - 1) / 2)).item())
-    right_padding = int(torch.ceil(tensor((kernel.size(3) - 1) / 2)).item())
-    top_padding = int(torch.floor(tensor((kernel.size(2) - 1) / 2)).item())
-    bottom_padding = int(torch.ceil(tensor((kernel.size(2) - 1) / 2)).item())
+    left_padding = int(math.floor((kernel.size(3) - 1) / 2))
+    right_padding = int(math.ceil((kernel.size(3) - 1) / 2))
+    top_padding = int(math.floor((kernel.size(2) - 1) / 2))
+    bottom_padding = int(math.ceil((kernel.size(2) - 1) / 2))
 
     padded = _symmetric_reflect_pad_2d(input, pad=(left_padding, right_padding, top_padding, bottom_padding))
     kernel = kernel.flip([2, 3])
@@ -89,7 +103,7 @@ def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
     return output
 
 
-def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor):
+def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
     preds_mean = conv2d(preds, window, stride=1, padding="same")
     target_mean = conv2d(target, window, stride=1, padding="same")
 
@@ -100,7 +114,7 @@ def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor):
     return preds_var, target_var, target_preds_cov
 
 
-def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int):
+def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int) -> Tensor:
     """Computes per channel Spatial Correlation Coefficient.
 
     Args:
@@ -142,7 +156,7 @@ def spatial_correlation_coefficient(
     target: Tensor,
     hp_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
     window_size: int = 8,
-):
+) -> Tensor:
     """Compute Spatial Correlation Coefficient (SCC_).
 
     Args:
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 64a660009b9..701474c307c 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -1,3 +1,16 @@
+# 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
 
 import torch
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 61e065b844c..d54fb2e4c87 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -1,3 +1,16 @@
+# 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 collections import namedtuple
 
 import numpy as np

From 2637b2396b5fa57fa415c3538162095bd7336fb9 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Thu, 30 Nov 2023 13:21:59 +0000
Subject: [PATCH 06/21] scc functional docstrings added. _hp_2d_laplacian
 function updated

---
 src/torchmetrics/functional/image/scc.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index cdc48ed9cf1..1b2e7205034 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -11,6 +11,7 @@
 # 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 math
 from typing import Tuple, Union
 
 import torch
@@ -72,6 +73,7 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
 
 
 def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
+    """Applies symmetric padding to the 2D image tensor input using `reflect` mode (d c b a | a b c d | d c b a)."""
     if isinstance(pad, int):
         pad = (pad, pad, pad, pad)
     if len(pad) != 4:
@@ -87,6 +89,7 @@ def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -
 
 
 def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
+    """Applies 2D signal convolution to the input tensor with the given kernel."""
     left_padding = int(math.floor((kernel.size(3) - 1) / 2))
     right_padding = int(math.ceil((kernel.size(3) - 1) / 2))
     top_padding = int(math.floor((kernel.size(2) - 1) / 2))
@@ -98,12 +101,17 @@ def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
 
 
 def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
-    output = _signal_convolve_2d(input, kernel)
-    output += _signal_convolve_2d(input, kernel)
+    """Applies 2-D Laplace filter to the input tensor with the given high pass filter."""
+    output = _signal_convolve_2d(input, kernel) * 2.
     return output
 
 
 def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
+    """Computes local variance and covariance of the input tensors."""
+
+    # This code is inspired by
+    # https://github.com/andrewekhalel/sewar/blob/master/sewar/full_ref.py#L187.
+
     preds_mean = conv2d(preds, window, stride=1, padding="same")
     target_mean = conv2d(target, window, stride=1, padding="same")
 

From 4a0058af35efbf89708665caabd83646a8581dc2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 30 Nov 2023 13:23:31 +0000
Subject: [PATCH 07/21] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/functional/image/scc.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 1b2e7205034..86600ed52f4 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -102,13 +102,11 @@ def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
 
 def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
     """Applies 2-D Laplace filter to the input tensor with the given high pass filter."""
-    output = _signal_convolve_2d(input, kernel) * 2.
-    return output
+    return _signal_convolve_2d(input, kernel) * 2.0
 
 
 def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
     """Computes local variance and covariance of the input tensors."""
-
     # This code is inspired by
     # https://github.com/andrewekhalel/sewar/blob/master/sewar/full_ref.py#L187.
 

From 06682361840c0c4564314d60e3a4e83aa0799591 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Thu, 30 Nov 2023 15:28:01 +0000
Subject: [PATCH 08/21] required failed checks resolved.

---
 src/torchmetrics/functional/image/scc.py | 20 +++++++++++---------
 src/torchmetrics/image/scc.py            |  7 +++++--
 tests/unittests/image/test_scc.py        |  9 +++++++--
 3 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 1b2e7205034..58238ca4dd2 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -72,37 +72,37 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
     return preds, target, hp_filter
 
 
-def _symmetric_reflect_pad_2d(input: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
+def _symmetric_reflect_pad_2d(input_img: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
     """Applies symmetric padding to the 2D image tensor input using `reflect` mode (d c b a | a b c d | d c b a)."""
     if isinstance(pad, int):
         pad = (pad, pad, pad, pad)
     if len(pad) != 4:
         raise ValueError(f"Expected padding to have length 4, but got {len(pad)}")
 
-    left_pad = input[:, :, :, 0 : pad[0]].flip(dims=[3])
-    right_pad = input[:, :, :, -pad[1] :].flip(dims=[3])
-    padded = torch.cat([left_pad, input, right_pad], dim=3)
+    left_pad = input_img[:, :, :, 0 : pad[0]].flip(dims=[3])
+    right_pad = input_img[:, :, :, -pad[1] :].flip(dims=[3])
+    padded = torch.cat([left_pad, input_img, right_pad], dim=3)
 
     top_pad = padded[:, :, 0 : pad[2], :].flip(dims=[2])
     bottom_pad = padded[:, :, -pad[3] :, :].flip(dims=[2])
     return torch.cat([top_pad, padded, bottom_pad], dim=2)
 
 
-def _signal_convolve_2d(input: Tensor, kernel: Tensor) -> Tensor:
+def _signal_convolve_2d(input_img: Tensor, kernel: Tensor) -> Tensor:
     """Applies 2D signal convolution to the input tensor with the given kernel."""
     left_padding = int(math.floor((kernel.size(3) - 1) / 2))
     right_padding = int(math.ceil((kernel.size(3) - 1) / 2))
     top_padding = int(math.floor((kernel.size(2) - 1) / 2))
     bottom_padding = int(math.ceil((kernel.size(2) - 1) / 2))
 
-    padded = _symmetric_reflect_pad_2d(input, pad=(left_padding, right_padding, top_padding, bottom_padding))
+    padded = _symmetric_reflect_pad_2d(input_img, pad=(left_padding, right_padding, top_padding, bottom_padding))
     kernel = kernel.flip([2, 3])
     return conv2d(padded, kernel, stride=1, padding=0)
 
 
-def _hp_2d_laplacian(input: Tensor, kernel: Tensor) -> Tensor:
+def _hp_2d_laplacian(input_img: Tensor, kernel: Tensor) -> Tensor:
     """Applies 2-D Laplace filter to the input tensor with the given high pass filter."""
-    output = _signal_convolve_2d(input, kernel) * 2.
+    output = _signal_convolve_2d(input_img, kernel) * 2.
     return output
 
 
@@ -162,7 +162,7 @@ def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, w
 def spatial_correlation_coefficient(
     preds: Tensor,
     target: Tensor,
-    hp_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
+    hp_filter: Tensor = None,
     window_size: int = 8,
 ) -> Tensor:
     """Compute Spatial Correlation Coefficient (SCC_).
@@ -188,6 +188,8 @@ def spatial_correlation_coefficient(
         tensor(1.)
 
     """
+    if hp_filter is None:
+        hp_filter = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
     preds, target, hp_filter = _scc_update(preds, target, hp_filter, window_size)
 
     per_channel = [
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 701474c307c..f406aa312fe 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -59,12 +59,15 @@ class SpatialCorrelationCoefficient(Metric):
 
     def __init__(
         self,
-        high_pass_filter: Tensor = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
-        window_size: int = 11,
+        high_pass_filter: 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
 
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index d54fb2e4c87..de96f6736f0 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -11,7 +11,7 @@
 # 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 collections import namedtuple
+from typing import NamedTuple
 
 import numpy as np
 import pytest
@@ -26,7 +26,9 @@
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
+class Input(NamedTuple):
+    preds: torch.Tensor
+    target: torch.Tensor
 _inputs = [
     Input(
         preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
@@ -52,15 +54,18 @@ def _reference_scc(preds, target):
 
 @pytest.mark.parametrize("preds, target", [(i.preds, i.target) for i in _inputs])
 class TestSpatialCorrelationCoefficient(MetricTester):
+    """Tests for SpatialCorrelationCoefficient metric."""
     atol = 1e-3
 
     @pytest.mark.parametrize("ddp", [True, False])
     def test_scc(self, preds, target, ddp):
+        """Test SpatialCorrelationCoefficient class usage."""
         self.run_class_metric_test(
             ddp, preds, target, metric_class=SpatialCorrelationCoefficient, reference_metric=_reference_scc
         )
 
     def test_scc_functional(self, preds, target):
+        """Test SpatialCorrelationCoefficient functional usage."""
         self.run_functional_metric_test(
             preds,
             target,

From 24bfc7766639027b7d33a1713e234b37ec7334f4 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:31:43 +0000
Subject: [PATCH 09/21] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/image/scc.py     | 7 +------
 tests/unittests/image/test_scc.py | 4 ++++
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index f406aa312fe..185a19ebf96 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -57,12 +57,7 @@ class SpatialCorrelationCoefficient(Metric):
     scc_score: Tensor
     total: Tensor
 
-    def __init__(
-        self,
-        high_pass_filter: Tensor = None,
-        window_size: int = 8,
-        **kwargs: Any
-    ) -> None:
+    def __init__(self, high_pass_filter: Tensor = None, window_size: int = 8, **kwargs: Any) -> None:
         super().__init__(**kwargs)
 
         if high_pass_filter is None:
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index de96f6736f0..2bb35b73dfe 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -26,9 +26,12 @@
 
 seed_all(42)
 
+
 class Input(NamedTuple):
     preds: torch.Tensor
     target: torch.Tensor
+
+
 _inputs = [
     Input(
         preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
@@ -55,6 +58,7 @@ def _reference_scc(preds, target):
 @pytest.mark.parametrize("preds, target", [(i.preds, i.target) for i in _inputs])
 class TestSpatialCorrelationCoefficient(MetricTester):
     """Tests for SpatialCorrelationCoefficient metric."""
+
     atol = 1e-3
 
     @pytest.mark.parametrize("ddp", [True, False])

From 33b6d9073660b179159a8a5602c796f6137fbcfa Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Thu, 30 Nov 2023 15:46:35 +0000
Subject: [PATCH 10/21] fixing the variable name mistake.

---
 src/torchmetrics/functional/image/scc.py | 2 +-
 tests/unittests/image/test_scc.py        | 8 ++------
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 807c73f1655..93b0c8fd1db 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -102,7 +102,7 @@ def _signal_convolve_2d(input_img: Tensor, kernel: Tensor) -> Tensor:
 
 def _hp_2d_laplacian(input_img: Tensor, kernel: Tensor) -> Tensor:
     """Applies 2-D Laplace filter to the input tensor with the given high pass filter."""
-    return _signal_convolve_2d(input, kernel) * 2.0
+    return _signal_convolve_2d(input_img, kernel) * 2.0
 
 
 def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index de96f6736f0..2522a087e0a 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -11,7 +11,6 @@
 # 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 NamedTuple
 
 import numpy as np
 import pytest
@@ -20,17 +19,14 @@
 from torchmetrics.functional.image import spatial_correlation_coefficient
 from torchmetrics.image import SpatialCorrelationCoefficient
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-class Input(NamedTuple):
-    preds: torch.Tensor
-    target: torch.Tensor
 _inputs = [
-    Input(
+    _Input(
         preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
         target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
     )

From f5a874f662050cac8893da77d80e34c5935203a7 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Mon, 4 Dec 2023 19:37:52 +0000
Subject: [PATCH 11/21] fixed even window size bug. changed atol to 1e-8. added
 None reduction in scc functional interface

---
 src/torchmetrics/functional/image/scc.py | 34 +++++++++++++++++-------
 tests/unittests/image/test_scc.py        |  8 +++---
 2 files changed, 29 insertions(+), 13 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 93b0c8fd1db..59a28aa6105 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import math
-from typing import Tuple, Union
+from typing import Tuple, Union, Optional
+from typing_extensions import Literal
 
 import torch
 from torch import Tensor, tensor
-from torch.nn.functional import conv2d
+from torch.nn.functional import conv2d, pad
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.distributed import reduce
@@ -110,12 +111,18 @@ def _local_variance_covariance(preds: Tensor, target: Tensor, window: Tensor) ->
     # This code is inspired by
     # https://github.com/andrewekhalel/sewar/blob/master/sewar/full_ref.py#L187.
 
-    preds_mean = conv2d(preds, window, stride=1, padding="same")
-    target_mean = conv2d(target, window, stride=1, padding="same")
+    left_padding = int(math.ceil((window.size(3) - 1) / 2))
+    right_padding = int(math.floor((window.size(3) - 1) / 2))
 
-    preds_var = conv2d(preds**2, window, stride=1, padding="same") - preds_mean**2
-    target_var = conv2d(target**2, window, stride=1, padding="same") - target_mean**2
-    target_preds_cov = conv2d(target * preds, window, stride=1, padding="same") - target_mean * preds_mean
+    preds = pad(preds, (left_padding, right_padding, left_padding, right_padding))
+    target = pad(target, (left_padding, right_padding, left_padding, right_padding))
+
+    preds_mean = conv2d(preds, window, stride=1, padding=0)
+    target_mean = conv2d(target, window, stride=1, padding=0)
+
+    preds_var = conv2d(preds**2, window, stride=1, padding=0) - preds_mean**2
+    target_var = conv2d(target**2, window, stride=1, padding=0) - target_mean**2
+    target_preds_cov = conv2d(target * preds, window, stride=1, padding=0) - target_mean * preds_mean
 
     return preds_var, target_var, target_preds_cov
 
@@ -162,6 +169,7 @@ def spatial_correlation_coefficient(
     target: Tensor,
     hp_filter: Tensor = None,
     window_size: int = 8,
+    reduction: Optional[Literal["mean", "none", None]] = "mean",
 ) -> Tensor:
     """Compute Spatial Correlation Coefficient (SCC_).
 
@@ -169,7 +177,8 @@ def spatial_correlation_coefficient(
         preds: predicted images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
         target: ground truth images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
         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
+        window_size: Local window size integer. default: 8,
+        reduction: Reduction method for output tensor. If ``None`` or ``"none"``, returns a tensor with the per sample results. default: ``"mean"``.
 
     Return:
         Tensor with scc score
@@ -188,6 +197,10 @@ def spatial_correlation_coefficient(
     """
     if hp_filter is None:
         hp_filter = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
+    if reduce is None:
+        reduction = "none"
+    if reduction not in ("mean", "none"):
+        raise ValueError(f"Expected reduction to be 'mean' or 'none', but got {reduction}")
     preds, target, hp_filter = _scc_update(preds, target, hp_filter, window_size)
 
     per_channel = [
@@ -196,4 +209,7 @@ def spatial_correlation_coefficient(
         )
         for i in range(preds.size(1))
     ]
-    return reduce(torch.cat(per_channel, dim=1), reduction="elementwise_mean")
+    if reduction == "none":
+        return torch.mean(torch.cat(per_channel, dim=1), dim=[1, 2, 3])
+    if reduction == "mean":
+        return reduce(torch.cat(per_channel, dim=1), reduction="elementwise_mean")
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 198c110c46f..6ff6fbb5657 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -27,8 +27,8 @@
 
 _inputs = [
     _Input(
-        preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
-        target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 128, 128),
+        preds=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 32, 32),
+        target=torch.randn(NUM_BATCHES, BATCH_SIZE, channels, 32, 32),
     )
     for channels in [1, 3]
 ]
@@ -52,7 +52,7 @@ def _reference_scc(preds, target):
 class TestSpatialCorrelationCoefficient(MetricTester):
     """Tests for SpatialCorrelationCoefficient metric."""
 
-    atol = 1e-3
+    atol = 1e-8
 
     @pytest.mark.parametrize("ddp", [True, False])
     def test_scc(self, preds, target, ddp):
@@ -67,5 +67,5 @@ def test_scc_functional(self, preds, target):
             preds,
             target,
             metric_functional=spatial_correlation_coefficient,
-            reference_metric=_reference_scc,
+            reference_metric=_reference_scc
         )

From a9c25eea0bcdc556cf34beb6b660b4e18530c60a Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Mon, 4 Dec 2023 20:05:33 +0000
Subject: [PATCH 12/21] added new tests for scc functional interface'

---
 tests/unittests/image/test_scc.py | 32 +++++++++++++++++++++++++++++--
 1 file changed, 30 insertions(+), 2 deletions(-)

diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 6ff6fbb5657..6a988740b45 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -32,6 +32,10 @@
     )
     for channels in [1, 3]
 ]
+_kernels = [
+    torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
+    torch.tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+]
 
 
 def _reference_scc(preds, target):
@@ -47,6 +51,22 @@ def _reference_scc(preds, target):
     ]
     return np.mean(scc)
 
+def _wrapped_reference_scc(win, ws, reduction):
+    """Wrapper around reference implementation of scc from sewar."""
+    def _wrapped(preds, target):
+        preds = torch.movedim(preds, 1, -1)
+        target = torch.movedim(target, 1, -1)
+        preds = preds.cpu().numpy()
+        target = target.cpu().numpy()
+        scc = [
+            sewar_scc(GT=target[batch], P=preds[batch], win=win, ws=ws) for batch in range(preds.shape[0])
+        ]
+        if reduction == 'mean':
+            return np.mean(scc)
+        if reduction == 'none':
+            return scc
+    return _wrapped
+
 
 @pytest.mark.parametrize("preds, target", [(i.preds, i.target) for i in _inputs])
 class TestSpatialCorrelationCoefficient(MetricTester):
@@ -61,11 +81,19 @@ def test_scc(self, preds, target, ddp):
             ddp, preds, target, metric_class=SpatialCorrelationCoefficient, reference_metric=_reference_scc
         )
 
-    def test_scc_functional(self, preds, target):
+    @pytest.mark.parametrize("hp_filter", _kernels)
+    @pytest.mark.parametrize("window_size", [8, 11])
+    @pytest.mark.parametrize("reduction", ['mean', 'none'])
+    def test_scc_functional(self, preds, target, hp_filter, window_size, reduction):
         """Test SpatialCorrelationCoefficient functional usage."""
         self.run_functional_metric_test(
             preds,
             target,
             metric_functional=spatial_correlation_coefficient,
-            reference_metric=_reference_scc
+            reference_metric=_wrapped_reference_scc(hp_filter, window_size, reduction),
+            metric_args={
+                "hp_filter": hp_filter,
+                "window_size": window_size,
+                "reduction": reduction,
+            }
         )

From 2d9300b96001f3c50ce1dcb00ef4b7a8ecff6051 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Mon, 4 Dec 2023 20:22:07 +0000
Subject: [PATCH 13/21] resolving failed mypy checks.

---
 src/torchmetrics/functional/image/scc.py | 2 +-
 src/torchmetrics/image/scc.py            | 4 ++--
 tests/unittests/image/test_scc.py        | 3 +--
 3 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 59a28aa6105..478ce3a4b44 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -167,7 +167,7 @@ def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, w
 def spatial_correlation_coefficient(
     preds: Tensor,
     target: Tensor,
-    hp_filter: Tensor = None,
+    hp_filter: Optional[Tensor] = None,
     window_size: int = 8,
     reduction: Optional[Literal["mean", "none", None]] = "mean",
 ) -> Tensor:
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 185a19ebf96..4631b9434ed 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -11,7 +11,7 @@
 # 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
+from typing import Any, Optional
 
 import torch
 from torch import Tensor, tensor
@@ -57,7 +57,7 @@ class SpatialCorrelationCoefficient(Metric):
     scc_score: Tensor
     total: Tensor
 
-    def __init__(self, high_pass_filter: Tensor = None, window_size: int = 8, **kwargs: Any) -> None:
+    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:
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 6a988740b45..05714d6c4c5 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -33,8 +33,7 @@
     for channels in [1, 3]
 ]
 _kernels = [
-    torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
-    torch.tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+    torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
 ]
 
 

From 58b39352e06fdead99e1b387bd9289e0fb56da9f Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 4 Dec 2023 20:24:03 +0000
Subject: [PATCH 14/21] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/functional/image/scc.py |  5 +++--
 tests/unittests/image/test_scc.py        | 20 ++++++++++----------
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 478ce3a4b44..876332b87cd 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import math
-from typing import Tuple, Union, Optional
-from typing_extensions import Literal
+from typing import Optional, Tuple, Union
 
 import torch
 from torch import Tensor, tensor
 from torch.nn.functional import conv2d, pad
+from typing_extensions import Literal
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.distributed import reduce
@@ -213,3 +213,4 @@ def spatial_correlation_coefficient(
         return torch.mean(torch.cat(per_channel, dim=1), dim=[1, 2, 3])
     if reduction == "mean":
         return reduce(torch.cat(per_channel, dim=1), reduction="elementwise_mean")
+    return None
diff --git a/tests/unittests/image/test_scc.py b/tests/unittests/image/test_scc.py
index 05714d6c4c5..6ef1443371c 100644
--- a/tests/unittests/image/test_scc.py
+++ b/tests/unittests/image/test_scc.py
@@ -32,9 +32,7 @@
     )
     for channels in [1, 3]
 ]
-_kernels = [
-    torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
-]
+_kernels = [torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])]
 
 
 def _reference_scc(preds, target):
@@ -50,20 +48,22 @@ def _reference_scc(preds, target):
     ]
     return np.mean(scc)
 
+
 def _wrapped_reference_scc(win, ws, reduction):
     """Wrapper around reference implementation of scc from sewar."""
+
     def _wrapped(preds, target):
         preds = torch.movedim(preds, 1, -1)
         target = torch.movedim(target, 1, -1)
         preds = preds.cpu().numpy()
         target = target.cpu().numpy()
-        scc = [
-            sewar_scc(GT=target[batch], P=preds[batch], win=win, ws=ws) for batch in range(preds.shape[0])
-        ]
-        if reduction == 'mean':
+        scc = [sewar_scc(GT=target[batch], P=preds[batch], win=win, ws=ws) for batch in range(preds.shape[0])]
+        if reduction == "mean":
             return np.mean(scc)
-        if reduction == 'none':
+        if reduction == "none":
             return scc
+        return None
+
     return _wrapped
 
 
@@ -82,7 +82,7 @@ def test_scc(self, preds, target, ddp):
 
     @pytest.mark.parametrize("hp_filter", _kernels)
     @pytest.mark.parametrize("window_size", [8, 11])
-    @pytest.mark.parametrize("reduction", ['mean', 'none'])
+    @pytest.mark.parametrize("reduction", ["mean", "none"])
     def test_scc_functional(self, preds, target, hp_filter, window_size, reduction):
         """Test SpatialCorrelationCoefficient functional usage."""
         self.run_functional_metric_test(
@@ -94,5 +94,5 @@ def test_scc_functional(self, preds, target, hp_filter, window_size, reduction):
                 "hp_filter": hp_filter,
                 "window_size": window_size,
                 "reduction": reduction,
-            }
+            },
         )

From 5a501ed3d08b9c16e191cddbe515811ab15453f9 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Mon, 4 Dec 2023 20:32:05 +0000
Subject: [PATCH 15/21] resolved long docstring line

---
 src/torchmetrics/functional/image/scc.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 478ce3a4b44..467f9208d96 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -178,7 +178,8 @@ def spatial_correlation_coefficient(
         target: ground truth images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
         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,
-        reduction: Reduction method for output tensor. If ``None`` or ``"none"``, returns a tensor with the per sample results. default: ``"mean"``.
+        reduction: Reduction method for output tensor. If ``None`` or ``"none"``, 
+                   returns a tensor with the per sample results. default: ``"mean"``.
 
     Return:
         Tensor with scc score

From 38c9f0f7fce28815e7fe64bf8df0e970ab5f777f Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 4 Dec 2023 20:40:20 +0000
Subject: [PATCH 16/21] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/functional/image/scc.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index de7d409eb98..68cf346ffaa 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -178,7 +178,7 @@ def spatial_correlation_coefficient(
         target: ground truth images of shape ``(N,C,H,W)`` or ``(N,H,W)``.
         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,
-        reduction: Reduction method for output tensor. If ``None`` or ``"none"``, 
+        reduction: Reduction method for output tensor. If ``None`` or ``"none"``,
                    returns a tensor with the per sample results. default: ``"mean"``.
 
     Return:

From 650d227e52fe49f8dcdea1768bc2f48b7aaa01d0 Mon Sep 17 00:00:00 2001
From: HoseinAkbarzadeh <hoseinakbarzadeh75@gmail.com>
Date: Tue, 5 Dec 2023 06:19:02 +0000
Subject: [PATCH 17/21] fixed example bug in docstring

---
 src/torchmetrics/functional/image/scc.py | 6 +++++-
 src/torchmetrics/image/scc.py            | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index de7d409eb98..f1397f0ccd0 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -194,11 +194,15 @@ def spatial_correlation_coefficient(
         >>> x = torch.randn(5, 16, 16)
         >>> scc(x, x)
         tensor(1.)
+        >>> x = torch.randn(5, 3, 16, 16)
+        >>> y = torch.randn(5, 3, 16, 16)
+        >>> scc(x, y, reduction="none")
+        tensor([0.0223, 0.0256, 0.0616, 0.0159, 0.0170])
 
     """
     if hp_filter is None:
         hp_filter = tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
-    if reduce is None:
+    if reduction is None:
         reduction = "none"
     if reduction not in ("mean", "none"):
         raise ValueError(f"Expected reduction to be 'mean' or 'none', but got {reduction}")
diff --git a/src/torchmetrics/image/scc.py b/src/torchmetrics/image/scc.py
index 4631b9434ed..15ea2b96ecf 100644
--- a/src/torchmetrics/image/scc.py
+++ b/src/torchmetrics/image/scc.py
@@ -46,7 +46,7 @@ class SpatialCorrelationCoefficient(Metric):
         >>> target = torch.randn([32, 3, 64, 64])
         >>> scc = SCC()
         >>> scc(preds, target)
-        tensor(0.0022)
+        tensor(0.0023)
 
     """
 

From 45b6af461d87011a4ebe729bb6da6ff1e89614f3 Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Wed, 20 Dec 2023 15:37:57 +0100
Subject: [PATCH 18/21] Update src/torchmetrics/functional/image/scc.py

Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
---
 src/torchmetrics/functional/image/scc.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/torchmetrics/functional/image/scc.py b/src/torchmetrics/functional/image/scc.py
index 328500b1acf..167ddbd37b5 100644
--- a/src/torchmetrics/functional/image/scc.py
+++ b/src/torchmetrics/functional/image/scc.py
@@ -74,7 +74,7 @@ def _scc_update(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: i
 
 
 def _symmetric_reflect_pad_2d(input_img: Tensor, pad: Union[int, Tuple[int, ...]]) -> Tensor:
-    """Applies symmetric padding to the 2D image tensor input using `reflect` mode (d c b a | a b c d | d c b a)."""
+    """Applies symmetric padding to the 2D image tensor input using ``reflect`` mode (d c b a | a b c d | d c b a)."""
     if isinstance(pad, int):
         pad = (pad, pad, pad, pad)
     if len(pad) != 4:

From 6c9308f97845a987484b9e42710abed6260c5838 Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Wed, 20 Dec 2023 15:38:33 +0100
Subject: [PATCH 19/21] changelog

---
 CHANGELOG.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e51fcd6f4c1..2cacd957a94 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,6 +28,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `aggregate`` argument to retrieval metrics ([#2220](https://github.com/Lightning-AI/torchmetrics/pull/2220))
 
 
+- Added `Spatial Correlation Coefficient` to image subpackage ([#2248](https://github.com/Lightning-AI/torchmetrics/pull/2248))
+
 ### Changed
 
 - Changed minimum supported Pytorch version from 1.8 to 1.10 ([#2145](https://github.com/Lightning-AI/torchmetrics/pull/2145))

From 3426755ccd064f6dbd280530d4f6e1c36141436d Mon Sep 17 00:00:00 2001
From: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Date: Thu, 21 Dec 2023 22:09:33 +0100
Subject: [PATCH 20/21] Apply suggestions from code review

---
 docs/source/links.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/links.rst b/docs/source/links.rst
index 9ff27f4c95a..488453b9442 100644
--- a/docs/source/links.rst
+++ b/docs/source/links.rst
@@ -167,4 +167,4 @@
 .. _FLORES-101: https://arxiv.org/abs/2106.03193
 .. _FLORES-200: https://arxiv.org/abs/2207.04672
 .. _averaging curve objects: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
-.. _SCC: https://www.tandfonline.com/doi/abs/10.1080/014311698215973
+.. _SCC: https://www.tandfonline.com/doi/10.1080/014311698215973

From b4892d567bd1a0772cd1721fea37a151a83af74f Mon Sep 17 00:00:00 2001
From: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Date: Thu, 21 Dec 2023 22:25:10 +0100
Subject: [PATCH 21/21] link

---
 docs/source/links.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/links.rst b/docs/source/links.rst
index 488453b9442..6ffe815325e 100644
--- a/docs/source/links.rst
+++ b/docs/source/links.rst
@@ -167,4 +167,4 @@
 .. _FLORES-101: https://arxiv.org/abs/2106.03193
 .. _FLORES-200: https://arxiv.org/abs/2207.04672
 .. _averaging curve objects: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
-.. _SCC: https://www.tandfonline.com/doi/10.1080/014311698215973
+.. _SCC: https://www.ingentaconnect.com/content/tandf/tres/1998/00000019/00000004/art00013