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scc.py
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scc.py
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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 math
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
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
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)
_check_same_shape(preds, target)
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."
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_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_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_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_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_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_img, 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.
left_padding = int(math.ceil((window.size(3) - 1) / 2))
right_padding = int(math.floor((window.size(3) - 1) / 2))
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
def _scc_per_channel_compute(preds: Tensor, target: Tensor, hp_filter: Tensor, window_size: int) -> Tensor:
"""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)
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: Optional[Tensor] = None,
window_size: int = 8,
reduction: Optional[Literal["mean", "none", None]] = "mean",
) -> Tensor:
"""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,
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
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.)
>>> 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 reduction 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 = [
_scc_per_channel_compute(
preds[:, i, :, :].unsqueeze(1), target[:, i, :, :].unsqueeze(1), hp_filter, window_size
)
for i in range(preds.size(1))
]
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")
return None