Source code for ignite.metrics.ssim
from typing import Callable, Sequence, Union
import torch
import torch.nn.functional as F
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
__all__ = ["SSIM"]
[docs]class SSIM(Metric):
"""
Computes Structual Similarity Index Measure
Args:
data_range (int or float): Range of the image. Typically, ``1.0`` or ``255``.
kernel_size (int or list or tuple of int): Size of the kernel. Default: (11, 11)
sigma (float or list or tuple of float): Standard deviation of the gaussian kernel.
Argument is used if ``gaussian=True``. Default: (1.5, 1.5)
k1 (float): Parameter of SSIM. Default: 0.01
k2 (float): Parameter of SSIM. Default: 0.03
gaussian (bool): ``True`` to use gaussian kernel, ``False`` to use uniform kernel
output_transform (callable, optional): A callable that is used to transform the
:class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
form expected by the metric.
Example:
To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
The output of the engine's ``process_function`` needs to be in the format of
``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``.
``y_pred`` and ``y`` can be un-normalized or normalized image tensors. Depending on that, the user might need
to adjust ``data_range``. ``y_pred`` and ``y`` should have the same shape.
.. code-block:: python
def process_function(engine, batch):
# ...
return y_pred, y
engine = Engine(process_function)
metric = SSIM(data_range=1.0)
metric.attach(engine, "ssim")
"""
def __init__(
self,
data_range: Union[int, float],
kernel_size: Union[int, Sequence[int]] = (11, 11),
sigma: Union[float, Sequence[float]] = (1.5, 1.5),
k1: float = 0.01,
k2: float = 0.03,
gaussian: bool = True,
output_transform: Callable = lambda x: x,
):
if isinstance(kernel_size, int):
self.kernel_size = [kernel_size, kernel_size]
elif isinstance(kernel_size, Sequence):
self.kernel_size = kernel_size
else:
raise ValueError("Argument kernel_size should be either int or a sequence of int.")
if isinstance(sigma, float):
self.sigma = [sigma, sigma]
elif isinstance(sigma, Sequence):
self.sigma = sigma
else:
raise ValueError("Argument sigma should be either float or a sequence of float.")
if any(x % 2 == 0 or x <= 0 for x in self.kernel_size):
raise ValueError("Expected kernel_size to have odd positive number. Got {}.".format(kernel_size))
if any(y <= 0 for y in self.sigma):
raise ValueError("Expected sigma to have positive number. Got {}.".format(sigma))
self.gaussian = gaussian
self.c1 = (k1 * data_range) ** 2
self.c2 = (k2 * data_range) ** 2
self.pad_h = (self.kernel_size[0] - 1) // 2
self.pad_w = (self.kernel_size[1] - 1) // 2
self._kernel = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma)
super(SSIM, self).__init__(output_transform=output_transform)
@reinit__is_reduced
def reset(self) -> None:
self._sum_of_batchwise_ssim = 0.0
self._num_examples = 0
self._kernel = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma)
def _uniform(self, kernel_size):
max, min = 2.5, -2.5
kernel = torch.arange(start=(1 - kernel_size) / 2, end=(1 + kernel_size) / 2, step=1, dtype=torch.float32)
for i, j in enumerate(kernel):
if min <= j <= max:
kernel[i] = 1 / (max - min)
else:
kernel[i] = 0
return kernel.unsqueeze(dim=0) # (1, kernel_size)
def _gaussian(self, kernel_size, sigma):
kernel = torch.arange(start=(1 - kernel_size) / 2, end=(1 + kernel_size) / 2, step=1, dtype=torch.float32)
gauss = torch.exp(-kernel.pow(2) / (2 * pow(sigma, 2)))
return (gauss / gauss.sum()).unsqueeze(dim=0) # (1, kernel_size)
def _gaussian_or_uniform_kernel(self, kernel_size, sigma):
if self.gaussian:
kernel_x = self._gaussian(kernel_size[0], sigma[0])
kernel_y = self._gaussian(kernel_size[1], sigma[1])
else:
kernel_x = self._uniform(kernel_size[0])
kernel_y = self._uniform(kernel_size[1])
return torch.matmul(kernel_x.t(), kernel_y) # (kernel_size, 1) * (1, kernel_size)
@reinit__is_reduced
def update(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
if y_pred.dtype != y.dtype:
raise TypeError(
"Expected y_pred and y to have the same data type. Got y_pred: {} and y: {}.".format(
y_pred.dtype, y.dtype
)
)
if y_pred.shape != y.shape:
raise ValueError(
"Expected y_pred and y to have the same shape. Got y_pred: {} and y: {}.".format(y_pred.shape, y.shape)
)
if len(y_pred.shape) != 4 or len(y.shape) != 4:
raise ValueError(
"Expected y_pred and y to have BxCxHxW shape. Got y_pred: {} and y: {}.".format(y_pred.shape, y.shape)
)
channel = y_pred.size(1)
if len(self._kernel.shape) < 4:
self._kernel = self._kernel.expand(channel, 1, -1, -1).to(device=y_pred.device)
y_pred = F.pad(y_pred, (self.pad_w, self.pad_w, self.pad_h, self.pad_h), mode="reflect")
y = F.pad(y, (self.pad_w, self.pad_w, self.pad_h, self.pad_h), mode="reflect")
input_list = torch.cat([y_pred, y, y_pred * y_pred, y * y, y_pred * y])
outputs = F.conv2d(input_list, self._kernel, groups=channel)
output_list = [outputs[x * y_pred.size(0) : (x + 1) * y_pred.size(0)] for x in range(len(outputs))]
mu_pred_sq = output_list[0].pow(2)
mu_target_sq = output_list[1].pow(2)
mu_pred_target = output_list[0] * output_list[1]
sigma_pred_sq = output_list[2] - mu_pred_sq
sigma_target_sq = output_list[3] - mu_target_sq
sigma_pred_target = output_list[4] - mu_pred_target
a1 = 2 * mu_pred_target + self.c1
a2 = 2 * sigma_pred_target + self.c2
b1 = mu_pred_sq + mu_target_sq + self.c1
b2 = sigma_pred_sq + sigma_target_sq + self.c2
ssim_idx = (a1 * a2) / (b1 * b2)
self._sum_of_batchwise_ssim += torch.mean(ssim_idx, (1, 2, 3), dtype=torch.float64)
self._num_examples += y.shape[0]
@sync_all_reduce("_sum_of_batchwise_ssim", "_num_examples")
def compute(self) -> torch.Tensor:
if self._num_examples == 0:
raise NotComputableError("SSIM must have at least one example before it can be computed.")
return torch.sum(self._sum_of_batchwise_ssim / self._num_examples)