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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: Range of the image. Typically, ``1.0`` or ``255``. kernel_size: Size of the kernel. Default: (11, 11) sigma: Standard deviation of the gaussian kernel. Argument is used if ``gaussian=True``. Default: (1.5, 1.5) k1: Parameter of SSIM. Default: 0.01 k2: Parameter of SSIM. Default: 0.03 gaussian: ``True`` to use gaussian kernel, ``False`` to use uniform kernel output_transform: 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. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: 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. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = SSIM(data_range=1.0) metric.attach(default_evaluator, 'ssim') preds = torch.rand([4, 3, 16, 16]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['ssim']) .. testoutput:: 0.9218971... .. versionadded:: 0.4.2 """ 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, device: Union[str, torch.device] = torch.device("cpu"), ): if isinstance(kernel_size, int): self.kernel_size = [kernel_size, kernel_size] # type: Sequence[int] 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] # type: Sequence[float] 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(f"Expected kernel_size to have odd positive number. Got {kernel_size}.") if any(y <= 0 for y in self.sigma): raise ValueError(f"Expected sigma to have positive number. Got {sigma}.") super(SSIM, self).__init__(output_transform=output_transform, device=device) 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)
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_ssim = torch.tensor(0.0, dtype=torch.float64, device=self._device) self._num_examples = 0 self._kernel = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma)
def _uniform(self, kernel_size: int) -> torch.Tensor: max, min = 2.5, -2.5 ksize_half = (kernel_size - 1) * 0.5 kernel = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, device=self._device) 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: int, sigma: float) -> torch.Tensor: ksize_half = (kernel_size - 1) * 0.5 kernel = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, device=self._device) gauss = torch.exp(-0.5 * (kernel / sigma).pow(2)) return (gauss / gauss.sum()).unsqueeze(dim=0) # (1, kernel_size) def _gaussian_or_uniform_kernel(self, kernel_size: Sequence[int], sigma: Sequence[float]) -> torch.Tensor: 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)
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.dtype != y.dtype: raise TypeError( f"Expected y_pred and y to have the same data type. Got y_pred: {y_pred.dtype} and y: {y.dtype}." ) if y_pred.shape != y.shape: raise ValueError( f"Expected y_pred and y to have the same shape. Got y_pred: {y_pred.shape} and y: {y.shape}." ) if len(y_pred.shape) != 4 or len(y.shape) != 4: raise ValueError( f"Expected y_pred and y to have BxCxHxW shape. Got y_pred: {y_pred.shape} and y: {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_ssim += torch.mean(ssim_idx, (1, 2, 3), dtype=torch.float64).sum().to(self._device) self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_ssim", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("SSIM must have at least one example before it can be computed.") return (self._sum_of_ssim / self._num_examples).item()

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