pytorch ssim损失函数

在 PyTorch 中，可以使用 `torch_ssim` 包中提供的 SSIM 损失函数来计算结构相似性指标。可以使用以下代码导入该包：

from pytorch_ssim import ssim

然后，可以使用 `ssim` 函数来计算 SSIM 损失，例如：

loss = 1 - ssim(output, target, data_range=output.max() - output.min())

其中，`output` 和 `target` 分别是模型输出和真实标签，`data_range` 是数据范围（通常为张量中的最大值减去最小值）。请注意，`torch_ssim` 包是基于 PyTorch 实现的，因此可以通过反向传播来计算梯度，并用于训练模型。

相关问题

ssim损失函数pytorch实现

好的，关于ssim损失函数的PyTorch实现，可以参考以下代码：

import torch
import torch.nn.functional as F

def ssim_loss(img1, img2, window_size=11, size_average=True, sigma=1.5):
    # Set the window size
    window = create_window(window_size, img1.size(1), sigma=sigma)

    # Compute the mean of the images
    mu1 = F.conv2d(img1, window, padding=window_size//2, groups=img1.size(1))
    mu2 = F.conv2d(img2, window, padding=window_size//2, groups=img1.size(1))

    # Compute the variances and the covariances
    sigma1_sq = F.conv2d(img1*img1, window, padding=window_size//2, groups=img1.size(1)) - mu1*mu1
    sigma2_sq = F.conv2d(img2*img2, window, padding=window_size//2, groups=img1.size(1)) - mu2*mu2
    sigma12 = F.conv2d(img1*img2, window, padding=window_size//2, groups=img1.size(1)) - mu1*mu2

    # Compute the SSIM index
    C1 = (0.01)**2
    C2 = (0.03)**2
    ssim_map = ((2*mu1*mu2 + C1)*(2*sigma12 + C2))/((mu1**2 + mu2**2 + C1)*(sigma1_sq + sigma2_sq + C2))
    ssim_index = torch.mean(ssim_map)

    # Compute the loss
    return 1 - ssim_index

def create_window(window_size, channel, sigma=1.5):
    # Create a 2D Gaussian window
    gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    gauss_2d = torch.mm(gauss.unsqueeze(1), gauss.unsqueeze(0))
    window = torch.Tensor(gauss_2d.expand(channel, 1, window_size, window_size).contiguous())

    # Normalize the window
    window = window / window.sum()

    return window

希望对你有帮助！

pytorch SSIM loss

在 PyTorch 中，可以使用 PyTorch 的 SSIM 损失函数来计算图像的结构相似性指数。SSIM 损失函数是一种用于测量图像质量的指标，它考虑了图像的亮度、对比度和结构等因素。下面是一个使用 PyTorch SSIM 损失函数的示例代码：

import torch
import torch.nn.functional as F
from math import exp

def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    return gauss/gauss.sum()

def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
    return window

def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False):
    _, channel, _, _ = img1.size()
    if window is None:
        real_size = min(window_size, img1.size()[2])
        window = create_window(real_size, channel).to(img1.device)
    mu1 = F.conv2d(img1, window, padding=real_size//2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=real_size//2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=real_size//2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=real_size//2, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window, padding=real_size//2, groups=channel) - mu1_mu2

    C1 = (0.01) ** 2
    C2 = (0.03) ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    if size_average:
        ret = ssim_map.mean()
    else:
        ret = ssim_map.mean(1).mean(1).mean(1)

    if full:
        return ret, ssim_map
    return ret

在上面的代码中，`ssim()` 函数接受两个输入图像 `img1` 和 `img2`，并返回它们的 SSIM 相似度。函数中使用了高斯滤波器来计算图像的均值和方差，以及结构相似性指数。如果需要返回 SSIM 相似度图，则可以将 `full` 参数设置为 `True`。