_window和self.window的区别

本文解释了@property方法与成员变量之间的区别。当使用@property但未声明成员变量时,默认会创建一个成员变量。例如,_window表示由@property创建的成员变量,而self.window则是一个普通的成员变量。

_window : 如果有@property方法但是没有声明成员变量, 则默认创建一个_window成员变量.

self.window: 普通的成员变量.

class SSIMLoss(nn.Module): """SSIM损失""" def __init__(self, window_size=11, size_average=True): super(SSIMLoss, self).__init__() self.window_size = window_size self.size_average = size_average self.channel = 1 self.window = self.create_window(window_size, self.channel) def gaussian(self, window_size, sigma=1.5): gauss = torch.Tensor([ torch.exp(torch.tensor(-(x - window_size//2)**2/float(2*sigma**2))) for x in range(window_size) ]) return gauss / gauss.sum() def create_window(self, window_size, channel): _1D_window = self.gaussian(window_size).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 forward(self, img1, img2): (_, channel, _, _) = img1.size() if channel == self.channel and self.window.data.type() == img1.data.type(): window = self.window else: window = self.create_window(self.window_size, channel) if img1.is_cuda: window = window.cuda(img1.get_device()) window = window.type_as(img1) self.window = window self.channel = channel return self._ssim(img1, img2, window, self.window_size, channel, self.size_average) def _ssim(self, img1, img2, window, window_size, channel, size_average=True): mu1 = F.conv2d(img1, window, padding=window_size//2, groups=channel) mu2 = F.conv2d(img2, window, padding=window_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=window_size//2, groups=channel) - mu1_sq sigma2_sq = F.conv2d(img2*img2, window, padding=window_size//2, groups=channel) - mu2_sq sigma12 = F.conv2d(img1*img2, window, padding=window_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: return ssim_map.mean() else: return ssim_map.mean(1).mean(1).mean(1)
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