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| # adapted from https://github.com/facebookresearch/barlowtwins | |
| from math import exp | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| class RedundancyReduction(torch.nn.Module): | |
| def __init__(self, lambd=1e-5, vector_dimensions=256): | |
| super().__init__() | |
| self.lambd = lambd | |
| self.bn = torch.nn.BatchNorm1d(vector_dimensions, affine=False) | |
| def forward(self, z1, z2): | |
| c = self.bn(z1).T @ self.bn(z2) | |
| c.div_(z1.size(0)) | |
| off_diag = off_diagonal(c).pow_(2).sum() | |
| return self.lambd * off_diag | |
| class BarlowTwinsLoss(torch.nn.Module): | |
| def __init__(self, lambd=1e-5, vector_dimensions=256): | |
| super().__init__() | |
| self.lambd = lambd | |
| self.bn = torch.nn.BatchNorm1d(vector_dimensions, affine=False) | |
| def forward(self, z1, z2): | |
| c = self.bn(z1).T @ self.bn(z2) | |
| c.div_(z1.size(0)) | |
| on_diag = torch.diagonal(c).add_(-1).pow_(2).sum() | |
| off_diag = off_diagonal(c).pow_(2).sum() | |
| loss = on_diag + self.lambd * off_diag | |
| return loss | |
| def off_diagonal(x): | |
| # return a flattened view of the off-diagonal elements of a square matrix | |
| n, m = x.shape | |
| assert n == m | |
| return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten() | |
| class TripletLoss(torch.nn.Module): | |
| def __init__(self, margin): | |
| super().__init__() | |
| self.cosine_similarity = torch.nn.CosineSimilarity() | |
| self.margin = margin | |
| def forward(self, | |
| anchor_embeddings, | |
| positive_embeddings, | |
| negative_embeddings): | |
| positive_distance = 1 - self.cosine_similarity(anchor_embeddings, positive_embeddings) | |
| negative_distance = 1 - self.cosine_similarity(anchor_embeddings, negative_embeddings) | |
| losses = torch.max(positive_distance - negative_distance + self.margin, | |
| torch.full_like(positive_distance, 0)) | |
| return torch.mean(losses) | |
| # The following is taken from https://github.com/NATSpeech/NATSpeech/blob/aef3aa8899c82e40a28e4f59d559b46b18ba87e8/utils/metrics/ssim.py | |
| 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 = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) | |
| return window | |
| def _ssim(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) | |
| class SSIM(torch.nn.Module): | |
| """ | |
| Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim | |
| """ | |
| def __init__(self, window_size=11, size_average=True): | |
| super(SSIM, self).__init__() | |
| self.window_size = window_size | |
| self.size_average = size_average | |
| self.channel = 1 | |
| self.window = create_window(window_size, self.channel) | |
| 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 = 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 _ssim(img1, img2, window, self.window_size, channel, self.size_average) | |
| window = None | |
| def ssim(img1, img2, window_size=11, size_average=True): | |
| (_, channel, _, _) = img1.size() | |
| global window | |
| if window is None: | |
| window = create_window(window_size, channel) | |
| if img1.is_cuda: | |
| window = window.cuda(img1.get_device()) | |
| window = window.type_as(img1) | |
| return _ssim(img1, img2, window, window_size, channel, size_average) | |