| | import torch |
| | import torch.nn.functional as F |
| | from torch.autograd import Variable |
| | from math import exp |
| | from lpips import LPIPS |
| |
|
| |
|
| | def smooth_l1_loss(pred, target, beta=1.0): |
| | diff = torch.abs(pred - target) |
| | loss = torch.where(diff < beta, 0.5 * diff ** 2 / beta, diff - 0.5 * beta) |
| | return loss.mean() |
| |
|
| |
|
| | def l1_loss(network_output, gt): |
| | return torch.abs((network_output - gt)).mean() |
| |
|
| |
|
| | def l2_loss(network_output, gt): |
| | return ((network_output - gt) ** 2).mean() |
| |
|
| |
|
| | 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 psnr(img1, img2, max_val=1.0): |
| | mse = F.mse_loss(img1, img2) |
| | return 20 * torch.log10(max_val / torch.sqrt(mse)) |
| |
|
| |
|
| | def ssim(img1, img2, window_size=11, size_average=True): |
| | channel = img1.size(-3) |
| | 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) |
| |
|
| | 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).mean(1).mean(1) |
| |
|
| |
|
| | loss_fn_vgg = None |
| | def lpips(img1, img2, value_range=(0, 1)): |
| | global loss_fn_vgg |
| | if loss_fn_vgg is None: |
| | loss_fn_vgg = LPIPS(net='vgg').cuda().eval() |
| | |
| | img1 = (img1 - value_range[0]) / (value_range[1] - value_range[0]) * 2 - 1 |
| | img2 = (img2 - value_range[0]) / (value_range[1] - value_range[0]) * 2 - 1 |
| | return loss_fn_vgg(img1, img2).mean() |
| |
|
| |
|
| | def normal_angle(pred, gt): |
| | pred = pred * 2.0 - 1.0 |
| | gt = gt * 2.0 - 1.0 |
| | norms = pred.norm(dim=-1) * gt.norm(dim=-1) |
| | cos_sim = (pred * gt).sum(-1) / (norms + 1e-9) |
| | cos_sim = torch.clamp(cos_sim, -1.0, 1.0) |
| | ang = torch.rad2deg(torch.acos(cos_sim[norms > 1e-9])).mean() |
| | if ang.isnan(): |
| | return -1 |
| | return ang |
| |
|
