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Running
on
Zero
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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()
# normalize to [-1, 1]
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
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