def __init__(self, criterion, geom_weight=1, level_nb=1):
self.level_nb = level_nb
self.SP = ScalePyramid()
if criterion == "l2":
self.criterion = torch.nn.MSELoss(reduction="none")
elif criterion == "l1":
self.criterion = torch.nn.L1Loss(reduction="none")
elif criterion == "ssim":
self.criterion = klosses.SSIM(reduction="none")
else:
raise ValueError(f"{criterion} not in [l2, l1, ssim]")
self.geom_weight = geom_weight
'piqa.PSNR': piqa.PSNR(),
}),
'SSIM': (2, {
'sk.ssim-np':
lambda x, y: sk.structural_similarity(
x,
y,
win_size=11,
multichannel=True,
gaussian_weights=True,
),
'piq.ssim':
piq.ssim,
'kornia.SSIM-halfloss':
kornia.SSIM(
window_size=11,
reduction='mean',
),
'piq.SSIM-loss':
piq.SSIMLoss(),
'IQA.SSIM-loss':
IQA.SSIM(),
'vainf.SSIM':
vainf.SSIM(data_range=1.),
'piqa.SSIM':
piqa.SSIM(),
}),
'MS-SSIM': (2, {
'piq.ms_ssim': piq.multi_scale_ssim,
'piq.MS_SSIM-loss': piq.MultiScaleSSIMLoss(),
'IQA.MS_SSIM-loss': IQA.MS_SSIM(),
'vainf.MS_SSIM': vainf.MS_SSIM(data_range=1.),
def __init__(self):
super(SSIMLoss, self).__init__()
self.ssim_loss_5x5 = losses.SSIM(5, reduction='none')
self.ssim_loss_11x11 = losses.SSIM(11, reduction='none')
def __init__(self) -> None:
super(MSE_SSIMLoss, self).__init__()
self.ssim = losses.SSIM(window_size=3, reduction='mean')
'PSNR': (2, {
'sk.psnr': sk.peak_signal_noise_ratio,
'piq.psnr': piq.psnr,
'kornia.PSNR': kornia.PSNRLoss(max_val=1.),
'piqa.PSNR': piqa.PSNR(),
}),
'SSIM': (2, {
'sk.ssim': lambda x, y: sk.structural_similarity(
x, y,
win_size=11,
multichannel=True,
gaussian_weights=True,
),
'piq.ssim': piq.ssim,
'kornia.SSIM-halfloss': kornia.SSIM(
window_size=11,
reduction='mean',
),
'piq.SSIM-loss': piq.SSIMLoss(),
'IQA.SSIM-loss': IQA.SSIM(),
'vainf.SSIM': vainf.SSIM(data_range=1.),
'piqa.SSIM': piqa.SSIM(),
}),
'MS-SSIM': (2, {
'piq.ms_ssim': piq.multi_scale_ssim,
'piq.MS_SSIM-loss': piq.MultiScaleSSIMLoss(),
'IQA.MS_SSIM-loss': IQA.MS_SSIM(),
'vainf.MS_SSIM': vainf.MS_SSIM(data_range=1.),
'piqa.MS_SSIM': piqa.MS_SSIM(),
}),
'LPIPS': (2, {
'piq.LPIPS': piq.LPIPS(),