PyTorch implementation of Laplacian pyramid loss
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import torch | |
def gauss_kernel(size=5, device=torch.device('cpu'), channels=3): | |
kernel = torch.tensor([[1., 4., 6., 4., 1], | |
[4., 16., 24., 16., 4.], | |
[6., 24., 36., 24., 6.], | |
[4., 16., 24., 16., 4.], | |
[1., 4., 6., 4., 1.]]) | |
kernel /= 256. | |
kernel = kernel.repeat(channels, 1, 1, 1) | |
kernel = kernel.to(device) | |
return kernel | |
def downsample(x): | |
return x[:, :, ::2, ::2] | |
def upsample(x): | |
cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3], device=x.device)], dim=3) | |
cc = cc.view(x.shape[0], x.shape[1], x.shape[2]*2, x.shape[3]) | |
cc = cc.permute(0,1,3,2) | |
cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]*2, device=x.device)], dim=3) | |
cc = cc.view(x.shape[0], x.shape[1], x.shape[2]*2, x.shape[3]*2) | |
x_up = cc.permute(0,1,3,2) | |
return conv_gauss(x_up, 4*gauss_kernel(channels=x.shape[1], device=x.device)) | |
def conv_gauss(img, kernel): | |
img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode='reflect') | |
out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1]) | |
return out | |
def laplacian_pyramid(img, kernel, max_levels=3): | |
current = img | |
pyr = [] | |
for level in range(max_levels): | |
filtered = conv_gauss(current, kernel) | |
down = downsample(filtered) | |
up = upsample(down) | |
diff = current-up | |
pyr.append(diff) | |
current = down | |
return pyr | |
class LapLoss(torch.nn.Module): | |
def __init__(self, max_levels=3, channels=3, device=torch.device('cpu')): | |
super(LapLoss, self).__init__() | |
self.max_levels = max_levels | |
self.gauss_kernel = gauss_kernel(channels=channels, device=device) | |
def forward(self, input, target): | |
pyr_input = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels) | |
pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels) | |
return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target)) |
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