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smooth_loss.py
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smooth_loss.py
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import torch
import torch.nn.functional as F
def laplacian_edge(img):
laplacian_filter = torch.Tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
filter = torch.reshape(laplacian_filter, [1, 1, 3, 3])
filter = filter.cuda()
lap_edge = F.conv2d(img, filter, stride=1, padding=1)
return lap_edge
def gradient_x(img):
sobel = torch.Tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
filter = torch.reshape(sobel,[1,1,3,3])
filter = filter.cuda()
gx = F.conv2d(img, filter, stride=1, padding=1)
return gx
def gradient_y(img):
sobel = torch.Tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
filter = torch.reshape(sobel, [1, 1,3,3])
filter = filter.cuda()
gy = F.conv2d(img, filter, stride=1, padding=1)
return gy
def charbonnier_penalty(s):
cp_s = torch.pow(torch.pow(s, 2) + 0.001**2, 0.5)
return cp_s
def get_saliency_smoothness(pred, gt, size_average=True):
alpha = 10
s1 = 10
s2 = 0
## first oder derivative: sobel
sal_x = torch.abs(gradient_x(pred))
sal_y = torch.abs(gradient_y(pred))
gt_x = gradient_x(gt)
gt_y = gradient_y(gt)
w_x = torch.exp(torch.abs(gt_x) * (-alpha))
w_y = torch.exp(torch.abs(gt_y) * (-alpha))
cps_x = charbonnier_penalty(sal_x * w_x)
cps_y = charbonnier_penalty(sal_y * w_y)
cps_xy = cps_x + cps_y
## second order derivative: laplacian
lap_sal = torch.abs(laplacian_edge(pred))
lap_gt = torch.abs(laplacian_edge(gt))
weight_lap = torch.exp(lap_gt * (-alpha))
weighted_lap = charbonnier_penalty(lap_sal*weight_lap)
smooth_loss = s1*torch.mean(cps_xy) + s2*torch.mean(weighted_lap)
return smooth_loss
class smoothness_loss(torch.nn.Module):
def __init__(self, size_average = True):
super(smoothness_loss, self).__init__()
self.size_average = size_average
def forward(self, pred, target):
return get_saliency_smoothness(pred, target, self.size_average)