class Get_gradient_nopadding_rgb(nn.Module):
def __init__(self):
super(Get_gradient_nopadding_rgb, self).__init__()
kernel_v = [[0, -1, 0],
[0, 0, 0],
[0, 1, 0]]
kernel_h = [[0, 0, 0],
[-1, 0, 1],
[0, 0, 0]]
kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False).cuda()
self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False).cuda()
def forward(self, x):
x0 = x[:, 0]
x1 = x[:, 1]
x2 = x[:, 2]
x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=1)
x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=1)
x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=1)
x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=1)
x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=1)
x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=1)
x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
x = torch.cat([x0, x1, x2], dim=1)
return x
