def forward(self, x):
x = torch.sum(x, dim=1)
print("sum after axis summing: {}".format(torch.sum(x)))
print("gaussian map sum: {}".format(
torch.sum(gaussian_map(x, self.sigma, self.w, self.gpu))))
x = gaussian_map(x, self.sigma, self.w, self.gpu) * x
print("sum after gaussian map: {}".format(torch.sum(x)))
#print("size is: {}".format(x.size()))
#print("sd is: {}".format(self.sigma))
return x
def __init__(self, gpu=False):
super(Smoothing, self).__init__()
self.conv = nn.Conv2d(1, 1, 3, bias=False, padding=1)
self.gpu = gpu
self.conv.weight = torch.nn.Parameter(gaussian_map(
torch.rand(3, 3), 1, 1, self.gpu).view(1, 1, 3, 3),
requires_grad=False)
print(self.conv.weight.size())
def forward(self, x):
x = gaussian_map(x, self.sigma, self.w, gpu) * x
return x