def get_M(self, F, tau=0.05):
assert (F.dim() == 4)
F_squared_sum = F.pow(2).sum(1, keepdim=True).pow(0.5)
F_normalized = self.normalize_0_to_1(F_squared_sum) # all feature normalize ~ [0,1]
M = self.Tensor(F_normalized.size())
M.copy_(torch.ge(F_normalized, tau)) # >= tau
return M
def stdv_channels(F):
assert (F.dim() == 4)
F_mean = mean_channels(F)
F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(
2, keepdim=True) / (F.size(2) * F.size(3))
x = F_variance.pow(0.5)
return x
def rescaled_contrast_layer(F):
assert (F.dim() == 4)
F_mean = mean_channels(F)
F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(
2, keepdim=True) / (F.size(2) * F.size(3))
# return F_mean / F_variance.pow(0.5)
# return - F_mean + F_variance
return -F_mean / F_variance.pow(0.5) + F_variance.pow(0.5)
def normalize_0_to_1(self, F):
assert (F.dim() == 4)
max_val = F.max()
min_val = F.min()
if max_val != min_val:
F_normalized = (F - min_val) / (max_val - min_val)
else:
F_normalized = self.Tensor(F.size()).fill_(0)
return F_normalized
def mean_channels(F):
assert (F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
