def normalize(x, eps=1e-6):
"""Apply min-max normalization."""
x = x.contiguous()
N, C, H, W = x.size()
x_ = x.view(N * C, -1)
max_val = porch.max(x_, dim=1, keepdim=True)[0]
min_val = porch.min(x_, dim=1, keepdim=True)[0]
x_ = (x_ - min_val) / (max_val - min_val + eps)
out = x_.view(N, C, H, W)
return out
def normalize(x, eps=1e-6):
"""Apply min-max normalization."""
# x = x.contiguous()
x = torch.varbase_to_tensor(x)
N, C, H, W = x.shape
x_ = x.view(N * C, -1)
max_val = torch.max(x_, dim=1, keepdim=True)[0]
min_val = torch.min(x_, dim=1, keepdim=True)[0]
x_ = (x_ - min_val) / (max_val - min_val + eps)
x_ = torch.varbase_to_tensor(x_)
out = x_.view(N, C, H, W)
return out
def min(self):
return torch.min(self)