I am trying to convert code from Pytorch to tensorflow, I don't have much idea about Pytorch, I like to use Tensorflow more, and I need some explanation about this class and some help to write that but in TensorFlow can anyone help me or share useful links Here is Pytorch code :
class DiffGroupNorm(torch.nn.Module):
def __init__(self, in_channels, groups, lamda=0.01, eps=1e-5, momentum=0.1,
affine=True, track_running_stats=True):
super().__init__()
self.in_channels = in_channels
self.groups = groups
self.lamda = lamda
self.lin = Linear(in_channels, groups, bias=False)
self.norm = BatchNorm1d(groups * in_channels, eps, momentum, affine,
track_running_stats)
self.reset_parameters()
def reset_parameters(self):
self.lin.reset_parameters()
self.norm.reset_parameters()
def forward(self, x: Tensor) -> Tensor:
""""""
F, G = self.in_channels, self.groups
s = self.lin(x).softmax(dim=-1) # [N, G]
out = s.unsqueeze(-1) * x.unsqueeze(-2) # [N, G, F]
out = self.norm(out.view(-1, G * F)).view(-1, G, F).sum(-2) # [N, F]
return x + self.lamda * out
@staticmethod
def group_distance_ratio(x: Tensor, y: Tensor, eps: float = 1e-5) -> float:
num_classes = int(y.max()) + 1
numerator = 0.
for i in range(num_classes):
mask = y == i
dist = torch.cdist(x[mask].unsqueeze(0), x[~mask].unsqueeze(0))
numerator += (1 / dist.numel()) * float(dist.sum())
numerator *= 1 / (num_classes - 1)**2
denominator = 0.
for i in range(num_classes):
mask = y == i
dist = torch.cdist(x[mask].unsqueeze(0), x[mask].unsqueeze(0))
denominator += (1 / dist.numel()) * float(dist.sum())
denominator *= 1 / num_classes
return numerator / (denominator + eps)
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'groups={self.groups})')