| import torch | |
| import torch.nn as nn | |
| class EvoNorm2d(nn.Module): | |
| __constants__ = ['num_features', 'eps', 'nonlinearity'] | |
| def __init__(self, num_features, eps=1e-5, nonlinearity=True, group=32): | |
| super(EvoNorm2d, self).__init__() | |
| self.num_features = num_features | |
| self.eps = eps | |
| self.nonlinearity = nonlinearity | |
| self.group = group | |
| self.weight = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) | |
| self.bias = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) | |
| if self.nonlinearity: | |
| self.v = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| nn.init.ones_(self.weight) | |
| nn.init.zeros_(self.bias) | |
| if self.nonlinearity: | |
| nn.init.ones_(self.v) | |
| def group_std(self, x, groups=32): | |
| N, C, H, W = x.shape | |
| x = torch.reshape(x, (N, groups, C // groups, H, W)) | |
| std = torch.std(x, (3, 4), keepdim=True) | |
| return torch.reshape(std + self.eps, (N, C, 1, 1)) | |
| def forward(self, x): | |
| if self.nonlinearity: | |
| num = x * torch.sigmoid(self.v * x) | |
| return num / self.group_std(x, self.group) * self.weight + self.bias | |
| else: | |
| return x * self.weight + self.bias |