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| import torch | |
| import torch.nn as nn | |
| from .block import Block | |
| class GroupNorm(Block): | |
| def __init__(self, num_channels, num_groups=32, eps=1e-6, *args, **kwargs) -> None: | |
| super().__init__(*args, **kwargs) | |
| self.norm = torch.nn.GroupNorm( | |
| num_groups=num_groups, num_channels=num_channels, eps=1e-6, affine=True | |
| ) | |
| def forward(self, x): | |
| return self.norm(x) | |
| def Normalize(in_channels, num_groups=32): | |
| return torch.nn.GroupNorm( | |
| num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True | |
| ) | |
| class ActNorm(nn.Module): | |
| def __init__(self, num_features, logdet=False, affine=True, | |
| allow_reverse_init=False): | |
| assert affine | |
| super().__init__() | |
| self.logdet = logdet | |
| self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) | |
| self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) | |
| self.allow_reverse_init = allow_reverse_init | |
| self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) | |
| def initialize(self, input): | |
| with torch.no_grad(): | |
| flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) | |
| mean = ( | |
| flatten.mean(1) | |
| .unsqueeze(1) | |
| .unsqueeze(2) | |
| .unsqueeze(3) | |
| .permute(1, 0, 2, 3) | |
| ) | |
| std = ( | |
| flatten.std(1) | |
| .unsqueeze(1) | |
| .unsqueeze(2) | |
| .unsqueeze(3) | |
| .permute(1, 0, 2, 3) | |
| ) | |
| self.loc.data.copy_(-mean) | |
| self.scale.data.copy_(1 / (std + 1e-6)) | |
| def forward(self, input, reverse=False): | |
| if reverse: | |
| return self.reverse(input) | |
| if len(input.shape) == 2: | |
| input = input[:,:,None,None] | |
| squeeze = True | |
| else: | |
| squeeze = False | |
| _, _, height, width = input.shape | |
| if self.training and self.initialized.item() == 0: | |
| self.initialize(input) | |
| self.initialized.fill_(1) | |
| h = self.scale * (input + self.loc) | |
| if squeeze: | |
| h = h.squeeze(-1).squeeze(-1) | |
| if self.logdet: | |
| log_abs = torch.log(torch.abs(self.scale)) | |
| logdet = height*width*torch.sum(log_abs) | |
| logdet = logdet * torch.ones(input.shape[0]).to(input) | |
| return h, logdet | |
| return h | |
| def reverse(self, output): | |
| if self.training and self.initialized.item() == 0: | |
| if not self.allow_reverse_init: | |
| raise RuntimeError( | |
| "Initializing ActNorm in reverse direction is " | |
| "disabled by default. Use allow_reverse_init=True to enable." | |
| ) | |
| else: | |
| self.initialize(output) | |
| self.initialized.fill_(1) | |
| if len(output.shape) == 2: | |
| output = output[:,:,None,None] | |
| squeeze = True | |
| else: | |
| squeeze = False | |
| h = output / self.scale - self.loc | |
| if squeeze: | |
| h = h.squeeze(-1).squeeze(-1) | |
| return h | |