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| import torch | |
| import torch.nn as nn | |
| def count_params(model): | |
| total_params = sum(p.numel() for p in model.parameters()) | |
| return total_params | |
| 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 | |
| class AbstractEncoder(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def encode(self, *args, **kwargs): | |
| raise NotImplementedError | |
| class Labelator(AbstractEncoder): | |
| """Net2Net Interface for Class-Conditional Model""" | |
| def __init__(self, n_classes, quantize_interface=True): | |
| super().__init__() | |
| self.n_classes = n_classes | |
| self.quantize_interface = quantize_interface | |
| def encode(self, c): | |
| c = c[:,None] | |
| if self.quantize_interface: | |
| return c, None, [None, None, c.long()] | |
| return c | |
| class SOSProvider(AbstractEncoder): | |
| # for unconditional training | |
| def __init__(self, sos_token, quantize_interface=True): | |
| super().__init__() | |
| self.sos_token = sos_token | |
| self.quantize_interface = quantize_interface | |
| def encode(self, x): | |
| # get batch size from data and replicate sos_token | |
| c = torch.ones(x.shape[0], 1)*self.sos_token | |
| c = c.long().to(x.device) | |
| if self.quantize_interface: | |
| return c, None, [None, None, c] | |
| return c |