(self, parameters, deterministic=False)
| 44 | |
| 45 | class DiagonalGaussianDistribution: |
| 46 | def __init__(self, parameters, deterministic=False): |
| 47 | self.parameters = parameters |
| 48 | self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) |
| 49 | self.logvar = torch.clamp(self.logvar, -30.0, 20.0) |
| 50 | self.deterministic = deterministic |
| 51 | self.std = torch.exp(0.5 * self.logvar) |
| 52 | self.var = torch.exp(self.logvar) |
| 53 | if self.deterministic: |
| 54 | self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) |
| 55 | |
| 56 | def sample(self): |
| 57 | x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) |
nothing calls this directly
no outgoing calls
no test coverage detected