(self, parameters, deterministic=False)
| 23 | |
| 24 | class DiagonalGaussianDistribution(object): |
| 25 | def __init__(self, parameters, deterministic=False): |
| 26 | self.parameters = parameters |
| 27 | self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) |
| 28 | self.logvar = torch.clamp(self.logvar, -30.0, 20.0) |
| 29 | self.deterministic = deterministic |
| 30 | self.std = torch.exp(0.5 * self.logvar) |
| 31 | self.var = torch.exp(self.logvar) |
| 32 | if self.deterministic: |
| 33 | self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) |
| 34 | |
| 35 | def sample(self, noise=None): |
| 36 | if noise is None: |
nothing calls this directly
no outgoing calls
no test coverage detected