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| from typing import Optional, Tuple | |
| import numpy as np | |
| import torch | |
| from diffusers.utils.torch_utils import randn_tensor | |
| class DiagonalGaussianDistribution(object): | |
| def __init__(self, parameters: torch.Tensor, deterministic: bool = False, _dim: int = 1): | |
| # Note: _dim is the new argument added here after copying from diffusers | |
| self.parameters = parameters | |
| self.mean, self.logvar = torch.chunk(parameters, 2, dim=_dim) | |
| self.logvar = torch.clamp(self.logvar, -30.0, 20.0) | |
| self.deterministic = deterministic | |
| self.std = torch.exp(0.5 * self.logvar) | |
| self.var = torch.exp(self.logvar) | |
| if self.deterministic: | |
| self.var = self.std = torch.zeros_like( | |
| self.mean, device=self.parameters.device, dtype=self.parameters.dtype | |
| ) | |
| def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor: | |
| # make sure sample is on the same device as the parameters and has same dtype | |
| sample = randn_tensor( | |
| self.mean.shape, | |
| generator=generator, | |
| device=self.parameters.device, | |
| dtype=self.parameters.dtype, | |
| ) | |
| x = self.mean + self.std * sample | |
| return x | |
| def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: | |
| if self.deterministic: | |
| return torch.Tensor([0.0]) | |
| else: | |
| if other is None: | |
| return 0.5 * torch.sum( | |
| torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, | |
| dim=[1, 2, 3], | |
| ) | |
| else: | |
| return 0.5 * torch.sum( | |
| torch.pow(self.mean - other.mean, 2) / other.var | |
| + self.var / other.var | |
| - 1.0 | |
| - self.logvar | |
| + other.logvar, | |
| dim=[1, 2, 3], | |
| ) | |
| def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor: | |
| if self.deterministic: | |
| return torch.Tensor([0.0]) | |
| logtwopi = np.log(2.0 * np.pi) | |
| return 0.5 * torch.sum( | |
| logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, | |
| dim=dims, | |
| ) | |
| def mode(self) -> torch.Tensor: | |
| return self.mean | |
| def _expand_linear_with_zeroed_weights( | |
| module: torch.nn.Linear, new_in_features: Optional[int] = None, new_out_features: Optional[int] = None | |
| ) -> torch.nn.Linear: | |
| if new_in_features is None: | |
| new_in_features = module.in_features | |
| if new_out_features is None: | |
| new_out_features = module.out_features | |
| bias = getattr(module, "bias", None) | |
| new_module = torch.nn.Linear(new_in_features, new_out_features, bias=bias is not None) | |
| new_module.to(device=module.weight.device, dtype=module.weight.dtype) | |
| new_module.weight.zero_() | |
| new_module.weight.data[: module.weight.data.shape[0], : module.weight.data.shape[1]].copy_(module.weight.data) | |
| if bias is not None: | |
| new_module.bias.zero_() | |
| new_module.bias.data[: bias.data.shape[0]].copy_(bias.data) | |
| return new_module | |
| def _expand_conv3d_with_zeroed_weights( | |
| module: torch.nn.Linear, new_in_channels: Optional[int] = None, new_out_channels: Optional[int] = None | |
| ) -> torch.nn.Conv3d: | |
| if new_in_channels is None: | |
| new_in_channels = module.in_channels | |
| if new_out_channels is None: | |
| new_out_channels = module.out_channels | |
| bias = getattr(module, "bias", None) | |
| new_module = torch.nn.Conv3d( | |
| new_in_channels, | |
| new_out_channels, | |
| kernel_size=module.kernel_size, | |
| stride=module.stride, | |
| padding=module.padding, | |
| dilation=module.dilation, | |
| groups=module.groups, | |
| bias=bias is not None, | |
| ) | |
| new_module.to(device=module.weight.device, dtype=module.weight.dtype) | |
| new_module.weight.zero_() | |
| new_module.weight.data[: module.weight.data.shape[0], : module.weight.data.shape[1]].copy_(module.weight.data) | |
| if bias is not None: | |
| new_module.bias.zero_() | |
| new_module.bias.data[: bias.data.shape[0]].copy_(bias.data) | |
| return new_module | |