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https://github.com/hpcaitech/Open-Sora.git
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68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
import numpy as np
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import torch
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# from taming.modules.losses.lpips import LPIPS # need to pip install https://github.com/CompVis/taming-transformers
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# from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
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"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
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## NOTE: not used since we only have 'GN'
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# def get_norm_layer(norm_type, dtype):
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# if norm_type == 'LN':
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# # supply a few args with partial function and pass the rest of the args when this norm_fn is called
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# norm_fn = functools.partial(nn.LayerNorm, dtype=dtype)
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# elif norm_type == 'GN': #
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# norm_fn = functools.partial(nn.GroupNorm, dtype=dtype)
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# elif norm_type is None:
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# norm_fn = lambda: (lambda x: x)
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# else:
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# raise NotImplementedError(f'norm_type: {norm_type}')
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# return norm_fn
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class DiagonalGaussianDistribution(object):
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def __init__(
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self,
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parameters,
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deterministic=False,
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):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device, dtype=self.mean.dtype)
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def sample(self):
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# torch.randn: standard normal distribution
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x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device, dtype=self.mean.dtype)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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if other is None: # SCH: assumes other is a standard normal distribution
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return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3, 4])
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=[1, 2, 3, 4],
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)
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def nll(self, sample, dims=[1, 2, 3, 4]): # TODO: what does this do?
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
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def mode(self):
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return self.mean
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