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7340df2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import torch
import torch.nn as nn
from typing import *
import numpy as np
from ..modules import sparse as sp
FP16_MODULES = (
nn.Conv1d,
nn.Conv2d,
nn.Conv3d,
nn.ConvTranspose1d,
nn.ConvTranspose2d,
nn.ConvTranspose3d,
nn.Linear,
sp.SparseConv3d,
sp.SparseInverseConv3d,
sp.SparseLinear,
)
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, FP16_MODULES):
for p in l.parameters():
p.data = p.data.half()
def convert_module_to_f32(l):
"""
Convert primitive modules to float32, undoing convert_module_to_f16().
"""
if isinstance(l, FP16_MODULES):
for p in l.parameters():
p.data = p.data.float()
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1):
self.feat_dim = feat_dim
self.parameters = parameters
if isinstance(parameters, list):
self.mean = parameters[0]
self.logvar = parameters[1]
else:
self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_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)
def sample(self):
x = self.mean + self.std * torch.randn_like(self.mean)
return x
def kl(self, other=None, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
def nll(self, sample, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([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):
return self.mean |