triposf/modules/utils.py

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