6084915af8
Adds support for GD models, courtesy of some maths from openai.
Also:
- Fixes requirement for eval{} even when it isn't being used
- Adds support for denormalizing an imagenet norm
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
"""
|
|
Helpers for various likelihood-based losses. These are ported from the original
|
|
Ho et al. diffusion models codebase:
|
|
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
import torch as th
|
|
|
|
|
|
def normal_kl(mean1, logvar1, mean2, logvar2):
|
|
"""
|
|
Compute the KL divergence between two gaussians.
|
|
|
|
Shapes are automatically broadcasted, so batches can be compared to
|
|
scalars, among other use cases.
|
|
"""
|
|
tensor = None
|
|
for obj in (mean1, logvar1, mean2, logvar2):
|
|
if isinstance(obj, th.Tensor):
|
|
tensor = obj
|
|
break
|
|
assert tensor is not None, "at least one argument must be a Tensor"
|
|
|
|
# Force variances to be Tensors. Broadcasting helps convert scalars to
|
|
# Tensors, but it does not work for th.exp().
|
|
logvar1, logvar2 = [
|
|
x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
|
|
for x in (logvar1, logvar2)
|
|
]
|
|
|
|
return 0.5 * (
|
|
-1.0
|
|
+ logvar2
|
|
- logvar1
|
|
+ th.exp(logvar1 - logvar2)
|
|
+ ((mean1 - mean2) ** 2) * th.exp(-logvar2)
|
|
)
|
|
|
|
|
|
def approx_standard_normal_cdf(x):
|
|
"""
|
|
A fast approximation of the cumulative distribution function of the
|
|
standard normal.
|
|
"""
|
|
return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
|
|
|
|
|
|
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
|
|
"""
|
|
Compute the log-likelihood of a Gaussian distribution discretizing to a
|
|
given image.
|
|
|
|
:param x: the target images. It is assumed that this was uint8 values,
|
|
rescaled to the range [-1, 1].
|
|
:param means: the Gaussian mean Tensor.
|
|
:param log_scales: the Gaussian log stddev Tensor.
|
|
:return: a tensor like x of log probabilities (in nats).
|
|
"""
|
|
assert x.shape == means.shape == log_scales.shape
|
|
centered_x = x - means
|
|
inv_stdv = th.exp(-log_scales)
|
|
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
|
|
cdf_plus = approx_standard_normal_cdf(plus_in)
|
|
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
|
|
cdf_min = approx_standard_normal_cdf(min_in)
|
|
log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
|
|
log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
|
|
cdf_delta = cdf_plus - cdf_min
|
|
log_probs = th.where(
|
|
x < -0.999,
|
|
log_cdf_plus,
|
|
th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
|
|
)
|
|
assert log_probs.shape == x.shape
|
|
return log_probs
|