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