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51 lines
4.2 KiB
Plaintext
51 lines
4.2 KiB
Plaintext
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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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Nz&at least one argument must be a Tensorç à?g ð¿é )Ú
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isinstanceÚthÚTensorÚtensorÚtoÚexp)Úmean1Úlogvar1Úmean2Úlogvar2r ÚobjÚxs ú]/maindata/data/shared/multimodal/zhengcong.fei/code/dit-moe/code/diffusion/diffusion_utils.pyÚ normal_klr
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ô ˜œ2Ÿ9™9Ô
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#ñ $ð
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3ñ 4ñ ð ùò s »AC c óº — ddt j t j dt j z « | dt j
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| d« z z z « z z S )zb
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A fast approximation of the cumulative distribution function of the
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standard normal.
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r ç ð?g @g÷Hmâä¦?é )r ÚtanhÚnpÚsqrtÚpiÚpow)r s r Úapprox_standard_normal_cdfr ' sH € ð
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}t j | « }||z }t j j t j | « t j
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Compute the log-likelihood of a continuous Gaussian distribution.
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:param x: the targets
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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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)r r Ú
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distributionsÚNormalÚ
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zeros_likeÚ ones_likeÚlog_prob)r ÚmeansÚ
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log_scalesÚ
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óh — | j |j cxk( r|j k( sJ ‚ J ‚| |z
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}t j | « }||dz z }t |« }||dz
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z }t |« }t j |j d¬« « } t j d|z
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t j | j d¬« « « « } | j | j k( sJ ‚| S )az
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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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