63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
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"""Modified from https://github.com/rwightman/pytorch-image-
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models/blob/master/timm/models/layers/drop.py."""
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import math
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import warnings
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import torch
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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"""Reference: https://people.sc.fsu.edu/~jburkardt/presentations
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/truncated_normal.pdf"""
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
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'The distribution of values may be incorrect.',
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stacklevel=2)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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lower_bound = norm_cdf((a - mean) / std)
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upper_bound = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * lower_bound - 1, 2 * upper_bound - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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Args:
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tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`
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mean (float): the mean of the normal distribution
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std (float): the standard deviation of the normal distribution
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a (float): the minimum cutoff value
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b (float): the maximum cutoff value
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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