# Source code for modulus.models.sfno.initialization

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import math
import torch
import warnings

def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):  # pragma: no cover
# Computes standard normal cumulative distribution function
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2,
)

# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)

# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)

# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()

# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.0))

# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor

[docs]def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):  # pragma: no cover
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:\mathcal{N}(\text{mean}, \text{std}^2)
with values outside :math:[a, b] redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:a \leq \text{mean} \leq b.
Args:
tensor: an n-dimensional torch.Tensor
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)

© Copyright 2023, NVIDIA Modulus Team. Last updated on Sep 22, 2023.