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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import math
import torch
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
"""Tensor initialization with truncated normal distribution.
Based on:
https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
https://github.com/rwightman/pytorch-image-models
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.
"""
def norm_cdf(x):
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
with torch.no_grad():
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
tensor.uniform_(2 * l - 1, 2 * u - 1)
tensor.erfinv_()
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
"""Tensor initialization with truncated normal distribution.
Based on:
https://github.com/rwightman/pytorch-image-models
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
"""
if std <= 0:
raise ValueError("the standard deviation should be greater than zero.")
if a >= b:
raise ValueError("minimum cutoff value (a) should be smaller than maximum cutoff value (b).")
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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