# Source code for towhee.models.utils.weight_init

# Copyright 2021 Ross Wightman . 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
# This code is modified by Zilliz.

import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out

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):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.

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.))

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

[docs]def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
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
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)

[docs]def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
if mode == 'fan_in':
denom = fan_in
elif mode == 'fan_out':
denom = fan_out
elif mode == 'fan_avg':
denom = (fan_in + fan_out) / 2

variance = scale / denom

if distribution == 'truncated_normal':
# constant is stddev of standard normal truncated to (-2, 2)
trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
elif distribution == 'normal':
tensor.normal_(std=math.sqrt(variance))
elif distribution == 'uniform':
bound = math.sqrt(3 * variance)
tensor.uniform_(-bound, bound)
else:
raise ValueError(f'invalid distribution {distribution}')

[docs]def lecun_normal_(tensor):
variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')