# Copyright (c) Alibaba, Inc. and its affiliates.
import logging
import math
import torch
import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init
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):
logging.warning(
'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
'The distribution of values may be incorrect.',
stacklevel=2)
with torch.no_grad():
# 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.))
tensor.add_(mean)
# 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.):
# (Tensor, float, float, float, float) -> Tensor
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
def _init_weights(module, init_linear='normal', std=0.01, bias=0.):
assert init_linear in ['normal', 'kaiming'], \
'Undefined init_linear: {}'.format(init_linear)
for m in module.modules():
if isinstance(m, nn.Linear):
if init_linear == 'normal':
normal_init(m, std=std, bias=bias)
else:
kaiming_init(m, mode='fan_in', nonlinearity='relu')
elif isinstance(
m,
(nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm, nn.SyncBatchNorm)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)