# Copyright (c) Open-MMLab. All rights reserved.
import numpy as np
import torch.nn as nn
def constant_init(module, val, bias=0):
if hasattr(module, 'weight') and module.weight is not None:
nn.init.constant_(module.weight, val)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def normal_init(module, mean=0, std=1, bias=0):
nn.init.normal_(module.weight, mean, std)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def uniform_init(module, a=0, b=1, bias=0):
nn.init.uniform_(module.weight, a, b)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def kaiming_init(module,
a=0,
mode='fan_out',
nonlinearity='relu',
bias=0,
distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.kaiming_uniform_(
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
else:
nn.init.kaiming_normal_(
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def caffe2_xavier_init(module, bias=0):
# `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
# Acknowledgment to FAIR's internal code
kaiming_init(
module,
a=1,
mode='fan_in',
nonlinearity='leaky_relu',
distribution='uniform')
[docs]def bias_init_with_prob(prior_prob):
"""initialize conv/fc bias value according to giving probablity."""
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
return bias_init