import math
import torch
def uniform(size, tensor):
if tensor is not None:
bound = 1.0 / math.sqrt(size)
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor is not None:
bound = math.sqrt(6 / ((1 + a**2) * fan))
tensor.data.uniform_(-bound, bound)
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def glorot_orthogonal(tensor, scale):
if tensor is not None:
torch.nn.init.orthogonal_(tensor.data)
scale /= ((tensor.size(-2) + tensor.size(-1)) * tensor.var())
tensor.data *= scale.sqrt()
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
def ones(tensor):
if tensor is not None:
tensor.data.fill_(1)
def normal(tensor, mean, std):
if tensor is not None:
tensor.data.normal_(mean, std)
def reset(nn):
def _reset(item):
if hasattr(item, 'reset_parameters'):
item.reset_parameters()
if nn is not None:
if hasattr(nn, 'children') and len(list(nn.children())) > 0:
for item in nn.children():
_reset(item)
else:
_reset(nn)