86 lines
2.3 KiB
Python
86 lines
2.3 KiB
Python
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch.autograd import Function
|
||
|
|
|
||
|
|
class LayerNorm(torch.nn.LayerNorm):
|
||
|
|
"""Layer normalization module.
|
||
|
|
:param int nout: output dim size
|
||
|
|
:param int dim: dimension to be normalized
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, nout, dim=-1, eps=1e-5):
|
||
|
|
"""Construct an LayerNorm object."""
|
||
|
|
super(LayerNorm, self).__init__(nout, eps=eps)
|
||
|
|
self.dim = dim
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
"""Apply layer normalization.
|
||
|
|
:param torch.Tensor x: input tensor
|
||
|
|
:return: layer normalized tensor
|
||
|
|
:rtype torch.Tensor
|
||
|
|
"""
|
||
|
|
if self.dim == -1:
|
||
|
|
return super(LayerNorm, self).forward(x)
|
||
|
|
return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
|
||
|
|
|
||
|
|
|
||
|
|
class Reshape(nn.Module):
|
||
|
|
def __init__(self, *args):
|
||
|
|
super(Reshape, self).__init__()
|
||
|
|
self.shape = args
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return x.view(self.shape)
|
||
|
|
|
||
|
|
|
||
|
|
class Permute(nn.Module):
|
||
|
|
def __init__(self, *args):
|
||
|
|
super(Permute, self).__init__()
|
||
|
|
self.args = args
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return x.permute(self.args)
|
||
|
|
|
||
|
|
|
||
|
|
def Linear(in_features, out_features, bias=True, init_type='xavier'):
|
||
|
|
m = nn.Linear(in_features, out_features, bias)
|
||
|
|
if init_type == 'xavier':
|
||
|
|
nn.init.xavier_uniform_(m.weight)
|
||
|
|
elif init_type == 'kaiming':
|
||
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_in')
|
||
|
|
if bias:
|
||
|
|
nn.init.constant_(m.bias, 0.)
|
||
|
|
return m
|
||
|
|
|
||
|
|
|
||
|
|
def Embedding(num_embeddings, embedding_dim, padding_idx=None, init_type='normal'):
|
||
|
|
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
|
||
|
|
if init_type == 'normal':
|
||
|
|
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
|
||
|
|
elif init_type == 'kaiming':
|
||
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_in')
|
||
|
|
if padding_idx is not None:
|
||
|
|
nn.init.constant_(m.weight[padding_idx], 0)
|
||
|
|
return m
|
||
|
|
|
||
|
|
|
||
|
|
class GradientReverseFunction(Function):
|
||
|
|
@staticmethod
|
||
|
|
def forward(ctx, input, coeff=1.):
|
||
|
|
ctx.coeff = coeff
|
||
|
|
output = input * 1.0
|
||
|
|
return output
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def backward(ctx, grad_output):
|
||
|
|
return grad_output.neg() * ctx.coeff, None
|
||
|
|
|
||
|
|
|
||
|
|
class GRL(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super(GRL, self).__init__()
|
||
|
|
|
||
|
|
def forward(self, *input):
|
||
|
|
return GradientReverseFunction.apply(*input)
|
||
|
|
|