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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
import os
import copy
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
def __init__(self, layer, N, length, d_model):
super(Encoder, self).__init__()
self.layers = layer
self.norm = LayerNorm(d_model)
self.pos_embedding_1 = nn.Parameter(torch.randn(1, length, d_model))
self.pos_embedding_2 = nn.Parameter(torch.randn(1, length, d_model))
self.pos_embedding_3 = nn.Parameter(torch.randn(1, length, d_model))
def forward(self, x, mask):
for i, layer in enumerate(self.layers):
if i == 0:
x += self.pos_embedding_1[:, :x.shape[1]]
elif i == 1:
x += self.pos_embedding_2[:, :x.shape[1]]
elif i == 2:
x += self.pos_embedding_3[:, :x.shape[1]]
x = layer(x, mask, i)
return x
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attn
class SublayerConnection(nn.Module):
def __init__(self, size, dropout, stride_num, i):
super(SublayerConnection, self).__init__()
self.norm = LayerNorm(size)
self.dropout = nn.Dropout(dropout)
self.pooling = nn.MaxPool1d(1, stride_num[i])
def forward(self, x, sublayer, i=-1, stride_num=-1):
if i != -1:
if stride_num[i] != 1:
res = self.pooling(x.permute(0, 2, 1))
res = res.permute(0, 2, 1)
return res + self.dropout(sublayer(self.norm(x)))
else:
return x + self.dropout(sublayer(self.norm(x)))
else:
return x + self.dropout(sublayer(self.norm(x)))
class EncoderLayer(nn.Module):
def __init__(self, size, self_attn, feed_forward, dropout, stride_num, i):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.stride_num = stride_num
self.sublayer = clones(SublayerConnection(size, dropout, stride_num, i), 2)
self.size = size
def forward(self, x, mask, i):
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
x = self.sublayer[1](x, self.feed_forward, i, self.stride_num)
return x
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None):
if mask is not None:
mask = mask.unsqueeze(1)
nbatches = query.size(0)
query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for l, x in zip(self.linears, (query, key, value))]
x, self.attn = attention(query, key, value, mask=mask,
dropout=self.dropout)
x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
return self.linears[-1](x)
class PositionwiseFeedForward(nn.Module):
def __init__(self, d_model, d_ff, dropout=0.1, number = -1, stride_num=-1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Conv1d(d_model, d_ff, kernel_size=1, stride=1)
self.w_2 = nn.Conv1d(d_ff, d_model, kernel_size=3, stride=stride_num[number], padding = 1)
self.gelu = nn.ReLU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = x.permute(0, 2, 1)
x = self.w_2(self.dropout(self.gelu(self.w_1(x))))
x = x.permute(0, 2, 1)
return x
class Transformer(nn.Module):
def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, length=27, stride_num=None, dropout=0.1):
super(Transformer, self).__init__()
self.length = length
self.stride_num = stride_num
self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout, length = self.length)
def forward(self, x, mask=None):
x = self.model(x, mask)
return x
def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
model_EncoderLayer = []
for i in range(N):
ff = PositionwiseFeedForward(d_model, d_ff, dropout, i, self.stride_num)
model_EncoderLayer.append(EncoderLayer(d_model, c(attn), c(ff), dropout, self.stride_num, i))
model_EncoderLayer = nn.ModuleList(model_EncoderLayer)
model = Encoder(model_EncoderLayer, N, length, d_model)
return model
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