80 lines
2.6 KiB
Python
Executable File
80 lines
2.6 KiB
Python
Executable File
''' Define the sublayers in encoder/decoder layer '''
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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from model.DSANET.Modules import ScaledDotProductAttention
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class MultiHeadAttention(nn.Module):
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''' Multi-Head Attention module '''
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def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
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super().__init__()
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self.n_head = n_head
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self.d_k = d_k
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self.d_v = d_v
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self.w_qs = nn.Linear(d_model, n_head * d_k)
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self.w_ks = nn.Linear(d_model, n_head * d_k)
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self.w_vs = nn.Linear(d_model, n_head * d_v)
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nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
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nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
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nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
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self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
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self.layer_norm = nn.LayerNorm(d_model)
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self.fc = nn.Linear(n_head * d_v, d_model)
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nn.init.xavier_normal_(self.fc.weight)
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self.dropout = nn.Dropout(dropout)
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def forward(self, q, k, v):
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d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
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sz_b, len_q, _ = q.size()
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sz_b, len_k, _ = k.size()
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sz_b, len_v, _ = v.size()
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residual = q
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q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
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k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
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v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
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q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k)
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k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k)
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v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v)
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output, attn = self.attention(q, k, v)
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output = output.view(n_head, sz_b, len_q, d_v)
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output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1)
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output = self.dropout(self.fc(output))
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output = self.layer_norm(output + residual)
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return output, attn
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class PositionwiseFeedForward(nn.Module):
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''' A two-feed-forward-layer module '''
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def __init__(self, d_in, d_hid, dropout=0.1):
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super().__init__()
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self.w_1 = nn.Conv1d(d_in, d_hid, 1)
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self.w_2 = nn.Conv1d(d_hid, d_in, 1)
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self.layer_norm = nn.LayerNorm(d_in)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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residual = x
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output = x.transpose(1, 2)
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output = self.w_2(F.relu(self.w_1(output)))
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output = output.transpose(1, 2)
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output = self.dropout(output)
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output = self.layer_norm(output + residual)
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return output
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