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3f792e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttnProjection(nn.Module):
def __init__(self, input_dim, n_heads, output_length):
super().__init__()
self.query = nn.Parameter(torch.randn(output_length, input_dim))
self.attn = nn.MultiheadAttention(
input_dim, n_heads, dropout=0.2, batch_first=True
)
self.norm1 = nn.LayerNorm(input_dim)
self.self_attn = nn.MultiheadAttention(
input_dim, n_heads, dropout=0.2, batch_first=True
)
self.norm2 = nn.LayerNorm(input_dim)
self.dropout = nn.Dropout(0.2)
nn.init.xavier_normal_(self.query)
def forward(self, x):
B = x.shape[0]
query = self.query.unsqueeze(0).repeat(B, 1, 1)
z = self.norm1(x)
z_attn = self.attn(query, z, z)[0]
z = z_attn
z = self.norm2(z)
z_attn = self.self_attn(z, z, z)[0]
z = z + self.dropout(z_attn)
z = z.contiguous().view(B, -1)
return z
class BiAttnPrediction(nn.Module):
def __init__(self, input_dim, n_heads):
super().__init__()
self.input_dim = input_dim
self.attn1 = nn.MultiheadAttention(
input_dim, n_heads, dropout=0.2, batch_first=True
)
self.norm1 = nn.LayerNorm(input_dim)
self.dropout1 = nn.Dropout(0.2)
self.attn2 = nn.MultiheadAttention(
input_dim, n_heads, dropout=0.2, batch_first=True
)
self.norm2 = nn.LayerNorm(input_dim)
self.dropout2 = nn.Dropout(0.2)
self.mlp = nn.Sequential(
nn.Linear(input_dim * 3, 1024),
nn.SiLU(),
nn.Dropout(0.2),
nn.Linear(1024, 512),
nn.SiLU(),
nn.Dropout(0.2),
nn.Linear(512, 256),
nn.SiLU(),
nn.Dropout(0.2),
nn.Linear(256, 1),
)
def forward(self, x1, x2):
B = x1.shape[0]
x1 = x1.view(B, -1, self.input_dim) # [B, M x D] -> [B, M, D]
x2 = x2.view(B, -1, self.input_dim) # [B, M x D] -> [B, M, D]
x1 = self.norm1(x1)
x2 = self.norm2(x2)
z1_attn = self.attn1(x2, x1, x1)[0]
z1 = x1 + self.dropout1(z1_attn)
z2_attn = self.attn2(x1, x2, x2)[0]
z2 = x2 + self.dropout2(z2_attn)
z1 = z1.mean(dim=1)
z2 = z2.mean(dim=1)
z = torch.cat([z1, z2, torch.abs(z1 - z2)], dim=1) # [B, D * 4]
z = self.mlp(z)
return z
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