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ddd3436 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import torch
import torch.nn as nn
import torch.nn.functional as F
class V_Attention(nn.Module):
def __init__(self, dim_I, num_heads=8, dropout=0.1, ffn_ratio=4.0):
super(V_Attention, self).__init__()
self.dim_I = dim_I
self.num_heads = num_heads
self.dropout = nn.Dropout(dropout)
self.cross_attn_a_to_b = nn.MultiheadAttention(
embed_dim=dim_I,
num_heads=num_heads,
kdim=dim_I,
vdim=dim_I,
dropout=dropout,
batch_first=True
)
self.ffn_i = nn.Sequential(
nn.Linear(dim_I, dim_I*4),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(dim_I*4, dim_I),
nn.LayerNorm(dim_I),
)
self.norm1_a = nn.LayerNorm(dim_I)
def forward(self, feat_I, mask=None):
B, N_a, _ = feat_I.shape
out_a, attn_a = self.cross_attn_a_to_b(
query=feat_I,
key=feat_I,
value=feat_I,
key_padding_mask=~mask,
need_weights=True
)
out_a = feat_I + self.dropout(self.ffn_i(out_a))
out_I = self.norm1_a(out_a)
return out_I
class VTS_Alignment(nn.Module):
def __init__(self, v_dim, TS_dim, embedding_dim=512, num_heads=8, dropout=0.1):
super().__init__()
self.TS_dim = TS_dim
self.embedding_dim = embedding_dim
t_dim2 = int(2 * self.TS_dim)
self.mlp_i = nn.Sequential(
nn.Linear(TS_dim, t_dim2),
nn.GELU(),
nn.Linear(t_dim2, TS_dim),
nn.LayerNorm(TS_dim),
)
self.mlp_t = nn.Sequential(
nn.Linear(TS_dim, t_dim2),
nn.GELU(),
nn.Linear(t_dim2, TS_dim),
nn.LayerNorm(TS_dim),
)
self.cross_attn_a_to_b = nn.MultiheadAttention(
embed_dim=TS_dim,
num_heads=num_heads,
kdim=TS_dim,
vdim=TS_dim,
dropout=dropout,
batch_first=True
)
self.cross_attn_b_to_a = nn.MultiheadAttention(
embed_dim=TS_dim,
num_heads=num_heads,
kdim=TS_dim,
vdim=TS_dim,
dropout=dropout,
batch_first=True
)
self.ffn_a = nn.Sequential(
nn.Linear(TS_dim, t_dim2),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(t_dim2, TS_dim)
)
self.ffn_b = nn.Sequential(
nn.Linear(TS_dim, t_dim2),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(t_dim2, TS_dim)
)
self.norm1_a = nn.LayerNorm(TS_dim)
self.norm2_a = nn.LayerNorm(TS_dim)
self.norm1_b = nn.LayerNorm(TS_dim)
self.norm2_b = nn.LayerNorm(TS_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, feat_I, feat_TS, mask=None):
feat_I = self.mlp_i(feat_I)
feat_TS = self.mlp_t(feat_TS)
out_a, _ = self.cross_attn_a_to_b(
query=feat_I,
key=feat_TS,
value=feat_TS,
key_padding_mask=~mask,
need_weights=True
)
out_a = feat_I + self.dropout(out_a)
out_a = self.norm1_a(out_a)
out_a = out_a + self.dropout(self.ffn_a(out_a))
x_I = self.norm2_a(out_a)
out_b, attn_b = self.cross_attn_b_to_a(
query=feat_TS,
key=feat_I,
value=feat_I,
key_padding_mask=~mask,
need_weights=True
)
out_b = feat_TS + self.dropout(out_b)
out_b = self.norm1_b(out_b)
out_b = out_b + self.dropout(self.ffn_b(out_b))
x_TS = self.norm2_b(out_b)
return x_I, x_TS
class router(nn.Module):
def __init__(self, dim, channel_num, num_tasks=2, topk=2, task_model='complex'):
super().__init__()
embed_dim = int(dim // 8)
self.task_model = task_model
self.l1 = nn.Linear(dim, embed_dim)
self.l2 = nn.Linear(embed_dim, channel_num)
self.topk = topk
self.task_embedding = nn.Embedding(num_tasks, embed_dim)
def forward(self, x, task_id=None):
original_shape = x.shape
x = x.view(-1, x.size(-1))
x = F.gelu(self.l1(x))
if task_id is not None:
task_id = int(task_id)
task_id = torch.tensor(task_id, device=x.device, dtype=torch.long)
task_bias = self.task_embedding(task_id)
task_emb = task_bias.unsqueeze(0).expand(x.size(0), -1)
x = x + task_emb
logits = self.l2(x)
topk_vals, topk_idx = torch.topk(logits, self.topk, dim=-1)
topk_probs = torch.softmax(topk_vals, dim=-1).to(logits.dtype)
probs = torch.zeros_like(logits)
probs.scatter_(-1, topk_idx, topk_probs)
probs = probs.view(*original_shape[:-1], -1)
return probs
class M_moe(nn.Module):
def __init__(self, dst_feature_dims):
super(M_moe, self).__init__()
self.dims = dst_feature_dims
self.Router = router(self.dims * 2, 3)
self.mlp_m = nn.Sequential(
nn.Linear(self.dims * 2, self.dims * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(self.dims * 2, self.dims),
)
def forward(self, F_M, F_T, F_I, router_input, mask=None):
B, T, _ = F_T.shape
F_M = self.mlp_m(F_M)
if mask is not None:
m_w_r = self.Router(router_input, 0)
c_fusion = (
F_T * m_w_r[:, :, 0:1] +
F_M * m_w_r[:, :, 1:2] +
F_I * m_w_r[:, :, 2:3]
)
return c_fusion, m_w_r
else:
m_w_c = self.Router(router_input, 1)
c_fusion = (
F_T * m_w_c[:, :, 0:1] +
F_M * m_w_c[:, :, 1:2] +
F_I * m_w_c[:, :, 2:3]
)
return c_fusion, m_w_c
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