File size: 16,042 Bytes
9157432 73f5c09 9157432 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 |
"""
here is the mian backbone for DLF
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...subNets import BertTextEncoder
from ...subNets.transformers_encoder.transformer import TransformerEncoder
from huggingface_hub import PyTorchModelHubMixin
class DLF(nn.Module, PyTorchModelHubMixin,
repo_url="https://github.com/pwang322/DLF",
paper_url="https://huggingface.co/papers/2412.12225",
tags=["sentiment-analysis"],
license="mit"):
def __init__(self, args):
super(DLF, self).__init__()
if args.use_bert:
self.text_model = BertTextEncoder(use_finetune=args.use_finetune, transformers=args.transformers,
pretrained=args.pretrained)
self.use_bert = args.use_bert
dst_feature_dims, nheads = args.dst_feature_dim_nheads
if args.dataset_name == 'mosi':
if args.need_data_aligned:
self.len_l, self.len_v, self.len_a = 50, 50, 50
else:
self.len_l, self.len_v, self.len_a = 50, 500, 375
if args.dataset_name == 'mosei':
if args.need_data_aligned:
self.len_l, self.len_v, self.len_a = 50, 50, 50
else:
self.len_l, self.len_v, self.len_a = 50, 500, 500
self.orig_d_l, self.orig_d_a, self.orig_d_v = args.feature_dims
self.d_l = self.d_a = self.d_v = dst_feature_dims
self.num_heads = nheads
self.layers = args.nlevels
self.attn_dropout = args.attn_dropout
self.attn_dropout_a = args.attn_dropout_a
self.attn_dropout_v = args.attn_dropout_v
self.relu_dropout = args.relu_dropout
self.embed_dropout = args.embed_dropout
self.res_dropout = args.res_dropout
self.output_dropout = args.output_dropout
self.text_dropout = args.text_dropout
self.attn_mask = args.attn_mask
combined_dim_low = self.d_a
combined_dim_high = self.d_a
combined_dim = (self.d_l + self.d_a + self.d_v ) + self.d_l * 3
output_dim = 1
# 1. Temporal convolutional layers for initial feature
self.proj_l = nn.Conv1d(self.orig_d_l, self.d_l, kernel_size=args.conv1d_kernel_size_l, padding=0, bias=False)
self.proj_a = nn.Conv1d(self.orig_d_a, self.d_a, kernel_size=args.conv1d_kernel_size_a, padding=0, bias=False)
self.proj_v = nn.Conv1d(self.orig_d_v, self.d_v, kernel_size=args.conv1d_kernel_size_v, padding=0, bias=False)
# 2. Modality-specific encoder
self.encoder_s_l = self.get_network(self_type='l', layers = self.layers)
self.encoder_s_v = self.get_network(self_type='v', layers = self.layers)
self.encoder_s_a = self.get_network(self_type='a', layers = self.layers)
# Modality-shared encoder
self.encoder_c = self.get_network(self_type='l', layers = self.layers)
# 3. Decoder for reconstruct three modalities
self.decoder_l = nn.Conv1d(self.d_l * 2, self.d_l, kernel_size=1, padding=0, bias=False)
self.decoder_v = nn.Conv1d(self.d_v * 2, self.d_v, kernel_size=1, padding=0, bias=False)
self.decoder_a = nn.Conv1d(self.d_a * 2, self.d_a, kernel_size=1, padding=0, bias=False)
# for calculate cosine sim between s_x
self.proj_cosine_l = nn.Linear(combined_dim_low * (self.len_l - args.conv1d_kernel_size_l + 1), combined_dim_low)
self.proj_cosine_v = nn.Linear(combined_dim_low * (self.len_v - args.conv1d_kernel_size_v + 1), combined_dim_low)
self.proj_cosine_a = nn.Linear(combined_dim_low * (self.len_a - args.conv1d_kernel_size_a + 1), combined_dim_low)
# for align c_l, c_v, c_a
self.align_c_l = nn.Linear(combined_dim_low * (self.len_l - args.conv1d_kernel_size_l + 1), combined_dim_low)
self.align_c_v = nn.Linear(combined_dim_low * (self.len_v - args.conv1d_kernel_size_v + 1), combined_dim_low)
self.align_c_a = nn.Linear(combined_dim_low * (self.len_a - args.conv1d_kernel_size_a + 1), combined_dim_low)
self.self_attentions_c_l = self.get_network(self_type='l')
self.self_attentions_c_v = self.get_network(self_type='v')
self.self_attentions_c_a = self.get_network(self_type='a')
self.proj1_c = nn.Linear(self.d_l * 3, self.d_l * 3)
self.proj2_c = nn.Linear(self.d_l * 3, self.d_l * 3)
self.out_layer_c = nn.Linear(self.d_l * 3, output_dim)
# 4 Multimodal Crossmodal Attentions
self.trans_l_with_a = self.get_network(self_type='la', layers = self.layers)
self.trans_l_with_v = self.get_network(self_type='lv', layers = self.layers)
self.trans_a_with_l = self.get_network(self_type='al')
self.trans_a_with_v = self.get_network(self_type='av')
self.trans_v_with_l = self.get_network(self_type='vl')
self.trans_v_with_a = self.get_network(self_type='va')
self.trans_l_mem = self.get_network(self_type='l_mem', layers=self.layers)
self.trans_a_mem = self.get_network(self_type='a_mem', layers=3)
self.trans_v_mem = self.get_network(self_type='v_mem', layers=3)
# 5. fc layers for shared features
self.proj1_l_low = nn.Linear(combined_dim_low * (self.len_l - args.conv1d_kernel_size_l + 1), combined_dim_low)
self.proj2_l_low = nn.Linear(combined_dim_low, combined_dim_low * (self.len_l - args.conv1d_kernel_size_l + 1))
self.out_layer_l_low = nn.Linear(combined_dim_low * (self.len_l - args.conv1d_kernel_size_l + 1), output_dim)
self.proj1_v_low = nn.Linear(combined_dim_low * (self.len_v - args.conv1d_kernel_size_v + 1), combined_dim_low)
self.proj2_v_low = nn.Linear(combined_dim_low, combined_dim_low * (self.len_v - args.conv1d_kernel_size_v + 1))
self.out_layer_v_low = nn.Linear(combined_dim_low * (self.len_v - args.conv1d_kernel_size_v + 1), output_dim)
self.proj1_a_low = nn.Linear(combined_dim_low * (self.len_a - args.conv1d_kernel_size_a + 1), combined_dim_low)
self.proj2_a_low = nn.Linear(combined_dim_low, combined_dim_low * (self.len_a - args.conv1d_kernel_size_a + 1))
self.out_layer_a_low = nn.Linear(combined_dim_low * (self.len_a - args.conv1d_kernel_size_a + 1), output_dim)
# 6. fc layers for specific features
self.proj1_l_high = nn.Linear(combined_dim_high, combined_dim_high)
self.proj2_l_high = nn.Linear(combined_dim_high, combined_dim_high)
self.out_layer_l_high = nn.Linear(combined_dim_high, output_dim)
self.proj1_v_high = nn.Linear(combined_dim_high, combined_dim_high)
self.proj2_v_high = nn.Linear(combined_dim_high, combined_dim_high)
self.out_layer_v_high = nn.Linear(combined_dim_high, output_dim)
self.proj1_a_high = nn.Linear(combined_dim_high, combined_dim_high)
self.proj2_a_high = nn.Linear(combined_dim_high, combined_dim_high)
self.out_layer_a_high = nn.Linear(combined_dim_high, output_dim)
# 7. project for fusion
self.projector_l = nn.Linear(self.d_l, self.d_l)
self.projector_v = nn.Linear(self.d_v, self.d_v)
self.projector_a = nn.Linear(self.d_a, self.d_a)
self.projector_c = nn.Linear(3 * self.d_l, 3 * self.d_l)
# 8. final project
self.proj1 = nn.Linear(combined_dim, combined_dim)
self.proj2 = nn.Linear(combined_dim, combined_dim)
self.out_layer = nn.Linear(combined_dim, output_dim)
def get_network(self, self_type='l', layers=-1):
if self_type in ['l', 'al', 'vl']:
embed_dim, attn_dropout = self.d_l, self.attn_dropout
elif self_type in ['a', 'la', 'va']:
embed_dim, attn_dropout = self.d_a, self.attn_dropout_a
elif self_type in ['v', 'lv', 'av']:
embed_dim, attn_dropout = self.d_v, self.attn_dropout_v
elif self_type == 'l_mem':
embed_dim, attn_dropout = self.d_l, self.attn_dropout
elif self_type == 'a_mem':
embed_dim, attn_dropout = self.d_a, self.attn_dropout
elif self_type == 'v_mem':
embed_dim, attn_dropout = self.d_v, self.attn_dropout
else:
raise ValueError("Unknown network type")
return TransformerEncoder(embed_dim=embed_dim,
num_heads=self.num_heads,
layers=max(self.layers, layers),
attn_dropout=attn_dropout,
relu_dropout=self.relu_dropout,
res_dropout=self.res_dropout,
embed_dropout=self.embed_dropout,
attn_mask=self.attn_mask)
def forward(self, text, audio, video):
#extraction
if self.use_bert:
text = self.text_model(text)
x_l = F.dropout(text.transpose(1, 2), p=self.text_dropout, training=self.training)
x_a = audio.transpose(1, 2)
x_v = video.transpose(1, 2)
proj_x_l = x_l if self.orig_d_l == self.d_l else self.proj_l(x_l)
proj_x_a = x_a if self.orig_d_a == self.d_a else self.proj_a(x_a)
proj_x_v = x_v if self.orig_d_v == self.d_v else self.proj_v(x_v)
proj_x_l = proj_x_l.permute(2, 0, 1)
proj_x_v = proj_x_v .permute(2, 0, 1)
proj_x_a = proj_x_a.permute(2, 0, 1)
#disentanglement
s_l = self.encoder_s_l(proj_x_l)
s_v = self.encoder_s_v(proj_x_v)
s_a = self.encoder_s_a(proj_x_a)
c_l = self.encoder_c(proj_x_l)
c_v = self.encoder_c(proj_x_v)
c_a = self.encoder_c(proj_x_a)
s_l = s_l.permute(1, 2, 0)
s_v = s_v.permute(1, 2, 0)
s_a = s_a.permute(1, 2, 0)
c_l = c_l.permute(1, 2, 0)
c_v = c_v.permute(1, 2, 0)
c_a = c_a.permute(1, 2, 0)
c_list = [c_l, c_v, c_a]
c_l_sim = self.align_c_l(c_l.contiguous().view(x_l.size(0), -1))
c_v_sim = self.align_c_v(c_v.contiguous().view(x_l.size(0), -1))
c_a_sim = self.align_c_a(c_a.contiguous().view(x_l.size(0), -1))
recon_l = self.decoder_l(torch.cat([s_l, c_list[0]], dim=1))
recon_v = self.decoder_v(torch.cat([s_v, c_list[1]], dim=1))
recon_a = self.decoder_a(torch.cat([s_a, c_list[2]], dim=1))
recon_l = recon_l.permute(2, 0, 1)
recon_v = recon_v.permute(2, 0, 1)
recon_a = recon_a.permute(2, 0, 1)
s_l_r = self.encoder_s_l(recon_l).permute(1, 2, 0)
s_v_r = self.encoder_s_v(recon_v).permute(1, 2, 0)
s_a_r = self.encoder_s_a(recon_a).permute(1, 2, 0)
s_l = s_l.permute(2, 0, 1)
s_v = s_v.permute(2, 0, 1)
s_a = s_a.permute(2, 0, 1)
c_l = c_l.permute(2, 0, 1)
c_v = c_v.permute(2, 0, 1)
c_a = c_a.permute(2, 0, 1)
#enhancement
hs_l_low = c_l.transpose(0, 1).contiguous().view(x_l.size(0), -1)
repr_l_low = self.proj1_l_low(hs_l_low)
hs_proj_l_low = self.proj2_l_low(
F.dropout(F.relu(repr_l_low, inplace=True), p=self.output_dropout, training=self.training))
hs_proj_l_low += hs_l_low
logits_l_low = self.out_layer_l_low(hs_proj_l_low)
hs_v_low = c_v.transpose(0, 1).contiguous().view(x_v.size(0), -1)
repr_v_low = self.proj1_v_low(hs_v_low)
hs_proj_v_low = self.proj2_v_low(
F.dropout(F.relu(repr_v_low, inplace=True), p=self.output_dropout, training=self.training))
hs_proj_v_low += hs_v_low
logits_v_low = self.out_layer_v_low(hs_proj_v_low)
hs_a_low = c_a.transpose(0, 1).contiguous().view(x_a.size(0), -1)
repr_a_low = self.proj1_a_low(hs_a_low)
hs_proj_a_low = self.proj2_a_low(
F.dropout(F.relu(repr_a_low, inplace=True), p=self.output_dropout, training=self.training))
hs_proj_a_low += hs_a_low
logits_a_low = self.out_layer_a_low(hs_proj_a_low)
c_l_att = self.self_attentions_c_l(c_l)
if type(c_l_att) == tuple:
c_l_att = c_l_att[0]
c_l_att = c_l_att[-1]
c_v_att = self.self_attentions_c_v(c_v)
if type(c_v_att) == tuple:
c_v_att = c_v_att[0]
c_v_att = c_v_att[-1]
c_a_att = self.self_attentions_c_a(c_a)
if type(c_a_att) == tuple:
c_a_att = c_a_att[0]
c_a_att = c_a_att[-1]
c_fusion = torch.cat([c_l_att, c_v_att, c_a_att], dim=1)
c_proj = self.proj2_c(
F.dropout(F.relu(self.proj1_c(c_fusion), inplace=True), p=self.output_dropout,
training=self.training))
c_proj += c_fusion
logits_c = self.out_layer_c(c_proj)
# LFA
# L --> L
h_ls = s_l
h_ls = self.trans_l_mem(h_ls)
if type(h_ls) == tuple:
h_ls = h_ls[0]
last_h_l = last_hs = h_ls[-1]
# A --> L
h_l_with_as = self.trans_l_with_a(s_l, s_a, s_a)
h_as = h_l_with_as
h_as = self.trans_a_mem(h_as)
if type(h_as) == tuple:
h_as = h_as[0]
last_h_a = last_hs = h_as[-1]
# V --> L
h_l_with_vs = self.trans_l_with_v(s_l, s_v, s_v)
h_vs = h_l_with_vs
h_vs = self.trans_v_mem(h_vs)
if type(h_vs) == tuple:
h_vs = h_vs[0]
last_h_v = last_hs = h_vs[-1]
hs_proj_l_high = self.proj2_l_high(
F.dropout(F.relu(self.proj1_l_high(last_h_l), inplace=True), p=self.output_dropout, training=self.training))
hs_proj_l_high += last_h_l
logits_l_high = self.out_layer_l_high(hs_proj_l_high)
hs_proj_v_high = self.proj2_v_high(
F.dropout(F.relu(self.proj1_v_high(last_h_v), inplace=True), p=self.output_dropout, training=self.training))
hs_proj_v_high += last_h_v
logits_v_high = self.out_layer_v_high(hs_proj_v_high)
hs_proj_a_high = self.proj2_a_high(
F.dropout(F.relu(self.proj1_a_high(last_h_a), inplace=True), p=self.output_dropout,
training=self.training))
hs_proj_a_high += last_h_a
logits_a_high = self.out_layer_a_high(hs_proj_a_high)
#fusion
last_h_l = torch.sigmoid(self.projector_l(hs_proj_l_high))
last_h_v = torch.sigmoid(self.projector_v(hs_proj_v_high))
last_h_a = torch.sigmoid(self.projector_a(hs_proj_a_high))
c_fusion = torch.sigmoid(self.projector_c(c_fusion))
last_hs = torch.cat([last_h_l, last_h_v, last_h_a, c_fusion], dim=1)
#prediction
last_hs_proj = self.proj2(
F.dropout(F.relu(self.proj1(last_hs), inplace=True), p=self.output_dropout, training=self.training))
last_hs_proj += last_hs
output = self.out_layer(last_hs_proj)
res = {
'origin_l': proj_x_l,
'origin_v': proj_x_v,
'origin_a': proj_x_a,
's_l': s_l,
's_v': s_v,
's_a': s_a,
'c_l': c_l,
'c_v': c_v,
'c_a': c_a,
's_l_r': s_l_r,
's_v_r': s_v_r,
's_a_r': s_a_r,
'recon_l': recon_l,
'recon_v': recon_v,
'recon_a': recon_a,
'c_l_sim': c_l_sim,
'c_v_sim': c_v_sim,
'c_a_sim': c_a_sim,
'logits_l_hetero': logits_l_high,
'logits_v_hetero': logits_v_high,
'logits_a_hetero': logits_a_high,
'logits_c': logits_c,
'output_logit': output
}
return res |