基于 Transformer 的多子空间多模态情感分析-数据代码

import torch

import as nn

import torchaudio

from transformers import ViTModel, Wav2Vec2Model, BertModel

# 定义模型结构

class MultiModalModel():

def __init__(self):

super(MultiModalModel, self).__init__()

# 视觉编码器

self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224')

self.vit_lstm = (input_size=768, hidden_size=256, batch_first=True)

# 音频编码器

self.wav2vec = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-base')

self.wav2vec_lstm = (input_size=768, hidden_size=256, batch_first=True)

# 文本编码器

self.bert = BertModel.from_pretrained('bert-base-uncased')

# 共享编码器

self.shared_encoder = (256, 128)

# Transformer模块

self.transformer = (d_model=128, nhead=8, num_encoder_layers=3)

# 双模态表示生成模块

self.fc1 = (128, 64)

# 预测层

self.fc2 = (64, 1)

def forward(self, images, audio, text):

# 视觉编码

v_features = self.vit(images).last_hidden_state

v_features, _ = self.vit_lstm(v_features)

# 音频编码

a_features = self.wav2vec(audio).last_hidden_state

a_features, _ = self.wav2vec_lstm(a_features)

# 文本编码

t_features = self.bert(text).last_hidden_state

# 共享编码

v_shared = self.shared_encoder(v_features)

a_shared = self.shared_encoder(a_features)

# Transformer融合

combined_features = ((v_shared, a_shared, t_features), dim=1)

transformer_out = self.transformer(combined_features)

# 双模态表示生成

bimodal_representation = self.fc1(transformer_out)

# 预测

output = self.fc2(bimodal_representation)

return output

# 生成模拟数据

images = (10, 3, 224, 224) # 模拟图像数据

audio = (10, 16000) # 模拟音频数据

text = (0, 30522, (10, 50)) # 模拟文本数据

# 初始化模型

model = MultiModalModel()

# 前向传播

output = model(images, audio, text)

print(output)