Python实现多模态AI图像文本语音融合处理深度好文

import torch
import torchvision.transforms as transforms
from torchvision.models import resnet50
from PIL import Image

# 图像预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载预训练的ResNet模型
model = resnet50(pretrained=True)
model.eval()

# 读取图像并进行预处理
image = Image.open("example.jpg")
image_tensor = transform(image).unsqueeze(0)

# 提取特征
with torch.no_grad():
    features = model(image_tensor)

print(features.shape)  # 输出特征向量的形状

from transformers import BertTokenizer, BertModel
import torch

# 加载预训练的BERT模型和分词器
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
model.eval()

# 文本预处理
text = "This is an example sentence."
tokens = tokenizer(text, return_tensors='pt')

# 提取特征
with torch.no_grad():
    outputs = model(**tokens)
    features = outputs.last_hidden_state

print(features.shape)  # 输出特征向量的形状

import librosa
import numpy as np

# 读取音频文件
audio_path = 'example.wav'
y, sr = librosa.load(audio_path, sr=None)

# 提取MFCC特征
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)

print(mfccs.shape)  # 输出特征向量的形状

import torch
import torch.nn as nn

class MultiModalFusion(nn.Module):
    def __init__(self):
        super(MultiModalFusion, self).__init__()
        self.image_fc = nn.Linear(2048, 512)
        self.text_fc = nn.Linear(768, 512)
        self.audio_fc = nn.Linear(13, 512)
        self.fusion_fc = nn.Linear(512 * 3, 256)
        self.output_fc = nn.Linear(256, 10)

    def forward(self, image_features, text_features, audio_features):
        image_out = torch.relu(self.image_fc(image_features))
        text_out = torch.relu(self.text_fc(text_features))
        audio_out = torch.relu(self.audio_fc(audio_features))

        fused_features = torch.cat((image_out, text_out, audio_out), dim=1)
        fused_out = torch.relu(self.fusion_fc(fused_features))
        output = self.output_fc(fused_out)
        return output

# 创建模型实例
model = MultiModalFusion()

# 模拟输入数据
image_features = torch.randn(1, 2048)
text_features = torch.randn(1, 768)
audio_features = torch.randn(1, 13)

# 前向传播
output = model(image_features, text_features, audio_features)
print(output.shape)  # 输出预测结果的形状