multi_modal_model.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import (
    BartForConditionalGeneration, 
    AutoModelForCausalLM, 
    BertModel, 
    Wav2Vec2Model,
    CLIPModel,
    AutoTokenizer
)
import numpy as np
import random
import copy

class MultiModalModel(nn.Module):
    def __init__(self):
        super(MultiModalModel, self).__init__()
        # 初始化子模型
        self.text_generator = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
        self.code_generator = AutoModelForCausalLM.from_pretrained('gpt2')
        self.nlp_encoder = BertModel.from_pretrained('bert-base-uncased')
        self.speech_encoder = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-base-960h')
        self.vision_encoder = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')

        # 初始化分词器和处理器
        self.text_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
        self.code_tokenizer = AutoTokenizer.from_pretrained('gpt2')
        self.nlp_tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
        self.speech_processor = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h')
        self.vision_processor = AutoTokenizer.from_pretrained('openai/clip-vit-base-patch32')
    
    def forward(self, task, inputs):
        if task == 'text_generation':
            attention_mask = inputs.attention_mask
            outputs = self.text_generator.generate(
                inputs.input_ids, 
                max_new_tokens=50, 
                pad_token_id=self.text_tokenizer.eos_token_id, 
                attention_mask=attention_mask,
                top_p=0.95,
                top_k=50,
                temperature=1.2,
                do_sample=True
            )
            return self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
        elif task == 'code_generation':
            attention_mask = inputs.attention_mask
            outputs = self.code_generator.generate(
                inputs.input_ids, 
                max_new_tokens=50, 
                pad_token_id=self.code_tokenizer.eos_token_id, 
                attention_mask=attention_mask,
                top_p=0.95,
                top_k=50,
                temperature=1.2,
                do_sample=True
            )
            return self.code_tokenizer.decode(outputs[0], skip_special_tokens=True)
        elif task == 'text_understanding':
            outputs = self.nlp_encoder(**inputs)
            return outputs.last_hidden_state
        elif task == 'speech_recognition':
            outputs = self.speech_encoder(**inputs).logits
            predicted_ids = torch.argmax(outputs, dim=-1)
            transcription = self.speech_processor.batch_decode(predicted_ids)[0]
            return transcription
        elif task == 'vision_understanding':
            outputs = self.vision_encoder.get_image_features(**inputs)
            return outputs

    def save_model(self, save_directory):
        os.makedirs(save_directory, exist_ok=True)
        torch.save(self.state_dict(), os.path.join(save_directory, 'multi_modal_model_state_dict.pth'))
        self.text_tokenizer.save_pretrained(os.path.join(save_directory, 'text_generator'))
        self.code_tokenizer.save_pretrained(os.path.join(save_directory, 'code_generator'))
        self.nlp_tokenizer.save_pretrained(os.path.join(save_directory, 'nlp_encoder'))
        self.speech_processor.save_pretrained(os.path.join(save_directory, 'speech_encoder'))
        self.vision_processor.save_pretrained(os.path.join(save_directory, 'vision_encoder'))

    def load_model(self, load_directory):
        self.load_state_dict(torch.load(os.path.join(load_directory, 'multi_modal_model_state_dict.pth')))
        self.text_tokenizer = AutoTokenizer.from_pretrained(os.path.join(load_directory, 'text_generator'))
        self.code_tokenizer = AutoTokenizer.from_pretrained(os.path.join(load_directory, 'code_generator'))
        self.nlp_tokenizer = AutoTokenizer.from_pretrained(os.path.join(load_directory, 'nlp_encoder'))
        self.speech_processor = AutoTokenizer.from_pretrained(os.path.join(load_directory, 'speech_encoder'))
        self.vision_processor = AutoTokenizer.from_pretrained(os.path.join(load_directory, 'vision_encoder'))

class EvolutionaryMultiModalNetwork(nn.Module):
    def __init__(self, device='cuda' if torch.cuda.is_available() else 'cpu'):
        super(EvolutionaryMultiModalNetwork, self).__init__()
        self.device = device
        self.multi_modal_model = MultiModalModel().to(self.device)
        self.mutation_params = {
            'mutation_rate': 0.2,  # 增加变异率
            'mutation_scale': 0.05  # 增加变异幅度
        }
    
    def mutate_model(self, model):
        """
        模型参数变异
        """
        for param in model.parameters():
            if param.requires_grad:
                noise = torch.normal(
                    mean=torch.zeros_like(param.data),
                    std=self.mutation_params['mutation_scale']
                ).to(self.device)
                if random.random() < self.mutation_params['mutation_rate']:
                    param.data.add_(noise)
        return model
    
    def evaluate_model(self, model, test_input):
        """
        模型评估
        """
        try:
            with torch.no_grad():
                output = model('text_generation', test_input)
                complexity = sum(p.numel() for p in model.parameters())
                performance = len(output)  # 示例性能评估指标
                return complexity, performance
        except Exception as e:
            print(f"模型评估错误: {e}")
            return 0, 0
    
    def save_models(self, save_dir='./model_checkpoints'):
        """
        保存模型
        """
        os.makedirs(save_dir, exist_ok=True)
        self.multi_modal_model.save_model(os.path.join(save_dir, 'multi_modal_model'))
        print(f"模型已保存到 {save_dir}")

    def evolutionary_training(self, epochs=5):
        """
        进化训练
        """
        print("🧬 开始进化训练...")
        
        for epoch in range(epochs):
            print(f"\n🌟 第 {epoch+1} 代:")
            
            # 模型变异
            self.multi_modal_model = self.mutate_model(self.multi_modal_model)
            
            # 模型评估
            test_input = self.multi_modal_model.text_tokenizer("Sample input for evaluation.", return_tensors='pt').to(self.device)
            complexity, performance = self.evaluate_model(self.multi_modal_model, test_input)
            print(f"多模态模型 - 复杂度: {complexity}, 性能: {performance:.4f}")

def main():
    # 设置随机种子
    torch.manual_seed(42)
    np.random.seed(42)
    random.seed(42)

    # 创建进化多模态神经网络
    evo_network = EvolutionaryMultiModalNetwork()
    
    # 打印模型信息
    evo_network.multi_modal_model.text_generator.config  # 打印模型配置示例
    
    # 进化训练
    evo_network.evolutionary_training(epochs=5)
    
    # 保存模型
    evo_network.save_models()

if __name__ == "__main__":
    main()