multi_modal_model.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import (
    BartForConditionalGeneration, 
    AutoModelForCausalLM, 
    BertModel, 
    Wav2Vec2ForCTC,
    CLIPModel,
    AutoTokenizer
)
import numpy as np
import random
import soundfile as sf
import resampy
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 = Wav2Vec2ForCTC.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')

        # 创建5层神经网络
        self.neural_network = nn.Sequential(
            nn.Linear(768, 1024),
            nn.ReLU(),
            nn.Linear(1024, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256)
        )
    
    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 self.neural_network(outputs.last_hidden_state)
        elif task == 'speech_recognition':
            inputs = self.speech_processor(audio=inputs, sampling_rate=16000, return_tensors="pt", padding=True)
            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

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,
            'mutation_scale': 0.05
        }
    
    def mutate_model(self, model):
        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, task, test_input):
        try:
            with torch.no_grad():
                output = model(task, 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 evolutionary_training(self, epochs=5):
        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_text = self.multi_modal_model.text_tokenizer("Hello, how are you?", return_tensors='pt').to(self.device)
            test_input_code = self.multi_modal_model.code_tokenizer("def add(a, b): return a + b", return_tensors='pt').to(self.device)
            
            # 加载音频文件并处理
            audio_path = "C:/Users/baby7/Desktop/推理/sample-3s.wav"
            audio_input, sample_rate = sf.read(audio_path)
            if audio_input.ndim > 1:
                audio_input = np.mean(audio_input, axis=1)  # 转换为单声道
            if sample_rate != 16000:
                audio_input = resampy.resample(audio_input, sample_rate, 16000)  # 重采样
            test_input_audio = torch.tensor(audio_input).to(self.device).unsqueeze(0)  # 添加 batch 维度
            
            complexity_text, performance_text = self.evaluate_model(self.multi_modal_model, 'text_generation', test_input_text)
            complexity_code, performance_code = self.evaluate_model(self.multi_modal_model, 'code_generation', test_input_code)
            complexity_audio, performance_audio = self.evaluate_model(self.multi_modal_model, 'speech_recognition', test_input_audio)
            
            print(f"多模态模型 (文本生成) - 复杂度: {complexity_text}, 性能: {performance_text:.4f}")
            print(f"多模态模型 (代码生成) - 复杂度: {complexity_code}, 性能: {performance_code:.4f}")
            print(f"多模态模型 (语音识别) - 复杂度: {complexity_audio}, 性能: {performance_audio:.4f}")

    def print_model_info(self):
        print(f"\n多模态模型结构:")
        print(self.multi_modal_model)
        print("\n参数统计:")
        total_params = sum(p.numel() for p in self.multi_modal_model.parameters())
        trainable_params = sum(p.numel() for p in self.multi_modal_model.parameters() if p.requires_grad)
        print(f"总参数: {total_params}")
        print(f"可训练参数: {trainable_params}")

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

    # 创建进化多模态网络实例
    evolutionary_network = EvolutionaryMultiModalNetwork()

    # 打印模型信息
    evolutionary_network.print_model_info()

    # 进行进化训练
    evolutionary_network.evolutionary_training(epochs=5)

if __name__ == "__main__":
    main()