Delete multi_modal_model.py

multi_modal_model.py

@@ -1,172 +0,0 @@
-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()