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()