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()