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main.py

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+import os
+import torch
+import torch.nn as nn
+import numpy as np
+import random
+from transformers import (
+    BartForConditionalGeneration, 
+    AutoModelForCausalLM, 
+    BertModel, 
+    Wav2Vec2Model,
+    CLIPModel,
+    AutoTokenizer
+)
+
+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':
+            # 确保 attention_mask 在 inputs 中
+            attention_mask = inputs.get('attention_mask')
+            print("输入数据:", inputs)
+            outputs = self.text_generator.generate(
+                inputs['input_ids'], 
+                max_new_tokens=100,  # 增加生成的最大新令牌数
+                pad_token_id=self.text_tokenizer.eos_token_id, 
+                attention_mask=attention_mask,
+                top_p=0.9,  # 调整 top_p 值
+                top_k=50,  # 保持 top_k 值
+                temperature=0.8,  # 调整 temperature 值
+                do_sample=True
+            )
+            print("生成的输出:", outputs)
+            return self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # 根据需要添加其他任务的逻辑...
+
+# 主函数
+if __name__ == "__main__":
+    # 初始化模型
+    model = MultiModalModel()
+
+    # 示例任务和输入数据
+    task = "text_generation"
+    input_text = "This is a sample input."
+    tokenizer = model.text_tokenizer
+    inputs = tokenizer(input_text, return_tensors='pt')
+
+    # 添加 attention_mask 键值对
+    inputs['attention_mask'] = torch.ones_like(inputs['input_ids'])
+
+    # 模型推理
+    result = model(task, inputs)
+    print("最终输出结果：", result)

multi_modal_model.py

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+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()

requirements.txt

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+torch
+numpy
+pandas
+scikit-learn
+code_generator
+nlp_encoder
+speech_encoder
+text_generator
+vision_encoder