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SJMT_model.pth

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 version https://git-lfs.github.com/spec/v1
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-size 2493181678
+oid sha256:3acba7b1a7aac988188da7e06a48d7db0ea263e2c9bf984412ad45f126b88f99
+size 2476637590

app.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, 
+    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()