run_local.py

import os
import torch
from model import AutoModel, Config

def load_model(model_path, config_path):
    """
    加载模型权重和配置
    """
    # 加载配置
    if not os.path.exists(config_path):
        raise FileNotFoundError(f"配置文件未找到: {config_path}")
    print(f"加载配置文件: {config_path}")
    config = Config()
    
    # 初始化模型
    model = AutoModel(config)
    
    # 加载权重
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"模型文件未找到: {model_path}")
    print(f"加载模型权重: {model_path}")
    state_dict = torch.load(model_path, map_location=torch.device("cpu"))
    model.load_state_dict(state_dict)
    model.eval()
    print("模型加载成功并设置为评估模式。")
    
    return model, config


def run_inference(model, config):
    """
    使用模型运行推理
    """
    # 模拟示例输入
    image = torch.randn(1, 3, 224, 224)  # 图像输入
    text = torch.randn(1, config.max_position_embeddings, config.hidden_size)  # 文本输入
    audio = torch.randn(1, config.audio_sample_rate)  # 音频输入

    # 模型推理
    outputs = model(image, text, audio)
    vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output = outputs

    # 打印结果
    print("\n推理结果:")
    print(f"VQA output shape: {vqa_output.shape}")
    print(f"Caption output shape: {caption_output.shape}")
    print(f"Retrieval output shape: {retrieval_output.shape}")
    print(f"ASR output shape: {asr_output.shape}")
    print(f"Realtime ASR output shape: {realtime_asr_output.shape}")

if __name__ == "__main__":
    # 文件路径
    model_path = "AutoModel.pth"
    config_path = "config.json"

    # 加载模型
    try:
        model, config = load_model(model_path, config_path)

        # 运行推理
        run_inference(model, config)
    except Exception as e:
        print(f"运行失败: {e}")