inspect_model.py

#!/usr/bin/env python3
"""
模型详细检查脚本 - 检查模型的关键点配置
"""

import os
import sys
import logging
import numpy as np
import cv2

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def inspect_model():
    """检查模型详细信息"""
    base_model_path = "models/kunin-mice-pose.v0.1.5n.pt"
    
    # 选择模型路径（与SingleMouseProcessor保持一致）
    model_path = select_model_path(base_model_path)
    
    try:
        from ultralytics import YOLO
        
        logger.info("正在加载模型...")
        model = YOLO(model_path, task="pose")
        
        # 检查模型配置
        logger.info("📊 模型详细信息:")
        
        # 获取模型名称信息
        if hasattr(model, 'model') and hasattr(model.model, 'names'):
            logger.info(f"  - 类别数量: {len(model.model.names)}")
            logger.info(f"  - 类别名称: {model.model.names}")
        
        if hasattr(model, 'model'):
            if hasattr(model.model, 'kpt_shape'):
                logger.info(f"  - 关键点形状: {model.model.kpt_shape}")
            elif hasattr(model.model, 'yaml') and 'kpt_shape' in model.model.yaml:
                logger.info(f"  - 关键点形状: {model.model.yaml['kpt_shape']}")
            
            # 检查模型的yaml配置
            if hasattr(model.model, 'yaml'):
                yaml_data = model.model.yaml
                logger.info(f"  - YAML配置:")
                for key in ['nc', 'kpt_shape', 'names']:
                    if key in yaml_data:
                        logger.info(f"    {key}: {yaml_data[key]}")
        
        # 使用真实的小鼠图像进行测试（如果有的话）
        test_images = [
            "test_mouse.jpg",
            "test_mouse.png", 
            "mouse_test.jpg",
            "sample.jpg"
        ]
        
        test_image = None
        for img_path in test_images:
            if os.path.exists(img_path):
                test_image = img_path
                break
        
        if test_image:
            logger.info(f"📸 使用测试图像: {test_image}")
            results = model(test_image, conf=0.1, verbose=True)  # 降低置信度阈值
        else:
            logger.info("📸 使用随机图像进行测试...")
            # 创建一个更像小鼠的测试图像
            test_img = np.random.randint(50, 200, (640, 640, 3), dtype=np.uint8)
            # 添加一些简单的形状
            cv2.circle(test_img, (320, 320), 50, (100, 100, 100), -1)
            cv2.ellipse(test_img, (320, 320), (80, 40), 0, 0, 360, (150, 150, 150), -1)
            
            results = model(test_img, conf=0.1, verbose=True)  # 降低置信度阈值
        
        result = results[0]
        
        logger.info("🔍 推理结果分析:")
        logger.info(f"  - 原始输出形状: {result.orig_shape}")
        
        if hasattr(result, 'boxes') and result.boxes is not None:
            logger.info(f"  - 检测框数量: {len(result.boxes)}")
            if len(result.boxes) > 0:
                logger.info(f"  - 置信度范围: {result.boxes.conf.min():.3f} - {result.boxes.conf.max():.3f}")
        else:
            logger.info("  - 没有检测到目标")
        
        if hasattr(result, 'keypoints') and result.keypoints is not None:
            kpts_data = result.keypoints.data
            logger.info(f"  - 关键点数据形状: {kpts_data.shape}")
            logger.info(f"  - 关键点数据类型: {kpts_data.dtype}")
            
            if len(kpts_data.shape) == 3:
                n_detections, n_keypoints, coords = kpts_data.shape
                logger.info(f"  - 检测数量: {n_detections}")
                logger.info(f"  - 每个检测的关键点数: {n_keypoints}")
                logger.info(f"  - 每个关键点的坐标数: {coords}")
                
                # 计算实际的关键点数量
                actual_keypoints = coords // 3 if coords % 3 == 0 else coords // 2
                logger.info(f"  - 推断的关键点数量: {actual_keypoints}")
                
                if n_detections > 0:
                    kpts = kpts_data[0]  # 第一个检测结果
                    visible_kpts = (kpts[:, 2] > 0.3).sum() if coords == 3 else (kpts[:, 0] > 0).sum()
                    logger.info(f"  - 可见关键点数量: {visible_kpts}")
                    
                    # 显示前几个关键点的值
                    logger.info("  - 前5个关键点:")
                    for i in range(min(5, n_keypoints)):
                        if coords == 3:
                            x, y, conf = kpts[i]
                            logger.info(f"    {i}: ({x:.1f}, {y:.1f}, {conf:.3f})")
                        else:
                            x, y = kpts[i]
                            logger.info(f"    {i}: ({x:.1f}, {y:.1f})")
        
        return True
        
    except Exception as e:
        logger.error(f"❌ 模型检查失败: {str(e)}")
        import traceback
        logger.error(traceback.format_exc())
        return False

def select_model_path(base_model_path: str) -> str:
    """根据GPU情况选择模型路径"""
    try:
        import torch
        # 检测是否有NVIDIA GPU
        if torch.cuda.is_available():
            nvidia_gpu_found = False
            for i in range(torch.cuda.device_count()):
                gpu_name = torch.cuda.get_device_name(i).lower()
                if 'nvidia' in gpu_name:
                    nvidia_gpu_found = True
                    break
            
            if nvidia_gpu_found:
                # 构建.engine模型路径
                engine_path = base_model_path.replace('.pt', '.engine')
                if os.path.exists(engine_path):
                    logger.info(f"🚀 检测到NVIDIA GPU，使用TensorRT模型: {engine_path}")
                    return engine_path
                else:
                    logger.info(f"⚠️ NVIDIA GPU已检测到，但TensorRT模型不存在: {engine_path}")
                    logger.info(f"📍 使用PyTorch模型: {base_model_path}")
                    return base_model_path
            else:
                logger.info(f"📍 检测到GPU但非NVIDIA，使用PyTorch模型: {base_model_path}")
                return base_model_path
        else:
            logger.info(f"📍 未检测到GPU，使用CPU模式，PyTorch模型: {base_model_path}")
            return base_model_path
            
    except Exception as e:
        logger.warning(f"⚠️ GPU检测失败，使用默认模型: {str(e)}")
        return base_model_path

def main():
    """主函数"""
    logger.info("🔍 开始模型详细检查...")
    
    success = inspect_model()
    
    if success:
        logger.info("✅ 模型检查完成！")
    else:
        logger.error("❌ 模型检查失败")

if __name__ == "__main__":
    main()