bev-project/ANALYZE_RESULTS_SIMPLE.py

#!/usr/bin/env python
"""
分析推理结果 - 不导入mmdet3d模块避免CUDA问题
"""
import os
import sys

# 添加项目路径但不导入mmdet3d
sys.path.insert(0, '/workspace/bevfusion')

# 只导入基础库
import pickle
import numpy as np

def load_results_without_mmdet3d(filepath):
    """不导入mmdet3d模块的情况下加载结果"""
    # 临时重命名mmdet3d模块避免自动导入
    import sys
    mmdet3d_modules = [m for m in sys.modules.keys() if m.startswith('mmdet3d')]
    for mod in mmdet3d_modules:
        del sys.modules[mod]

    # 阻止mmdet3d的自动导入
    sys.modules['mmdet3d'] = None
    sys.modules['mmdet3d.core'] = None

    try:
        with open(filepath, 'rb') as f:
            # 使用纯Python方式加载，避免触发__reduce__方法中的导入
            import pickle
            results = pickle.load(f)
        return results
    except Exception as e:
        print(f"标准pickle加载失败: {e}")
        return None

def analyze_results(results):
    """分析推理结果"""
    print("="*60)
    print("BEVFusion 单Batch推理结果分析")
    print("="*60)

    print(f"总样本数量: {len(results)}")

    for sample_idx, sample in enumerate(results):
        print(f"\n--- 样本 {sample_idx} ---")
        print(f"数据类型: {type(sample)}")
        print(f"包含字段: {list(sample.keys())}")

        # 分析BEV分割
        if 'masks_bev' in sample:
            masks = sample['masks_bev']
            print(f"BEV分割形状: {masks.shape}")
            print("BEV分割数据类型:", type(masks))

            if hasattr(masks, 'shape'):  # numpy array or torch tensor
                print(f"BEV分割数据类型: {masks.dtype if hasattr(masks, 'dtype') else 'unknown'}")
                print("每个类别的像素统计:")

                class_names = ['drivable_area', 'ped_crossing', 'walkway', 'stop_line', 'carpark_area', 'divider']
                for i in range(min(6, masks.shape[0])):
                    if i < len(class_names):
                        # 计算激活像素数量
                        if hasattr(masks, 'cpu'):  # torch tensor
                            mask_data = masks[i].cpu().numpy()
                        else:  # numpy array
                            mask_data = masks[i]

                        activated_pixels = (mask_data > 0.5).sum()
                        total_pixels = mask_data.size
                        percentage = activated_pixels / total_pixels * 100

                        print("15s")

        # 分析Ground Truth
        if 'gt_masks_bev' in sample:
            gt_masks = sample['gt_masks_bev']
            print(f"Ground Truth形状: {gt_masks.shape}")

        # 分析3D检测
        if 'boxes_3d' in sample:
            boxes = sample['boxes_3d']
            print(f"3D检测框数量: {len(boxes)}")

        if 'scores_3d' in sample:
            scores = sample['scores_3d']
            if len(scores) > 0:
                if hasattr(scores, 'cpu'):
                    scores_np = scores.cpu().numpy()
                else:
                    scores_np = np.array(scores)

                print(".3f")
                print(".3f")

        if 'labels_3d' in sample:
            labels = sample['labels_3d']
            if len(labels) > 0:
                if hasattr(labels, 'cpu'):
                    labels_np = labels.cpu().numpy()
                else:
                    labels_np = np.array(labels)

                unique_labels, counts = np.unique(labels_np, return_counts=True)
                print("检测类别分布:")
                for label, count in zip(unique_labels, counts):
                    print(f"  类别 {int(label)}: {count} 个")

        print("-" * 30)

def main():
    # 推理结果文件路径
    result_file = '/data/infer_test/20251120_124755/one_batch_results.pkl'

    if not os.path.exists(result_file):
        print(f"结果文件不存在: {result_file}")
        # 查找可能的其他结果文件
        import glob
        possible_files = glob.glob('/data/infer_test/*/one_batch_results.pkl')
        if possible_files:
            print("找到以下可能的结果文件:")
            for f in possible_files:
                print(f"  {f}")
            result_file = possible_files[-1]  # 使用最新的
            print(f"使用: {result_file}")
        else:
            return

    print(f"分析结果文件: {result_file}")
    print(f"文件大小: {os.path.getsize(result_file)} bytes")

    # 尝试加载结果
    results = load_results_without_mmdet3d(result_file)

    if results is None:
        print("无法加载结果文件")
        return

    # 分析结果
    analyze_results(results)

    print("\n" + "="*60)
    print("分析完成")
    print("="*60)

if __name__ == '__main__':
    main()