#!/usr/bin/env python
"""
只评估BEV分割的脚本，避免3D检测为空的问题
"""
import os
import sys
import torch

# 设置环境
os.environ['PATH'] = '/opt/conda/bin:' + os.environ.get('PATH', '')
os.environ['LD_LIBRARY_PATH'] = '/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:' + os.environ.get('LD_LIBRARY_PATH', '')
os.environ['PYTHONPATH'] = '/workspace/bevfusion:' + os.environ.get('PYTHONPATH', '')

def main():
    import mmcv
    from mmcv import Config
    from mmdet3d.datasets import build_dataloader, build_dataset
    from mmdet3d.models import build_model
    from mmcv.runner import load_checkpoint
    from mmdet3d.apis import single_gpu_test

    # 加载配置
    config_file = 'configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_BEV2X_phase4b_rmtppad_segmentation.yaml'
    cfg = Config.fromfile(config_file)

    # 设置测试模式
    cfg.data.val.test_mode = True

    # 构建数据集
    print("构建数据集...")
    from mmdet3d.datasets import NuScenesDataset

    val_config = {
        'type': 'NuScenesDataset',
        'dataset_root': cfg.dataset_root,
        'ann_file': cfg.dataset_root + 'nuscenes_infos_val.pkl',
        'pipeline': cfg.evaluation.pipeline,
        'object_classes': cfg.object_classes,
        'map_classes': cfg.map_classes,
        'modality': {
            'use_lidar': True,
            'use_camera': True,
            'use_radar': False,
            'use_map': False,
            'use_external': False
        },
        'test_mode': True,
        'use_valid_flag': False,
        'box_type_3d': 'LiDAR',
        'load_interval': cfg.data.val.load_interval if hasattr(cfg.data.val, 'load_interval') else 1
    }

    dataset = build_dataset(val_config)
    print(f"数据集大小: {len(dataset)}")

    # 构建数据加载器
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=1,
        workers_per_gpu=0,
        dist=False,
        shuffle=False,
    )

    # 构建模型
    print("构建模型...")
    cfg.model.train_cfg = None
    model = build_model(cfg.model, test_cfg=cfg.get("test_cfg"))
    print(f"模型heads: {list(model.heads.keys())}")

    # 加载checkpoint
    checkpoint_path = 'runs/run-4c8ec7e5-fabdc997/epoch_1.pth'
    print(f"加载checkpoint: {checkpoint_path}")
    checkpoint = load_checkpoint(model, checkpoint_path, map_location="cpu")

    if "CLASSES" in checkpoint.get("meta", {}):
        model.CLASSES = checkpoint["meta"]["CLASSES"]
    else:
        model.CLASSES = dataset.CLASSES

    # 单GPU测试
    print("开始BEV分割评估...")
    model = model.cuda()
    outputs = single_gpu_test(model, data_loader)

    print(f"推理完成，结果数量: {len(outputs)}")

    # 保存结果
    output_file = '/data/eval_fast/bev_only_results.pkl'
    mmcv.dump(outputs, output_file)
    print(f"结果已保存至: {output_file}")

    # 手动评估BEV分割
    print("开始BEV分割评估...")

    # 过滤只包含分割结果的输出
    seg_results = []
    for sample in outputs:
        if 'masks_bev' in sample and 'gt_masks_bev' in sample:
            seg_results.append({
                'masks_bev': sample['masks_bev'],
                'gt_masks_bev': sample['gt_masks_bev']
            })

    print(f"找到 {len(seg_results)} 个有效的分割样本")

    if seg_results:
        # 计算分割指标
        total_iou = 0
        valid_samples = 0

        for sample in seg_results:
            pred_mask = sample['masks_bev']
            gt_mask = sample['gt_masks_bev']

            if hasattr(pred_mask, 'cpu'):
                pred_mask = pred_mask.cpu().numpy()
            if hasattr(gt_mask, 'cpu'):
                gt_mask = gt_mask.cpu().numpy()

            # 计算mIoU (简化版，只计算第一个类别)
            pred_flat = pred_mask[0].flatten()  # 假设第一个类别是主要类别
            gt_flat = gt_mask[0].flatten()

            # 简单IoU计算
            intersection = ((pred_flat > 0.5) & (gt_flat > 0.5)).sum()
            union = ((pred_flat > 0.5) | (gt_flat > 0.5)).sum()

            if union > 0:
                iou = intersection / union
                total_iou += iou
                valid_samples += 1

        if valid_samples > 0:
            avg_iou = total_iou / valid_samples
            print(".4f")
        else:
            print("没有有效的分割样本进行评估")

    print("BEV分割评估完成！")

if __name__ == '__main__':
    main()