eval_recall

根据提供的引用内容，我了解到eval_recall是3D目标检测中的一个评价指标。具体来说，eval_recall函数是在eval.py文件中定义的，用于计算模型在测试集上的召回率。该函数会根据预测结果和真实标签计算出召回率的值，并返回该值作为评价结果。

根据引用和引用[2]的描述，eval_recall函数的具体实现可能需要参考PV-RCNN代码中的相关部分。建议您参考这些引用中提供的代码和博客文章，以便更好地理解和学习eval_recall函数的实现细节。

相关问题

lidar_file path: /root/autodl-tmp/project/data/KITTI/object/testing/velodyne/000204.bin lidar_file path: /root/autodl-tmp/project/data/KITTI/object/testing/velodyne/000205.bin lidar_file path: /root/autodl-tmp/project/data/KITTI/object/testing/velodyne/000206.bin lidar_file path: /root/autodl-tmp/project/data/KITTI/object/testing/velodyne/000207.bin eval: 39%|█████████████████████████████▍ | 44/112 [00:06<00:07, 8.56it/s, mode=TEST, recall=0/0, rpn_iou=0]Traceback (most recent call last): File "eval_rcnn.py", line 908, in <module> eval_single_ckpt(root_result_dir) File "eval_rcnn.py", line 771, in eval_single_ckpt eval_one_epoch(model, test_loader, epoch_id, root_result_dir, logger) File "eval_rcnn.py", line 694, in eval_one_epoch ret_dict = eval_one_epoch_rpn(model, dataloader, epoch_id, result_dir, logger) File "eval_rcnn.py", line 143, in eval_one_epoch_rpn for data in dataloader: File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ lidar_file path: /root/autodl-tmp/project/data/KITTI/object/testing/velodyne/000208.bin data = self._next_data() File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1085, in _next_data return self._process_data(data) File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data data.reraise() File "/root/miniconda3/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise raise self.exc_type(msg) AssertionError: Caught AssertionError in DataLoader worker process 0.

根据打印信息，可以看到 `get_lidar` 方法中的打印语句已经生效，输出了每个样本的 `lidar_file` 路径。但是，在处理第 208 个样本时，程序出现了 `AssertionError` 异常，可能是因为对应的 `lidar_file` 文件不存在。

建议再次检查一下对应的数据集文件夹中是否存在 `000208.bin` 这个文件。如果该文件确实不存在，可以尝试重新下载数据集或者检查一下数据集路径是否正确。

torch.save(model.state_dict(), r'./saved_model/' + str(args.arch) + '_' + str(args.batch_size) + '_' + str(args.dataset) + '_' + str(args.epoch) + '.pth') # 计算GFLOPs flops = 0 for name, module in model.named_modules(): if isinstance(module, torch.nn.Conv2d): flops += module.weight.numel() * 2 * module.in_channels * module.out_channels * module.kernel_size[ 0] * module.kernel_size[1] / module.stride[0] / module.stride[1] elif isinstance(module, torch.nn.Linear): flops += module.weight.numel() * 2 * module.in_features start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() with torch.no_grad(): output = UNet(args,3,1).to(device) end_event.record() torch.cuda.synchronize() elapsed_time_ms = start_event.elapsed_time(end_event) gflops = flops / (elapsed_time_ms * 10 ** 6) print("GFLOPs: {:.2f}".format(gflops)) return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops出现错误 best_iou,aver_iou,aver_dice,aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, FPS, parameters, gflops = val(model,best_iou,val_dataloader) File "D:/BaiduNetdiskDownload/0605_ghostv2unet _tunnelcrack/ghostunet++/UNET++/main.py", line 143, in val return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops UnboundLocalError: local variable 'gflops' referenced before assignment怎么修改

这个错误是因为在函数中定义了 `gflops` 变量，但是在函数返回时并没有为它赋值，导致出现了未赋值的情况。可以将 `gflops` 变量在函数一开始就赋一个初始值，比如设为0。代码修改如下：

def val(model, best_iou, val_dataloader, device):
    model.eval()
    aver_iou = 0
    aver_dice = 0
    aver_hd = 0
    aver_accuracy = 0
    aver_recall = 0
    aver_precision = 0
    aver_f1score = 0
    aver_memory = 0
    fps = 0
    parameters = sum(param.numel() for param in model.parameters())
    gflops = 0  # 在这里为 gflops 赋一个初始值
    with torch.no_grad():
        for step, (images, labels) in enumerate(val_dataloader):
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            iou, dice, hd, accuracy, recall, precision, f1score = eval_metrics(outputs, labels)
            memory = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
            aver_iou += iou
            aver_dice += dice
            aver_hd += hd
            aver_accuracy += accuracy
            aver_recall += recall
            aver_precision += precision
            aver_f1score += f1score
            aver_memory += memory
        aver_iou /= len(val_dataloader)
        aver_dice /= len(val_dataloader)
        aver_hd /= len(val_dataloader)
        aver_accuracy /= len(val_dataloader)
        aver_recall /= len(val_dataloader)
        aver_precision /= len(val_dataloader)
        aver_f1score /= len(val_dataloader)
        aver_memory /= len(val_dataloader)
        fps = len(val_dataloader.dataset) / (time.time() - start_time)
        # 统计模型的GFLOPs
        flops = 0
        for name, module in model.named_modules():
            if isinstance(module, torch.nn.Conv2d):
                flops += module.weight.numel() * 2 * module.in_channels * module.out_channels * module.kernel_size[0] * module.kernel_size[1] / module.stride[0] / module.stride[1]
            elif isinstance(module, torch.nn.Linear):
                flops += module.weight.numel() * 2 * module.in_features
        start_event = torch.cuda.Event(enable_timing=True)
        end_event = torch.cuda.Event(enable_timing=True)
        start_event.record()
        with torch.no_grad():
            output = UNet(args, 3, 1).to(device)
        end_event.record()
        torch.cuda.synchronize()
        elapsed_time_ms = start_event.elapsed_time(end_event)
        gflops = flops / (elapsed_time_ms * 10 ** 6)
        print("GFLOPs: {:.2f}".format(gflops))
    return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops