python代码中 for 循环异常中断

Question

python代码中 for 循环异常中断

if args.deploy == 'finetune':
            print("Start selecting the best lr...")
            best_acc = 0
            for lr in [0, 0.0001, 0.0005, 0.001]:
                model.lr = lr
                test_stats = evaluate(data_loader_val, model, criterion, device, seed=1234, ep=5) #test_stats：测试结果统计信息
                acc = test_stats['acc1']
                print(f"*lr = {lr}: acc1 = {acc}")
                if acc > best_acc:
                    best_acc = acc
                    best_lr = lr
            model.lr = best_lr
            print(f"### Selected lr = {best_lr}")

在这个代码中第一个学习率0 一切正常，到了第二个学习率 就直接总时间变成0了，没有进行任何操作，直接就回到了打印阶段

def evaluate(data_loaders, model, criterion, device, seed=None, ep=None):
    if isinstance(data_loaders, dict):
        test_stats_lst = {}
        test_stats_glb = {}

        for j, (source, data_loader) in enumerate(data_loaders.items()):
            print(f'* Evaluating {source}:')
            seed_j = seed + j if seed else None
            test_stats = _evaluate(data_loader, model, criterion, device, seed_j)
            test_stats_lst[source] = test_stats
            test_stats_glb[source] = test_stats['acc1']

        # apart from individual's acc1, accumulate metrics over all domains to compute mean
        for k in test_stats_lst[source].keys():
            test_stats_glb[k] = torch.tensor([test_stats[k] for test_stats in test_stats_lst.values()]).mean().item()

        return test_stats_glb
    elif isinstance(data_loaders, torch.utils.data.DataLoader): # when args.eval = True
        return _evaluate(data_loaders, model, criterion, device, seed, ep)
    else:
        warnings.warn(f'The structure of {data_loaders} is not recognizable.')
        return _evaluate(data_loaders, model, criterion, device, seed)

```python
def _evaluate(data_loader, model, criterion, device, seed=None, ep=None):
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('n_ways', utils.SmoothedValue(window_size=1, fmt='{value:d}'))
    metric_logger.add_meter('n_imgs', utils.SmoothedValue(window_size=1, fmt='{value:d}'))
    metric_logger.add_meter('acc1', utils.SmoothedValue(window_size=len(data_loader.dataset)))
    metric_logger.add_meter('acc5', utils.SmoothedValue(window_size=len(data_loader.dataset)))


    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    if seed is not None:
        data_loader.generator.manual_seed(seed)

    for ii, batch in enumerate(metric_logger.log_every(data_loader, 10, header)):
        if ep is not None:
            if ii > ep:
                break

        batch = to_device(batch, device)
        SupportTensor, SupportLabel, x, y = batch

        # compute output
        with torch.cuda.amp.autocast():
            output = model(SupportTensor, SupportLabel, x) #logits

        output = output.view(x.shape[0] * x.shape[1], -1)
        y = y.view(-1)
        loss = criterion(output, y)
        acc1, acc5 = accuracy(output, y, topk=(1, 5))

        batch_size = x.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
        metric_logger.update(n_ways=SupportLabel.max()+1)
        metric_logger.update(n_imgs=SupportTensor.shape[1] + x.shape[1])

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))

    ret_dict = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
    ret_dict['acc_std'] = metric_logger.meters['acc1'].std

    return ret_dict

Answer 1

print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' 这一行代码中访问了 loss 属性，但是 MetricLogger 类中并没有定义对应的属性。
你可以在 update 方法中记录 loss 的逻辑改为添加一个新的 meter，然后在 getattr 方法中增加对应的属性访问器。