NotImplementedError: There were no tensor arguments to this function

Question

When I evaluated ActionFormer model which is trained on the WEAR dataset, I met the following error:

Traceback (most recent call last):
File "./eval.py", line 127, in \<module\>
main(args)
File "./eval.py", line 94, in main
mAP = valid_one_epoch(
File "/home/kunpeng/actionformer_release/libs/utils/train_utils.py", line 418, in valid_one_epoch
results\['t-start'\] = torch.cat(results\['t-start'\]).numpy()
NotImplementedError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::\_cat.  This usually means that this function requires a non-empty list of Tensors, or that you (the operator writer) forgot to register a fallback function.  Available functions are \[CPU, CUDA, QuantizedCPU, BackendSelect, Python, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradLazy, AutogradXPU, AutogradMLC, AutogradHPU, AutogradNestedTensor, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, AutocastCPU, Autocast, Batched, VmapMode, Functionalize\].

The code of valid_one_epoch function is:

def valid_one_epoch(
    val_loader,
    model,
    curr_epoch,
    ext_score_file = None,
    evaluator = None,
    output_file = None,
    tb_writer = None,
    print_freq = 20
):
    """Test the model on the validation set"""
    # either evaluate the results or save the results
    assert (evaluator is not None) or (output_file is not None)

    # set up meters
    batch_time = AverageMeter()
    # switch to evaluate mode
    model.eval()
    # dict for results (for our evaluation code)
    results = {
        'video-id': [],
        't-start' : [],
        't-end': [],
        'label': [],
        'score': []
    }

    # loop over validation set
    start = time.time()
    for iter_idx, video_list in enumerate(val_loader, 0):
        # forward the model (wo. grad)
        with torch.no_grad():
            output = model(video_list)
            # unpack the results into ANet format
            num_vids = len(output)
            for vid_idx in range(num_vids):
                if output[vid_idx]['segments'].shape[0] > 0:
                    results['video-id'].extend(
                        [output[vid_idx]['video_id']] *
                        output[vid_idx]['segments'].shape[0]
                    )
                    results['t-start'].append(output[vid_idx]['segments'][:, 0])
                    results['t-end'].append(output[vid_idx]['segments'][:, 1])
                    results['label'].append(output[vid_idx]['labels'])
                    results['score'].append(output[vid_idx]['scores'])

        # printing
        if (iter_idx != 0) and iter_idx % (print_freq) == 0:
            # measure elapsed time (sync all kernels)
            torch.cuda.synchronize()
            batch_time.update((time.time() - start) / print_freq)
            start = time.time()

            # print timing
            print('Test: [{0:05d}/{1:05d}]\t'
                  'Time {batch_time.val:.2f} ({batch_time.avg:.2f})'.format(
                  iter_idx, len(val_loader), batch_time=batch_time))

    # gather all stats and evaluate
    results['t-start'] = torch.cat(results['t-start']).numpy()
    results['t-end'] = torch.cat(results['t-end']).numpy()
    results['label'] = torch.cat(results['label']).numpy()
    results['score'] = torch.cat(results['score']).numpy()

    if evaluator is not None:
        if ext_score_file is not None and isinstance(ext_score_file, str):
            results = postprocess_results(results, ext_score_file)
        # call the evaluator
        _, mAP, _ = evaluator.evaluate(results, verbose=True)
    else:
        # dump to a pickle file that can be directly used for evaluation
        with open(output_file, "wb") as f:
            pickle.dump(results, f)
        mAP = 0.0

    # log mAP to tb_writer
    if tb_writer is not None:
        tb_writer.add_scalar('validation/mAP', mAP, curr_epoch)

    return mAP

I checked the val_loader has been assigned a value.

batch_sampler:<torch.utils.data.sampler.BatchSampler object at 0x7f22c2ecd4c0>

batch_size:1

dataset:<libs.datasets.wear.WEARDataset object at 0x7f22c2ebef40>

But when I print the len of it, it was 0.

>>> val_loader = make_data_loader(
        val_dataset,
        False,
        None,
        1,
        cfg['loader']['num_workers']
    )
>>> print(f"Length of val_loader: {len(val_loader)}")
Length of val_loader: 0

I have tried to change the batch size but this was not solved.

Can anyone tell me where the problem is?