WZMIAOMIAO/deep-learning-for-image-processing · error · Exception

Please run accumulate() first

Error message

Please run accumulate() first

What it means

The COCO-style evaluator's summarize() requires that accumulate() has run first, because self.eval is only populated by accumulate(). If self.eval is None/empty, summarize raises Exception('Please run accumulate() first'). It enforces the correct call order: evaluate() -> accumulate() -> summarize().

Source

Thrown at pytorch_object_detection/yolov3_spp/validation.py:80

    stats, print_list = [0] * 12, [""] * 12
    stats[0], print_list[0] = _summarize(1)
    stats[1], print_list[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])
    stats[2], print_list[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2])
    stats[3], print_list[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2])
    stats[4], print_list[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2])
    stats[5], print_list[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2])
    stats[6], print_list[6] = _summarize(0, maxDets=self.params.maxDets[0])
    stats[7], print_list[7] = _summarize(0, maxDets=self.params.maxDets[1])
    stats[8], print_list[8] = _summarize(0, maxDets=self.params.maxDets[2])
    stats[9], print_list[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2])
    stats[10], print_list[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2])
    stats[11], print_list[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2])

    print_info = "\n".join(print_list)

    if not self.eval:
        raise Exception('Please run accumulate() first')

    return stats, print_info


def main(parser_data):
    device = torch.device(parser_data.device if torch.cuda.is_available() else "cpu")
    print("Using {} device training.".format(device.type))

    # read class_indict
    label_json_path = './data/pascal_voc_classes.json'
    assert os.path.exists(label_json_path), "json file {} dose not exist.".format(label_json_path)
    with open(label_json_path, 'r') as f:
        class_dict = json.load(f)

    category_index = {v: k for k, v in class_dict.items()}

    data_dict = parse_data_cfg(parser_data.data)
    test_path = data_dict["valid"]

View on GitHub (pinned to 1ec3fe6f37)

Solutions

1. Ensure the loop calls evaluator.accumulate() after evaluate() and before summarize().
2. Check that accumulate() is not skipped by a conditional/early return when predictions exist.
3. If self.eval can legitimately be empty, guard with `if evaluator.eval: evaluator.summarize()`.

Example fix

// before
for images, targets in val_loader:
    evaluator.update(predictions)
evaluator.summarize()
// after
for images, targets in val_loader:
    evaluator.update(predictions)
evaluator.accumulate()
evaluator.summarize()

if evaluator.eval is None:
    evaluator.accumulate()
evaluator.summarize()

def can_summarize(evaluator) -> bool:
    return getattr(evaluator, 'eval', None) is not None

try:
    stats, info = evaluator.summarize()
except Exception as e:
    if 'accumulate' in str(e):
        evaluator.accumulate(); stats, info = evaluator.summarize()
    else:
        raise

When it happens

Trigger: Calling evaluator.summarize() (from validation.py main) without having called evaluator.accumulate() beforehand, or accumulate() being skipped on an error/no-data path.

Common situations: Writing custom validation loops that call summarize directly; early-exit code paths that skip accumulation when the dataloader is empty; refactoring that reordered accumulate/summarize calls.

Related errors

• sampler should be an instance of torch.utils.data.Sampler, b
• VOCdevkit dose not in path:'{}'.
• return_layers are not present in model
• Expected target boxes to be a tensorof shape [N, 4], got {:}
• Expected target boxes to be of type Tensor, got {:}.

AI-assisted analysis of WZMIAOMIAO/deep-learning-for-image-processing@1ec3fe6f37 (2026-08-30). Data as JSON: /api/errors/9bcffe5af06337a7. Report an issue: GitHub.