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

Please run accumulate() first

Error message

Please run accumulate() first

What it means

COCOEvalWrapper.summarize() raises if self.eval is unset, meaning accumulate() (the underlying COCO eval) was never run. COCO API's summarize needs evalImgs populated by accumulate(); calling summarize directly after evaluate() skips that step.

Source

Thrown at pytorch_object_detection/retinaNet/validation.py:87

    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))

    data_transform = {
        "val": transforms.Compose([transforms.ToTensor()])
    }

    # read class_indict
    label_json_path = './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)

View on GitHub (pinned to 1ec3fe6f37)

Solutions

1. Call accumulate() (or the full evaluate() method that includes it) before summarize()
2. Use the wrapper's evaluate() entry point which chains evaluate->accumulate->summarize
3. Guard summarize behind 'if self.eval:' check in caller code

Example fix

// before
stats, info = coco_evaluator.summarize()
// after
coco_evaluator.evaluate()
coco_evaluator.accumulate()
stats, info = coco_evaluator.summarize()

assert coco_evaluator.eval is not None, 'call accumulate() before summarize()'

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

try:
    stats, info = evaluator.summarize()
except Exception as e:
    print(f'Eval pipeline incomplete: {e}; run evaluate/accumulate first')

When it happens

Trigger: Calling evaluator.summarize() in validation main() without first calling evaluator.evaluate() and evaluator.accumulate(); early-exit before accumulate() when stats are missing.

Common situations: Copying COCO eval code piecemeal (evaluate/accumulate/summarize pipeline) and dropping the accumulate step; catching an exception in evaluate and still calling summarize; running summarize twice when first run had no eval.

Related errors

• Please run accumulate() first
• not support iou_type: {self.iou_type}
• Unknown iou type {}
• Please run accumulate() first
• not support iou_type: {self.iou_type}

AI-assisted analysis of WZMIAOMIAO/deep-learning-for-image-processing@1ec3fe6f37 (2026-08-30). Data as JSON: /api/errors/04a961792352cfb7. Report an issue: GitHub.