Function evaluate_rcnn

sotabench/sotabench.py:42–85

(model_name, paper_arxiv_id, cfg_list, model_file)

def evaluate_rcnn(model_name, paper_arxiv_id, cfg_list, model_file):
    evaluator = COCOEvaluator(
        root=COCO_ROOT, model_name=model_name, paper_arxiv_id=paper_arxiv_id
    )
    category_id_to_coco_id = {
        v: k for k, v in COCODetection.COCO_id_to_category_id.items()
    }

    cfg.update_args(cfg_list)  # TODO backup/restore config
    finalize_configs(False)
    MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
    predcfg = PredictConfig(
        model=MODEL,
        session_init=SmartInit(model_file),
        input_names=MODEL.get_inference_tensor_names()[0],
        output_names=MODEL.get_inference_tensor_names()[1],
    )
    predictor = OfflinePredictor(predcfg)

    def xyxy_to_xywh(box):
        box[2] -= box[0]
        box[3] -= box[1]
        return box

    df = get_eval_dataflow("coco_val2017")
    df.reset_state()
    for img, img_id in tqdm.tqdm(df, total=len(df)):
        results = predict_image(img, predictor)
        res = [
            {
                "image_id": img_id,
                "category_id": category_id_to_coco_id.get(
                    int(r.class_id), int(r.class_id)
                ),
                "bbox": xyxy_to_xywh([round(float(x), 4) for x in r.box]),
                "score": round(float(r.score), 3),
            }
            for r in results
        ]
        evaluator.add(res)
        if evaluator.cache_exists:
            break

    evaluator.save()


download(