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| """Helper functions for computing evaluation scores""" | |
| import numpy as np | |
| from cirtorch.utils.evaluate import compute_map | |
| def compute_map_and_log(dataset, ranks, gnd, kappas=(1, 5, 10), logger=None): | |
| """Computed mAP and log it | |
| :param str dataset: Dataset to compute the mAP on (e.g. roxford5k) | |
| :param np.ndarray ranks: 2D matrix of ints corresponding to previously computed ranks | |
| :param dict gnd: Ground-truth dataset structure | |
| :param list kappas: Compute mean precision at each kappa | |
| :param logging.Logger logger: If not None, use it to log mAP and all mP@kappa | |
| :return tuple: mAP and mP@kappa (medium difficulty for roxford5k and rparis6k) | |
| """ | |
| # new evaluation protocol | |
| if dataset.startswith('roxford5k') or dataset.startswith('rparis6k'): | |
| gnd_t = [] | |
| for gndi in gnd: | |
| g = {} | |
| g['ok'] = np.concatenate([gndi['easy']]) | |
| g['junk'] = np.concatenate([gndi['junk'], gndi['hard']]) | |
| gnd_t.append(g) | |
| mapE, apsE, mprE, prsE = compute_map(ranks, gnd_t, kappas) | |
| gnd_t = [] | |
| for gndi in gnd: | |
| g = {} | |
| g['ok'] = np.concatenate([gndi['easy'], gndi['hard']]) | |
| g['junk'] = np.concatenate([gndi['junk']]) | |
| gnd_t.append(g) | |
| mapM, apsM, mprM, prsM = compute_map(ranks, gnd_t, kappas) | |
| gnd_t = [] | |
| for gndi in gnd: | |
| g = {} | |
| g['ok'] = np.concatenate([gndi['hard']]) | |
| g['junk'] = np.concatenate([gndi['junk'], gndi['easy']]) | |
| gnd_t.append(g) | |
| mapH, apsH, mprH, prsH = compute_map(ranks, gnd_t, kappas) | |
| if logger: | |
| fmap = lambda x: np.around(x*100, decimals=2) | |
| logger.info(f"Evaluated {dataset}: mAP E: {fmap(mapE)}, M: {fmap(mapM)}, H: {fmap(mapH)}") | |
| logger.info(f"Evaluated {dataset}: mP@k{kappas} E: {fmap(mprE)}, M: {fmap(mprM)}, H: {fmap(mprH)}") | |
| scores = {"map_easy": mapE.item(), "mp@k_easy": mprE, "ap_easy": apsE, "p@k_easy": prsE, | |
| "map_medium": mapM.item(), "mp@k_medium": mprM, "ap_medium": apsM, "p@k_medium": prsM, | |
| "map_hard": mapH.item(), "mp@k_hard": mprH, "ap_hard": apsH, "p@k_hard": prsH} | |
| return scores | |
| # old evaluation protocol | |
| map_score, ap_scores, prk, pr_scores = compute_map(ranks, gnd, kappas=kappas) | |
| if logger: | |
| fmap = lambda x: np.around(x*100, decimals=2) | |
| logger.info(f"Evaluated {dataset}: mAP {fmap(map_score)}, mP@k {fmap(prk)}") | |
| return {"map": map_score, "mp@k": prk, "ap": ap_scores, "p@k": pr_scores} | |