# credits: https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/metrics/rank.py import numpy as np import warnings from collections import defaultdict try: from .rank_cylib.rank_cy import evaluate_cy IS_CYTHON_AVAI = True except ImportError: IS_CYTHON_AVAI = False warnings.warn( 'Cython evaluation (very fast so highly recommended) is ' 'unavailable, now use python evaluation.' ) def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank): """Evaluation with cuhk03 metric Key: one image for each gallery identity is randomly sampled for each query identity. Random sampling is performed num_repeats times. """ num_repeats = 10 num_q, num_g = distmat.shape if num_g < max_rank: max_rank = num_g print( 'Note: number of gallery samples is quite small, got {}'. format(num_g) ) indices = np.argsort(distmat, axis=1) matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32) # compute cmc curve for each query all_cmc = [] all_AP = [] num_valid_q = 0. # number of valid query for q_idx in range(num_q): # get query pid and camid q_pid = q_pids[q_idx] q_camid = q_camids[q_idx] # remove gallery samples that have the same pid and camid with query order = indices[q_idx] remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid) keep = np.invert(remove) # compute cmc curve raw_cmc = matches[q_idx][ keep] # binary vector, positions with value 1 are correct matches if not np.any(raw_cmc): # this condition is true when query identity does not appear in gallery continue kept_g_pids = g_pids[order][keep] g_pids_dict = defaultdict(list) for idx, pid in enumerate(kept_g_pids): g_pids_dict[pid].append(idx) cmc = 0. for repeat_idx in range(num_repeats): mask = np.zeros(len(raw_cmc), dtype=np.bool) for _, idxs in g_pids_dict.items(): # randomly sample one image for each gallery person rnd_idx = np.random.choice(idxs) mask[rnd_idx] = True masked_raw_cmc = raw_cmc[mask] _cmc = masked_raw_cmc.cumsum() _cmc[_cmc > 1] = 1 cmc += _cmc[:max_rank].astype(np.float32) cmc /= num_repeats all_cmc.append(cmc) # compute AP num_rel = raw_cmc.sum() tmp_cmc = raw_cmc.cumsum() tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)] tmp_cmc = np.asarray(tmp_cmc) * raw_cmc AP = tmp_cmc.sum() / num_rel all_AP.append(AP) num_valid_q += 1. assert num_valid_q > 0, 'Error: all query identities do not appear in gallery' all_cmc = np.asarray(all_cmc).astype(np.float32) all_cmc = all_cmc.sum(0) / num_valid_q mAP = np.mean(all_AP) return all_cmc, mAP def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank): """Evaluation with market1501 metric Key: for each query identity, its gallery images from the same camera view are discarded. """ num_q, num_g = distmat.shape if num_g < max_rank: max_rank = num_g print('Note: number of gallery samples is quite small, got {}'.format(num_g)) indices = np.argsort(distmat, axis=1) matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32) # compute cmc curve for each query all_cmc = [] all_AP = [] all_INP = [] num_valid_q = 0. # number of valid query for q_idx in range(num_q): # get query pid and camid q_pid = q_pids[q_idx] q_camid = q_camids[q_idx] # remove gallery samples that have the same pid and camid with query order = indices[q_idx] remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid) keep = np.invert(remove) # compute cmc curve raw_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches if not np.any(raw_cmc): # this condition is true when query identity does not appear in gallery continue cmc = raw_cmc.cumsum() pos_idx = np.where(raw_cmc == 1) max_pos_idx = np.max(pos_idx) inp = cmc[max_pos_idx] / (max_pos_idx + 1.0) all_INP.append(inp) cmc[cmc > 1] = 1 all_cmc.append(cmc[:max_rank]) num_valid_q += 1. # compute average precision # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision num_rel = raw_cmc.sum() tmp_cmc = raw_cmc.cumsum() tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)] tmp_cmc = np.asarray(tmp_cmc) * raw_cmc AP = tmp_cmc.sum() / num_rel all_AP.append(AP) assert num_valid_q > 0, 'Error: all query identities do not appear in gallery' all_cmc = np.asarray(all_cmc).astype(np.float32) all_cmc = all_cmc.sum(0) / num_valid_q mAP = np.mean(all_AP) mINP = np.mean(all_INP) return all_cmc, mAP, mINP def evaluate_py( distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03 ): if use_metric_cuhk03: return eval_cuhk03( distmat, q_pids, g_pids, q_camids, g_camids, max_rank ) else: return eval_market1501( distmat, q_pids, g_pids, q_camids, g_camids, max_rank ) def evaluate_rank( distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True ): """Evaluates CMC rank. Args: distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery). q_pids (numpy.ndarray): 1-D array containing person identities of each query instance. g_pids (numpy.ndarray): 1-D array containing person identities of each gallery instance. q_camids (numpy.ndarray): 1-D array containing camera views under which each query instance is captured. g_camids (numpy.ndarray): 1-D array containing camera views under which each gallery instance is captured. max_rank (int, optional): maximum CMC rank to be computed. Default is 50. use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03. Default is False. This should be enabled when using cuhk03 classic split. use_cython (bool, optional): use cython code for evaluation. Default is True. This is highly recommended as the cython code can speed up the cmc computation by more than 10x. This requires Cython to be installed. """ if use_cython and IS_CYTHON_AVAI: return evaluate_cy( distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03 ) else: return evaluate_py( distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03 )