# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True # credits: https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/metrics/rank_cylib/rank_cy.pyx import cython import numpy as np cimport numpy as np from collections import defaultdict """ Compiler directives: https://github.com/cython/cython/wiki/enhancements-compilerdirectives Cython tutorial: https://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html Credit to https://github.com/luzai """ # Main interface cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False): distmat = np.asarray(distmat, dtype=np.float32) q_pids = np.asarray(q_pids, dtype=np.int64) g_pids = np.asarray(g_pids, dtype=np.int64) q_camids = np.asarray(q_camids, dtype=np.int64) g_camids = np.asarray(g_camids, dtype=np.int64) if use_metric_cuhk03: return eval_cuhk03_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank) return eval_market1501_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank) cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids, long[:]q_camids, long[:]g_camids, long max_rank): cdef long num_q = distmat.shape[0] cdef long num_g = distmat.shape[1] if num_g < max_rank: max_rank = num_g print('Note: number of gallery samples is quite small, got {}'.format(num_g)) cdef: long num_repeats = 10 long[:,:] indices = np.argsort(distmat, axis=1) long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64) float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32) float[:] all_AP = np.zeros(num_q, dtype=np.float32) float num_valid_q = 0. # number of valid query long q_idx, q_pid, q_camid, g_idx long[:] order = np.zeros(num_g, dtype=np.int64) long keep float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches float[:] masked_raw_cmc = np.zeros(num_g, dtype=np.float32) float[:] cmc, masked_cmc long num_g_real, num_g_real_masked, rank_idx, rnd_idx unsigned long meet_condition float AP long[:] kept_g_pids, mask float num_rel float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32) float tmp_cmc_sum 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 for g_idx in range(num_g): order[g_idx] = indices[q_idx, g_idx] num_g_real = 0 meet_condition = 0 kept_g_pids = np.zeros(num_g, dtype=np.int64) for g_idx in range(num_g): if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid): raw_cmc[num_g_real] = matches[q_idx][g_idx] kept_g_pids[num_g_real] = g_pids[order[g_idx]] num_g_real += 1 if matches[q_idx][g_idx] > 1e-31: meet_condition = 1 if not meet_condition: # this condition is true when query identity does not appear in gallery continue # cuhk03-specific setting g_pids_dict = defaultdict(list) # overhead! for g_idx in range(num_g_real): g_pids_dict[kept_g_pids[g_idx]].append(g_idx) cmc = np.zeros(max_rank, dtype=np.float32) for _ in range(num_repeats): mask = np.zeros(num_g_real, dtype=np.int64) for _, idxs in g_pids_dict.items(): # randomly sample one image for each gallery person rnd_idx = np.random.choice(idxs) #rnd_idx = idxs[0] # use deterministic for debugging mask[rnd_idx] = 1 num_g_real_masked = 0 for g_idx in range(num_g_real): if mask[g_idx] == 1: masked_raw_cmc[num_g_real_masked] = raw_cmc[g_idx] num_g_real_masked += 1 masked_cmc = np.zeros(num_g, dtype=np.float32) function_cumsum(masked_raw_cmc, masked_cmc, num_g_real_masked) for g_idx in range(num_g_real_masked): if masked_cmc[g_idx] > 1: masked_cmc[g_idx] = 1 for rank_idx in range(max_rank): cmc[rank_idx] += masked_cmc[rank_idx] / num_repeats for rank_idx in range(max_rank): all_cmc[q_idx, rank_idx] = cmc[rank_idx] # compute average precision # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision function_cumsum(raw_cmc, tmp_cmc, num_g_real) num_rel = 0 tmp_cmc_sum = 0 for g_idx in range(num_g_real): tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx] num_rel += raw_cmc[g_idx] all_AP[q_idx] = tmp_cmc_sum / num_rel num_valid_q += 1. assert num_valid_q > 0, 'Error: all query identities do not appear in gallery' # compute averaged cmc cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32) for rank_idx in range(max_rank): for q_idx in range(num_q): avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx] avg_cmc[rank_idx] /= num_valid_q cdef float mAP = 0 for q_idx in range(num_q): mAP += all_AP[q_idx] mAP /= num_valid_q return np.asarray(avg_cmc).astype(np.float32), mAP cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids, long[:]q_camids, long[:]g_camids, long max_rank): cdef long num_q = distmat.shape[0] cdef long num_g = distmat.shape[1] if num_g < max_rank: max_rank = num_g print('Note: number of gallery samples is quite small, got {}'.format(num_g)) cdef: long[:,:] indices = np.argsort(distmat, axis=1) long[:] matches float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32) float[:] all_AP = np.zeros(num_q, dtype=np.float32) float[:] all_INP = np.zeros(num_q, dtype=np.float32) float num_valid_q = 0. # number of valid query long valid_index = 0 long q_idx, q_pid, q_camid, g_idx long[:] order = np.zeros(num_g, dtype=np.int64) long keep float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches float[:] cmc = np.zeros(num_g, dtype=np.float32) long max_pos_idx = 0 float inp long num_g_real, rank_idx unsigned long meet_condition float num_rel float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32) float tmp_cmc_sum for q_idx in range(num_q): # get query pid and camid q_pid = q_pids[q_idx] q_camid = q_camids[q_idx] for g_idx in range(num_g): order[g_idx] = indices[q_idx, g_idx] num_g_real = 0 meet_condition = 0 matches = (np.asarray(g_pids)[np.asarray(order)] == q_pid).astype(np.int64) # remove gallery samples that have the same pid and camid with query for g_idx in range(num_g): if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid): raw_cmc[num_g_real] = matches[g_idx] num_g_real += 1 # this condition is true if query appear in gallery if matches[g_idx] > 1e-31: meet_condition = 1 if not meet_condition: # this condition is true when query identity does not appear in gallery continue # compute cmc function_cumsum(raw_cmc, cmc, num_g_real) # compute mean inverse negative penalty # reference : https://github.com/mangye16/ReID-Survey/blob/master/utils/reid_metric.py max_pos_idx = 0 for g_idx in range(num_g_real): if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx): max_pos_idx = g_idx inp = cmc[max_pos_idx] / (max_pos_idx + 1.0) all_INP[valid_index] = inp for g_idx in range(num_g_real): if cmc[g_idx] > 1: cmc[g_idx] = 1 for rank_idx in range(max_rank): all_cmc[q_idx, rank_idx] = cmc[rank_idx] num_valid_q += 1. # compute average precision # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision function_cumsum(raw_cmc, tmp_cmc, num_g_real) num_rel = 0 tmp_cmc_sum = 0 for g_idx in range(num_g_real): tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx] num_rel += raw_cmc[g_idx] all_AP[valid_index] = tmp_cmc_sum / num_rel valid_index += 1 assert num_valid_q > 0, 'Error: all query identities do not appear in gallery' # compute averaged cmc cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32) for rank_idx in range(max_rank): for q_idx in range(num_q): avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx] avg_cmc[rank_idx] /= num_valid_q return np.asarray(avg_cmc).astype(np.float32), np.asarray(all_AP[:valid_index]), np.asarray(all_INP[:valid_index]) # Compute the cumulative sum cdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n): cdef long i dst[0] = src[0] for i in range(1, n): dst[i] = src[i] + dst[i - 1]