Changed cython types from long to int64_t to support Windows
Carmel Barak
parent
57dad66ce7
commit
7cedf08a5b
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@ -2,6 +2,7 @@
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from __future__ import print_function
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import numpy as np
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from libc.stdint cimport int64_t, uint64_t
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import cython
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@ -33,41 +34,41 @@ cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_met
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return eval_market1501_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
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cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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long[:]q_camids, long[:]g_camids, long max_rank):
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cdef long num_q = distmat.shape[0]
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cdef long num_g = distmat.shape[1]
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cpdef eval_cuhk03_cy(float[:,:] distmat, int64_t[:] q_pids, int64_t[:]g_pids,
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int64_t[:]q_camids, int64_t[:]g_camids, int64_t max_rank):
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cdef int64_t num_q = distmat.shape[0]
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cdef int64_t num_g = distmat.shape[1]
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if num_g < max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.format(num_g))
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cdef:
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long num_repeats = 10
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long[:,:] indices = np.argsort(distmat, axis=1)
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long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
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int64_t num_repeats = 10
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int64_t[:,:] indices = np.argsort(distmat, axis=1)
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int64_t[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
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float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
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float[:] all_AP = np.zeros(num_q, dtype=np.float32)
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float num_valid_q = 0. # number of valid query
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long q_idx, q_pid, q_camid, g_idx
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long[:] order = np.zeros(num_g, dtype=np.int64)
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long keep
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int64_t q_idx, q_pid, q_camid, g_idx
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int64_t[:] order = np.zeros(num_g, dtype=np.int64)
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int64_t keep
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float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
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float[:] masked_raw_cmc = np.zeros(num_g, dtype=np.float32)
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float[:] cmc, masked_cmc
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long num_g_real, num_g_real_masked, rank_idx, rnd_idx
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unsigned long meet_condition
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int64_t num_g_real, num_g_real_masked, rank_idx, rnd_idx
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uint64_t meet_condition
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float AP
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long[:] kept_g_pids, mask
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int64_t[:] kept_g_pids, mask
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float num_rel
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float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
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float tmp_cmc_sum
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for q_idx in range(num_q):
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# get query pid and camid
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q_pid = q_pids[q_idx]
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@ -87,7 +88,7 @@ cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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num_g_real += 1
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if matches[q_idx][g_idx] > 1e-31:
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meet_condition = 1
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if not meet_condition:
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# this condition is true when query identity does not appear in gallery
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continue
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@ -100,7 +101,7 @@ cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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cmc = np.zeros(max_rank, dtype=np.float32)
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for _ in range(num_repeats):
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mask = np.zeros(num_g_real, dtype=np.int64)
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for _, idxs in g_pids_dict.items():
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# randomly sample one image for each gallery person
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rnd_idx = np.random.choice(idxs)
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@ -121,7 +122,7 @@ cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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for rank_idx in range(max_rank):
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cmc[rank_idx] += masked_cmc[rank_idx] / num_repeats
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for rank_idx in range(max_rank):
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all_cmc[q_idx, rank_idx] = cmc[rank_idx]
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# compute average precision
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@ -143,7 +144,7 @@ cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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for q_idx in range(num_q):
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avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
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avg_cmc[rank_idx] /= num_valid_q
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cdef float mAP = 0
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for q_idx in range(num_q):
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mAP += all_AP[q_idx]
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@ -152,37 +153,37 @@ cpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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return np.asarray(avg_cmc).astype(np.float32), mAP
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cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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long[:]q_camids, long[:]g_camids, long max_rank):
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cdef long num_q = distmat.shape[0]
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cdef long num_g = distmat.shape[1]
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cpdef eval_market1501_cy(float[:,:] distmat, int64_t[:] q_pids, int64_t[:]g_pids,
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int64_t[:]q_camids, int64_t[:]g_camids, int64_t max_rank):
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cdef int64_t num_q = distmat.shape[0]
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cdef int64_t num_g = distmat.shape[1]
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if num_g < max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.format(num_g))
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cdef:
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long[:,:] indices = np.argsort(distmat, axis=1)
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long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
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int64_t[:,:] indices = np.argsort(distmat, axis=1)
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int64_t[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)
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float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)
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float[:] all_AP = np.zeros(num_q, dtype=np.float32)
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float num_valid_q = 0. # number of valid query
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long q_idx, q_pid, q_camid, g_idx
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long[:] order = np.zeros(num_g, dtype=np.int64)
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long keep
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int64_t q_idx, q_pid, q_camid, g_idx
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int64_t[:] order = np.zeros(num_g, dtype=np.int64)
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int64_t keep
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float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches
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float[:] cmc = np.zeros(num_g, dtype=np.float32)
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long num_g_real, rank_idx
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unsigned long meet_condition
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int64_t num_g_real, rank_idx
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uint64_t meet_condition
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float num_rel
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float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)
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float tmp_cmc_sum
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for q_idx in range(num_q):
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# get query pid and camid
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q_pid = q_pids[q_idx]
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@ -193,14 +194,14 @@ cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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order[g_idx] = indices[q_idx, g_idx]
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num_g_real = 0
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meet_condition = 0
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for g_idx in range(num_g):
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if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):
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raw_cmc[num_g_real] = matches[q_idx][g_idx]
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num_g_real += 1
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if matches[q_idx][g_idx] > 1e-31:
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meet_condition = 1
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if not meet_condition:
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# this condition is true when query identity does not appear in gallery
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continue
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@ -233,7 +234,7 @@ cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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for q_idx in range(num_q):
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avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]
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avg_cmc[rank_idx] /= num_valid_q
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cdef float mAP = 0
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for q_idx in range(num_q):
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mAP += all_AP[q_idx]
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@ -243,8 +244,8 @@ cpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,
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# Compute the cumulative sum
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cdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n):
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cdef long i
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cdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, int64_t n):
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cdef int64_t i
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dst[0] = src[0]
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for i in range(1, n):
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dst[i] = src[i] + dst[i - 1]
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