add Gait3D support

opengait/evaluation/metric.py

@@ -86,3 +86,73 @@ def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1)
     mAP = np.mean(all_AP)
 
     return ACC, mAP
+
+
+def evaluate_rank(distmat, p_lbls, g_lbls, max_rank=50):
+    '''
+    Copy from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/utils/rank.py#L12-L63
+    '''
+    num_p, 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_lbls[indices] == p_lbls[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each probe
+    all_cmc = []
+    all_AP = []
+    all_INP = []
+    num_valid_p = 0.  # number of valid probe
+
+    for p_idx in range(num_p):
+        # compute cmc curve
+        # binary vector, positions with value 1 are correct matches
+        raw_cmc = matches[p_idx]
+        if not np.any(raw_cmc):
+            # this condition is true when probe identity does not appear in gallery
+            continue
+
+        cmc = raw_cmc.cumsum()
+
+        pos_idx = np.where(raw_cmc == 1)    # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index
+        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_p += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = raw_cmc.sum()
+        pos_idx = np.where(raw_cmc == 1)    # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index
+        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_p += 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_p > 0, 'Error: all probe identities do not appear in gallery'
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_p
+
+    return all_cmc, all_AP, all_INP