--- a
+++ b/opengait/evaluation/metric.py
@@ -0,0 +1,208 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+
+from utils import is_tensor
+
+
+def cuda_dist(x, y, metric='euc'):
+    x = torch.from_numpy(x).cuda()
+    y = torch.from_numpy(y).cuda()
+    if metric == 'cos':
+        x = F.normalize(x, p=2, dim=1)  # n c p
+        y = F.normalize(y, p=2, dim=1)  # n c p
+    num_bin = x.size(2)
+    n_x = x.size(0)
+    n_y = y.size(0)
+    dist = torch.zeros(n_x, n_y).cuda()
+    for i in range(num_bin):
+        _x = x[:, :, i]
+        _y = y[:, :, i]
+        if metric == 'cos':
+            dist += torch.matmul(_x, _y.transpose(0, 1))
+        else:
+            _dist = torch.sum(_x ** 2, 1).unsqueeze(1) + torch.sum(_y ** 2, 1).unsqueeze(
+                0) - 2 * torch.matmul(_x, _y.transpose(0, 1))
+            dist += torch.sqrt(F.relu(_dist))
+    return 1 - dist/num_bin if metric == 'cos' else dist / num_bin
+
+
+def mean_iou(msk1, msk2, eps=1.0e-9):
+    if not is_tensor(msk1):
+        msk1 = torch.from_numpy(msk1).cuda()
+    if not is_tensor(msk2):
+        msk2 = torch.from_numpy(msk2).cuda()
+    n = msk1.size(0)
+    inter = msk1 * msk2
+    union = ((msk1 + msk2) > 0.).float()
+    miou = inter.view(n, -1).sum(-1) / (union.view(n, -1).sum(-1) + eps)
+    return miou
+
+
+def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1):
+    num_q, _ = distmat.shape
+    # indices = np.argsort(distmat, axis=1)
+    # matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    all_ACC = []
+    all_AP = []
+    num_valid_q = 0.  # number of valid query
+    for q_idx in range(num_q):
+        q_idx_dist = distmat[q_idx]
+        q_idx_glabels = g_pids
+        if q_views is not None and g_views is not None:
+            q_idx_mask = np.isin(g_views, q_views[q_idx], invert=True) | np.isin(
+                g_pids, q_pids[q_idx], invert=True)
+            q_idx_dist = q_idx_dist[q_idx_mask]
+            q_idx_glabels = q_idx_glabels[q_idx_mask]
+
+        assert(len(q_idx_glabels) >
+               0), "No gallery after excluding identical-view cases!"
+        q_idx_indices = np.argsort(q_idx_dist)
+        q_idx_matches = (q_idx_glabels[q_idx_indices]
+                         == q_pids[q_idx]).astype(np.int32)
+
+        # binary vector, positions with value 1 are correct matches
+        # orig_cmc = matches[q_idx]
+        orig_cmc = q_idx_matches
+        cmc = orig_cmc.cumsum()
+        cmc[cmc > 1] = 1
+        all_ACC.append(cmc[rank-1])
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = orig_cmc.sum()
+
+        if num_rel > 0:
+            num_valid_q += 1.
+            tmp_cmc = orig_cmc.cumsum()
+            tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
+            tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+            AP = tmp_cmc.sum() / num_rel
+            all_AP.append(AP)
+
+    # all_ACC = np.asarray(all_ACC).astype(np.float32)
+    ACC = np.mean(all_ACC)
+    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()
+        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
+
+
+def evaluate_many(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
+    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)   # 对应位置变成从小到大的序号
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(
+        np.int32)  # 根据indices调整顺序 g_pids[indices]
+    # print(matches)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    all_INP = []
+    num_valid_q = 0.
+    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
+        # binary vector, positions with value 1 are correct matches
+        orig_cmc = matches[q_idx][keep]
+        if not np.any(orig_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        cmc = orig_cmc.cumsum()
+
+        pos_idx = np.where(orig_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 = orig_cmc.sum()
+        tmp_cmc = orig_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * orig_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