import torch import numpy as np import torch.nn.functional as F from opengait.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