| 88 | |
| 89 | |
| 90 | class R1_mAP_eval(): |
| 91 | def __init__(self, num_query, max_rank=50, feat_norm=True, reranking=False): |
| 92 | super(R1_mAP_eval, self).__init__() |
| 93 | self.num_query = num_query |
| 94 | self.max_rank = max_rank |
| 95 | self.feat_norm = feat_norm |
| 96 | self.reranking = reranking |
| 97 | |
| 98 | def reset(self): |
| 99 | self.feats = [] |
| 100 | self.pids = [] |
| 101 | self.camids = [] |
| 102 | |
| 103 | def update(self, output): # called once for each batch |
| 104 | feat, pid, camid = output |
| 105 | self.feats.append(feat.cpu()) |
| 106 | self.pids.extend(np.asarray(pid)) |
| 107 | self.camids.extend(np.asarray(camid)) |
| 108 | |
| 109 | def compute(self): # called after each epoch |
| 110 | feats = torch.cat(self.feats, dim=0) |
| 111 | if self.feat_norm: |
| 112 | print("The test feature is normalized") |
| 113 | feats = torch.nn.functional.normalize(feats, dim=1, p=2) # along channel |
| 114 | # query |
| 115 | qf = feats[:self.num_query] |
| 116 | q_pids = np.asarray(self.pids[:self.num_query]) |
| 117 | q_camids = np.asarray(self.camids[:self.num_query]) |
| 118 | # gallery |
| 119 | gf = feats[self.num_query:] |
| 120 | g_pids = np.asarray(self.pids[self.num_query:]) |
| 121 | |
| 122 | g_camids = np.asarray(self.camids[self.num_query:]) |
| 123 | if self.reranking: |
| 124 | print('=> Enter reranking') |
| 125 | # distmat = re_ranking(qf, gf, k1=20, k2=6, lambda_value=0.3) |
| 126 | distmat = re_ranking(qf, gf, k1=50, k2=15, lambda_value=0.3) |
| 127 | |
| 128 | else: |
| 129 | print('=> Computing DistMat with euclidean_distance') |
| 130 | distmat = euclidean_distance(qf, gf) |
| 131 | cmc, mAP = eval_func(distmat, q_pids, g_pids, q_camids, g_camids) |
| 132 | |
| 133 | return cmc, mAP, distmat, self.pids, self.camids, qf, gf |