def rank(dist): r = cmc(dist, queryset.ids, testset.ids, queryset.cameras, testset.cameras, separate_camera_set=False, single_gallery_shot=False, first_match_break=True) m_ap = mean_ap(dist, queryset.ids, testset.ids, queryset.cameras, testset.cameras) return r, m_ap
def rank(dist): r = cmc(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras, self.queryset.clothes, self.testset.clothes, separate_camera_set=False, single_gallery_shot=False, first_match_break=True, cloth_changing_settings=False) m_ap = mean_ap( dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras, self.queryset.clothes, self.testset.clothes, cloth_changing_settings=False) return r, m_ap
def evaluate(self): self.model.eval() print('extract features, this may take a few minutes') qf = extract_feature(self.model, tqdm(self.query_loader)).numpy() gf = extract_feature(self.model, tqdm(self.test_loader)).numpy() ######################### re rank########################## q_g_dist = np.dot(qf, np.transpose(gf)) q_q_dist = np.dot(qf, np.transpose(qf)) g_g_dist = np.dot(gf, np.transpose(gf)) dist = re_ranking(q_g_dist, q_q_dist, g_g_dist) queryset = self.queryset testset = self.testset m_ap = mean_ap(dist, queryset, testset) print('[With Re-Ranking] mAP: {:.4f}'.format(m_ap)) #########################no re rank########################## dist = cdist(qf, gf) m_ap = mean_ap(dist, queryset, testset) print('[Without Re-Ranking] mAP: {:.4f}'.format(m_ap))