def eval(): net.is_train = False net.eval() print("Evaluating......") query = np.concatenate([ net(inputs.to(device)).detach().cpu().numpy() for inputs, _ in queryloader ]) test = np.concatenate([ net(inputs.to(device)).detach().cpu().numpy() for inputs, _ in testloader ]) dist = cdist(query, test) r = eval_tools.cmc(dist, query_set.ids, test_set.ids, query_set.cameras, test_set.cameras, separate_camera_set=False, single_gallery_shot=False, first_match_break=True, same_cam_valid=True) m_ap = eval_tools.mean_ap(dist, query_set.ids, test_set.ids, query_set.cameras, test_set.cameras, same_cam_valid=True) print( colored( 'model:%s mAP=%f, r@1=%f, r@3=%f, r@5=%f, r@10=%f' % (os.path.basename(args.checkpoint), m_ap, r[0], r[2], r[4], r[9]), "red"))
def eval(epoch): net.is_train = False net.eval() query = np.concatenate([ net(inputs.to(device)).detach().cpu().numpy() for inputs, _ in queryloader ]) test = np.concatenate([ net(inputs.to(device)).detach().cpu().numpy() for inputs, _ in testloader ]) dist = cdist(query, test) r = eval_tools.cmc(dist, data.query.ids, data.test.ids, data.query.cameras, data.test.cameras, separate_camera_set=False, single_gallery_shot=False, first_match_break=True, same_cam_valid=True) m_ap = eval_tools.mean_ap(dist, data.query.ids, data.test.ids, data.query.cameras, data.test.cameras, same_cam_valid=True) print( colored( 'epoch[%d]: mAP=%f, r@1=%f, r@3=%f, r@5=%f, r@10=%f' % (epoch + 1, m_ap, r[0], r[2], r[4], r[9]), "yellow"))