先展示S-test函数
@torch.no_grad()
def S_test(net, args, test_loader, logger, step, test_info, subset='test'):net.eval()snippet_result = {}snippet_result['version'] = 'VERSION 1.3'snippet_result['results'] = {}snippet_result['external_data'] = {'used': True, 'details': 'Features from I3D Network'}if subset == 'train':snippet_result['bkg_score'] = {}num_correct = 0.num_total = 0.for sample in tqdm(test_loader):_data, _vid_label, _vid_name, _vid_len, _vid_duration = sample['data'], sample['vid_label'], sample['vid_name'], sample['vid_len'], sample['vid_duration']outputs = net(_data.to(args.device))_vid_score, _cas_fuse = outputs['vid_score'], outputs['cas_fuse']for b in range(_data.shape[0]):vid_name = _vid_name[b]vid_len = _vid_len[b].item()vid_duration = _vid_duration[b].item()# >> caculate video-level predictionlabel_np = _vid_label[b].unsqueeze(0).numpy()score_np = _vid_score[b].cpu().numpy()pred_np = np.zeros_like(score_np)pred_np[np.where(score_np < args.class_thresh)] = 0pred_np[np.where(score_np >= args.class_thresh)] = 1if pred_np.sum() == 0:pred_np[np.argmax(score_np)] = 1correct_pred = np.sum(label_np == pred_np, axis=1)num_correct += np.sum((correct_pred == args.num_class).astype(np.float32))num_total += correct_pred.shape[0]# >> post-processcas_fuse = _cas_fuse[b]num_segments = _data[b].shape[0]# class-specific scorecas_S = cas_fuse[:, :-1]pred = np.where(score_np >= args.class_thresh)[0]if len(pred) == 0:pred = np.array([np.argmax(score_np)])cas_pred = cas_S.cpu().numpy()[:, pred] cas_pred = np.reshape(cas_pred, (num_segments, -1, 1))cas_pred = utils.upgrade_resolution(cas_pred, args.scale)# class-agnostic scoreagnostic_score = 1 - cas_fuse[:, -1].unsqueeze(1)agnostic_score = agnostic_score.expand((-1, args.num_class))agnostic_score = agnostic_score.cpu().numpy()[:, pred]agnostic_score = np.reshape(agnostic_score, (num_segments, -1, 1))agnostic_score = utils.upgrade_resolution(agnostic_score, args.scale)# >> save outputif subset == 'train':snippet_result['bkg_score'][vid_name] = cas_fuse[:, -1].cpu().numpy()# >> generate proposalsproposal_dict = {}for i in range(len(args.act_thresh_cas)):cas_temp = cas_pred.copy()zero_location = np.where(cas_temp[:, :, 0] < args.act_thresh_cas[i])cas_temp[zero_location] = 0seg_list = []for c in range(len(pred)):pos = np.where(cas_temp[:, c, 0] > 0)seg_list.append(pos)proposals = utils.get_proposal_oic(args, seg_list, cas_temp, score_np, pred, vid_len, num_segments, vid_duration)for i in range(len(proposals)):class_id = proposals[i][0][2]if class_id not in proposal_dict.keys():proposal_dict[class_id] = []proposal_dict[class_id] += proposals[i]for i in range(len(args.act_thresh_agnostic)):cas_temp = cas_pred.copy()agnostic_score_temp = agnostic_score.copy()zero_location = np.where(agnostic_score_temp[:, :, 0] < args.act_thresh_agnostic[i])agnostic_score_temp[zero_location] = 0seg_list = []for c in range(len(pred)):pos = np.where(agnostic_score_temp[:, c, 0] > 0)seg_list.append(pos)proposals = utils.get_proposal_oic(args, seg_list, cas_temp, score_np, pred, vid_len, num_segments, vid_duration)for i in range(len(proposals)):class_id = proposals[i][0][2]if class_id not in proposal_dict.keys():proposal_dict[class_id] = []proposal_dict[class_id] += proposals[i]if args.mode == 'train' or args.mode == 'infer':final_proposals = utils.post_process(args, vid_name, proposal_dict, test_loader)else:final_proposals = []for class_id in proposal_dict.keys():temp_proposal = proposal_dict[class_id]final_proposals += temp_proposalfinal_proposals = utils.result2json(args, final_proposals)snippet_result['results'][vid_name] = final_proposalsjson_path = os.path.join(args.output_path_s1, 'snippet_result_{}.json'.format(subset, args.seed))with open(json_path, 'w') as f:json.dump(snippet_result, f, cls=NumpyArrayEncoder)if args.mode == 'train' or args.mode == 'infer':test_acc = num_correct / num_totalprint("TEST ACC:{:.4f}".format(test_acc))test_map = log_evaluate(args, step, test_acc, logger, json_path, test_info, subset)return test_map
初始化了snippet_result = {}，用于存放结果。
_data, _vid_label, _vid_name, _vid_len, _vid_duration = sample['data'], sample['vid_label'], sample['vid_name'], sample['vid_len'], sample['vid_duration']
outputs = net(_data.to(args.device))
_vid_score, _cas_fuse = outputs['vid_score'], outputs['cas_fuse']
模型推理得到输出，和训练的区别是
vid_score的计算方式有所不同
if vid_labels is None:vid_score = torch.mean(topk_scores, dim=1)else:vid_score = (torch.mean(topk_scores, dim=1) * vid_labels) + \(torch.mean
