def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model = LocNet(cfg) # model.apply(weight_init) model.cuda() # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth' # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2545.pth' # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet13_checkpoint_best_cas_epoch35_map_0.2348.pth' # weight_file = '' weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_inv_0_save_model/checkpoint_best_cas_inv0_epoch69_0.2636.pth' # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_save_model/checkpoint_best_cas_only_cas_epoch134_0.1957.pth' # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_individual_attention_2048k1_2048k1_2048k1_only_cam_svae_model/checkpoint_best_cas_only_cam_epoch96_0.1714.pth' res_dir = os.path.join(cfg.BASIC.CKPT_DIR, cfg.TEST.RESULT_DIR, 'vis/cas_gt_idx_minmax_norm_std') if not os.path.exists(res_dir): os.makedirs(res_dir) from utils.utils import load_weights model = load_weights(model, weight_file) epoch = 600 output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate( cfg, val_loader, model, epoch) evaluate_mAP(cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE) evaluate_mAP(cfg, output_json_file_cam, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE) is_minmax_norm = True evaluate_vis_cas_minmax_norm_std(cfg, val_loader, model, res_dir, is_minmax_norm)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project # prepare_env(cfg) cas_dir = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/000_thumos_29.13_save_model_Frame_wise_accuracy/save_for_post_process" epoch = 901 datas = list() file_name_list = os.listdir(cas_dir) for file_name in file_name_list: data = np.load(os.path.join(cas_dir, file_name)) datas.append(data) output_json_file_cas, test_acc_cas = evaluate_from_offline_cas( cfg, datas, epoch) if cfg.BASIC.VERBOSE: print('test_acc, cas %f' % (test_acc_cas)) mAP, average_mAP = evaluate_mAP( cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project # prepare_env(cfg) base_branch_json = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/075_cas.json' cam_branch_json = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/089_cam.json' evaluate_mAP(cfg, base_branch_json, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE) evaluate_mAP(cfg, cam_branch_json, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE) base_branch_data = json.load(open(base_branch_json, 'r')) base_branch_results = base_branch_data['results'] cam_branch_data = json.load(open(cam_branch_json, 'r')) cam_branch_results = cam_branch_data['results'] for vid_name in cam_branch_results.keys(): cam_branch_results[vid_name].extend(base_branch_results[vid_name]) output_dict = {'version': 'VERSION 1.3', 'results': cam_branch_results, 'external_data': {}} result_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/cat.json' outfile = open(result_file, 'w') json.dump(output_dict, outfile) outfile.close() evaluate_mAP(cfg, result_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model = LocNet(cfg) # model.apply(weight_init) model.cuda() # weight_file = '' weight_file = '/disk/yangle/Short-Actions/ECM/output/thumos14/ECM_baseline/checkpoint_best_150.pth' res_dir = os.path.join(cfg.BASIC.CKPT_DIR, cfg.TEST.RESULT_DIR, 'vis/ECM_thumos_score') if not os.path.exists(res_dir): os.makedirs(res_dir) from utils.utils import load_weights model = load_weights(model, weight_file) epoch = 600 # output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch) output_json_file_cas = '/disk/yangle/Short-Actions/ECM/output/thumos14/ECM_baseline/vis/ecm.json' evaluate_mAP(cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project # prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model_cas = LocNet(cfg) # model.apply(weight_init) model_cam = LocNet(cfg) model_cas.cuda() model_cam.cuda() # weight_file = "" # weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth" # weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2394.pth" # weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet13/anet13_same_as_anet12_seed1_epoch45_TOPK_K_R_0.6_LR_DECAY26_save_every_model/checkpoint_best_cas_epoch35.pth" weight_file_cas = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model/checkpoint_best_cas_epoch75_0.2055.pth" weight_file_cam = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model/checkpoint_best_cam_epoch89_0.176.pth" from utils.utils import load_weights model_cas = load_weights(model_cas, weight_file_cas) model_cam = load_weights(model_cam, weight_file_cam) epoch = 911 output_json_file_cas, test_acc_cas = evaluate_fuse_sequence( cfg, val_loader, model_cas, model_cam, epoch) # output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch) if cfg.BASIC.VERBOSE: print('test_acc, cas %f' % (test_acc_cas)) mAP, average_mAP = evaluate_mAP( cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model = LocNet(cfg) # model.apply(weight_init) model.cuda() # weight_file = "" weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth" # weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2394.pth" # weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet13_checkpoint_best_cas_epoch35_map_0.2348.pth" epoch = 801 from utils.utils import load_weights model = load_weights(model, weight_file) # actions_json_file = evaluate(cfg, val_loader, model, epoch) # # evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE)) # output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(cfg, val_loader, model, epoch) output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch) if cfg.BASIC.VERBOSE: print('test_acc, cas %f' % (test_acc_cas)) mAP, average_mAP = evaluate_mAP( cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project # prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model = LocNet(cfg) # model.apply(weight_init) model.cuda() # weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet12/anet12_0.2350_cam_inv_1_seed7_epoch36_TOPK_K_R_0.25__save_model_LR_decay/anet12_checkpoint_best_cas_0.2394.pth' weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet13/anet13_same_as_anet12_seed1_epoch45_TOPK_K_R_0.6_LR_DECAY26_save_every_model/anet13_checkpoint_best_cas_epoch28_0.2178.pth" epoch = 603 from utils.utils import load_weights model = load_weights(model, weight_file) # actions_json_file = evaluate(cfg, val_loader, model, epoch) # # evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE)) # output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(cfg, val_loader, model, epoch) output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch) if cfg.BASIC.VERBOSE: print('test_acc, cas %f' % (test_acc_cas)) mAP, average_mAP = evaluate_mAP( cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
def post_process(cfg, actions_json_file, writer, best_mAP, info, epoch, name): mAP, average_mAP = evaluate_mAP( cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE) for i in range(len(cfg.TEST.IOU_TH)): writer.add_scalar('z_mAP@{}/{}'.format(cfg.TEST.IOU_TH[i], name), mAP[i], epoch) writer.add_scalar('Average mAP/{}'.format(name), average_mAP, epoch) if cfg.DATASET.NAME == "THUMOS14": # use [email protected] as the metric mAP_5 = mAP[4] if mAP_5 > best_mAP: best_mAP = mAP_5 info = [epoch, average_mAP, mAP] elif cfg.DATASET.NAME == "ActivityNet1.3" or cfg.DATASET.NAME == "ActivityNet1.2": if average_mAP > best_mAP: best_mAP = average_mAP info = [epoch, average_mAP, mAP] return writer, best_mAP, info
def main(): args = args_parser() update_config(args.cfg) if cfg.BASIC.SHOW_CFG: pprint.pprint(cfg) # prepare running environment for the whole project # prepare_env(cfg) # dataloader val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT) val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY) # network model = LocNet(cfg) # model.apply(weight_init) model.cuda() # weight_file = "" weight_file = "/disk/yangle/Short-Actions/ECM/output/thumos14/ECM_baseline/checkpoint_best_150.pth" epoch = 601 from utils.utils import load_weights model = load_weights(model, weight_file) # actions_json_file = evaluate(cfg, val_loader, model, epoch) # # evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE)) # output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(cfg, val_loader, model, epoch) output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch) if cfg.BASIC.VERBOSE: print('test_acc, cas %f' % (test_acc_cas)) mAP, average_mAP = evaluate_mAP(cfg, output_json_file_cas, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)