def main(): args = parse_args() cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # update configs according to CLI args if args.work_dir is not None: cfg.work_dir = args.work_dir if args.resume_from is not None: cfg.resume_from = args.resume_from cfg.gpus = args.gpus # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # init logger before other steps logger = get_root_logger(cfg.log_level) logger.info('Distributed training: {}'.format(distributed)) # set random seeds if args.seed is not None: logger.info('Set random seed to {}'.format(args.seed)) set_random_seed(args.seed) model = build_detector(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) train_dataset = build_dataset(cfg.data.train) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict(mmdet_version=__version__, config=cfg.text, CLASSES=train_dataset.CLASSES) # add an attribute for visualization convenience model.CLASSES = train_dataset.CLASSES train_detector(model, train_dataset, cfg, distributed=distributed, validate=args.validate, logger=logger)
def main(): args = parse_args() if args.out is not None and not args.out.endswith(('.json', '.pickle')): raise ValueError('The output file must be a pkl file.') for i in range(args.checkpoint_start, args.checkpoint_end): cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if not args.mean_teacher: while not osp.exists(args.checkpoint + str(i) + '.pth'): time.sleep(5) while i+1 != args.checkpoint_end and not osp.exists(args.checkpoint + str(i+1) + '.pth'): time.sleep(5) checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth', map_location='cpu') else: while not osp.exists(args.checkpoint + str(i) + '.pth.stu'): time.sleep(5) while i+1 != args.checkpoint_end and not osp.exists(args.checkpoint + str(i+1) + '.pth.stu'): time.sleep(5) checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth.stu', map_location='cpu') checkpoint['meta'] = dict() # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.save_img, args.save_img_dir) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) res = [] for id, boxes in enumerate(outputs): boxes=boxes[0] if type(boxes) == list: boxes = boxes[0] boxes[:, [2, 3]] -= boxes[:, [0, 1]] if len(boxes) > 0: for box in boxes: # box[:4] = box[:4] / 0.6 temp = dict() temp['image_id'] = id+1 temp['category_id'] = 1 temp['bbox'] = box[:4].tolist() temp['score'] = float(box[4]) res.append(temp) with open(args.out, 'w') as f: json.dump(res, f) MRs = validate('datasets/crowdhuman/validation.json', args.out) print(MRs) print('Checkpoint %d: [Reasonable: %.2f%%], [Bare: %.2f%%], [Partial: %.2f%%], [Heavy: %.2f%%]' % (i, MRs[0] * 100, MRs[1] * 100, MRs[2] * 100, MRs[3] * 100))
def main(): args = parse_args() if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.save_img, args.save_img_dir) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) eval_types = args.eval if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = args.out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_files = results2json(dataset, outputs, args.out) coco_eval(result_files, eval_types, dataset.coco) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = args.out + '.{}'.format(name) result_files = results2json(dataset, outputs_, result_file) coco_eval(result_files, eval_types, dataset.coco)