def load_detector(run_id):
run_dir = EXP_DIR / run_id
cfg = yaml.load((run_dir / 'config.yaml').read_text(), Loader=yaml.FullLoader)
cfg = check_update_config(cfg)
label_to_category_id = cfg.label_to_category_id
model = create_model_detector(cfg, len(label_to_category_id))
ckpt = torch.load(run_dir / 'checkpoint.pth.tar')
ckpt = ckpt['state_dict']
model.load_state_dict(ckpt)
model = model.cuda().eval()
model.cfg = cfg
model.config = cfg
model = Detector(model)
return model
def make_eval_configs(args, model_training, epoch):
model = model_training.module
model.config = args
model.cfg = args
detector = Detector(model)
configs = []
for ds_name in args.test_ds_names:
cfg = argparse.ArgumentParser('').parse_args([])
cfg.ds_name = ds_name
cfg.save_dir = args.save_dir / f'dataset={ds_name}/epoch={epoch}'
cfg.n_workers = args.n_dataloader_workers
cfg.pred_bsz = 16
cfg.eval_bsz = 16
cfg.n_frames = None
cfg.skip_evaluation = False
cfg.skip_model_predictions = False
cfg.external_predictions = True
cfg.n_frames = args.n_test_frames
configs.append(cfg)
return configs, detector