def main(argv): if not len(argv) >= 2: print('Usage: ./trainer.py <config_file, e.g., configs/bottle_0_t5> ' '[model_dir (/tmp/model)]') exit(0) config_file = argv[1] if len(argv) > 2: model_dir = argv[2] else: model_dir = '/tmp/model' fname = os.path.join(utils.KEYPOSE_PATH, config_file + '.yaml') with open(fname, 'r') as f: params, _, _ = utils.get_params(param_file=f) dset_dir = os.path.join(utils.KEYPOSE_PATH, params.dset_dir) # Configuration has the dset directory, now get more info from there. with open(fname, 'r') as f: params, _, _ = utils.get_params( param_file=f, cam_file=os.path.join(os.path.join(dset_dir, 'data_params.pbtxt'))) params.model_dir = model_dir params.dset_dir = dset_dir print('Parameters to train and eval:\n', params.make_dict()) train_and_eval( params, model_fn=est.est_model_fn, input_fn=inp.create_input_fn, save_checkpoints_secs=600, eval_throttle_secs=600, eval_steps=1000, )
def main(argv): if not len(argv) >= 3: print('Usage: ./predict.py <model_dir> <image_dir> [show|<object>]') exit(0) model_dir = argv[1] image_dir = argv[2] mesh_file = None if len(argv) > 3: mesh_file = argv[3] fname = os.path.join(model_dir, 'params.yaml') with open(fname, 'r') as f: params, camera_model, camera_image = utils.get_params( param_file=f, cam_file=os.path.join(model_dir, 'data_params.pbtxt'), cam_image_file=os.path.join(image_dir, 'data_params.pbtxt')) predict(model_dir, image_dir, params, camera_model, camera_image, mesh_file=mesh_file)