from .base_options import BaseOptions


class TestOptions(BaseOptions):
    def initialize(self):
        BaseOptions.initialize(self)
        self.parser.add_argument(
            '--ntest', type=int, default=float("inf"), help='# of test examples.')
        self.parser.add_argument(
            '--results_dir', type=str, default='./results/', help='saves results here.')
        self.parser.add_argument(
            '--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
        self.parser.add_argument(
            '--phase', type=str, default='test', help='train, val, test, etc')
        self.parser.add_argument('--which_epoch', type=str, default='latest',
                                 help='which epoch to load? set to latest to use latest cached model')
        self.parser.add_argument(
            '--how_many', type=int, default=1000, help='how many test images to run')
        self.parser.add_argument('--serial_batches', action='store_false',
                                 help='if true, takes images in order to make batches, otherwise takes them randomly')
        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy',
                                 help='the path for clustered results of encoded features')
        self.parser.add_argument('--use_encoded_image', action='store_true',
                                 help='if specified, encode the real image to get the feature map')
        self.parser.add_argument(
            "--export_onnx", type=str, help="export ONNX model to a given file")
        self.parser.add_argument("--engine", type=str,
                                 help="run serialized TRT engine")
        self.parser.add_argument(
            "--onnx", type=str, help="run ONNX model via TRT")
        self.isTrain = False