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=50, help='how many test images to run') 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.parser.add_argument("--Arc_path", type=str, default='models/BEST_checkpoint.tar', help="run ONNX model via TRT") self.parser.add_argument("--pic_a_path", type=str, default='crop_224/gdg.jpg', help="people a") self.parser.add_argument("--pic_b_path", type=str, default='crop_224/zrf.jpg', help="people b") self.parser.add_argument("--output_path", type=str, default='output/', help="people b") self.isTrain = False