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### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
from .base_options import BaseOptions
class TestOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--start_img', type=str, default="test.jpg", help='starting image for recursive generation')
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("--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("-d", "--data_type", default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
self.isTrain = False
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