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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
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