chenxuanhong
23 lines
1.8 KiB
Python
23 lines
1.8 KiB
Python
from .base_options import BaseOptions
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class TestOptions(BaseOptions):
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def initialize(self):
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BaseOptions.initialize(self)
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self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
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self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
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self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
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self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
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self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
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self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')
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self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
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self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
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self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
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self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
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self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")
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self.parser.add_argument("--Arc_path", type=str, default='models/BEST_checkpoint.tar', help="run ONNX model via TRT")
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self.parser.add_argument("--pic_a_path", type=str, default='crop_224/gdg.jpg', help="people a")
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self.parser.add_argument("--pic_b_path", type=str, default='crop_224/zrf.jpg', help="people b")
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self.parser.add_argument("--output_path", type=str, default='output/', help="people b")
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self.isTrain = False
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