47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
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from torch import optim
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class BaseConfig(object):
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"""
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Default parameters for all config files.
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"""
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def __init__(self):
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"""
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Set the defaults.
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"""
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self.img_dir = "inria/Train/pos"
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self.lab_dir = "inria/Train/pos/yolo-labels"
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self.cfgfile = "cfg/yolo.cfg"
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self.weightfile = "weights/yolo.weights"
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self.printfile = "non_printability/30values.txt"
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self.patch_size = 300
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self.start_learning_rate = 0.03
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self.patch_name = 'base'
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self.scheduler_factory = lambda x: optim.lr_scheduler.ReduceLROnPlateau(x, 'min', patience=50)
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self.max_tv = 0
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self.batch_size = 20
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self.loss_target = lambda obj, cls: obj * cls
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class ReproducePaperObj(BaseConfig):
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"""
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Reproduce the results from the paper: Generate a patch that minimises object score.
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"""
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def __init__(self):
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super().__init__()
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self.batch_size = 8
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self.patch_size = 300
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self.patch_name = 'ObjectOnlyPaper'
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self.max_tv = 0.165
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self.loss_target = lambda obj, cls: obj
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