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import os |
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from yolox.data import get_yolox_datadir |
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from yolox.exp import Exp as MyExp |
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class Exp(MyExp): |
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def __init__(self): |
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super(Exp, self).__init__() |
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self.num_classes = 20 |
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self.depth = 0.33 |
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self.width = 0.50 |
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self.warmup_epochs = 1 |
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self.mosaic_prob = 1.0 |
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self.mixup_prob = 1.0 |
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self.hsv_prob = 1.0 |
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self.flip_prob = 0.5 |
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self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0] |
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def get_dataset(self, cache: bool, cache_type: str = "ram"): |
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from yolox.data import VOCDetection, TrainTransform |
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return VOCDetection( |
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data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"), |
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image_sets=[('2007', 'trainval'), ('2012', 'trainval')], |
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img_size=self.input_size, |
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preproc=TrainTransform( |
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max_labels=50, |
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flip_prob=self.flip_prob, |
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hsv_prob=self.hsv_prob), |
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cache=cache, |
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cache_type=cache_type, |
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) |
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def get_eval_dataset(self, **kwargs): |
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from yolox.data import VOCDetection, ValTransform |
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legacy = kwargs.get("legacy", False) |
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return VOCDetection( |
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data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"), |
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image_sets=[('2007', 'test')], |
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img_size=self.test_size, |
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preproc=ValTransform(legacy=legacy), |
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) |
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def get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False): |
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from yolox.evaluators import VOCEvaluator |
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return VOCEvaluator( |
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dataloader=self.get_eval_loader(batch_size, is_distributed, |
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testdev=testdev, legacy=legacy), |
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img_size=self.test_size, |
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confthre=self.test_conf, |
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nmsthre=self.nmsthre, |
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num_classes=self.num_classes, |
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) |
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