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| import os
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| import torch.nn as nn
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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.depth = 0.33
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| self.width = 0.25
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| self.scale = (0.5, 1.5)
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| self.random_size = (10, 20)
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| self.test_size = (416, 416)
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| self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
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| self.enable_mixup = False
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| def get_model(self, sublinear=False):
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| def init_yolo(M):
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| for m in M.modules():
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| if isinstance(m, nn.BatchNorm2d):
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| m.eps = 1e-3
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| m.momentum = 0.03
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| if "model" not in self.__dict__:
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| from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
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| in_channels = [256, 512, 1024]
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| backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, depthwise=True)
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| head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, depthwise=True)
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| self.model = YOLOX(backbone, head)
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| self.model.apply(init_yolo)
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| self.model.head.initialize_biases(1e-2)
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| return self.model
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