text: #!/usr/bin/env python3
  # -*- coding:utf-8 -*-
  # Copyright (c) Megvii, Inc. and its affiliates.
  
  import os
  import torch.nn as nn
  
  from yolox.exp import Exp as MyExp
  
  
  class Exp(MyExp):
      def __init__(self):
          super(Exp, self).__init__()
          self.depth = 0.33
          self.width = 0.25
          self.scale = (0.5, 1.5)
          self.random_size = (10, 20)
          self.test_size = (416, 416)
          self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
          self.mixup = 1.0
          self.mosaic = 1.0
  
          # Define yourself dataset path
          self.data_dir = "datasets/coco128"
          self.train_ann = "instances_train2017.json"
          self.val_ann = "instances_val2017.json"
  
          self.num_classes = 71
  
      def get_model(self, sublinear=False):
  
          def init_yolo(M):
              for m in M.modules():
                  if isinstance(m, nn.BatchNorm2d):
                      m.eps = 1e-3
                      m.momentum = 0.03
          if "model" not in self.__dict__:
              from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
              in_channels = [256, 512, 1024]
              # NANO model use depthwise = True, which is main difference.
              backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, depthwise=True)
              head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, depthwise=True)
              self.model = YOLOX(backbone, head)
  
          self.model.apply(init_yolo)
          self.model.head.initialize_biases(1e-2)
          return self.model
  