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#!/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.enable_mixup = False
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
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