40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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# Copyright (c) Megvii, Inc. and its affiliates.
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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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# NANO model use depthwise = True, which is main difference.
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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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