unet3 动态分辨率支持
【摘要】
网络结构代码:
# -*- coding: utf-8 -*-import osimport timeimport numpy as npimport cv2import torchimport torch.nn as nnimport torch.nn.functional as torch_ffrom torch.nn impor...
网络结构代码:
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# -*- coding: utf-8 -*-
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import os
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import time
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import numpy as np
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import cv2
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import torch
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import torch.nn as nn
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import torch.nn.functional as torch_f
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from torch.nn import init
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def weights_init_normal(m):
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classname = m.__class__.__name__
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#print(classname)
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if classname.find('Conv') != -1:
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init.normal_(m.weight.data, 0.0, 0.02)
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elif classname.find('Linear') != -1:
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init.normal_(m.weight.data, 0.0, 0.02)
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elif classname.find('BatchNorm') != -1:
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init.normal_(m.weight.data, 1.0, 0.02)
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init.constant_(m.bias.data, 0.0)
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def weights_init_xavier(m):
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classname = m.__class__.__name__
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#print(classname)
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if classname.find('Conv') != -1:
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init.xavier_normal_(m.weight.data, gain=1)
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elif classname.find('Linear') != -1:
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init.xavier_normal_(m.weight.data, gain=1)
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elif classname.find('Ba
文章来源: blog.csdn.net,作者:AI视觉网奇,版权归原作者所有,如需转载,请联系作者。
原文链接:blog.csdn.net/jacke121/article/details/122375415
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