unet_3plus

1060 320*320 8ms，cpu800 ms

# -*- coding: utf-8 -*-
import os
import time

import numpy as np
import cv2

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from numpy import *

from torch.nn import init

def weights_init_normal(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('Linear') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)


def weights_init_xavier(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.xavier_normal_(m.weight.data, gain=1)
    elif classname.find('Linear') !=