时间戳

首页 > 解决方案 > 如何阅读这个修改后的unet？

python - 如何阅读这个修改后的unet？

问题描述

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from PIL import Image
import matplotlib.pyplot as plt

class Model_Down(nn.Module):
    """
    Convolutional (Downsampling) Blocks.

    nd = Number of Filters
    kd = Kernel size

    """
    def __init__(self,in_channels, nd = 128, kd = 3, padding = 1, stride = 2):
        super(Model_Down,self).__init__()
        self.padder = nn.ReflectionPad2d(padding)
        self.conv1 = nn.Conv2d(in_channels = in_channels, out_channels = nd, kernel_size = kd, stride = stride)
        self.bn1 = nn.BatchNorm2d(nd)

        self.conv2 = nn.Conv2d(in_channels = nd, out_channels = nd, kernel_size = kd, stride = 1)
        self.bn2 = nn.BatchNorm2d(nd)

        self.relu = nn.LeakyReLU()

    def forward(self, x):
        x = self.padder(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.padder(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        return x

class Model_Skip(nn.Module):
    """

    Skip Connections

    ns = Number of filters
    ks = Kernel size

    """
    def __init__(self,in_channels = 128, ns = 4, ks = 1, padding = 0, stride = 1):
        super(Model_Skip, self).__init__()
        self.conv = nn.Conv2d(in_channels = in_channels, out_channels = ns, kernel_size = ks, stride = stride, padding = padding)
        self.bn = nn.BatchNorm2d(ns)
        self.relu = nn.LeakyReLU()

    def forward(self,x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Model_Up(nn.Module):
    """
    Convolutional (Downsampling) Blocks.

    nd = Number of Filters
    kd = Kernel size

    """
    def __init__(self, in_channels = 132, nu = 128, ku = 3, padding = 1):
        super(Model_Up, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.padder = nn.ReflectionPad2d(padding)
        self.conv1 = nn.Conv2d(in_channels = in_channels, out_channels = nu, kernel_size = ku, stride = 1, padding = 0)
        self.bn2 = nn.BatchNorm2d(nu)

        self.conv2 =  nn.Conv2d(in_channels = nu, out_channels = nu, kernel_size = 1, stride = 1, padding = 0) #According to supmat.pdf ku = 1 for second layer
        self.bn3 = nn.BatchNorm2d(nu)

        self.relu = nn.LeakyReLU()

    def forward(self,x):
        x = self.bn1(x)
        x = self.padder(x)
        x = self.conv1(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn3(x)
        x = self.relu(x)
        x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')
        return x


class Model(nn.Module):
    def __init__(self, length = 5, in_channels = 32, out_channels = 3, nu = [128,128,128,128,128] , nd =
                    [128,128,128,128,128], ns = [4,4,4,4,4], ku = [3,3,3,3,3], kd = [3,3,3,3,3], ks = [1,1,1,1,1]):
        super(Model,self).__init__()
        assert length == len(nu), 'Hyperparameters do not match network depth.'

        self.length = length

        self.downs = nn.ModuleList([Model_Down(in_channels = nd[i-1], nd = nd[i], kd = kd[i]) if i != 0 else
                                        Model_Down(in_channels = in_channels, nd = nd[i], kd = kd[i]) for i in range(self.length)])

        self.skips = nn.ModuleList([Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i]) for i in range(self.length)])

        self.ups = nn.ModuleList([Model_Up(in_channels = ns[i]+nu[i+1], nu = nu[i], ku = ku[i]) if i != self.length-1 else
                                        Model_Up(in_channels = ns[i], nu = nu[i], ku = ku[i]) for i in range(self.length-1,-1,-1)]) #Elements ordered backwards

        self.conv_out = nn.Conv2d(nu[0],out_channels,1,padding = 0)
        self.sigm = nn.Sigmoid()

    def forward(self,x):
        s = [] #Skip Activations

        #Downpass
        for i in range(self.length):
            x = self.downs[i].forward(x)
            s.append(self.skips[i].forward(x))

        #Uppass
        for i in range(self.length):
            if (i == 0):
                x = self.ups[i].forward(s[-1])
            else:
                x = self.ups[i].forward(torch.cat([x,s[self.length-1-i]],axis = 1))

        x = self.sigm(self.conv_out(x)) #Squash to RGB ([0,1]) format
        return x

这段代码是UNet我正在修改的。我面临着难以阅读和理解的代码以及跳过连接如何连接到上采样的问题。任何人都可以请解释一下，或者可以用更简单、更容易理解的方式来写，而无需nn.ModuleList.

有人可以使用图表显示该网络的外观吗？

这是我获取此代码并试图理解它的 github链接repo 链接。

标签： pythondeep-learningpytorchimage-segmentation

解决方案

这是主要模型forward(x)方法的功能等效项。它更加冗长，但它正在“解开”操作流程，使其更容易理解。

我假设列表参数的长度始终是5（i 在 [0, 4] 范围内，包括在内），因此我可以正确解包（并且它遵循默认的参数集）。

def unet_function(x, in_channels = 32, out_channels = 3, nu = [128,128,128,128,128],
                  nd = [128,128,128,128,128], ns = [4,4,4,4,4], ku = [3,3,3,3,3],
                  kd = [3,3,3,3,3], ks = [1,1,1,1,1]):


    ################################
    # DOWN PASS ####################
    ################################

    #########
    # i = 0 #
    #########

    # First Down
    # Model_Down(in_channels = in_channels, nd = nd[i], kd = kd[i])
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2D(in_channels=in_channels, out_channels=nd[0], kernel_size=kd[0], stride=2)(x)
    x = nn.BatchNorm2d(nd[0])(x)
    x = nn.LeakyRelu()(x)
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2d(in_channels = nd[0], out_channels=nd[0], kernel_size = kd[0], stride=1)(x)
    x = nn.BatchNorm2d(nd[0])(x)
    x = nn.LeakyRelu()(x)

    # First skip
    # Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i])
    s0 = nn.Conv2D(in_channels=nd[0], out_channels=ns[0])(x)
    s0 = nn.BatchNorm2d(ns[0])(s0)
    s0 = nn.LeakyreLU()(s0)


    #########
    # i = 1 #
    #########

    # Second Down
    # Model_Down(in_channels = nd[i-1], nd = nd[i], kd = kd[i])
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2D(in_channels=nd[0], out_channels=nd[0], kernel_size=kd[1], stride=2)(x)
    x = nn.BatchNorm2d(nd[0])(x)
    x = nn.LeakyRelu()(x)
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2d(in_channels = nd[0], out_channels=nd[0], kernel_size = kd[1], stride=1)(x)
    x = nn.BatchNorm2d(nd[0])(x)
    x = nn.LeakyRelu()(x)

    # Second skip
    # Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i])
    s1 = nn.Conv2D(in_channels=nd[1], out_channels=ns[1])(x)
    s1 = nn.BatchNorm2d(ns[1])(s1)
    s1 = nn.LeakyreLU()(s1)


    #########
    # i = 2 #
    #########

    # Third Down
    # Model_Down(in_channels = nd[i-1], nd = nd[i], kd = kd[i])
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2D(in_channels=nd[1], out_channels=nd[1], kernel_size=kd[2], stride=2)(x)
    x = nn.BatchNorm2d(nd[1])(x)
    x = nn.LeakyRelu()(x)
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2d(in_channels = nd[1], out_channels=nd[0], kernel_size = kd[2], stride=1)(x)
    x = nn.BatchNorm2d(nd[1])(x)
    x = nn.LeakyRelu()(x)

    # Third skip
    # Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i])
    s2 = nn.Conv2D(in_channels=nd[2], out_channels=ns[2])(x)
    s2 = nn.BatchNorm2d(ns[2])(s2)
    s2 = nn.LeakyreLU()(s2)


    #########
    # i = 3 #
    #########

    # Fourth Down
    # Model_Down(in_channels = nd[i-1], nd = nd[i], kd = kd[i])
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2D(in_channels=nd[2], out_channels=nd[2], kernel_size=kd[3], stride=2)(x)
    x = nn.BatchNorm2d(nd[2])(x)
    x = nn.LeakyRelu()(x)
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2d(in_channels = nd[2], out_channels=nd[2], kernel_size = kd[3], stride=1)(x)
    x = nn.BatchNorm2d(nd[2])(x)
    x = nn.LeakyRelu()(x)

    # Fourth skip
    # Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i])
    s3 = nn.Conv2D(in_channels=nd[3], out_channels=ns[3])(x)
    s3 = nn.BatchNorm2d(ns[3])(s3)
    s3 = nn.LeakyreLU()(s3)


    #########
    # i = 4 #
    #########

    # Fifth Down
    # Model_Down(in_channels = nd[i-1], nd = nd[i], kd = kd[i])
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2D(in_channels=nd[3], out_channels=nd[3], kernel_size=kd[4], stride=2)(x)
    x = nn.BatchNorm2d(nd[3])(x)
    x = nn.LeakyRelu()(x)
    x = nn.ReflectionPad2d(padding=1)(x)
    x = nn.Conv2d(in_channels = nd[3], out_channels=nd[3], kernel_size = kd[4], stride=1)(x)
    x = nn.BatchNorm2d(nd[2])(x)
    x = nn.LeakyRelu()(x)

    # Fifth skip
    # Model_Skip(in_channels = nd[i], ns = ns[i], ks = ks[i])
    x = nn.Conv2D(in_channels=nd[4], out_channels=ns[4])(x)
    x = nn.BatchNorm2d(ns[4])(x)
    x = nn.LeakyreLU()(x)



    ################################
    # UP PASS ######################
    ################################

    #########
    # i = 4 #
    #########

    # First Up
    # Model_Up(in_channels = ns[i], nu = nu[i], ku = ku[i])
    x = nn.BatchNorm2d(in_channel=ns[4])(x)
    x = nn.ReflectionPad2d(padding)(x)
    x = nn.Conv2d(in_channels=ns[4], out_channels=nu[4], kernel_size=ku[4], stride=1, padding=0)(x)
    x = nn.BatchNorm2d(nu[4])(x)
    x = nn.LeakyReLU()(x)
    x = nn.Conv2d(in_channels = nu[4], out_channels=nu[4], kernel_size = 1, stride = 1, padding = 0)(x)
    x = nn.BatchNorm2d(nu[4])(x)
    x = nn.LeakyReLU()(x)
    x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')


    #########
    # i = 3 #
    #########

    # Second Up
    # self.ups[i].forward(torch.cat([x,s[self.length-1-i]],axis = 1))
    x = torch.cat([x,s3], axis=1) # IMPORTANT HERE
    # Model_Up(in_channels = ns[i]+nu[i+1], nu = nu[i], ku = ku[i])
    x = nn.BatchNorm2d(in_channel=ns[3]+nu[4])(x)
    x = nn.ReflectionPad2d(padding)(x)
    x = nn.Conv2d(in_channels=ns[3]+nu[4], out_channels=nu[3], kernel_size=ku[3], stride=1, padding=0)(x)
    x = nn.BatchNorm2d(nu[3])(x)
    x = nn.LeakyReLU()(x)
    x = nn.Conv2d(in_channels = ns[3]+nu[4], out_channels=nu[3], kernel_size = 1, stride = 1, padding = 0)(x)
    x = nn.BatchNorm2d(nu[3])(x)
    x = nn.LeakyReLU()(x)
    x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')


    #########
    # i = 2 #
    #########

    # Third Up
    # self.ups[i].forward(torch.cat([x,s[self.length-1-i]],axis = 1))
    x = torch.cat([x,s2], axis=1) # IMPORTANT HERE
    # Model_Up(in_channels = ns[i]+nu[i+1], nu = nu[i], ku = ku[i])
    x = nn.BatchNorm2d(in_channel=ns[2]+nu[3])(x)
    x = nn.ReflectionPad2d(padding)(x)
    x = nn.Conv2d(in_channels=ns[2]+nu[3], out_channels=nu[2], kernel_size=ku[2], stride=1, padding=0)(x)
    x = nn.BatchNorm2d(nu[2])(x)
    x = nn.LeakyReLU()(x)
    x = nn.Conv2d(in_channels = ns[2]+nu[3], out_channels=nu[2], kernel_size = 1, stride = 1, padding = 0)(x)
    x = nn.BatchNorm2d(nu[2])(x)
    x = nn.LeakyReLU()(x)
    x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')


    #########
    # i = 1 #
    #########

    # Fourth Up
    # self.ups[i].forward(torch.cat([x,s[self.length-1-i]],axis = 1))
    x = torch.cat([x,s1], axis=1) # IMPORTANT HERE
    # Model_Up(in_channels = ns[i]+nu[i+1], nu = nu[i], ku = ku[i])
    x = nn.BatchNorm2d(in_channel=ns[1]+nu[2])(x)
    x = nn.ReflectionPad2d(padding)(x)
    x = nn.Conv2d(in_channels=ns[1]+nu[2], out_channels=nu[1], kernel_size=ku[1], stride=1, padding=0)(x)
    x = nn.BatchNorm2d(nu[1])(x)
    x = nn.LeakyReLU()(x)
    x = nn.Conv2d(in_channels = ns[1]+nu[2], out_channels=nu[1], kernel_size = 1, stride = 1, padding = 0)(x)
    x = nn.BatchNorm2d(nu[1])(x)
    x = nn.LeakyReLU()(x)
    x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')    


    #########
    # i = 0 #
    #########

    # Fifth Up
    # self.ups[i].forward(torch.cat([x,s[self.length-1-i]],axis = 1))
    x = torch.cat([x,s0], axis=1) # IMPORTANT HERE
    # Model_Up(in_channels = ns[i]+nu[i+1], nu = nu[i], ku = ku[i])
    x = nn.BatchNorm2d(in_channel=ns[0]+nu[1])(x)
    x = nn.ReflectionPad2d(padding)(x)
    x = nn.Conv2d(in_channels=ns[0]+nu[1], out_channels=nu[0], kernel_size=ku[0], stride=1, padding=0)(x)
    x = nn.BatchNorm2d(nu[0])(x)
    x = nn.LeakyReLU()(x)
    x = nn.Conv2d(in_channels = nu[0], out_channels=nu[0], kernel_size = 1, stride = 1, padding = 0)(x)
    x = nn.BatchNorm2d(nu[0])(x)
    x = nn.LeakyReLU()(x)
    x = F.interpolate(x, scale_factor = 2, mode = 'bilinear')


    ################################
    # OUT ##########################
    ################################

    x = nn.Conv2d(in_channels=nu[0], out_channels=out_channels, kernel_size=1, padding = 0)
    return nn.Sigmoid()(x) #Squash to RGB ([0,1]) format

最重要的两个部分是：

  1. skips张量在代码的x并行部分中被处理，而不是干扰 main x "pathway"。

  2. 然后从零件产生的张量从最后一个开始skip反馈到“主路径”。我将这些张量保留为单个变量s0 to s3，以便更明显。

从这张照片中，您可以清楚地看到向下部分喂食后面的部分。s0是最长的灰色箭头，它在最后一个卷积层组之前 连接到“主路径”。网络 （不是同一个U-Net）

您也可以从中理解为什么我们不需要存储 a s4：它直接馈送到下一层，因此不需要将其存储为单独的变量。

该Module版本确实存储了它，但这只是因为它方便地存储在最后以相反顺序读取的列表中。将它们存储在列表中的另一个明显原因是，我们可以通过相应地更改参数来拥有任意数量的Up和Down部分。

推荐阅读