Models:
Samuel Callahan
SCallahan/
pytorch-mri-segmentation
0
Downloads: 1
[f2ca4d]: / architectures / hrnet_3D / smallhighresnet_3D.py

Download this file

125 lines (103 with data), 4.2 kB 

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
import torch.nn as nn

import math

import torch.utils.model_zoo as model_zoo

import torch

import numpy as np

import sys

import torch.nn.init as init

affine_par = True



class HighResNetBlock(nn.Module):



    def __init__(self, inplanes, outplanes, padding_=1, stride=1, dilation_ = 1):

        super(HighResNetBlock, self).__init__()



        self.conv1 = nn.Conv3d(inplanes, outplanes, kernel_size=3, stride=1, 

                                padding=padding_, bias=False, dilation = dilation_)

        self.conv2 = nn.Conv3d(outplanes, outplanes, kernel_size=3, stride=1, 

                                padding=padding_, bias=False, dilation = dilation_)

        #2 convolutions of same dilation. residual block

        self.bn1 = nn.BatchNorm3d(outplanes, affine = affine_par)

        for i in self.bn1.parameters():

            i.requires_grad = False



        self.bn2 = nn.BatchNorm3d(outplanes, affine = affine_par)

        for i in self.bn2.parameters():

            i.requires_grad = False



        self.relu = nn.PReLU()

        self.diff_dims = (inplanes != outplanes)



        self.downsample = nn.Sequential(

            nn.Conv3d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False),

            nn.BatchNorm3d(outplanes, affine = affine_par)

        )

        for i in self.downsample._modules['1'].parameters():

                i.requires_grad = False



    def forward(self, x):

        residual = x



        out = self.conv1(x)

        out = self.bn1(out)

        out = self.relu(out)



        out = self.conv2(out)

        out = self.bn2(out)

        

        if self.diff_dims:

            residual = self.downsample(residual)



        out += residual

        out = self.relu(out)



        return out



class SmallHighResNet(nn.Module):

    def __init__(self,NoLabels):

        super(SmallHighResNet,self).__init__()

        self.conv1 = nn.Conv3d(1, 8, kernel_size=3, stride=8, padding=1, bias=False)

        self.bn1 = nn.BatchNorm3d(8, affine = affine_par)

        for i in self.bn1.parameters():

            i.requires_grad = False

        self.relu = nn.PReLU()



        self.block1_1 = HighResNetBlock(inplanes=8, outplanes=8, padding_=1, dilation_=1)



        self.block2_1 = HighResNetBlock(inplanes=8, outplanes=16,padding_=2, dilation_=2)

        self.block2_2 = HighResNetBlock(inplanes=16, outplanes=16, padding_=2, dilation_=2)



        self.block3_1 = HighResNetBlock(inplanes=16, outplanes=16, padding_=4, dilation_=4)

        self.block3_2 = HighResNetBlock(inplanes=16, outplanes=16, padding_=4, dilation_=4)



        self.conv2 = nn.Conv3d(16, NoLabels, kernel_size=1, stride=1, padding=0, bias=False)



    def forward(self,x):

        out = self.conv1(x)

        out = self.bn1(out)

        out = self.relu(out)

        #print('A', out.size())

        #res blocks (dilation = 1)

        out = self.block1_1(out)

        #print('B', out.size())

        out = self.block1_1(out)

        #print('C', out.size())

        out = self.block1_1(out)

        #print('D', out.size())



        #res blocks (dilation = 2)

        out = self.block2_1(out)

        #print('E', out.size())

        out = self.block2_2(out)

        #print('F', out.size())

        out = self.block2_2(out)

        #print('G', out.size())



        #res blocks (dilation = 4)

        out = self.block3_1(out)

        #print('H', out.size())

        out = self.block3_2(out)

        #print('I', out.size())

        out = self.block3_2(out)

        #print('J', out.size())

        out = self.conv2(out)

        s0 = x.size()[2]

        s1 = x.size()[3]

        s2 = x.size()[4]

        self.interp = nn.Upsample(size = (s0, s1, s2), mode='trilinear')

        out = self.interp(out)

        #print('K', out.size())

        return out



def getSmallHRNet(NoLabels=3):

    model = SmallHighResNet(NoLabels)

    for m in model.modules():

        if isinstance(m,nn.Conv3d):

            init.kaiming_uniform(m.weight)

        elif isinstance(m, nn.Sequential):

            for m_1 in m.modules():

                if isinstance(m_1, nn.Conv3d):

                    init.kaiming_uniform(m_1.weight)

    return model



#or m in net.modules():

#m.weight.data.fill_(1)

#m.bias.data.fill_(0)