Hyunji / A-Performance-Evaluation-of-CNN-for-Brain-Age-Prediction-Using-Structural-MRI-Data

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3D-CNN, VGGNet model

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import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(121, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
# nn.Dropout2d(),
nn.Conv2d(64, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 16, 3, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.classifier = nn.Sequential(
nn.Linear(4320, 1024),
# nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(1024, 512),
# nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(),
nn.Linear(512, 1),
# nn.BatchNorm1d(),
nn.ReLU()
)
# self.linear1 = nn.Linear()
def forward(self, x):
x = self.features(x)
x = self.classifier(x.view(-1, x.shape[1]*x.shape[2]*x.shape[3]))
# print(x.shape)
return x
class VGGBasedModel(nn.Module):
def __init__(self):
super(VGGBasedModel, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(121, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(512, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Linear(3072, 2048),
# nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Dropout(),
nn.Linear(512, 1),
# nn.BatchNorm1d(),
nn.ReLU()
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x.view(-1, x.shape[1]*x.shape[2]*x.shape[3]))
# print(x.shape)
return x
class VGGBasedModel2D(nn.Module):
def __init__(self):
super(VGGBasedModel2D, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(512, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Linear(3072, 2048),
# nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Dropout(),
nn.Linear(512, 1),
# nn.BatchNorm1d(),
nn.ReLU()
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x.view(-1, x.shape[1]*x.shape[2]*x.shape[3]))
# print(x.shape)
return x
class Model3D(nn.Module):
def __init__(self):
super(Model3D, self).__init__()
self.block1 = nn.Sequential(
nn.Conv3d(1, 8, 3, stride=1),
nn.ReLU(),
nn.Conv3d(8, 8, 3, stride=1),
nn.BatchNorm3d(8),
nn.ReLU(),
nn.MaxPool3d(2, stride=2))
self.block2 = nn.Sequential(
nn.Conv3d(8, 16, 3, stride=1),
nn.ReLU(),
nn.Conv3d(16, 16, 3, stride=1),
nn.BatchNorm3d(16),
nn.ReLU(),
nn.MaxPool3d(2, stride=2))
self.block3 = nn.Sequential(
nn.Conv3d(16, 32, 3, stride=1),
nn.ReLU(),
nn.Conv3d(32, 32, 3, stride=1),
nn.BatchNorm3d(32),
nn.ReLU(),
nn.MaxPool3d(2, stride=2))
self.block4 = nn.Sequential(
nn.Conv3d(32, 64, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv3d(64, 64, 3, stride=1, padding=1),
nn.BatchNorm3d(64),
nn.ReLU(),
nn.MaxPool3d(2, stride=2))
self.block5 = nn.Sequential(
nn.Conv3d(64, 128, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv3d(128, 128, 3, stride=1, padding=1),
nn.BatchNorm3d(128),
nn.ReLU(),
nn.MaxPool3d(2, stride=2)
)
self.classifier = nn.Linear(1536, 1)
def forward(self, x):
x = self.block1(x)
# print(x.shape)
x = self.block2(x)
# print(x.shape)
x = self.block3(x)
# print(x.shape)
x = self.block4(x)
# print(x.shape)
x = self.block5(x)
# print(x.shape)
x = self.classifier(x.view(-1, x.shape[1]*x.shape[2]*x.shape[3]*x.shape[4]))
# print(x.shape)
return x
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