brain age 3d

+""" 3D brain age model"""
+from box import Box
+from torch import nn
+from torch.nn import init
+
+
+def conv_blk(in_channel, out_channel):
+    return nn.Sequential(
+        nn.Conv3d(in_channel, out_channel, kernel_size=3, stride=1, padding=1),
+        nn.InstanceNorm3d(out_channel), nn.MaxPool3d(2, stride=2), nn.ReLU()
+    )
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.conv1 = conv_blk(1, 32)
+        self.conv2 = conv_blk(32, 64)
+        self.conv3 = conv_blk(64, 128)
+        self.conv4 = conv_blk(128, 256)
+        self.conv5 = conv_blk(256, 256)
+
+        self.conv6 = nn.Sequential(nn.Conv3d(256, 64, kernel_size=1, stride=1),
+                                   nn.InstanceNorm3d(64), nn.ReLU(),
+                                   nn.AvgPool3d(kernel_size=(2, 3, 2)))
+
+        self.drop = nn.Dropout3d(p=0.5)
+
+        self.output = nn.Conv3d(64, 1, kernel_size=1, stride=1)
+
+        init.constant_(self.output.bias, 62.68)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        x = self.conv6(x)
+        x = self.drop(x)
+        x = self.output(x)
+        return Box({"y_pred": x})
+
+
+def get_arch(*args, **kwargs):
+    return {"net": Model()}