Delete brain_age_3d.py

-""" 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()}