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MBEPredictor.py
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MBEPredictor.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class myTransformer1D(nn.Module):
def __init__(self, input_dim, output_dim):
super(myTransformer1D, self).__init__()
self.relu = nn.ReLU()
self.li1_a = nn.Linear(input_dim, output_dim)
self.li1_b = nn.Linear(input_dim, output_dim)
self.li1_c = nn.Linear(input_dim, output_dim)
self.li2_a = nn.Linear(output_dim, output_dim)
self.li2_b = nn.Linear(output_dim, output_dim)
self.li2_c = nn.Linear(output_dim, output_dim)
self.li3 = nn.Linear(output_dim, output_dim)
self.li4 = nn.Linear(output_dim, output_dim)
def forward(self, x):
a = self.relu(self.li1_a(x))
a = self.relu(self.li2_a(a))
b = self.relu(self.li1_b(x))
b = self.relu(self.li2_b(b))
c = self.relu(self.li1_c(x))
c = self.relu(self.li2_c(c))
x = a * b + c
x = self.relu(self.li3(x))
x = self.relu(self.li4(x))
return x
class MBEPredictor(nn.Module):
def __init__(self):
super(MBEPredictor, self).__init__()
self.Conv3D_G = nn.Sequential(
nn.Conv3d(1, 8, (3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
nn.BatchNorm3d(8),
nn.ReLU(),
nn.Conv3d(8, 8, (3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
nn.BatchNorm3d(8),
nn.ReLU(),
nn.Conv3d(8, 8, (3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
nn.BatchNorm3d(8),
nn.ReLU(),
nn.Conv3d(8, 1, (3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
nn.BatchNorm3d(1),
nn.ReLU()
)
self.myTransformer1D_G = nn.Sequential(
myTransformer1D(686, 512),
myTransformer1D(512, 512),
myTransformer1D(512, 512)
)
self.linear = nn.Linear(512, 1)
self.relu = nn.ReLU()
def forward(self, x1, x2):
x1 = self.Conv3D_G(x1)
x1 = x1.view(x1.size(0), -1)
x2 = self.Conv3D_G(x2)
x2 = x2.view(x2.size(0), -1)
x = torch.cat((x1, x2), dim=1)
x = self.myTransformer1D_G(x)
x = self.linear(x)
x = self.relu(x)
return x
def loadCO2Model():
return torch.randn(1, 1, 111, 111, 111)
def demo():
net = MBEPredictor()
x1 = torch.randn(1, 1, 111, 111, 111)
x2 = torch.randn(1, 1, 111, 111, 111)
y = net(x1, x2)
print(y)
if __name__ == '__main__':
demo()