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import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Model with same padding
Conv5 uses a large filter size to aggregate the features from the whole box"""
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv3d(8, 32, 3, padding="same")
self.conv2 = nn.Conv3d(32, 64, 3, padding="same")
self.conv3 = nn.Conv3d(64, 80, 3, padding="same")
self.conv4 = nn.Conv3d(80, 20, 3, padding="same")
self.conv5 = nn.Conv3d(20, 20, 20, padding="same")
self.conv6 = nn.Conv3d(20, 16, 3, padding="same")
self.conv7 = nn.Conv3d(16, 1, 3, padding="same")
self.dropout1 = nn.Dropout(0.2)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = self.conv3(x)
x = F.relu(x)
x = self.conv4(x)
x = F.relu(x)
x = self.conv5(x)
x = F.relu(x)
x = self.dropout1(x)
x = self.conv6(x)
x = F.relu(x)
x = self.conv7(x)
x = torch.sigmoid(x)
return x
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