Simplenet: Replace all functional operator calls with explicit layers

models/cifar10/simplenet_cifar.py

@@ -19,26 +19,32 @@ import torch.nn.functional as F
 
 __all__ = ['simplenet_cifar']
 
+
 class Simplenet(nn.Module):
     def __init__(self):
         super(Simplenet, self).__init__()
         self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
+        self.relu_conv1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(2, 2)
         self.conv2 = nn.Conv2d(6, 16, 5)
+        self.relu_conv2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(2, 2)
         self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.relu_fc1 = nn.ReLU()
         self.fc2 = nn.Linear(120, 84)
+        self.relu_fc2 = nn.ReLU()
         self.fc3 = nn.Linear(84, 10)
 
     def forward(self, x):
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool1(self.relu_conv1(self.conv1(x)))
+        x = self.pool2(self.relu_conv2(self.conv2(x)))
         x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        #x = nn.Threshold(0.2, 0.0)#ActivationZeroThreshold(x)
+        x = self.relu_fc1(self.fc1(x))
+        x = self.relu_fc2(self.fc2(x))
         x = self.fc3(x)
         return x
 
+
 def simplenet_cifar():
     model = Simplenet()
     return model