update network

Author: dontLoveBugs

main.py

@@ -26,7 +26,7 @@ def parse_command():
 
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
     parser.add_argument('--resume', default=None, type=str, metavar='PATH')
-    parser.add_argument('--model', type=str, default='plain', choices=model_names)
+    parser.add_argument('--model', type=str, default='deform', choices=model_names)
     parser.add_argument('--batch-size', type=int, default=32, metavar='N',
                         help='input batch size for training (default: 32)')
     parser.add_argument('--test-batch-size', type=int, default=32, metavar='N',

network/network.py

@@ -58,7 +58,6 @@ def __init__(self):
         self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
         self.bn3 = nn.BatchNorm2d(128)
 
-        self.offsets = nn.Conv2d(128, 18, kernel_size=3, padding=1)
         self.conv4 = deform_conv.DeformConv2D(128, 128, kernel_size=3, padding=1)
         self.bn4 = nn.BatchNorm2d(128)
 
@@ -73,8 +72,7 @@ def forward(self, x):
         x = F.relu(self.conv3(x))
         x = self.bn3(x)
         # deformable convolution
-        offsets = self.offsets(x)
-        x = F.relu(self.conv4(x, offsets))
+        x = F.relu(self.conv4(x))
         x = self.bn4(x)
 
         x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1)
@@ -95,7 +93,6 @@ def __init__(self):
         self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
         self.bn3 = nn.BatchNorm2d(128)
 
-        self.offsets = nn.Conv2d(128, 18, kernel_size=3, padding=1)
         self.conv4 = deform_conv_v2.DeformConv2D(128, 128, kernel_size=3, padding=1)
         self.bn4 = nn.BatchNorm2d(128)
 
@@ -110,8 +107,7 @@ def forward(self, x):
         x = F.relu(self.conv3(x))
         x = self.bn3(x)
         # deformable convolution
-        offsets = self.offsets(x)
-        x = F.relu(self.conv4(x, offsets))
+        x = F.relu(self.conv4(x))
         x = self.bn4(x)
 
         x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1)