[models.cnn3d] fix qa.

src/mednet/models/cnn3d.py

@@ -69,18 +69,30 @@ class Conv3DNet(Model):
 
         self.model_transforms = []
 
-         # First convolution block
-        self.conv3d_1_1 = nn.Conv3d(in_channels=1, out_channels=4, kernel_size=3, stride=1, padding=1)
-        self.conv3d_1_2 = nn.Conv3d(in_channels=4, out_channels=16, kernel_size=3, stride=1, padding=1)
-        self.conv3d_1_3 = nn.Conv3d(in_channels=1, out_channels=16, kernel_size=1,stride=1)
+        # First convolution block
+        self.conv3d_1_1 = nn.Conv3d(
+            in_channels=1, out_channels=4, kernel_size=3, stride=1, padding=1
+        )
+        self.conv3d_1_2 = nn.Conv3d(
+            in_channels=4, out_channels=16, kernel_size=3, stride=1, padding=1
+        )
+        self.conv3d_1_3 = nn.Conv3d(
+            in_channels=1, out_channels=16, kernel_size=1, stride=1
+        )
         self.batch_norm_1_1 = nn.BatchNorm3d(4)
         self.batch_norm_1_2 = nn.BatchNorm3d(16)
         self.batch_norm_1_3 = nn.BatchNorm3d(16)
 
         # Second convolution block
-        self.conv3d_2_1 = nn.Conv3d(in_channels=16, out_channels=24, kernel_size=3, stride=1, padding=1)
-        self.conv3d_2_2 = nn.Conv3d(in_channels=24, out_channels=32, kernel_size=3, stride=1, padding=1)
-        self.conv3d_2_3 = nn.Conv3d(in_channels=16, out_channels=32, kernel_size=1, stride=1)
+        self.conv3d_2_1 = nn.Conv3d(
+            in_channels=16, out_channels=24, kernel_size=3, stride=1, padding=1
+        )
+        self.conv3d_2_2 = nn.Conv3d(
+            in_channels=24, out_channels=32, kernel_size=3, stride=1, padding=1
+        )
+        self.conv3d_2_3 = nn.Conv3d(
+            in_channels=16, out_channels=32, kernel_size=1, stride=1
+        )
         self.batch_norm_2_1 = nn.BatchNorm3d(24)
         self.batch_norm_2_2 = nn.BatchNorm3d(32)
         self.batch_norm_2_3 = nn.BatchNorm3d(32)
@@ -116,7 +128,7 @@ class Conv3DNet(Model):
         self.pool = nn.MaxPool3d(2)
         self.global_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
         self.dropout = nn.Dropout(0.3)
-        self.fc1 = nn.Linear(64,32)
+        self.fc1 = nn.Linear(64, 32)
         self.fc2 = nn.Linear(32, num_classes)
 
     def forward(self, x):
@@ -137,7 +149,7 @@ class Conv3DNet(Model):
         x = (x + F.relu(self.batch_norm_2_3(self.conv3d_2_3(_x)))) / 2
         x = self.pool(x)
 
-        # Third convolution block 
+        # Third convolution block
 
         _x = x
         x = F.relu(self.batch_norm_3_1(self.conv3d_3_1(x)))
@@ -152,7 +164,6 @@ class Conv3DNet(Model):
         x = F.relu(self.batch_norm_4_2(self.conv3d_4_2(x)))
         x = (x + F.relu(self.batch_norm_4_3(self.conv3d_4_3(_x)))) / 2
 
-
         x = self.global_pool(x)
         x = x.view(x.size(0), x.size(1))