deleted pool_indices - not needed for upconv and leaking memory

BrainMapperUNet.py

@@ -99,18 +99,18 @@ class BrainMapperUNet3D(nn.Module):
             probability_map (torch.tensor): Output forward passed tensor through the U-net block
         """
 
-        Y_encoder_1, Y_np1, pool_indices1 = self.encoderBlock1.forward(X)
-        Y_encoder_2, Y_np2, pool_indices2 = self.encoderBlock2.forward(
+        Y_encoder_1, Y_np1, _ = self.encoderBlock1.forward(X)
+        Y_encoder_2, Y_np2, _ = self.encoderBlock2.forward(
             Y_encoder_1)
 
         del Y_encoder_1
 
-        Y_encoder_3, Y_np3, pool_indices3 = self.encoderBlock3.forward(
+        Y_encoder_3, Y_np3, _ = self.encoderBlock3.forward(
             Y_encoder_2)
 
         del Y_encoder_2
 
-        Y_encoder_4, Y_np4, pool_indices4 = self.encoderBlock4.forward(
+        Y_encoder_4, Y_np4, _ = self.encoderBlock4.forward(
             Y_encoder_3)
 
         del Y_encoder_3
@@ -120,24 +120,24 @@ class BrainMapperUNet3D(nn.Module):
         del Y_encoder_4
 
         Y_decoder_1 = self.decoderBlock1.forward(
-            Y_bottleNeck, Y_np4, pool_indices4)
+            Y_bottleNeck, Y_np4)
 
-        del Y_bottleNeck, Y_np4, pool_indices4
+        del Y_bottleNeck, Y_np4
         
         Y_decoder_2 = self.decoderBlock2.forward(
-            Y_decoder_1, Y_np3, pool_indices3)
+            Y_decoder_1, Y_np3)
 
-        del Y_decoder_1, Y_np3, pool_indices3
+        del Y_decoder_1, Y_np3
 
         Y_decoder_3 = self.decoderBlock3.forward(
-            Y_decoder_2, Y_np2, pool_indices2)
+            Y_decoder_2, Y_np2)
 
-        del Y_decoder_2, Y_np2, pool_indices2
+        del Y_decoder_2, Y_np2
 
         Y_decoder_4 = self.decoderBlock4.forward(
-            Y_decoder_3, Y_np1, pool_indices1)
+            Y_decoder_3, Y_np1)
 
-        del Y_decoder_3, Y_np1, pool_indices1
+        del Y_decoder_3, Y_np1
 
         probability_map = self.classifier.forward(Y_decoder_4)