Parameter Adjustmens and Ensemble Model Implementation

modules/utils.py

@@ -24,17 +24,6 @@ class F_x(object):
 
 class ShapeMixin:
 
-    @property
-    def shape(self):
-        x = torch.randn(self.in_shape).unsqueeze(0)
-        output: torch.Tensor = self(x)
-        return output.shape[1:]
-
-
-# Utility - Modules
-###################
-class Flatten(nn.Module):
-
     @property
     def shape(self):
         try:
@@ -45,6 +34,11 @@ class Flatten(nn.Module):
             print(e)
             return -1
 
+
+# Utility - Modules
+###################
+class Flatten(ShapeMixin, nn.Module):
+
     def __init__(self, in_shape, to=-1):
         assert isinstance(to, int) or isinstance(to, tuple)
         super(Flatten, self).__init__()
@@ -172,29 +166,6 @@ class LightningBaseModule(pl.LightningModule, ABC):
         self.apply(weight_initializer)
 
 
-class BaseModuleMixin_Dataloaders(ABC):
-
-    # Dataloaders
-    # ================================================================================
-    # Train Dataloader
-    def train_dataloader(self):
-        return DataLoader(dataset=self.dataset.train_dataset, shuffle=True,
-                          batch_size=self.params.batch_size,
-                          num_workers=self.params.worker)
-
-    # Test Dataloader
-    def test_dataloader(self):
-        return DataLoader(dataset=self.dataset.test_dataset, shuffle=True,
-                          batch_size=self.params.batch_size,
-                          num_workers=self.params.worker)
-
-    # Validation Dataloader
-    def val_dataloader(self):
-        return DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
-                          batch_size=self.params.batch_size,
-                          num_workers=self.params.worker)
-
-
 class FilterLayer(nn.Module):
 
     def __init__(self):
@@ -253,7 +224,7 @@ class HorizontalSplitter(nn.Module):
         self.in_shape = in_shape
 
         self.channel, self.height, self.width = self.in_shape
-        self.new_height = (self.height // self.n) + 1 if self.height % self.n != 0 else 0
+        self.new_height = (self.height // self.n) + (1 if self.height % self.n != 0 else 0)
 
         self.shape = (self.channel, self.new_height, self.width)
         self.autopad = AutoPadToShape(self.shape)