[fix] Dropout bug fix

autoPyTorch/pipeline/components/setup/network_backbone/ResNetBackbone.py

@@ -41,7 +41,7 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> None:
                     out_features=self.config["num_units_%d" % i],
                     blocks_per_group=self.config["blocks_per_group_%d" % i],
                     last_block_index=(i - 1) * self.config["blocks_per_group_%d" % i],
-                    dropout=self.config['use_dropout']
+                    dropout=self.config[f'dropout_{i}'] if self.config['use_dropout'] else None,
                 )
             )
 
@@ -52,7 +52,7 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> None:
         return backbone
 
     def _add_group(self, in_features: int, out_features: int,
-                   blocks_per_group: int, last_block_index: int, dropout: bool
+                   blocks_per_group: int, last_block_index: int, dropout: Optional[float]
                    ) -> nn.Module:
         """
         Adds a group into the main backbone.
@@ -206,7 +206,7 @@ def __init__(
         out_features: int,
         blocks_per_group: int,
         block_index: int,
-        dropout: bool,
+        dropout: Optional[float],
         activation: nn.Module
     ):
         super(ResBlock, self).__init__()

autoPyTorch/pipeline/components/setup/network_backbone/ShapedResNetBackbone.py

@@ -38,10 +38,14 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> None:
         self.config.update(
             {"num_units_%d" % (i): num for i, num in enumerate(neuron_counts)}
         )
-        if self.config['use_dropout'] and self.config["max_dropout"] > 0.05:
+        # we are skipping the last layer, as the function get_shaped_neuron_counts
+        # is built for getting neuron counts, so it will add the out_features to
+        # the last layer. However, in dropout we dont want to have that, we just
+        # want to use the shape and not worry about the output.
+        if self.config['use_dropout']:
             dropout_shape = get_shaped_neuron_counts(
-                self.config['resnet_shape'], 0, 0, 1000, self.config['num_groups']
-            )
+                self.config['resnet_shape'], 0, 0, 1000, self.config['num_groups'] + 1
+            )[:-1]
 
             dropout_shape = [
                 dropout / 1000 * self.config["max_dropout"] for dropout in dropout_shape
@@ -61,7 +65,7 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> None:
                     out_features=self.config["num_units_%d" % i],
                     blocks_per_group=self.config["blocks_per_group"],
                     last_block_index=(i - 1) * self.config["blocks_per_group"],
-                    dropout=self.config['use_dropout']
+                    dropout=self.config[f'dropout_{i}'] if self.config['use_dropout'] else None
                 )
             )