[fix] Dropout bug fix (#247)

* fix dropout bug

* fix dropout shape discrepancy

* Fix unit test bug

* Add tests for dropout shape asper comments from fransisco

* Fix flake

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
                 )
             )
 

test/test_pipeline/components/setup/test_setup.py

@@ -22,7 +22,10 @@
     SchedulerChoice
 )
 from autoPyTorch.pipeline.components.setup.network_backbone import NetworkBackboneChoice
+from autoPyTorch.pipeline.components.setup.network_backbone.ResNetBackbone import ResBlock
+from autoPyTorch.pipeline.components.setup.network_backbone.ShapedResNetBackbone import ShapedResNetBackbone
 from autoPyTorch.pipeline.components.setup.network_backbone.base_network_backbone import NetworkBackboneComponent
+from autoPyTorch.pipeline.components.setup.network_backbone.utils import get_shaped_neuron_counts
 from autoPyTorch.pipeline.components.setup.network_head import NetworkHeadChoice
 from autoPyTorch.pipeline.components.setup.network_head.base_network_head import NetworkHeadComponent
 from autoPyTorch.pipeline.components.setup.network_initializer import (
@@ -33,7 +36,10 @@
     BaseOptimizerComponent,
     OptimizerChoice
 )
-from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
+from autoPyTorch.utils.hyperparameter_search_space_update import (
+    HyperparameterSearchSpace,
+    HyperparameterSearchSpaceUpdates
+)
 
 
 class DummyLR(BaseLRComponent):
@@ -417,6 +423,58 @@ def test_add_network_backbone(self):
         # clear addons
         base_network_backbone_choice._addons = ThirdPartyComponents(NetworkBackboneComponent)
 
+    @pytest.mark.parametrize('resnet_shape', ['funnel', 'long_funnel',
+                                              'diamond', 'hexagon',
+                                              'brick', 'triangle',
+                                              'stairs'])
+    def test_dropout(self, resnet_shape):
+        # ensures that dropout is assigned to the resblock as expected
+        dataset_properties = {"task_type": constants.TASK_TYPES_TO_STRING[1]}
+        max_dropout = 0.5
+        num_groups = 4
+        config_space = ShapedResNetBackbone.get_hyperparameter_search_space(dataset_properties=dataset_properties,
+                                                                            use_dropout=HyperparameterSearchSpace(
+                                                                                hyperparameter='use_dropout',
+                                                                                value_range=[True],
+                                                                                default_value=True),
+                                                                            max_dropout=HyperparameterSearchSpace(
+                                                                                hyperparameter='max_dropout',
+                                                                                value_range=[max_dropout],
+                                                                                default_value=max_dropout),
+                                                                            resnet_shape=HyperparameterSearchSpace(
+                                                                                hyperparameter='resnet_shape',
+                                                                                value_range=[resnet_shape],
+                                                                                default_value=resnet_shape),
+                                                                            num_groups=HyperparameterSearchSpace(
+                                                                                hyperparameter='num_groups',
+                                                                                value_range=[num_groups],
+                                                                                default_value=num_groups),
+                                                                            blocks_per_group=HyperparameterSearchSpace(
+                                                                                hyperparameter='blocks_per_group',
+                                                                                value_range=[1],
+                                                                                default_value=1
+                                                                            )
+                                                                            )
+
+        config = config_space.sample_configuration().get_dictionary()
+        resnet_backbone = ShapedResNetBackbone(**config)
+        resnet_backbone.build_backbone((100, 5))
+        dropout_probabilites = [resnet_backbone.config[key] for key in resnet_backbone.config if 'dropout_' in key]
+        dropout_shape = get_shaped_neuron_counts(
+            resnet_shape, 0, 0, 1000, num_groups + 1
+        )[:-1]
+
+        dropout_shape = [
+            dropout / 1000 * max_dropout for dropout in dropout_shape
+        ]
+        blocks_dropout = []
+        for block in resnet_backbone.backbone:
+            if isinstance(block, torch.nn.Sequential):
+                for inner_block in block:
+                    if isinstance(inner_block, ResBlock):
+                        blocks_dropout.append(inner_block.dropout)
+        assert dropout_probabilites == dropout_shape == blocks_dropout
+
 
 class TestNetworkHead:
     def test_all_heads_available(self):