backbones and heads are now handled by a BackboneHeadNet, leaving the…

… chance to still create custom networks that are not split into backbone and head

autoPyTorch/pipeline/components/setup/network/BackboneHeadNet.py

@@ -0,0 +1,112 @@
+from typing import Any, Dict, Optional, Tuple
+
+import ConfigSpace as CS
+import numpy as np
+from ConfigSpace.configuration_space import ConfigurationSpace
+from ConfigSpace.hyperparameters import (
+    CategoricalHyperparameter
+)
+from torch import nn
+
+from autoPyTorch.pipeline.components.setup.network.backbone import get_available_backbones, BaseBackbone, MLPBackbone, \
+    ShapedMLPBackbone
+from autoPyTorch.pipeline.components.setup.network.base_network import BaseNetworkComponent
+from autoPyTorch.pipeline.components.setup.network.head import get_available_heads, BaseHead, FullyConnectedHead
+
+
+class BackboneHeadNet(BaseNetworkComponent):
+    """
+    Implementation of a dynamic network, that consists of a backbone and a head
+    """
+
+    def __init__(
+            self,
+            random_state: Optional[np.random.RandomState] = None,
+            **kwargs: Any
+    ):
+        super().__init__(
+            random_state=random_state,
+        )
+        self.config = kwargs
+        self._backbones = get_available_backbones()
+        self._heads = get_available_heads()
+        self._backbones = get_available_backbones()
+        self._heads = get_available_heads()
+
+    @staticmethod
+    def get_properties(dataset_properties: Optional[Dict[str, str]] = None) -> Dict[str, Any]:
+        return {
+            "shortname": "BackboneHeadNet",
+            "name": "BackboneHeadNet",
+        }
+
+    @staticmethod
+    def get_hyperparameter_search_space(dataset_properties: Optional[Dict[str, str]] = None,
+                                        **kwargs: Any) -> ConfigurationSpace:
+        cs = ConfigurationSpace()
+        backbones = get_available_backbones()
+        heads = get_available_heads()
+
+        # filter backbones and heads for those who support the current task type
+        task = dataset_properties["task_type"]
+        backbones = {name: backbone for name, backbone in backbones.items() if task in backbone.supported_tasks}
+        heads = {name: head for name, head in heads.items() if task in head.supported_tasks}
+
+        backbone_hp = CategoricalHyperparameter("backbone", choices=backbones.keys())
+        head_hp = CategoricalHyperparameter("head", choices=heads.keys())
+        cs.add_hyperparameters([backbone_hp, head_hp])
+
+        # for each backbone and head, add a conditional search space if this backbone or head is chosen
+        for backbone_name in backbones.keys():
+            backbone_cs = backbones[backbone_name].get_hyperparameter_search_space(dataset_properties)
+            cs.add_configuration_space(backbone_name,
+                                       backbone_cs,
+                                       parent_hyperparameter={"parent": backbone_hp, "value": backbone_name})
+
+        for head_name in heads.keys():
+            head_cs: ConfigurationSpace = heads[head_name].get_hyperparameter_search_space(dataset_properties)
+            cs.add_configuration_space(head_name,
+                                       head_cs,
+                                       parent_hyperparameter={"parent": head_hp, "value": head_name})
+        return cs
+
+    def build_network(self, input_shape: Tuple[int, ...], output_shape: Tuple[int, ...]) -> nn.Module:
+        """This method returns a pytorch network, that is dynamically built
+
+            a self.config that is network specific, and contains the additional
+            configuration hyperparameters to build a domain specific network
+        """
+        backbone_name = self.config["backbone"]
+        head_name = self.config["head"]
+        Backbone = self._backbones[backbone_name]
+        Head = self._heads[head_name]
+
+        backbone = Backbone(**{k.replace(backbone_name, "").replace(":", ""): v
+                               for k, v in self.config.items() if
+                               k.startswith(backbone_name)})
+        backbone_module = backbone.build_backbone(input_shape=input_shape)
+        backbone_output_shape = backbone.get_output_shape(input_shape=input_shape)
+
+        head = Head(**{k.replace(head_name, "").replace(":", ""): v
+                       for k, v in self.config.items() if
+                       k.startswith(head_name)})
+        head_module = head.build_head(input_shape=backbone_output_shape, output_shape=output_shape)
+
+        return nn.Sequential(backbone_module, head_module)
+
+    def __str__(self) -> str:
+        """ Allow a nice understanding of what components where used """
+        info = vars(self)
+        # Remove unwanted info
+        info.pop('network', None)
+        info.pop('random_state', None)
+        return f"{self.config['backbone']} -> {self.config['head']} ({str(info)})"
+
+
+if __name__ == "__main__":
+    cs = BackboneHeadNet.get_hyperparameter_search_space(dataset_properties={"task_type": "tabular_classification"})
+    print(cs)
+    sample = cs.sample_configuration()
+    bnet = BackboneHeadNet(**sample)
+    print(bnet)
+    net = BackboneHeadNet(**sample).build_network(**{"input_shape": (10,), "output_shape": (10,)})

autoPyTorch/pipeline/components/setup/network/ResNet.py

@@ -39,15 +39,11 @@ class ResNet(BaseNetworkComponent):
 
     def __init__(
         self,
-        intermediate_activation: str,
-        final_activation: Optional[str] = None,
         random_state: Optional[np.random.RandomState] = None,
         **kwargs: Any
     ):
 
         super().__init__(
-            intermediate_activation=intermediate_activation,
-            final_activation=final_activation,
             random_state=random_state,
         )
         self.config = kwargs
@@ -130,7 +126,6 @@ def get_hyperparameter_search_space(dataset_properties: Optional[Dict] = None,
                                         min_num_units: int = 10,
                                         max_num_units: int = 1024,
                                         ) -> ConfigurationSpace:
-
         cs = ConfigurationSpace()
 
         # The number of groups that will compose the resnet. That is,