Accept torch datasets to initialize components

prototorch/components/components.py

@@ -25,15 +25,16 @@ class Components(torch.nn.Module):
         if initialized_components is not None:
             self._components = Parameter(initialized_components)
             if number_of_components is not None or initializer is not None:
-                warnings.warn(
-                    "Arguments ignored while initializing Components")
+                wmsg = "Arguments ignored while initializing Components"
+                warnings.warn(wmsg)
         else:
             self._initialize_components(number_of_components, initializer)
 
     def _initialize_components(self, number_of_components, initializer):
         if not isinstance(initializer, ComponentsInitializer):
-            emsg = f"`initializer` has to be some kind of `ComponentsInitializer`. " \
-                f"You provided: {initializer=} instead."
+            emsg = f"`initializer` has to be some subtype of " \
+                f"{ComponentsInitializer}. " \
+                f"You have provided: {initializer=} instead."
             raise TypeError(emsg)
         self._components = Parameter(
             initializer.generate(number_of_components))

prototorch/components/initializers.py

@@ -1,11 +1,30 @@
 """ProtoTroch Initializers."""
+import warnings
 from collections.abc import Iterable
 
 import torch
+from torch.utils.data import DataLoader, Dataset
+
+
+def parse_init_arg(arg):
+    if isinstance(arg, Dataset):
+        data, labels = next(iter(DataLoader(arg, batch_size=len(arg))))
+        # data = data.view(len(arg), -1)  # flatten
+    else:
+        data, labels = arg
+        if not isinstance(data, torch.Tensor):
+            wmsg = f"Converting data to {torch.Tensor}."
+            warnings.warn(wmsg)
+            data = torch.Tensor(data)
+        if not isinstance(labels, torch.Tensor):
+            wmsg = f"Converting labels to {torch.Tensor}."
+            warnings.warn(wmsg)
+            labels = torch.Tensor(labels)
+    return data, labels
 
 
 # Components
-class ComponentsInitializer:
+class ComponentsInitializer(object):
     def generate(self, number_of_components):
         raise NotImplementedError("Subclasses should implement this!")
 
@@ -63,8 +82,9 @@ class MeanInitializer(PositionAwareInitializer):
 
 
 class ClassAwareInitializer(ComponentsInitializer):
-    def __init__(self, data, labels):
+    def __init__(self, arg):
         super().__init__()
+        data, labels = parse_init_arg(arg)
         self.data = data
         self.labels = labels
 
@@ -73,8 +93,8 @@ class ClassAwareInitializer(ComponentsInitializer):
 
 
 class StratifiedMeanInitializer(ClassAwareInitializer):
-    def __init__(self, data, labels):
-        super().__init__(data, labels)
+    def __init__(self, arg):
+        super().__init__(arg)
 
         self.initializers = []
         for clabel in self.clabels:
@@ -89,8 +109,8 @@ class StratifiedMeanInitializer(ClassAwareInitializer):
 
 
 class StratifiedSelectionInitializer(ClassAwareInitializer):
-    def __init__(self, data, labels, noise=None):
-        super().__init__(data, labels)
+    def __init__(self, arg, *, noise=None):
+        super().__init__(arg)
         self.noise = noise
 
         self.initializers = []