Cleanup components
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@ -16,21 +16,23 @@ from torch.nn.parameter import Parameter
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class Components(torch.nn.Module):
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"""Components is a set of learnable Tensors."""
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def __init__(self,
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number_of_components=None,
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ncomps=None,
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initializer=None,
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*,
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initialized_components=None):
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super().__init__()
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self.ncomps = ncomps
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# Ignore all initialization settings if initialized_components is given.
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if initialized_components is not None:
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self.register_parameter("_components",
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Parameter(initialized_components))
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if number_of_components is not None or initializer is not None:
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if ncomps is not None or initializer is not None:
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wmsg = "Arguments ignored while initializing Components"
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warnings.warn(wmsg)
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else:
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self._initialize_components(number_of_components, initializer)
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self._initialize_components(initializer)
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def _precheck_initializer(self, initializer):
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if not isinstance(initializer, ComponentsInitializer):
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@ -39,9 +41,9 @@ class Components(torch.nn.Module):
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f"You have provided: {initializer=} instead."
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raise TypeError(emsg)
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def _initialize_components(self, number_of_components, initializer):
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def _initialize_components(self, initializer):
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self._precheck_initializer(initializer)
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_components = initializer.generate(number_of_components)
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_components = initializer.generate(self.ncomps)
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self.register_parameter("_components", Parameter(_components))
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@property
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@ -72,17 +74,16 @@ class LabeledComponents(Components):
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self._labels = component_labels
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else:
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_labels = self._initialize_labels(distribution)
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super().__init__(number_of_components=len(_labels),
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initializer=initializer)
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self.register_buffer('_labels', _labels)
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super().__init__(len(_labels), initializer=initializer)
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self.register_buffer("_labels", _labels)
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def _initialize_components(self, number_of_components, initializer):
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def _initialize_components(self, initializer):
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if isinstance(initializer, ClassAwareInitializer):
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self._precheck_initializer(initializer)
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self._components = Parameter(
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initializer.generate(number_of_components, self.distribution))
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_components = initializer.generate(self.ncomps, self.distribution)
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self.register_parameter("_components", Parameter(_components))
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else:
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super()._initialize_components(number_of_components, initializer)
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super()._initialize_components(initializer)
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def _initialize_labels(self, distribution):
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if type(distribution) == tuple:
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@ -130,14 +131,12 @@ class ReasoningComponents(Components):
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self.register_parameter("_reasonings", reasonings)
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else:
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self._initialize_reasonings(reasonings)
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super().__init__(number_of_components=len(self._reasonings),
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initializer=initializer)
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super().__init__(len(self._reasonings), initializer=initializer)
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def _initialize_reasonings(self, reasonings):
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if type(reasonings) == tuple:
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num_classes, number_of_components = reasonings
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reasonings = ZeroReasoningsInitializer(num_classes,
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number_of_components)
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nclasses, ncomps = reasonings
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reasonings = ZeroReasoningsInitializer(nclasses, ncomps)
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_reasonings = reasonings.generate()
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self.register_parameter("_reasonings", _reasonings)
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