Dynamically remove components
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27b43b06a7
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@ -41,8 +41,6 @@ class Components(torch.nn.Module):
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initialized_components=None):
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super().__init__()
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self.num_components = num_components
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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_components(initialized_components)
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@ -50,7 +48,12 @@ class Components(torch.nn.Module):
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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(initializer)
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self._initialize_components(initializer, num_components)
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@property
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def num_components(self):
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# return len(self._components)
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return self._components.shape[0]
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def _register_components(self, components):
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self.register_parameter("_components", Parameter(components))
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@ -62,17 +65,31 @@ 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, initializer):
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def _initialize_components(self, initializer, num_components):
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self._precheck_initializer(initializer)
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_components = initializer.generate(self.num_components)
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_components = initializer.generate(num_components)
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self._register_components(_components)
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def increase_components(self, initializer, num=1):
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self._precheck_initializer(initializer)
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_new = initializer.generate(num)
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_components = torch.cat([self._components, _new])
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def add_components(self,
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initializer=None,
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num=1,
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*,
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initialized_components=None):
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if initialized_components is not None:
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_components = torch.cat([self._components, initialized_components])
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else:
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self._precheck_initializer(initializer)
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_new = initializer.generate(num)
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_components = torch.cat([self._components, _new])
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self._register_components(_components)
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def remove_components(self, indices=None):
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mask = torch.ones(self.num_components, dtype=torch.bool)
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mask[indices] = False
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_components = self._components[mask]
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self._register_components(_components)
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return mask
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@property
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def components(self):
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"""Tensor containing the component tensors."""
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@ -101,7 +118,7 @@ class LabeledComponents(Components):
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self._labels = component_labels
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else:
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labels = get_labels_object(distribution)
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self.distribution = labels.distribution
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self.initial_distribution = labels.distribution
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_labels = labels.generate()
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super().__init__(len(_labels), initializer=initializer)
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self._register_labels(_labels)
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@ -109,21 +126,21 @@ class LabeledComponents(Components):
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def _register_labels(self, labels):
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self.register_buffer("_labels", labels)
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def _update_distribution(self, distribution):
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self.distribution = [
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old + new for old, new in zip(self.distribution, distribution)
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]
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@property
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def distribution(self):
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clabels, counts = torch.unique(self._labels, sorted=True, return_counts=True)
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return dict(zip(clabels.tolist(), counts.tolist()))
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def _initialize_components(self, initializer):
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def _initialize_components(self, initializer, num_components):
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if isinstance(initializer, ClassAwareInitializer):
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self._precheck_initializer(initializer)
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_components = initializer.generate(self.num_components,
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self.distribution)
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_components = initializer.generate(num_components,
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self.initial_distribution)
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self._register_components(_components)
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else:
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super()._initialize_components(initializer)
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super()._initialize_components(initializer, num_components)
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def increase_components(self, initializer, distribution=[1]):
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def add_components(self, initializer, distribution=[1]):
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self._precheck_initializer(initializer)
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# Labels
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@ -140,8 +157,13 @@ class LabeledComponents(Components):
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_components = torch.cat([self._components, _new])
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self._register_components(_components)
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# Housekeeping
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self._update_distribution(labels.distribution)
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def remove_components(self, indices=None):
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# Components
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mask = super().remove_components(indices)
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# Labels
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_labels = self._labels[mask]
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self._register_labels(_labels)
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@property
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def component_labels(self):
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