135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
"""ProtoTorch components modules."""
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import warnings
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from typing import Tuple
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import torch
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from prototorch.components.initializers import (ComponentsInitializer,
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EqualLabelInitializer,
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ZeroReasoningsInitializer)
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from prototorch.functions.initializers import get_initializer
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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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initializer=None,
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*,
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initialized_components=None,
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dtype=torch.float32):
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super().__init__()
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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._components = Parameter(initialized_components)
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if number_of_components 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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def _initialize_components(self, number_of_components, initializer):
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if not isinstance(initializer, ComponentsInitializer):
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emsg = f"`initializer` has to be some subtype of " \
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f"{ComponentsInitializer}. " \
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f"You have provided: {initializer=} instead."
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raise TypeError(emsg)
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self._components = Parameter(
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initializer.generate(number_of_components))
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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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return self._components.detach().cpu()
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def forward(self):
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return self._components
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def extra_repr(self):
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return f"components.shape: {tuple(self._components.shape)}"
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class LabeledComponents(Components):
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"""LabeledComponents generate a set of components and a set of labels.
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Every Component has a label assigned.
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"""
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def __init__(self,
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labels=None,
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initializer=None,
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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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super().__init__(initialized_components=initialized_components[0])
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self._labels = initialized_components[1]
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else:
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self._initialize_labels(labels)
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super().__init__(number_of_components=len(self._labels),
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initializer=initializer)
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def _initialize_labels(self, labels):
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if type(labels) == tuple:
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num_classes, prototypes_per_class = labels
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labels = EqualLabelInitializer(num_classes, prototypes_per_class)
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self._labels = labels.generate()
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@property
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def component_labels(self):
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"""Tensor containing the component tensors."""
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return self._labels.detach().cpu()
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def forward(self):
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return super().forward(), self._labels
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class ReasoningComponents(Components):
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"""ReasoningComponents generate a set of components and a set of reasoning matrices.
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Every Component has a reasoning matrix assigned.
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A reasoning matrix is a Nx2 matrix, where N is the number of Classes. The
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first element is called positive reasoning :math:`p`, the second negative
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reasoning :math:`n`. A components can reason in favour (positive) of a
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class, against (negative) a class or not at all (neutral).
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It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
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\leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
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three element probability distribution.
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"""
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def __init__(self,
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reasonings=None,
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initializer=None,
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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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super().__init__(initialized_components=initialized_components[0])
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self._reasonings = initialized_components[1]
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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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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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self._reasonings = reasonings.generate()
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@property
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def reasonings(self):
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"""Returns Reasoning Matrix.
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Dimension NxCx2
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"""
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return self._reasonings.detach().cpu()
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def forward(self):
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return super().forward(), self._reasonings
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