159 lines
5.7 KiB
Python
159 lines
5.7 KiB
Python
"""ProtoTorch components modules."""
|
|
|
|
import warnings
|
|
from typing import Tuple
|
|
|
|
import torch
|
|
from prototorch.components.initializers import (ClassAwareInitializer,
|
|
ComponentsInitializer,
|
|
EqualLabelsInitializer,
|
|
UnequalLabelsInitializer,
|
|
ZeroReasoningsInitializer)
|
|
from prototorch.functions.initializers import get_initializer
|
|
from torch.nn.parameter import Parameter
|
|
|
|
|
|
class Components(torch.nn.Module):
|
|
"""Components is a set of learnable Tensors."""
|
|
def __init__(self,
|
|
ncomps=None,
|
|
initializer=None,
|
|
*,
|
|
initialized_components=None):
|
|
super().__init__()
|
|
|
|
self.ncomps = ncomps
|
|
|
|
# Ignore all initialization settings if initialized_components is given.
|
|
if initialized_components is not None:
|
|
self.register_parameter("_components",
|
|
Parameter(initialized_components))
|
|
if ncomps is not None or initializer is not None:
|
|
wmsg = "Arguments ignored while initializing Components"
|
|
warnings.warn(wmsg)
|
|
else:
|
|
self._initialize_components(initializer)
|
|
|
|
def _precheck_initializer(self, initializer):
|
|
if not isinstance(initializer, ComponentsInitializer):
|
|
emsg = f"`initializer` has to be some subtype of " \
|
|
f"{ComponentsInitializer}. " \
|
|
f"You have provided: {initializer=} instead."
|
|
raise TypeError(emsg)
|
|
|
|
def _initialize_components(self, initializer):
|
|
self._precheck_initializer(initializer)
|
|
_components = initializer.generate(self.ncomps)
|
|
self.register_parameter("_components", Parameter(_components))
|
|
|
|
@property
|
|
def components(self):
|
|
"""Tensor containing the component tensors."""
|
|
return self._components.detach()
|
|
|
|
def forward(self):
|
|
return self._components
|
|
|
|
def extra_repr(self):
|
|
return f"components.shape: {tuple(self._components.shape)}"
|
|
|
|
|
|
class LabeledComponents(Components):
|
|
"""LabeledComponents generate a set of components and a set of labels.
|
|
|
|
Every Component has a label assigned.
|
|
"""
|
|
def __init__(self,
|
|
distribution=None,
|
|
initializer=None,
|
|
*,
|
|
initialized_components=None):
|
|
if initialized_components is not None:
|
|
components, component_labels = initialized_components
|
|
super().__init__(initialized_components=components)
|
|
self._labels = component_labels
|
|
else:
|
|
_labels = self._initialize_labels(distribution)
|
|
super().__init__(len(_labels), initializer=initializer)
|
|
self.register_buffer("_labels", _labels)
|
|
|
|
def _initialize_components(self, initializer):
|
|
if isinstance(initializer, ClassAwareInitializer):
|
|
self._precheck_initializer(initializer)
|
|
_components = initializer.generate(self.ncomps, self.distribution)
|
|
self.register_parameter("_components", Parameter(_components))
|
|
else:
|
|
super()._initialize_components(initializer)
|
|
|
|
def _initialize_labels(self, distribution):
|
|
if type(distribution) == dict:
|
|
labels = EqualLabelsInitializer(
|
|
distribution["num_classes"],
|
|
distribution["prototypes_per_class"])
|
|
elif type(distribution) == tuple:
|
|
num_classes, prototypes_per_class = distribution
|
|
labels = EqualLabelsInitializer(num_classes, prototypes_per_class)
|
|
elif type(distribution) == list:
|
|
labels = UnequalLabelsInitializer(distribution)
|
|
|
|
self.distribution = labels.distribution
|
|
return labels.generate()
|
|
|
|
@property
|
|
def component_labels(self):
|
|
"""Tensor containing the component tensors."""
|
|
return self._labels.detach()
|
|
|
|
def forward(self):
|
|
return super().forward(), self._labels
|
|
|
|
|
|
class ReasoningComponents(Components):
|
|
"""ReasoningComponents generate a set of components and a set of reasoning matrices.
|
|
|
|
Every Component has a reasoning matrix assigned.
|
|
|
|
A reasoning matrix is a Nx2 matrix, where N is the number of Classes. The
|
|
first element is called positive reasoning :math:`p`, the second negative
|
|
reasoning :math:`n`. A components can reason in favour (positive) of a
|
|
class, against (negative) a class or not at all (neutral).
|
|
|
|
It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
|
|
\leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
|
|
three element probability distribution.
|
|
|
|
"""
|
|
def __init__(self,
|
|
reasonings=None,
|
|
initializer=None,
|
|
*,
|
|
initialized_components=None):
|
|
if initialized_components is not None:
|
|
components, reasonings = initialized_components
|
|
|
|
super().__init__(initialized_components=components)
|
|
self.register_parameter("_reasonings", reasonings)
|
|
else:
|
|
self._initialize_reasonings(reasonings)
|
|
super().__init__(len(self._reasonings), initializer=initializer)
|
|
|
|
def _initialize_reasonings(self, reasonings):
|
|
if type(reasonings) == tuple:
|
|
nclasses, ncomps = reasonings
|
|
reasonings = ZeroReasoningsInitializer(nclasses, ncomps)
|
|
|
|
_reasonings = reasonings.generate()
|
|
self.register_parameter("_reasonings", _reasonings)
|
|
|
|
@property
|
|
def reasonings(self):
|
|
"""Returns Reasoning Matrix.
|
|
|
|
Dimension NxCx2
|
|
|
|
"""
|
|
return self._reasonings.detach()
|
|
|
|
def forward(self):
|
|
return super().forward(), self._reasonings
|