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Author SHA1 Message Date
Jensun Ravichandran
17b45249f4 Add scaffolding 2021-06-08 01:13:59 +02:00
4 changed files with 114 additions and 1 deletions

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"""ProtoTroch Initializers.""" """ProtoTroch Component and Label Initializers."""
import warnings import warnings
from collections.abc import Iterable from collections.abc import Iterable
from itertools import chain from itertools import chain

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@ -1,5 +1,7 @@
"""ProtoTorch modules.""" """ProtoTorch modules."""
from .competitions import * from .competitions import *
from .initializers import *
from .pooling import * from .pooling import *
from .transformations import *
from .wrappers import LambdaLayer, LossLayer from .wrappers import LambdaLayer, LossLayer

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"""ProtoTroch Module Initializers."""
import torch
# Transformations
class MatrixInitializer(object):
def __init__(self, *args, **kwargs):
...
def generate(self, shape):
raise NotImplementedError("Subclasses should implement this!")
class ZerosInitializer(MatrixInitializer):
def generate(self, shape):
return torch.zeros(shape)
class OnesInitializer(MatrixInitializer):
def __init__(self, scale=1.0):
super().__init__()
self.scale = scale
def generate(self, shape):
return torch.ones(shape) * self.scale
class UniformInitializer(MatrixInitializer):
def __init__(self, minimum=0.0, maximum=1.0, scale=1.0):
super().__init__()
self.minimum = minimum
self.maximum = maximum
self.scale = scale
def generate(self, shape):
return torch.ones(shape).uniform_(self.minimum,
self.maximum) * self.scale
class DataAwareInitializer(MatrixInitializer):
def __init__(self, data, transform=torch.nn.Identity()):
super().__init__()
self.data = data
self.transform = transform
def __del__(self):
del self.data
class EigenVectorInitializer(DataAwareInitializer):
def generate(self, shape):
# TODO
raise NotImplementedError()
# Aliases
EV = EigenVectorInitializer
Random = RandomInitializer = UniformInitializer
Zeros = ZerosInitializer
Ones = OnesInitializer

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"""ProtoTorch Transformation Layers."""
import torch
from torch.nn.parameter import Parameter
from .initializers import MatrixInitializer
def _precheck_initializer(initializer):
if not isinstance(initializer, MatrixInitializer):
emsg = f"`initializer` has to be some subtype of " \
f"{MatrixInitializer}. " \
f"You have provided: {initializer=} instead."
raise TypeError(emsg)
class Omega(torch.nn.Module):
"""The Omega mapping used in GMLVQ."""
def __init__(self,
num_replicas=1,
input_dim=None,
latent_dim=None,
initializer=None,
*,
initialized_weights=None):
super().__init__()
if initialized_weights is not None:
self._register_weights(initialized_weights)
else:
if num_replicas == 1:
shape = (input_dim, latent_dim)
else:
shape = (num_replicas, input_dim, latent_dim)
self._initialize_weights(shape, initializer)
def _register_weights(self, weights):
self.register_parameter("_omega", Parameter(weights))
def _initialize_weights(self, shape, initializer):
_precheck_initializer(initializer)
_omega = initializer.generate(shape)
self._register_weights(_omega)
def forward(self):
return self._omega
def extra_repr(self):
return f"(omega): (shape: {tuple(self._omega.shape)})"