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Feature/mlmlvq #17

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824dfce
Implement a prototypical 2-layer version of GMLVQ
juvilius Nov 3, 2023
1786031
adjust omega_matrix property
juvilius Nov 6, 2023
c6f718a
GMLMLVQ: allow for 2 or more omega layers
juvilius Nov 7, 2023
78f8b6c
remove accidental LiteralString import
juvilius Nov 7, 2023
adafb49
masks -> ParameterList(requires_grad=False)
juvilius Nov 7, 2023
1fe2951
style: ran pre-commit
juvilius Apr 30, 2024
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5 changes: 3 additions & 2 deletions src/prototorch/models/callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,8 @@
from .extras import ConnectionTopology

if TYPE_CHECKING:
from prototorch.models import GLVQ, GrowingNeuralGas
from prototorch.models.glvq import GLVQ
from prototorch.models.knn import GrowingNeuralGas


class PruneLoserPrototypes(pl.Callback):
Expand Down Expand Up @@ -61,7 +62,7 @@ def on_train_epoch_end(self, trainer, pl_module: "GLVQ"):
return_counts=True)
distribution = dict(zip(labels.tolist(), counts.tolist()))

logging.info(f"Re-adding pruned prototypes...")
logging.info("Re-adding pruned prototypes...")
logging.debug(f"distribution={distribution}")

pl_module.add_prototypes(
Expand Down
52 changes: 50 additions & 2 deletions src/prototorch/models/glvq.py
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Original file line number Diff line number Diff line change
@@ -1,21 +1,23 @@
"""Models based on the GLVQ framework."""

import torch
from numpy.typing import NDArray
from prototorch.core.competitions import wtac
from prototorch.core.distances import (
ML_omega_distance,
lomega_distance,
omega_distance,
squared_euclidean_distance,
)
from prototorch.core.initializers import EyeLinearTransformInitializer
from prototorch.core.initializers import LLTI, EyeLinearTransformInitializer
from prototorch.core.losses import (
GLVQLoss,
lvq1_loss,
lvq21_loss,
)
from prototorch.core.transforms import LinearTransform
from prototorch.nn.wrappers import LambdaLayer, LossLayer
from torch.nn.parameter import Parameter
from torch.nn import Parameter, ParameterList

from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
from .extras import ltangent_distance, orthogonalization
Expand Down Expand Up @@ -175,6 +177,7 @@ class GRLVQ(SiameseGLVQ):
TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.

"""

_relevances: torch.Tensor

def __init__(self, hparams, **kwargs):
Expand Down Expand Up @@ -229,6 +232,51 @@ def lambda_matrix(self):
return lam.detach().cpu()


class GMLMLVQ(GLVQ):
"""Generalized Multi-Layer Matrix Learning Vector Quantization.
Masks are applied to the omega layers to achieve sparsity and constrain
learning to certain items of each omega.

Implemented as a regular GLVQ network that simply uses a different distance
function. This makes it easier to implement a localized variant.
"""

# Parameters
_omegas: list[torch.Tensor]
masks: list[torch.Tensor]

def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", ML_omega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)

# Additional parameters
self._masks = ParameterList([
Parameter(mask, requires_grad=False)
for mask in kwargs.get("masks")
])
self._omegas = ParameterList(
[LLTI(mask).generate(1, 1) for mask in self._masks])

@property
def omega_matrices(self):
return [_omega.detach().cpu() for _omega in self._omegas]

@property
def lambda_matrix(self):
# TODO update to respective lambda calculation rules.
omega = self._omega.detach() # (input_dim, latent_dim)
lam = omega @ omega.T
return lam.detach().cpu()

def compute_distances(self, x):
protos, _ = self.proto_layer()
distances = self.distance_layer(x, protos, self._omegas, self._masks)
return distances

def extra_repr(self):
return f"(omegas): (shapes: {[tuple(_omega.shape) for _omega in self._omegas]})"


class GMLVQ(GLVQ):
"""Generalized Matrix Learning Vector Quantization.

Expand Down