prototorch_models/prototorch/models/glvq.py
2021-06-04 22:20:32 +02:00

313 lines
11 KiB
Python

"""Models based on the GLVQ framework."""
import torch
from prototorch.functions.activations import get_activation
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import (
lomega_distance,
omega_distance,
squared_euclidean_distance,
)
from prototorch.functions.helper import get_flat
from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
from prototorch.modules import LambdaLayer, LossLayer
from torch.nn.parameter import Parameter
from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
class GLVQ(SupervisedPrototypeModel):
"""Generalized Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
# Default hparams
self.hparams.setdefault("transfer_fn", "identity")
self.hparams.setdefault("transfer_beta", 10.0)
# Layers
transfer_fn = get_activation(self.hparams.transfer_fn)
self.transfer_layer = LambdaLayer(transfer_fn)
# Loss
self.loss = LossLayer(glvq_loss)
# Prototype metrics
self.initialize_prototype_win_ratios()
def initialize_prototype_win_ratios(self):
self.register_buffer(
"prototype_win_ratios",
torch.zeros(self.num_prototypes, device=self.device))
def on_epoch_start(self):
self.initialize_prototype_win_ratios()
def log_prototype_win_ratios(self, distances):
batch_size = len(distances)
prototype_wc = torch.zeros(self.num_prototypes,
dtype=torch.long,
device=self.device)
wi, wc = torch.unique(distances.min(dim=-1).indices,
sorted=True,
return_counts=True)
prototype_wc[wi] = wc
prototype_wr = prototype_wc / batch_size
self.prototype_win_ratios = torch.vstack([
self.prototype_win_ratios,
prototype_wr,
])
def shared_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
out = self.compute_distances(x)
plabels = self.proto_layer.component_labels
mu = self.loss(out, y, prototype_labels=plabels)
batch_loss = self.transfer_layer(mu, beta=self.hparams.transfer_beta)
loss = batch_loss.sum(dim=0)
return out, loss
def training_step(self, batch, batch_idx, optimizer_idx=None):
out, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
self.log_prototype_win_ratios(out)
self.log("train_loss", train_loss)
self.log_acc(out, batch[-1], tag="train_acc")
return train_loss
def validation_step(self, batch, batch_idx):
# `model.eval()` and `torch.no_grad()` handled by pl
out, val_loss = self.shared_step(batch, batch_idx)
self.log("val_loss", val_loss)
self.log_acc(out, batch[-1], tag="val_acc")
return val_loss
def test_step(self, batch, batch_idx):
# `model.eval()` and `torch.no_grad()` handled by pl
out, test_loss = self.shared_step(batch, batch_idx)
self.log_acc(out, batch[-1], tag="test_acc")
return test_loss
def test_epoch_end(self, outputs):
test_loss = 0.0
for batch_loss in outputs:
test_loss += batch_loss.item()
self.log("test_loss", test_loss)
# TODO
# def predict_step(self, batch, batch_idx, dataloader_idx=None):
# pass
class SiameseGLVQ(GLVQ):
"""GLVQ in a Siamese setting.
GLVQ model that applies an arbitrary transformation on the inputs and the
prototypes before computing the distances between them. The weights in the
transformation pipeline are only learned from the inputs.
"""
def __init__(self,
hparams,
backbone=torch.nn.Identity(),
both_path_gradients=False,
**kwargs):
distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
self.backbone = backbone
self.both_path_gradients = both_path_gradients
def configure_optimizers(self):
proto_opt = self.optimizer(self.proto_layer.parameters(),
lr=self.hparams.proto_lr)
optimizer = None
if list(self.backbone.parameters()):
# only add an optimizer is the backbone has trainable parameters
# otherwise, the next line fails
bb_opt = self.optimizer(self.backbone.parameters(),
lr=self.hparams.bb_lr)
optimizer = [proto_opt, bb_opt]
else:
optimizer = proto_opt
if self.lr_scheduler is not None:
scheduler = self.lr_scheduler(optimizer,
**self.lr_scheduler_kwargs)
sch = {
"scheduler": scheduler,
"interval": "step",
} # called after each training step
return optimizer, [sch]
else:
return optimizer
def compute_distances(self, x):
protos, _ = self.proto_layer()
latent_x = self.backbone(x)
self.backbone.requires_grad_(self.both_path_gradients)
latent_protos = self.backbone(protos)
self.backbone.requires_grad_(True)
distances = self.distance_layer(latent_x, latent_protos)
return distances
def predict_latent(self, x, map_protos=True):
"""Predict `x` assuming it is already embedded in the latent space.
Only the prototypes are embedded in the latent space using the
backbone.
"""
self.eval()
with torch.no_grad():
protos, plabels = self.proto_layer()
if map_protos:
protos = self.backbone(protos)
d = self.distance_layer(x, protos)
y_pred = wtac(d, plabels)
return y_pred
class GRLVQ(SiameseGLVQ):
"""Generalized Relevance Learning Vector Quantization.
TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
"""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", omega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
relevances = torch.ones(self.hparams.input_dim, device=self.device)
self.register_parameter("_relevances", Parameter(relevances))
# Override the backbone.
self.backbone = LambdaLayer(lambda x: x @ torch.diag(self.relevances),
name="relevances")
@property
def relevance_profile(self):
return self.relevances.detach().cpu()
def compute_distances(self, x):
protos, _ = self.proto_layer()
distances = self.distance_layer(x, protos, torch.diag(self.relevances))
return distances
class SiameseGMLVQ(SiameseGLVQ):
"""Generalized Matrix Learning Vector Quantization.
Implemented as a Siamese network with a linear transformation backbone.
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
# Override the backbone.
self.backbone = torch.nn.Linear(self.hparams.input_dim,
self.hparams.latent_dim,
bias=False)
@property
def omega_matrix(self):
return self.backbone.weight.detach().cpu()
@property
def lambda_matrix(self):
omega = self.backbone.weight # (latent_dim, input_dim)
lam = omega.T @ omega
return lam.detach().cpu()
def compute_distances(self, x):
protos, _ = self.proto_layer()
x, protos = get_flat(x, protos)
latent_x = self.backbone(x)
self.backbone.requires_grad_(self.both_path_gradients)
latent_protos = self.backbone(protos)
self.backbone.requires_grad_(True)
distances = self.distance_layer(latent_x, latent_protos)
return distances
class LVQMLN(SiameseGLVQ):
"""Learning Vector Quantization Multi-Layer Network.
GLVQ model that applies an arbitrary transformation on the inputs, BUT NOT
on the prototypes before computing the distances between them. This of
course, means that the prototypes no longer live the input space, but
rather in the embedding space.
"""
def compute_distances(self, x):
latent_protos, _ = self.proto_layer()
latent_x = self.backbone(x)
distances = self.distance_layer(latent_x, latent_protos)
return distances
class GMLVQ(GLVQ):
"""Generalized Matrix Learning Vector Quantization.
Implemented as a regular GLVQ network that simply uses a different distance
function.
"""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", omega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
omega = torch.randn(self.hparams.input_dim,
self.hparams.latent_dim,
device=self.device)
self.register_parameter("_omega", Parameter(omega))
def compute_distances(self, x):
protos, _ = self.proto_layer()
distances = self.distance_layer(x, protos, self._omega)
return distances
def extra_repr(self):
return f"(omega): (shape: {tuple(self._omega.shape)})"
class LGMLVQ(GMLVQ):
"""Localized and Generalized Matrix Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", lomega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
# Re-register `_omega` to override the one from the super class.
omega = torch.randn(
self.num_prototypes,
self.hparams.input_dim,
self.hparams.latent_dim,
device=self.device,
)
self.register_parameter("_omega", Parameter(omega))
class GLVQ1(GLVQ):
"""Generalized Learning Vector Quantization 1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.loss = LossLayer(lvq1_loss)
self.optimizer = torch.optim.SGD
class GLVQ21(GLVQ):
"""Generalized Learning Vector Quantization 2.1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.loss = LossLayer(lvq21_loss)
self.optimizer = torch.optim.SGD
class ImageGLVQ(ImagePrototypesMixin, GLVQ):
"""GLVQ for training on image data.
GLVQ model that constrains the prototypes to the range [0, 1] by clamping
after updates.
"""
class ImageGMLVQ(ImagePrototypesMixin, GMLVQ):
"""GMLVQ for training on image data.
GMLVQ model that constrains the prototypes to the range [0, 1] by clamping
after updates.
"""