prototorch_models/prototorch/models/glvq.py

307 lines
10 KiB
Python
Raw Normal View History

"""Models based on the GLVQ framework."""
2021-04-21 12:51:34 +00:00
import torch
from torch.nn.parameter import Parameter
from ..core.competitions import wtac
from ..core.distances import lomega_distance, omega_distance, squared_euclidean_distance
from ..core.initializers import EyeTransformInitializer
from ..core.losses import glvq_loss, lvq1_loss, lvq21_loss
from ..nn.activations import get_activation
from ..nn.wrappers import LambdaLayer, LossLayer
2021-06-04 20:20:32 +00:00
from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
2021-04-21 12:51:34 +00:00
2021-06-04 20:20:32 +00:00
class GLVQ(SupervisedPrototypeModel):
2021-04-21 12:51:34 +00:00
"""Generalized Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
2021-06-04 20:20:32 +00:00
super().__init__(hparams, **kwargs)
2021-06-04 20:20:32 +00:00
# Default hparams
2021-05-19 14:30:19 +00:00
self.hparams.setdefault("transfer_fn", "identity")
2021-05-04 18:56:16 +00:00
self.hparams.setdefault("transfer_beta", 10.0)
2021-05-31 09:19:06 +00:00
# Layers
2021-06-04 20:20:32 +00:00
transfer_fn = get_activation(self.hparams.transfer_fn)
self.transfer_layer = LambdaLayer(transfer_fn)
2021-06-04 20:20:32 +00:00
# Loss
self.loss = LossLayer(glvq_loss)
2021-04-21 12:51:34 +00:00
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,
])
2021-05-19 14:57:51 +00:00
def shared_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
2021-06-04 20:20:32 +00:00
out = self.compute_distances(x)
plabels = self.proto_layer.labels
2021-05-19 14:57:51 +00:00
mu = self.loss(out, y, prototype_labels=plabels)
2021-05-31 09:19:06 +00:00
batch_loss = self.transfer_layer(mu, beta=self.hparams.transfer_beta)
2021-05-19 14:57:51 +00:00
loss = batch_loss.sum(dim=0)
return out, loss
2021-05-18 17:49:16 +00:00
2021-05-19 14:57:51 +00:00
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)
2021-05-19 14:57:51 +00:00
self.log_acc(out, batch[-1], tag="train_acc")
2021-05-19 14:30:19 +00:00
return train_loss
2021-05-19 14:57:51 +00:00
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)
2021-05-19 14:30:19 +00:00
self.log("val_loss", val_loss)
2021-05-19 14:57:51 +00:00
self.log_acc(out, batch[-1], tag="val_acc")
2021-05-19 14:30:19 +00:00
return val_loss
2021-05-19 14:57:51 +00:00
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)
2021-05-20 12:03:31 +00:00
self.log_acc(out, batch[-1], tag="test_acc")
2021-05-20 12:20:23 +00:00
return test_loss
def test_epoch_end(self, outputs):
2021-05-20 12:40:02 +00:00
test_loss = 0.0
2021-05-20 12:20:23 +00:00
for batch_loss in outputs:
2021-05-20 12:40:02 +00:00
test_loss += batch_loss.item()
self.log("test_loss", test_loss)
# TODO
2021-05-19 14:57:51 +00:00
# def predict_step(self, batch, batch_idx, dataloader_idx=None):
# pass
2021-04-21 12:51:34 +00:00
2021-05-21 11:33:57 +00:00
class SiameseGLVQ(GLVQ):
2021-04-27 12:35:17 +00:00
"""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.
2021-04-29 21:37:22 +00:00
2021-04-27 12:35:17 +00:00
"""
def __init__(self,
hparams,
backbone=torch.nn.Identity(),
both_path_gradients=False,
2021-04-27 12:35:17 +00:00
**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
2021-05-03 11:20:49 +00:00
2021-05-21 11:33:57 +00:00
def configure_optimizers(self):
proto_opt = self.optimizer(self.proto_layer.parameters(),
lr=self.hparams.proto_lr)
2021-06-07 15:00:38 +00:00
# Only add a backbone optimizer if backbone has trainable parameters
if (bb_params := list(self.backbone.parameters())):
bb_opt = self.optimizer(bb_params, lr=self.hparams.bb_lr)
optimizers = [proto_opt, bb_opt]
2021-05-21 11:33:57 +00:00
else:
2021-06-07 15:00:38 +00:00
optimizers = [proto_opt]
2021-06-04 13:55:06 +00:00
if self.lr_scheduler is not None:
2021-06-07 15:00:38 +00:00
schedulers = []
for optimizer in optimizers:
scheduler = self.lr_scheduler(optimizer,
**self.lr_scheduler_kwargs)
schedulers.append(scheduler)
return optimizers, schedulers
2021-06-04 13:55:06 +00:00
else:
2021-06-07 15:00:38 +00:00
return optimizers
2021-05-21 11:33:57 +00:00
2021-06-04 20:20:32 +00:00
def compute_distances(self, x):
protos, _ = self.proto_layer()
x, protos = [arr.view(arr.size(0), -1) for arr in (x, protos)]
2021-04-27 12:35:17 +00:00
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
2021-04-27 12:35:17 +00:00
2021-05-21 11:33:57 +00:00
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)
2021-05-21 11:33:57 +00:00
y_pred = wtac(d, plabels)
return y_pred
2021-04-29 21:37:22 +00:00
2021-06-07 15:00:38 +00:00
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 GRLVQ(SiameseGLVQ):
"""Generalized Relevance Learning Vector Quantization.
2021-05-06 16:42:06 +00:00
2021-06-07 15:00:38 +00:00
Implemented as a Siamese network with a linear transformation backbone.
TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
2021-06-07 15:00:38 +00:00
"""
def __init__(self, hparams, **kwargs):
2021-06-07 15:00:38 +00:00
super().__init__(hparams, **kwargs)
# Additional parameters
relevances = torch.ones(self.hparams.input_dim, device=self.device)
self.register_parameter("_relevances", Parameter(relevances))
2021-06-07 15:00:38 +00:00
# Override the backbone
self.backbone = LambdaLayer(lambda x: x @ torch.diag(self._relevances),
name="relevance scaling")
2021-05-21 13:42:45 +00:00
2021-05-06 16:42:06 +00:00
@property
def relevance_profile(self):
2021-06-07 15:00:38 +00:00
return self._relevances.detach().cpu()
2021-05-06 16:42:06 +00:00
2021-06-07 15:00:38 +00:00
def extra_repr(self):
return f"(relevances): (shape: {tuple(self._relevances.shape)})"
2021-05-06 16:42:06 +00:00
class SiameseGMLVQ(SiameseGLVQ):
"""Generalized Matrix Learning Vector Quantization.
Implemented as a Siamese network with a linear transformation backbone.
"""
2021-04-29 21:37:22 +00:00
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
2021-06-07 15:00:38 +00:00
# Override the backbone
self.backbone = torch.nn.Linear(self.hparams.input_dim,
self.hparams.latent_dim,
bias=False)
2021-05-07 13:24:47 +00:00
@property
def omega_matrix(self):
return self.backbone.weight.detach().cpu()
2021-05-07 13:24:47 +00:00
@property
def lambda_matrix(self):
omega = self.backbone.weight # (latent_dim, input_dim)
lam = omega.T @ omega
2021-05-07 13:24:47 +00:00
return lam.detach().cpu()
class GMLVQ(GLVQ):
"""Generalized Matrix Learning Vector Quantization.
Implemented as a regular GLVQ network that simply uses a different distance
2021-06-07 15:00:38 +00:00
function. This makes it easier to implement a localized variant.
"""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", omega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
2021-06-07 15:00:38 +00:00
# Additional parameters
omega_initializer = kwargs.get("omega_initializer",
EyeTransformInitializer())
omega = omega_initializer.generate(self.hparams.input_dim,
self.hparams.latent_dim)
self.register_parameter("_omega", Parameter(omega))
self.backbone = LambdaLayer(lambda x: x @ self._omega,
name="omega matrix")
2021-06-07 15:00:38 +00:00
@property
def omega_matrix(self):
return self._omega.detach().cpu()
2021-06-04 20:20:32 +00:00
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)
2021-06-07 15:00:38 +00:00
# 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))
2021-05-18 17:49:16 +00:00
class GLVQ1(GLVQ):
2021-05-21 13:42:45 +00:00
"""Generalized Learning Vector Quantization 1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
2021-06-04 20:20:32 +00:00
self.loss = LossLayer(lvq1_loss)
self.optimizer = torch.optim.SGD
2021-05-18 17:49:16 +00:00
class GLVQ21(GLVQ):
2021-05-21 13:42:45 +00:00
"""Generalized Learning Vector Quantization 2.1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
2021-06-04 20:20:32 +00:00
self.loss = LossLayer(lvq21_loss)
self.optimizer = torch.optim.SGD
2021-06-04 20:20:32 +00:00
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.
"""
2021-06-04 20:20:32 +00:00
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.
"""