julius/prototorch_models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Stop passing component initializers as hparams

Browse Source 
Pass the component initializer as an hparam slows down the script very much. The
API has now been changed to pass it as a kwarg to the models instead.

The example scripts have also been updated to reflect the new changes.

Also, ImageGMLVQ and an example script `gmlvq_mnist.py` that uses it have also
been added.
This commit is contained in:
Jensun Ravichandran
2021-05-12 16:36:22 +02:00
parent 1498c4bde5
commit ca39aa00d5
11 changed files with 172 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
prototorch/models/__init__.py
View File

@@ -2,7 +2,7 @@ from importlib.metadata import PackageNotFoundError, version
from .cbc import CBC
from .glvq import (GLVQ, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN, ImageGLVQ,
SiameseGLVQ)
ImageGMLVQ, SiameseGLVQ)
from .knn import KNN
from .neural_gas import NeuralGas
from .vis import *

10
prototorch/models/abstract.py
View File

@@ -8,6 +8,11 @@ class AbstractPrototypeModel(pl.LightningModule):
def prototypes(self):
return self.proto_layer.components.detach().cpu()
@property
def components(self):
"""Only an alias for the prototypes."""
return self.prototypes
def configure_optimizers(self):
optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
scheduler = ExponentialLR(optimizer,
@@ -19,3 +24,8 @@ class AbstractPrototypeModel(pl.LightningModule):
"interval": "step",
} # called after each training step
return [optimizer], [sch]
class PrototypeImageModel(pl.LightningModule):
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
self.proto_layer.components.data.clamp_(0.0, 1.0)

31
prototorch/models/glvq.py
View File

@@ -5,9 +5,18 @@ from prototorch.functions.activations import get_activation
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import (euclidean_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.mappings import OmegaMapping
from .abstract import AbstractPrototypeModel
from .abstract import AbstractPrototypeModel, PrototypeImageModel
class GLVQ(AbstractPrototypeModel):
"""Generalized Learning Vector Quantization."""
from .abstract import AbstractPrototypeModel, PrototypeImageModel
class GLVQ(AbstractPrototypeModel):
@@ -18,6 +27,7 @@ class GLVQ(AbstractPrototypeModel):
self.save_hyperparameters(hparams)
self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
prototype_initializer = kwargs.get("prototype_initializer", None)
# Default Values
self.hparams.setdefault("distance", euclidean_distance)
@@ -26,7 +36,7 @@ class GLVQ(AbstractPrototypeModel):
self.proto_layer = LabeledComponents(
distribution=self.hparams.distribution,
initializer=self.hparams.prototype_initializer)
initializer=prototype_initializer)
self.transfer_function = get_activation(self.hparams.transfer_function)
self.train_acc = torchmetrics.Accuracy()
@@ -44,7 +54,6 @@ class GLVQ(AbstractPrototypeModel):
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
x, y = train_batch
x = x.view(x.size(0), -1) # flatten
dis = self(x)
plabels = self.proto_layer.component_labels
mu = self.loss(dis, y, prototype_labels=plabels)
@@ -95,15 +104,14 @@ class LVQ21(GLVQ):
self.optimizer = torch.optim.SGD
class ImageGLVQ(GLVQ):
class ImageGLVQ(GLVQ, PrototypeImageModel):
"""GLVQ for training on image data.
GLVQ model that constrains the prototypes to the range [0, 1] by clamping
after updates.
"""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
self.proto_layer.components.data.clamp_(0.0, 1.0)
pass
class SiameseGLVQ(GLVQ):
@@ -235,6 +243,7 @@ class GMLVQ(GLVQ):
def forward(self, x):
protos, _ = self.proto_layer()
x, protos = get_flat(x, protos)
latent_x = self.omega_layer(x)
latent_protos = self.omega_layer(protos)
dis = squared_euclidean_distance(latent_x, latent_protos)
@@ -256,6 +265,16 @@ class GMLVQ(GLVQ):
return y_pred.numpy()
class ImageGMLVQ(GMLVQ, PrototypeImageModel):
"""GMLVQ for training on image data.
GMLVQ model that constrains the prototypes to the range [0, 1] by clamping
after updates.
"""
pass
class LVQMLN(GLVQ):
"""Learning Vector Quantization Multi-Layer Network.

59
prototorch/models/vis.py
View File

@@ -3,6 +3,7 @@ import os
import numpy as np
import pytorch_lightning as pl
import torch
import torchvision
from matplotlib import pyplot as plt
from matplotlib.offsetbox import AnchoredText
from prototorch.utils.celluloid import Camera
@@ -270,6 +271,7 @@ class Vis2DAbstract(pl.Callback):
border=1,
resolution=50,
show_protos=True,
show=True,
tensorboard=False,
show_last_only=False,
pause_time=0.1,
@@ -290,6 +292,7 @@ class Vis2DAbstract(pl.Callback):
self.border = border
self.resolution = resolution
self.show_protos = show_protos
self.show = show
self.tensorboard = tensorboard
self.show_last_only = show_last_only
self.pause_time = pause_time
@@ -352,10 +355,11 @@ class Vis2DAbstract(pl.Callback):
def log_and_display(self, trainer, pl_module):
if self.tensorboard:
self.add_to_tensorboard(trainer, pl_module)
if not self.block:
plt.pause(self.pause_time)
else:
plt.show(block=True)
if self.show:
if not self.block:
plt.pause(self.pause_time)
else:
plt.show(block=True)
def on_train_end(self, trainer, pl_module):
plt.show()
@@ -458,3 +462,50 @@ class VisNG2D(Vis2DAbstract):
)
self.log_and_display(trainer, pl_module)
class VisImgComp(Vis2DAbstract):
def __init__(self,
*args,
random_data=0,
dataformats="CHW",
nrow=2,
**kwargs):
super().__init__(*args, **kwargs)
self.random_data = random_data
self.dataformats = dataformats
self.nrow = nrow
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
if self.show:
components = pl_module.components
grid = torchvision.utils.make_grid(components, nrow=self.nrow)
plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
self.log_and_display(trainer, pl_module)
def add_to_tensorboard(self, trainer, pl_module):
tb = pl_module.logger.experiment
components = pl_module.components
grid = torchvision.utils.make_grid(components, nrow=self.nrow)
tb.add_image(
tag="Components",
img_tensor=grid,
global_step=trainer.current_epoch,
dataformats=self.dataformats,
)
if self.random_data:
ind = np.random.choice(len(self.x_train),
size=self.random_data,
replace=False)
data_img = self.x_train[ind]
grid = torchvision.utils.make_grid(data_img, nrow=self.nrow)
tb.add_image(tag="Data",
img_tensor=grid,
global_step=trainer.current_epoch,
dataformats=self.dataformats)