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Merge branch 'dev' of github.com:si-cim/prototorch_models into dev

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Jensun Ravichandran 2021-05-31 00:32:49 +02:00
commit 7b7bc3693d
4 changed files with 53 additions and 149 deletions
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5
examples/cli/gmlvq.py
View File

@ -1,12 +1,11 @@
"""GLVQ example using the MNIST dataset."""
from prototorch.models import ImageGLVQ
from prototorch.models.data import train_on_mnist
from pytorch_lightning.utilities.cli import LightningCLI
from mnist import TrainOnMNIST
class GLVQMNIST(TrainOnMNIST, ImageGLVQ):
class GLVQMNIST(train_on_mnist(batch_size=64), ImageGLVQ):
"""Model Definition."""

7
examples/probabilistic.py
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@ -33,11 +33,12 @@ if __name__ == "__main__":
)
# Initialize the model
model = pt.models.probabilistic.RSLVQ(
model = pt.models.probabilistic.LikelihoodRatioLVQ(
#model = pt.models.probabilistic.RSLVQ(
hparams,
optimizer=torch.optim.Adam,
prototype_initializer=pt.components.SSI(train_ds, noise=2),
#prototype_initializer=pt.components.UniformInitializer(2),
#prototype_initializer=pt.components.SSI(train_ds, noise=2),
prototype_initializer=pt.components.UniformInitializer(2),
)
# Callbacks

26
examples/cli/mnist.py → prototorch/models/data.py
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@ -10,15 +10,12 @@ class MNISTDataModule(pl.LightningDataModule):
super().__init__()
self.batch_size = batch_size
# When doing distributed training, Datamodules have two optional arguments for
# granular control over download/prepare/splitting data:
# OPTIONAL, called only on 1 GPU/machine
# Download mnist dataset as side-effect, only called on the first cpu
def prepare_data(self):
MNIST("~/datasets", train=True, download=True)
MNIST("~/datasets", train=False, download=True)
# OPTIONAL, called for every GPU/machine (assigning state is OK)
# called for every GPU/machine (assigning state is OK)
def setup(self, stage=None):
# Transforms
transform = transforms.Compose([
@ -28,13 +25,17 @@ class MNISTDataModule(pl.LightningDataModule):
if stage in (None, "fit"):
mnist_train = MNIST("~/datasets", train=True, transform=transform)
self.mnist_train, self.mnist_val = random_split(
mnist_train, [55000, 5000])
mnist_train,
[55000, 5000],
)
if stage == (None, "test"):
self.mnist_test = MNIST("~/datasets",
self.mnist_test = MNIST(
"~/datasets",
train=False,
transform=transform)
transform=transform,
)
# Return the dataloader for each split
# Dataloaders
def train_dataloader(self):
mnist_train = DataLoader(self.mnist_train, batch_size=self.batch_size)
return mnist_train
@ -48,8 +49,11 @@ class MNISTDataModule(pl.LightningDataModule):
return mnist_test
class TrainOnMNIST(pl.LightningModule):
datamodule = MNISTDataModule(batch_size=256)
def train_on_mnist(batch_size=256) -> type:
class DataClass(pl.LightningModule):
datamodule = MNISTDataModule(batch_size=batch_size)
def prototype_initializer(self, **kwargs):
return pt.components.Zeros((28, 28, 1))
return DataClass

158
prototorch/models/probabilistic.py
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@ -1,100 +1,26 @@
"""Probabilistic GLVQ methods"""
import torch
from prototorch.functions.competitions import stratified_sum
from prototorch.functions.losses import (log_likelihood_ratio_loss,
robust_soft_loss)
from prototorch.functions.transform import gaussian
from .glvq import GLVQ
# HELPER
# TODO: Refactor into general files, if useful
def probability(distance, variance):
return torch.exp(-(distance * distance) / (2 * variance))
def grouped_sum(value: torch.Tensor,
labels: torch.LongTensor) -> (torch.Tensor, torch.LongTensor):
"""Group-wise average for (sparse) grouped tensors
Args:
value (torch.Tensor): values to average (# samples, latent dimension)
labels (torch.LongTensor): labels for embedding parameters (# samples,)
Returns:
result (torch.Tensor): (# unique labels, latent dimension)
new_labels (torch.LongTensor): (# unique labels,)
Examples:
>>> samples = torch.Tensor([
[0.15, 0.15, 0.15], #-> group / class 1
[0.2, 0.2, 0.2 ], #-> group / class 3
[0.4, 0.4, 0.4 ], #-> group / class 3
[0.0, 0.0, 0.0 ] #-> group / class 0
])
>>> labels = torch.LongTensor([1, 5, 5, 0])
>>> result, new_labels = groupby_mean(samples, labels)
>>> result
tensor([[0.0000, 0.0000, 0.0000],
[0.1500, 0.1500, 0.1500],
[0.3000, 0.3000, 0.3000]])
>>> new_labels
tensor([0, 1, 5])
"""
uniques = labels.unique(sorted=True).tolist()
labels = labels.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
labels = torch.LongTensor(list(map(key_val.get, labels)))
labels = labels.view(labels.size(0), 1).expand(-1, value.size(1))
unique_labels = labels.unique(dim=0)
result = torch.zeros_like(unique_labels, dtype=torch.float).scatter_add_(
0, labels, value)
return result.T
def likelihood_loss(probabilities, target, prototype_labels):
uniques = prototype_labels.unique(sorted=True).tolist()
labels = target.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
target_indices = torch.LongTensor(list(map(key_val.get, labels)))
whole_probability = probabilities.sum(dim=1)
correct_probability = probabilities[torch.arange(len(probabilities)),
target_indices]
wrong_probability = whole_probability - correct_probability
likelihood = correct_probability / wrong_probability
log_likelihood = torch.log(likelihood)
return log_likelihood
def robust_soft_loss(probabilities, target, prototype_labels):
uniques = prototype_labels.unique(sorted=True).tolist()
labels = target.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
target_indices = torch.LongTensor(list(map(key_val.get, labels)))
whole_probability = probabilities.sum(dim=1)
correct_probability = probabilities[torch.arange(len(probabilities)),
target_indices]
likelihood = correct_probability / whole_probability
log_likelihood = torch.log(likelihood)
return log_likelihood
class LikelihoodRatioLVQ(GLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
"""
def __init__(self, hparams, **kwargs):
class ProbabilisticLVQ(GLVQ):
def __init__(self, hparams, rejection_confidence=1.0, **kwargs):
super().__init__(hparams, **kwargs)
self.conditional_distribution = probability
self.conditional_distribution = gaussian
self.rejection_confidence = rejection_confidence
def predict(self, x):
probabilities = self.forward(x)
confidence, prediction = torch.max(probabilities, dim=1)
prediction[confidence < self.rejection_confidence] = -1
return prediction
def forward(self, x):
distances = self._forward(x)
@ -104,7 +30,7 @@ class LikelihoodRatioLVQ(GLVQ):
posterior = conditional * prior
plabels = torch.LongTensor(self.proto_layer.component_labels)
y_pred = grouped_sum(posterior.T, plabels)
y_pred = stratified_sum(posterior.T, plabels)
return y_pred
@ -112,52 +38,26 @@ class LikelihoodRatioLVQ(GLVQ):
X, y = batch
out = self.forward(X)
plabels = self.proto_layer.component_labels
batch_loss = -likelihood_loss(out, y, prototype_labels=plabels)
batch_loss = -self.loss_fn(out, y, plabels)
loss = batch_loss.sum(dim=0)
return loss
def predict(self, x):
probabilities = self.forward(x)
confidence, prediction = torch.max(probabilities, dim=1)
prediction[confidence < 0.1] = -1
return prediction
class RSLVQ(GLVQ):
class LikelihoodRatioLVQ(ProbabilisticLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.conditional_distribution = probability
def forward(self, x):
distances = self._forward(x)
conditional = self.conditional_distribution(distances,
self.hparams.variance)
prior = 1.0 / torch.Tensor(self.proto_layer.distribution).sum().item()
posterior = conditional * prior
plabels = torch.LongTensor(self.proto_layer.component_labels)
y_pred = grouped_sum(posterior.T, plabels)
return y_pred
def training_step(self, batch, batch_idx, optimizer_idx=None):
X, y = batch
out = self.forward(X)
plabels = self.proto_layer.component_labels
batch_loss = -robust_soft_loss(out, y, prototype_labels=plabels)
loss = batch_loss.sum(dim=0)
return loss
def predict(self, x):
probabilities = self.forward(x)
confidence, prediction = torch.max(probabilities, dim=1)
#prediction[confidence < 0.1] = -1
return prediction
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = log_likelihood_ratio_loss
__all__ = ["LikelihoodRatioLVQ", "probability", "grouped_sum"]
class RSLVQ(ProbabilisticLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = robust_soft_loss
__all__ = ["LikelihoodRatioLVQ", "RSLVQ"]