prototorch_models/prototorch/models/neural_gas.py

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2021-04-23 15:30:23 +00:00
import pytorch_lightning as pl
import torch
from prototorch.functions.distances import euclidean_distance
from prototorch.modules import Prototypes1D
from prototorch.modules.losses import NeuralGasEnergy
class EuclideanDistance(torch.nn.Module):
def forward(self, x, y):
return euclidean_distance(x, y)
class ConnectionTopology(torch.nn.Module):
def __init__(self, agelimit, num_prototypes):
super().__init__()
self.agelimit = agelimit
self.num_prototypes = num_prototypes
self.cmat = torch.zeros((self.num_prototypes, self.num_prototypes))
self.age = torch.zeros_like(self.cmat)
def forward(self, d):
order = torch.argsort(d, dim=1)
for element in order:
i0, i1 = element[0], element[1]
self.cmat[i0][i1] = 1
self.age[i0][i1] = 0
self.age[i0][self.cmat[i0] == 1] += 1
self.cmat[i0][self.age[i0] > self.agelimit] = 0
def extra_repr(self):
return f"agelimit: {self.agelimit}"
class NeuralGas(pl.LightningModule):
def __init__(self, hparams, **kwargs):
super().__init__()
self.save_hyperparameters(hparams)
# Default Values
self.hparams.setdefault("agelimit", 10)
self.hparams.setdefault("lm", 1)
self.hparams.setdefault("prototype_initializer", "zeros")
self.proto_layer = Prototypes1D(
input_dim=self.hparams.input_dim,
nclasses=self.hparams.nclasses,
prototypes_per_class=self.hparams.prototypes_per_class,
prototype_initializer=self.hparams.prototype_initializer,
**kwargs,
)
self.distance_layer = EuclideanDistance()
self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
self.topology_layer = ConnectionTopology(
agelimit=self.hparams.agelimit,
num_prototypes=len(self.proto_layer.prototypes),
)
def training_step(self, train_batch, batch_idx):
x, _ = train_batch
protos, _ = self.proto_layer()
d = self.distance_layer(x, protos)
cost, order = self.energy_layer(d)
self.topology_layer(d)
return cost
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
return optimizer