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nets.py
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nets.py
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# Collection of models
import torch
import torch.nn as nn
from losses import LocalMetricRegularizer, DistTopLoss, LocalMetricRegularizerMask
class onlyLMRNet(nn.Module):
def __init__(self, dist_mat, edge_mask):
super(onlyLMRNet, self).__init__()
self.metric_loss = LocalMetricRegularizerMask(dist_mat, edge_mask)
def forward(self, embedding):
# Compute the metric loss
return self.metric_loss(embedding)
class DimensionReductionNet(nn.Module):
def __init__(self, dist_mat, dist_thresh, alpha,
hom_dims, hom_weights, k, num_subsets, p):
super(DimensionReductionNet, self).__init__()
self.metric_loss = LocalMetricRegularizer(dist_mat, dist_thresh)
self.top_loss = DistTopLoss(dist_mat, hom_dims, hom_weights, k, p)
self.num_subsets = num_subsets
self.alpha = alpha
def forward(self, embedding):
# Compute the metric loss
metric_loss = self.metric_loss(embedding)
# Compute the average topological loss
top_loss = torch.tensor(0., requires_grad=True)
for _ in range(self.num_subsets):
top_loss = top_loss + self.top_loss(embedding)
return self.alpha * metric_loss + (1-self.alpha) * (top_loss / self.num_subsets)
class DimensionReductionNetMask(nn.Module):
def __init__(self, dist_mat, edge_mask, alpha,
hom_dims, hom_weights, k, num_subsets, p):
super(DimensionReductionNetMask, self).__init__()
self.metric_loss = LocalMetricRegularizerMask(dist_mat, edge_mask)
self.top_loss = DistTopLoss(dist_mat, hom_dims, hom_weights, k, p)
self.num_subsets = num_subsets
self.alpha = alpha
def forward(self, embedding):
# Compute the metric loss
metric_loss = self.metric_loss(embedding)
# Compute the average topological loss
top_loss = torch.tensor(0., requires_grad=True)
for _ in range(self.num_subsets):
top_loss = top_loss + self.top_loss(embedding)
return self.alpha * metric_loss + (1-self.alpha) * (top_loss / self.num_subsets)