Temporarily remove GTLVQ
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Jensun Ravichandran
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import torch
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from prototorch.components import LabeledComponents, StratifiedMeanInitializer
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from prototorch.functions.distances import euclidean_distance_matrix
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from prototorch.functions.normalization import orthogonalization
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from torch import nn
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class GTLVQ(nn.Module):
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r""" Generalized Tangent Learning Vector Quantization
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Parameters
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----------
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num_classes: int
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Number of classes of the given classification problem.
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subspace_data: torch.tensor of shape (n_batch,feature_dim,feature_dim)
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Subspace data for the point approximation, required
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prototype_data: torch.tensor of shape (n_init_data,feature_dim) (optional)
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prototype data for initalization of the prototypes used in GTLVQ.
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subspace_size: int (default=256,optional)
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Subspace dimension of the Projectors. Currently only supported
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with tagnent_projection_type=global.
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tangent_projection_type: string
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Specifies the tangent projection type
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options: local
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local_proj
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global
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local: computes the tangent distances without emphasizing projected
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data. Only distances are available
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local_proj: computs tangent distances and returns the projected data
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for further use. Be careful: data is repeated by number of prototypes
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global: Number of subspaces is set to one and every prototypes
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uses the same.
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prototypes_per_class: int (default=2,optional)
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Number of prototypes per class
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feature_dim: int (default=256)
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Dimensionality of the feature space specified as integer.
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Prototype dimension.
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Notes
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-----
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The GTLVQ [1] is a prototype-based classification learning model. The
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GTLVQ uses the Tangent-Distances for a local point approximation
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of an assumed data manifold via prototypial representations.
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The GTLVQ requires subspace projectors for transforming the data
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and prototypes into the affine subspace. Every prototype is
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equipped with a specific subpspace and represents a point
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approximation of the assumed manifold.
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In practice prototypes and data are projected on this manifold
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and pairwise euclidean distance computes.
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References
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----------
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.. [1] Saralajew, Sascha; Villmann, Thomas: Transfer learning
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in classification based on manifolc. models and its relation
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to tangent metric learning. In: 2017 International Joint
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Conference on Neural Networks (IJCNN).
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Bd. 2017-May : IEEE, 2017, S. 1756–1765
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"""
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def __init__(
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self,
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num_classes,
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subspace_data=None,
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prototype_data=None,
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subspace_size=256,
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tangent_projection_type="local",
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prototypes_per_class=2,
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feature_dim=256,
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):
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super(GTLVQ, self).__init__()
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self.num_protos = num_classes * prototypes_per_class
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self.num_protos_class = prototypes_per_class
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self.subspace_size = feature_dim if subspace_size is None else subspace_size
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self.feature_dim = feature_dim
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self.num_classes = num_classes
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cls_initializer = StratifiedMeanInitializer(prototype_data)
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cls_distribution = {
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"num_classes": num_classes,
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"prototypes_per_class": prototypes_per_class,
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}
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self.cls = LabeledComponents(cls_distribution, cls_initializer)
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if subspace_data is None:
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raise ValueError("Init Data must be specified!")
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self.tpt = tangent_projection_type
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with torch.no_grad():
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if self.tpt == "local":
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self.init_local_subspace(subspace_data, subspace_size,
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self.num_protos)
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elif self.tpt == "global":
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self.init_gobal_subspace(subspace_data, subspace_size)
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else:
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self.subspaces = None
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def forward(self, x):
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if self.tpt == "local":
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dis = self.local_tangent_distances(x)
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elif self.tpt == "gloabl":
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dis = self.global_tangent_distances(x)
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else:
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dis = (x @ self.cls.prototypes.T) / (
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torch.norm(x, dim=1, keepdim=True) @ torch.norm(
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self.cls.prototypes, dim=1, keepdim=True).T)
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return dis
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def init_gobal_subspace(self, data, num_subspaces):
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_, _, v = torch.svd(data)
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subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
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subspaces = subspace[:, :num_subspaces]
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self.subspaces = nn.Parameter(subspaces, requires_grad=True)
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def init_local_subspace(self, data, num_subspaces, num_protos):
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data = data - torch.mean(data, dim=0)
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_, _, v = torch.svd(data, some=False)
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v = v[:, :num_subspaces]
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subspaces = v.unsqueeze(0).repeat_interleave(num_protos, 0)
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self.subspaces = nn.Parameter(subspaces, requires_grad=True)
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def global_tangent_distances(self, x):
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# Tangent Projection
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x, projected_prototypes = (
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x @ self.subspaces,
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self.cls.prototypes @ self.subspaces,
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)
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# Euclidean Distance
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return euclidean_distance_matrix(x, projected_prototypes)
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def local_tangent_distances(self, x):
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# Tangent Distance
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x = x.unsqueeze(1).expand(x.size(0), self.cls.num_components,
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x.size(-1))
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protos = self.cls()[0].unsqueeze(0).expand(x.size(0),
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self.cls.num_components,
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x.size(-1))
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projectors = torch.eye(
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self.subspaces.shape[-2], device=x.device) - torch.bmm(
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self.subspaces, self.subspaces.permute([0, 2, 1]))
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diff = (x - protos)
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diff = diff.permute([1, 0, 2])
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diff = torch.bmm(diff, projectors)
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diff = torch.norm(diff, 2, dim=-1).T
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return diff
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def get_parameters(self):
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return {
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"params": self.cls.components,
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}, {
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"params": self.subspaces
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}
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def orthogonalize_subspace(self):
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if self.subspaces is not None:
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with torch.no_grad():
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ortho_subpsaces = (orthogonalization(self.subspaces)
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if self.tpt == "global" else
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torch.nn.init.orthogonal_(self.subspaces))
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self.subspaces.copy_(ortho_subpsaces)
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