Temporarily remove tangent distance
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Jensun Ravichandran
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d26a626677
@ -1,15 +1,7 @@
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"""ProtoTorch distances"""
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import numpy as np
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import torch
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# from prototorch.functions.helper import (
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# _check_shapes,
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# _int_and_mixed_shape,
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# equal_int_shape,
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# get_flat,
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# )
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def squared_euclidean_distance(x, y):
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r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
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@ -102,160 +94,5 @@ def lomega_distance(x, y, omegas):
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return distances
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# def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
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# r"""Computes an euclidean distances matrix given two distinct vectors.
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# last dimension must be the vector dimension!
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# compute the distance via the identity of the dot product. This avoids the memory overhead due to the subtraction!
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# - ``x.shape = (number_of_x_vectors, vector_dim)``
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# - ``y.shape = (number_of_y_vectors, vector_dim)``
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# output: matrix of distances (number_of_x_vectors, number_of_y_vectors)
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# """
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# for tensor in [x, y]:
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# if tensor.ndim != 2:
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# raise ValueError(
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# "The tensor dimension must be two. You provide: tensor.ndim=" +
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# str(tensor.ndim) + ".")
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# if not equal_int_shape([tuple(x.shape)[1]], [tuple(y.shape)[1]]):
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# raise ValueError(
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# "The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]="
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# + str(tuple(x.shape)[1]) + " and tuple(y.shape)(y)[1]=" +
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# str(tuple(y.shape)[1]) + ".")
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# y = torch.transpose(y)
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# diss = (torch.sum(x**2, axis=1, keepdims=True) - 2 * torch.dot(x, y) +
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# torch.sum(y**2, axis=0, keepdims=True))
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# if not squared:
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# if epsilon == 0:
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# diss = torch.sqrt(diss)
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# else:
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# diss = torch.sqrt(torch.max(diss, epsilon))
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# return diss
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# def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
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# r"""Tangent distances based on the tensorflow implementation of Sascha Saralajews
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# For more info about Tangen distances see
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# DOI:10.1109/IJCNN.2016.7727534.
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# The subspaces is always assumed as transposed and must be orthogonal!
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# For local non sparse signals subspaces must be provided!
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# - shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
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# - shape(protos): proto_number x dim1 x dim2 x ... x dimN
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# - shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN) x prod(projected_atom_shape)
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# subspace should be orthogonalized
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# Pytorch implementation of Sascha Saralajew's tensorflow code.
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# Translation by Christoph Raab
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# """
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# signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
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# proto_shape, proto_int_shape = _int_and_mixed_shape(protos)
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# subspace_int_shape = tuple(subspaces.shape)
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# # check if the shapes are correct
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# _check_shapes(signal_int_shape, proto_int_shape)
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# atom_axes = list(range(3, len(signal_int_shape)))
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# # for sparse signals, we use the memory efficient implementation
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# if signal_int_shape[1] == 1:
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# signals = torch.reshape(signals, [-1, np.prod(signal_shape[3:])])
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# if len(atom_axes) > 1:
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# protos = torch.reshape(protos, [proto_shape[0], -1])
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# if subspaces.ndim == 2:
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# # clean solution without map if the matrix_scope is global
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# projectors = torch.eye(subspace_int_shape[-2]) - torch.dot(
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# subspaces, torch.transpose(subspaces))
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# projected_signals = torch.dot(signals, projectors)
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# projected_protos = torch.dot(protos, projectors)
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# diss = euclidean_distance_matrix(projected_signals,
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# projected_protos,
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# squared=squared,
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# epsilon=epsilon)
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# diss = torch.reshape(
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# diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
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# return torch.permute(diss, [0, 2, 1])
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# else:
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# # no solution without map possible --> memory efficient but slow!
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# projectors = torch.eye(subspace_int_shape[-2]) - torch.bmm(
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# subspaces,
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# subspaces) # K.batch_dot(subspaces, subspaces, [2, 2])
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# projected_protos = (protos @ subspaces
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# ).T # K.batch_dot(projectors, protos, [1, 1]))
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# def projected_norm(projector):
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# return torch.sum(torch.dot(signals, projector)**2, axis=1)
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# diss = (torch.transpose(map(projected_norm, projectors)) -
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# 2 * torch.dot(signals, projected_protos) +
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# torch.sum(projected_protos**2, axis=0, keepdims=True))
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# if not squared:
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# if epsilon == 0:
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# diss = torch.sqrt(diss)
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# else:
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# diss = torch.sqrt(torch.max(diss, epsilon))
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# diss = torch.reshape(
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# diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
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# return torch.permute(diss, [0, 2, 1])
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# else:
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# signals = signals.permute([0, 2, 1] + atom_axes)
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# diff = signals - protos
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# # global tangent space
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# if subspaces.ndim == 2:
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# # Scope Projectors
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# projectors = subspaces #
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# # Scope: Tangentspace Projections
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# diff = torch.reshape(
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# diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
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# projected_diff = diff @ projectors
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# projected_diff = torch.reshape(
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# projected_diff,
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# (signal_shape[0], signal_shape[2], signal_shape[1]) +
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# signal_shape[3:],
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# )
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# diss = torch.norm(projected_diff, 2, dim=-1)
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# return diss.permute([0, 2, 1])
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# # local tangent spaces
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# else:
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# # Scope: Calculate Projectors
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# projectors = subspaces
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# # Scope: Tangentspace Projections
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# diff = torch.reshape(
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# diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
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# diff = diff.permute([1, 0, 2])
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# projected_diff = torch.bmm(diff, projectors)
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# projected_diff = torch.reshape(
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# projected_diff,
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# (signal_shape[1], signal_shape[0], signal_shape[2]) +
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# signal_shape[3:],
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# )
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# diss = torch.norm(projected_diff, 2, dim=-1)
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# return diss.permute([1, 0, 2]).squeeze(-1)
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# Aliases
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sed = squared_euclidean_distance
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