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6 Commits

Author SHA1 Message Date
Alexander Engelsberger
09c80e2d54 Merge branch 'master' into kernel_distances 2021-05-11 16:10:56 +02:00
Alexander Engelsberger
65e0637b17 Fix RBF Kernel Dimensions. 2021-04-27 17:58:05 +02:00
Alexander Engelsberger
209f9e641b Fix kernel dimensions. 2021-04-27 16:56:56 +02:00
Alexander Engelsberger
ba537fe1d5 Automatic formatting. 2021-04-27 15:43:10 +02:00
Alexander Engelsberger
b0cd2de18e Batch Kernel. [Ineficient] 2021-04-27 15:38:34 +02:00
Alexander Engelsberger
7d353f5b5a Kernel Distances. 2021-04-27 12:06:15 +02:00
5 changed files with 221 additions and 7 deletions

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@ -3,8 +3,11 @@
import numpy as np
import torch
from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
equal_int_shape)
from prototorch.functions.helper import (
_check_shapes,
_int_and_mixed_shape,
equal_int_shape,
)
def squared_euclidean_distance(x, y):
@ -261,5 +264,86 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
return diss.permute([1, 0, 2]).squeeze(-1)
class KernelDistance:
r"""Kernel Distance
Distance based on a kernel function.
"""
def __init__(self, kernel_fn):
self.kernel_fn = kernel_fn
def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
return self._single_call(x_batch, y_batch)
def _single_call(self, x, y):
remove_dims = []
if len(x.shape) == 1:
x = x.unsqueeze(0)
remove_dims.append(0)
if len(y.shape) == 1:
y = y.unsqueeze(0)
remove_dims.append(-1)
output = self.kernel_fn(x, x).diag().unsqueeze(1) - 2 * self.kernel_fn(
x, y) + self.kernel_fn(y, y).diag()
for dim in remove_dims:
output.squeeze_(dim)
return torch.sqrt(output)
class BatchKernelDistance:
r"""Kernel Distance
Distance based on a kernel function.
"""
def __init__(self, kernel_fn):
self.kernel_fn = kernel_fn
def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
remove_dims = 0
# Extend Single inputs
if len(x_batch.shape) == 1:
x_batch = x_batch.unsqueeze(0)
remove_dims += 1
if len(y_batch.shape) == 1:
y_batch = y_batch.unsqueeze(0)
remove_dims += 1
# Loop over batches
output = torch.FloatTensor(len(x_batch), len(y_batch))
for i, x in enumerate(x_batch):
for j, y in enumerate(y_batch):
output[i][j] = self._single_call(x, y)
for _ in range(remove_dims):
output.squeeze_(0)
return output
def _single_call(self, x, y):
kappa_xx = self.kernel_fn(x, x)
kappa_xy = self.kernel_fn(x, y)
kappa_yy = self.kernel_fn(y, y)
squared_distance = kappa_xx - 2 * kappa_xy + kappa_yy
return torch.sqrt(squared_distance)
class SquaredKernelDistance(KernelDistance):
r"""Squared Kernel Distance
Kernel distance without final squareroot.
"""
def single_call(self, x, y):
kappa_xx = self.kernel_fn(x, x)
kappa_xy = self.kernel_fn(x, y)
kappa_yy = self.kernel_fn(y, y)
return kappa_xx - 2 * kappa_xy + kappa_yy
# Aliases
sed = squared_euclidean_distance
sed = squared_euclidean_distance

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@ -0,0 +1,28 @@
"""
Experimental Kernels
"""
import torch
class ExplicitKernel:
def __init__(self, projection=torch.nn.Identity()):
self.projection = projection
def __call__(self, x, y):
return self.projection(x) @ self.projection(y).T
class RadialBasisFunctionKernel:
def __init__(self, sigma) -> None:
self.s2 = sigma * sigma
def __call__(self, x, y):
remove_dim = False
if len(x.shape) > 1:
x = x.unsqueeze(1)
remove_dim = True
output = torch.exp(-torch.sum((x - y)**2, dim=-1) / (2 * self.s2))
if remove_dim:
output = output.squeeze(1)
return output

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@ -1,8 +1,7 @@
import torch
from torch import nn
from prototorch.functions.distances import (euclidean_distance_matrix,
tangent_distance)
from prototorch.functions.distances import euclidean_distance_matrix, tangent_distance
from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
from prototorch.functions.normalization import orthogonalization
from prototorch.modules.prototypes import Prototypes1D

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@ -5,8 +5,13 @@ import unittest
import numpy as np
import torch
from prototorch.functions import (activations, competitions, distances,
initializers, losses)
from prototorch.functions import (
activations,
competitions,
distances,
initializers,
losses,
)
class TestActivations(unittest.TestCase):

98
tests/test_kernels.py Normal file
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@ -0,0 +1,98 @@
"""ProtoTorch kernels test suite."""
import unittest
import numpy as np
import torch
from prototorch.functions.distances import KernelDistance
from prototorch.functions.kernels import ExplicitKernel, RadialBasisFunctionKernel
class TestExplicitKernel(unittest.TestCase):
def setUp(self):
self.single_x = torch.randn(1024)
self.single_y = torch.randn(1024)
self.batch_x = torch.randn(32, 1024)
self.batch_y = torch.randn(32, 1024)
def test_single_values(self):
kernel = ExplicitKernel()
self.assertEqual(
kernel(self.single_x, self.single_y).shape, torch.Size([]))
def test_single_batch(self):
kernel = ExplicitKernel()
self.assertEqual(
kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
def test_batch_single(self):
kernel = ExplicitKernel()
self.assertEqual(
kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
def test_batch_values(self):
kernel = ExplicitKernel()
self.assertEqual(
kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
class TestRadialBasisFunctionKernel(unittest.TestCase):
def setUp(self):
self.single_x = torch.randn(1024)
self.single_y = torch.randn(1024)
self.batch_x = torch.randn(32, 1024)
self.batch_y = torch.randn(32, 1024)
def test_single_values(self):
kernel = RadialBasisFunctionKernel(1)
self.assertEqual(
kernel(self.single_x, self.single_y).shape, torch.Size([]))
def test_single_batch(self):
kernel = RadialBasisFunctionKernel(1)
self.assertEqual(
kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
def test_batch_single(self):
kernel = RadialBasisFunctionKernel(1)
self.assertEqual(
kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
def test_batch_values(self):
kernel = RadialBasisFunctionKernel(1)
self.assertEqual(
kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
class TestKernelDistance(unittest.TestCase):
def setUp(self):
self.single_x = torch.randn(1024)
self.single_y = torch.randn(1024)
self.batch_x = torch.randn(32, 1024)
self.batch_y = torch.randn(32, 1024)
self.kernel = ExplicitKernel()
def test_single_values(self):
distance = KernelDistance(self.kernel)
self.assertEqual(
distance(self.single_x, self.single_y).shape, torch.Size([]))
def test_single_batch(self):
distance = KernelDistance(self.kernel)
self.assertEqual(
distance(self.single_x, self.batch_y).shape, torch.Size([32]))
def test_batch_single(self):
distance = KernelDistance(self.kernel)
self.assertEqual(
distance(self.batch_x, self.single_y).shape, torch.Size([32]))
def test_batch_values(self):
distance = KernelDistance(self.kernel)
self.assertEqual(
distance(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))