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Improve documentation.

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This commit is contained in:
Alexander Engelsberger 2021-04-21 14:55:54 +02:00
parent c42df6e203
commit 34973808b8
5 changed files with 164 additions and 102 deletions
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20
docs/source/api.rst
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@ -11,8 +11,26 @@ Datasets
Functions
--------------------------------------
.. automodule:: prototorch.functions
**Dimensions:**
- :math:`B` ... Batch size
- :math:`P` ... Number of prototypes
- :math:`n_x` ... Data dimension for vectorial data
- :math:`n_w` ... Data dimension for vectorial prototypes
Activations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. automodule:: prototorch.functions.activations
:members:
:exclude-members: register_activation, get_activation
:undoc-members:
Distances
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. automodule:: prototorch.functions.distances
:members:
:exclude-members: sed
:undoc-members:
Modules

39
prototorch/datasets/abstract.py
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@ -14,6 +14,7 @@ import torch
class Dataset(torch.utils.data.Dataset):
"""Abstract dataset class to be inherited."""
_repr_indent = 2
def __init__(self, root):
@ -30,8 +31,9 @@ class Dataset(torch.utils.data.Dataset):
class ProtoDataset(Dataset):
"""Abstract dataset class to be inherited."""
training_file = 'training.pt'
test_file = 'test.pt'
training_file = "training.pt"
test_file = "test.pt"
def __init__(self, root, train=True, download=True, verbose=True):
super().__init__(root)
@ -39,43 +41,44 @@ class ProtoDataset(Dataset):
self.verbose = verbose
if download:
self.download()
self._download()
if not self._check_exists():
raise RuntimeError('Dataset not found. '
'You can use download=True to download it')
raise RuntimeError(
"Dataset not found. " "You can use download=True to download it"
)
data_file = self.training_file if self.train else self.test_file
self.data, self.targets = torch.load(
os.path.join(self.processed_folder, data_file))
os.path.join(self.processed_folder, data_file)
)
@property
def raw_folder(self):
return os.path.join(self.root, self.__class__.__name__, 'raw')
return os.path.join(self.root, self.__class__.__name__, "raw")
@property
def processed_folder(self):
return os.path.join(self.root, self.__class__.__name__, 'processed')
return os.path.join(self.root, self.__class__.__name__, "processed")
@property
def class_to_idx(self):
return {_class: i for i, _class in enumerate(self.classes)}
def _check_exists(self):
return (os.path.exists(
os.path.join(self.processed_folder, self.training_file))
and os.path.exists(
os.path.join(self.processed_folder, self.test_file)))
return os.path.exists(
os.path.join(self.processed_folder, self.training_file)
) and os.path.exists(os.path.join(self.processed_folder, self.test_file))
def __repr__(self):
head = 'Dataset ' + self.__class__.__name__
body = ['Number of datapoints: {}'.format(self.__len__())]
head = "Dataset " + self.__class__.__name__
body = ["Number of datapoints: {}".format(self.__len__())]
if self.root is not None:
body.append('Root location: {}'.format(self.root))
body.append("Root location: {}".format(self.root))
body += self.extra_repr().splitlines()
lines = [head] + [' ' * self._repr_indent + line for line in body]
return '\n'.join(lines)
lines = [head] + [" " * self._repr_indent + line for line in body]
return "\n".join(lines)
def extra_repr(self):
return f"Split: {'Train' if self.train is True else 'Test'}"
@ -83,5 +86,5 @@ class ProtoDataset(Dataset):
def __len__(self):
return len(self.data)
def download(self):
def _download(self):
raise NotImplementedError

49
prototorch/datasets/tecator.py
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@ -46,42 +46,45 @@ from prototorch.datasets.abstract import ProtoDataset
class Tecator(ProtoDataset):
"""Tecator dataset for classification."""
resources = [
('1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0',
'ba5607c580d0f91bb27dc29d13c2f8df'),
"""
`Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
for classification.
"""
_resources = [
("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0", "ba5607c580d0f91bb27dc29d13c2f8df"),
] # (google_storage_id, md5hash)
classes = ['0 - low_fat', '1 - high_fat']
classes = ["0 - low_fat", "1 - high_fat"]
def __getitem__(self, index):
img, target = self.data[index], int(self.targets[index])
return img, target
def download(self):
def _download(self):
"""Download the data if it doesn't exist in already."""
if self._check_exists():
return
if self.verbose:
print('Making directories...')
print("Making directories...")
os.makedirs(self.raw_folder, exist_ok=True)
os.makedirs(self.processed_folder, exist_ok=True)
if self.verbose:
print('Downloading...')
for fileid, md5 in self.resources:
filename = 'tecator.npz'
download_file_from_google_drive(fileid,
root=self.raw_folder,
filename=filename,
md5=md5)
print("Downloading...")
for fileid, md5 in self._resources:
filename = "tecator.npz"
download_file_from_google_drive(
fileid, root=self.raw_folder, filename=filename, md5=md5
)
if self.verbose:
print('Processing...')
with np.load(os.path.join(self.raw_folder, 'tecator.npz'),
allow_pickle=False) as f:
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
print("Processing...")
with np.load(
os.path.join(self.raw_folder, "tecator.npz"), allow_pickle=False
) as f:
x_train, y_train = f["x_train"], f["y_train"]
x_test, y_test = f["x_test"], f["y_test"]
training_set = [
torch.tensor(x_train, dtype=torch.float32),
torch.tensor(y_train),
@ -91,12 +94,10 @@ class Tecator(ProtoDataset):
torch.tensor(y_test),
]
with open(os.path.join(self.processed_folder, self.training_file),
'wb') as f:
with open(os.path.join(self.processed_folder, self.training_file), "wb") as f:
torch.save(training_set, f)
with open(os.path.join(self.processed_folder, self.test_file),
'wb') as f:
with open(os.path.join(self.processed_folder, self.test_file), "wb") as f:
torch.save(test_set, f)
if self.verbose:
print('Done!')
print("Done!")

157
prototorch/functions/distances.py
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@ -1,15 +1,24 @@
"""ProtoTorch distance functions."""
import torch
from prototorch.functions.helper import equal_int_shape, _int_and_mixed_shape, _check_shapes
from prototorch.functions.helper import (
equal_int_shape,
_int_and_mixed_shape,
_check_shapes,
)
import numpy as np
def squared_euclidean_distance(x, y):
"""Compute the squared Euclidean distance between :math:`x` and :math:`y`.
r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
Expected dimension of x is 2.
Expected dimension of y is 2.
Compute :math:`{\langle \bm x - \bm y \rangle}_2`
:param `torch.tensor` x: Two dimensional vector
:param `torch.tensor` y: Two dimensional vector
**Alias:**
``prototorch.functions.distances.sed``
"""
expanded_x = x.unsqueeze(dim=1)
batchwise_difference = y - expanded_x
@ -19,10 +28,15 @@ def squared_euclidean_distance(x, y):
def euclidean_distance(x, y):
"""Compute the Euclidean distance between :math:`x` and :math:`y`.
r"""Compute the Euclidean distance between :math:`x` and :math:`y`.
Expected dimension of x is 2.
Expected dimension of y is 2.
Compute :math:`\sqrt{{\langle \bm x - \bm y \rangle}_2}`
:param `torch.tensor` x: Input Tensor of shape :math:`X \times N`
:param `torch.tensor` y: Input Tensor of shape :math:`Y \times N`
:returns: Distance Tensor of shape :math:`X \times Y`
:rtype: `torch.tensor`
"""
distances_raised = squared_euclidean_distance(x, y)
distances = torch.sqrt(distances_raised)
@ -30,10 +44,17 @@ def euclidean_distance(x, y):
def lpnorm_distance(x, y, p):
r"""Compute :math:`{\langle x, y \rangle}_p`.
r"""
Calculates the lp-norm between :math:`\bm x` and :math:`\bm y`.
Also known as Minkowski distance.
Expected dimension of x is 2.
Expected dimension of y is 2.
Compute :math:`{\| \bm x - \bm y \|}_p`.
Calls ``torch.cdist``
:param `torch.tensor` x: Two dimensional vector
:param `torch.tensor` y: Two dimensional vector
:param p: p parameter of the lp norm
"""
distances = torch.cdist(x, y, p=p)
return distances
@ -42,11 +63,11 @@ def lpnorm_distance(x, y, p):
def omega_distance(x, y, omega):
r"""Omega distance.
Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
Expected dimension of x is 2.
Expected dimension of y is 2.
Expected dimension of omega is 2.
:param `torch.tensor` x: Two dimensional vector
:param `torch.tensor` y: Two dimensional vector
:param `torch.tensor` omega: Two dimensional matrix
"""
projected_x = x @ omega
projected_y = y @ omega
@ -57,48 +78,55 @@ def omega_distance(x, y, omega):
def lomega_distance(x, y, omegas):
r"""Localized Omega distance.
Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
Expected dimension of x is 2.
Expected dimension of y is 2.
Expected dimension of omegas is 3.
:param `torch.tensor` x: Two dimensional vector
:param `torch.tensor` y: Two dimensional vector
:param `torch.tensor` omegas: Three dimensional matrix
"""
projected_x = x @ omegas
projected_y = torch.diagonal(y @ omegas).T
expanded_y = torch.unsqueeze(projected_y, dim=1)
batchwise_difference = expanded_y - projected_x
differences_squared = batchwise_difference**2
differences_squared = batchwise_difference ** 2
distances = torch.sum(differences_squared, dim=2)
distances = distances.permute(1, 0)
return distances
def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
r""" Computes an euclidean distanes matrix given two distinct vectors.
r"""Computes an euclidean distances matrix given two distinct vectors.
last dimension must be the vector dimension!
compute the distance via the identity of the dot product. This avoids the memory overhead due to the subtraction!
x.shape = (number_of_x_vectors, vector_dim)
y.shape = (number_of_y_vectors, vector_dim)
- ``x.shape = (number_of_x_vectors, vector_dim)``
- ``y.shape = (number_of_y_vectors, vector_dim)``
output: matrix of distances (number_of_x_vectors, number_of_y_vectors)
"""
for tensor in [x, y]:
if tensor.ndim != 2:
raise ValueError(
'The tensor dimension must be two. You provide: tensor.ndim=' +
str(tensor.ndim) + '.')
"The tensor dimension must be two. You provide: tensor.ndim="
+ str(tensor.ndim)
+ "."
)
if not equal_int_shape([tuple(x.shape)[1]], [tuple(y.shape)[1]]):
raise ValueError(
'The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]='
+ str(tuple(x.shape)[1]) + ' and tuple(y.shape)(y)[1]=' +
str(tuple(y.shape)[1]) + '.')
"The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]="
+ str(tuple(x.shape)[1])
+ " and tuple(y.shape)(y)[1]="
+ str(tuple(y.shape)[1])
+ "."
)
y = torch.transpose(y)
diss = torch.sum(x**2, axis=1,
keepdims=True) - 2 * torch.dot(x, y) + torch.sum(
y**2, axis=0, keepdims=True)
diss = (
torch.sum(x ** 2, axis=1, keepdims=True)
- 2 * torch.dot(x, y)
+ torch.sum(y ** 2, axis=0, keepdims=True)
)
if not squared:
if epsilon == 0:
@ -110,13 +138,19 @@ def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
r""" Tangent distances based on the tensorflow implementation of Sascha Saralajews
For more info about Tangen distances see DOI:10.1109/IJCNN.2016.7727534.
r"""Tangent distances based on the tensorflow implementation of Sascha Saralajews
For more info about Tangen distances see
DOI:10.1109/IJCNN.2016.7727534.
The subspaces is always assumed as transposed and must be orthogonal!
For local non sparse signals subspaces must be provided!
shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
shape(protos): proto_number x dim1 x dim2 x ... x dimN
shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN) x prod(projected_atom_shape)
- shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
- shape(protos): proto_number x dim1 x dim2 x ... x dimN
- shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN) x prod(projected_atom_shape)
subspace should be orthogonalized
Pytorch implementation of Sascha Saralajew's tensorflow code.
Translation by Christoph Raab
@ -139,18 +173,19 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
if subspaces.ndim == 2:
# clean solution without map if the matrix_scope is global
projectors = torch.eye(subspace_int_shape[-2]) - torch.dot(
subspaces, torch.transpose(subspaces))
subspaces, torch.transpose(subspaces)
)
projected_signals = torch.dot(signals, projectors)
projected_protos = torch.dot(protos, projectors)
diss = euclidean_distance_matrix(projected_signals,
projected_protos,
squared=squared,
epsilon=epsilon)
diss = euclidean_distance_matrix(
projected_signals, projected_protos, squared=squared, epsilon=epsilon
)
diss = torch.reshape(
diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
)
return torch.permute(diss, [0, 2, 1])
@ -158,18 +193,21 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# no solution without map possible --> memory efficient but slow!
projectors = torch.eye(subspace_int_shape[-2]) - torch.bmm(
subspaces,
subspaces) #K.batch_dot(subspaces, subspaces, [2, 2])
subspaces, subspaces
) # K.batch_dot(subspaces, subspaces, [2, 2])
projected_protos = (protos @ subspaces
).T #K.batch_dot(projectors, protos, [1, 1]))
projected_protos = (
protos @ subspaces
).T # K.batch_dot(projectors, protos, [1, 1]))
def projected_norm(projector):
return torch.sum(torch.dot(signals, projector)**2, axis=1)
return torch.sum(torch.dot(signals, projector) ** 2, axis=1)
diss = torch.transpose(map(projected_norm, projectors)) \
- 2 * torch.dot(signals, projected_protos) \
+ torch.sum(projected_protos**2, axis=0, keepdims=True)
diss = (
torch.transpose(map(projected_norm, projectors))
- 2 * torch.dot(signals, projected_protos)
+ torch.sum(projected_protos ** 2, axis=0, keepdims=True)
)
if not squared:
if epsilon == 0:
@ -178,7 +216,8 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
diss = torch.sqrt(torch.max(diss, epsilon))
diss = torch.reshape(
diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
)
return torch.permute(diss, [0, 2, 1])
@ -189,17 +228,18 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# global tangent space
if subspaces.ndim == 2:
#Scope Projectors
# Scope Projectors
projectors = subspaces #
#Scope: Tangentspace Projections
# Scope: Tangentspace Projections
diff = torch.reshape(
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
)
projected_diff = diff @ projectors
projected_diff = torch.reshape(
projected_diff,
(signal_shape[0], signal_shape[2], signal_shape[1]) +
signal_shape[3:])
(signal_shape[0], signal_shape[2], signal_shape[1]) + signal_shape[3:],
)
diss = torch.norm(projected_diff, 2, dim=-1)
return diss.permute([0, 2, 1])
@ -211,13 +251,14 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# Scope: Tangentspace Projections
diff = torch.reshape(
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
)
diff = diff.permute([1, 0, 2])
projected_diff = torch.bmm(diff, projectors)
projected_diff = torch.reshape(
projected_diff,
(signal_shape[1], signal_shape[0], signal_shape[2]) +
signal_shape[3:])
(signal_shape[1], signal_shape[0], signal_shape[2]) + signal_shape[3:],
)
diss = torch.norm(projected_diff, 2, dim=-1)
return diss.permute([1, 0, 2]).squeeze(-1)

1
setup.py
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@ -8,7 +8,6 @@
ProtoTorch Core Package
"""
from setuptools import setup
from setuptools import find_packages