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Alexander Engelsberger 2021-04-23 17:24:53 +02:00
parent e1d56595c1
commit 7c30ffe2c7
28 changed files with 393 additions and 321 deletions
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30
docs/source/conf.py
View File

@ -12,9 +12,8 @@
#
import os
import sys
sys.path.insert(0, os.path.abspath("../../"))
import sphinx_rtd_theme
sys.path.insert(0, os.path.abspath("../../"))
# -- Project information -----------------------------------------------------
@ -128,15 +127,12 @@ latex_elements = {
# The paper size ("letterpaper" or "a4paper").
#
# "papersize": "letterpaper",
# The font size ("10pt", "11pt" or "12pt").
#
# "pointsize": "10pt",
# Additional stuff for the LaTeX preamble.
#
# "preamble": "",
# Latex figure (float) alignment
#
# "figure_align": "htbp",
@ -146,15 +142,21 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, "prototorch.tex", "ProtoTorch Documentation",
"Jensun Ravichandran", "manual"),
(
master_doc,
"prototorch.tex",
"ProtoTorch Documentation",
"Jensun Ravichandran",
"manual",
),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)]
man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
1)]
# -- Options for Texinfo output -------------------------------------------
@ -162,9 +164,15 @@ man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, "prototorch", "ProtoTorch Documentation", author, "prototorch",
"Prototype-based machine learning in PyTorch.",
"Miscellaneous"),
(
master_doc,
"prototorch",
"ProtoTorch Documentation",
author,
"prototorch",
"Prototype-based machine learning in PyTorch.",
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.

32
examples/glvq_iris.py
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@ -3,13 +3,14 @@
import numpy as np
import torch
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torchinfo import summary
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import euclidean_distance
from prototorch.modules.losses import GLVQLoss
from prototorch.modules.prototypes import Prototypes1D
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torchinfo import summary
# Prepare and preprocess the data
scaler = StandardScaler()
@ -29,7 +30,8 @@ class Model(torch.nn.Module):
prototypes_per_class=3,
nclasses=3,
prototype_initializer="stratified_random",
data=[x_train, y_train])
data=[x_train, y_train],
)
def forward(self, x):
protos = self.proto_layer.prototypes
@ -61,8 +63,10 @@ for epoch in range(70):
with torch.no_grad():
pred = wtac(dis, plabels)
correct = pred.eq(y_in.view_as(pred)).sum().item()
acc = 100. * correct / len(x_train)
print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%")
acc = 100.0 * correct / len(x_train)
print(
f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%"
)
# Take a gradient descent step
optimizer.zero_grad()
@ -83,13 +87,15 @@ for epoch in range(70):
ax.set_ylabel("Data dimension 2")
cmap = "viridis"
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
ax.scatter(protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor="k",
marker="D",
s=50)
ax.scatter(
protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor="k",
marker="D",
s=50,
)
# Paint decision regions
x = np.vstack((x_train, protos))

12
examples/gmlvq_tecator.py
View File

@ -20,11 +20,13 @@ class Model(torch.nn.Module):
"""GMLVQ model as a siamese network."""
super().__init__()
x, y = train_data.data, train_data.targets
self.p1 = Prototypes1D(input_dim=100,
prototypes_per_class=2,
nclasses=2,
prototype_initializer="stratified_random",
data=[x, y])
self.p1 = Prototypes1D(
input_dim=100,
prototypes_per_class=2,
nclasses=2,
prototype_initializer="stratified_random",
data=[x, y],
)
self.omega = torch.nn.Linear(in_features=100,
out_features=100,
bias=False)

120
examples/gtlvq_mnist.py
View File

@ -13,8 +13,9 @@ import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from prototorch.modules.losses import GLVQLoss
from prototorch.functions.helper import calculate_prototype_accuracy
from prototorch.modules.losses import GLVQLoss
from prototorch.modules.models import GTLVQ
# Parameters and options
@ -26,32 +27,40 @@ momentum = 0.5
log_interval = 10
cuda = "cuda:1"
random_seed = 1
device = torch.device(cuda if torch.cuda.is_available() else 'cpu')
device = torch.device(cuda if torch.cuda.is_available() else "cpu")
# Configures reproducability
torch.manual_seed(random_seed)
np.random.seed(random_seed)
# Prepare and preprocess the data
train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
'./files/',
train=True,
download=True,
transform=torchvision.transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
batch_size=batch_size_train,
shuffle=True)
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST(
"./files/",
train=True,
download=True,
transform=torchvision.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]),
),
batch_size=batch_size_train,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
'./files/',
train=False,
download=True,
transform=torchvision.transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
batch_size=batch_size_test,
shuffle=True)
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST(
"./files/",
train=False,
download=True,
transform=torchvision.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]),
),
batch_size=batch_size_test,
shuffle=True,
)
# Define the GLVQ model plus appropriate feature extractor
@ -67,25 +76,34 @@ class CNNGTLVQ(torch.nn.Module):
):
super(CNNGTLVQ, self).__init__()
#Feature Extractor - Simple CNN
self.fe = nn.Sequential(nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
nn.Conv2d(32, 64, 3, 1), nn.ReLU(),
nn.MaxPool2d(2), nn.Dropout(0.25),
nn.Flatten(), nn.Linear(9216, bottleneck_dim),
nn.Dropout(0.5), nn.LeakyReLU(),
nn.LayerNorm(bottleneck_dim))
# Feature Extractor - Simple CNN
self.fe = nn.Sequential(
nn.Conv2d(1, 32, 3, 1),
nn.ReLU(),
nn.Conv2d(32, 64, 3, 1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(0.25),
nn.Flatten(),
nn.Linear(9216, bottleneck_dim),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.LayerNorm(bottleneck_dim),
)
# Forward pass of subspace and prototype initialization data through feature extractor
subspace_data = self.fe(subspace_data)
prototype_data[0] = self.fe(prototype_data[0])
# Initialization of GTLVQ
self.gtlvq = GTLVQ(num_classes,
subspace_data,
prototype_data,
tangent_projection_type=tangent_projection_type,
feature_dim=bottleneck_dim,
prototypes_per_class=prototypes_per_class)
self.gtlvq = GTLVQ(
num_classes,
subspace_data,
prototype_data,
tangent_projection_type=tangent_projection_type,
feature_dim=bottleneck_dim,
prototypes_per_class=prototypes_per_class,
)
def forward(self, x):
# Feature Extraction
@ -103,20 +121,24 @@ subspace_data = torch.cat(
prototype_data = next(iter(train_loader))
# Build the CNN GTLVQ model
model = CNNGTLVQ(10,
subspace_data,
prototype_data,
tangent_projection_type="local",
bottleneck_dim=128).to(device)
model = CNNGTLVQ(
10,
subspace_data,
prototype_data,
tangent_projection_type="local",
bottleneck_dim=128,
).to(device)
# Optimize using SGD optimizer from `torch.optim`
optimizer = torch.optim.Adam([{
'params': model.fe.parameters()
}, {
'params': model.gtlvq.parameters()
}],
lr=learning_rate)
criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
optimizer = torch.optim.Adam(
[{
"params": model.fe.parameters()
}, {
"params": model.gtlvq.parameters()
}],
lr=learning_rate,
)
criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
# Training loop
for epoch in range(n_epochs):
@ -139,8 +161,8 @@ for epoch in range(n_epochs):
if batch_idx % log_interval == 0:
acc = calculate_prototype_accuracy(distances, y_train, plabels)
print(
f'Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
Train Acc: {acc.item():02.02f}')
f"Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
Train Acc: {acc.item():02.02f}")
# Test
with torch.no_grad():
@ -154,9 +176,9 @@ for epoch in range(n_epochs):
i = torch.argmin(test_distances, 1)
correct += torch.sum(y_test == test_plabels[i])
total += y_test.size(0)
print('Accuracy of the network on the test images: %d %%' %
print("Accuracy of the network on the test images: %d %%" %
(torch.true_divide(correct, total) * 100))
# Save the model
PATH = './glvq_mnist_model.pth'
PATH = "./glvq_mnist_model.pth"
torch.save(model.state_dict(), PATH)

28
examples/lgmlvq_iris.py
View File

@ -22,10 +22,12 @@ class Model(torch.nn.Module):
def __init__(self):
"""Local-GMLVQ model."""
super().__init__()
self.p1 = Prototypes1D(input_dim=2,
prototype_distribution=[1, 2, 2],
prototype_initializer="stratified_random",
data=[x_train, y_train])
self.p1 = Prototypes1D(
input_dim=2,
prototype_distribution=[1, 2, 2],
prototype_initializer="stratified_random",
data=[x_train, y_train],
)
omegas = torch.zeros(5, 2, 2)
self.omegas = torch.nn.Parameter(omegas)
eye_(self.omegas)
@ -76,14 +78,16 @@ for epoch in range(100):
ax.set_xlabel("Data dimension 1")
ax.set_ylabel("Data dimension 2")
cmap = "viridis"
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
ax.scatter(protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor='k',
marker='D',
s=50)
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
ax.scatter(
protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor="k",
marker="D",
s=50,
)
# Paint decision regions
x = np.vstack((x_train, protos))

16
prototorch/__init__.py
View File

@ -5,8 +5,6 @@
# #############################################
__version__ = "0.3.0-dev0"
from prototorch import datasets, functions, modules
__all_core__ = [
"datasets",
"functions",
@ -17,6 +15,7 @@ __all_core__ = [
# Plugin Loader
# #############################################
import pkgutil
import pkg_resources
__path__ = pkgutil.extend_path(__path__, __name__)
@ -25,7 +24,8 @@ __path__ = pkgutil.extend_path(__path__, __name__)
def discover_plugins():
return {
entry_point.name: entry_point.load()
for entry_point in pkg_resources.iter_entry_points("prototorch.plugins")
for entry_point in pkg_resources.iter_entry_points(
"prototorch.plugins")
}
@ -33,12 +33,10 @@ discovered_plugins = discover_plugins()
locals().update(discovered_plugins)
# Generate combines __version__ and __all__
version_plugins = "\n".join(
[
"- " + name + ": v" + plugin.__version__
for name, plugin in discovered_plugins.items()
]
)
version_plugins = "\n".join([
"- " + name + ": v" + plugin.__version__
for name, plugin in discovered_plugins.items()
])
if version_plugins != "":
version_plugins = "\nPlugins: \n" + version_plugins

2
prototorch/datasets/__init__.py
View File

@ -3,5 +3,5 @@
from .tecator import Tecator
__all__ = [
'Tecator',
"Tecator",
]

21
prototorch/datasets/tecator.py
View File

@ -52,7 +52,8 @@ class Tecator(ProtoDataset):
"""
_resources = [
("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0", "ba5607c580d0f91bb27dc29d13c2f8df"),
("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0",
"ba5607c580d0f91bb27dc29d13c2f8df"),
] # (google_storage_id, md5hash)
classes = ["0 - low_fat", "1 - high_fat"]
@ -74,15 +75,15 @@ class Tecator(ProtoDataset):
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
)
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:
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 = [
@ -94,9 +95,11 @@ 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:

10
prototorch/functions/__init__.py
View File

@ -4,9 +4,9 @@ from .activations import identity, sigmoid_beta, swish_beta
from .competitions import knnc, wtac
__all__ = [
'identity',
'sigmoid_beta',
'swish_beta',
'knnc',
'wtac',
"identity",
"sigmoid_beta",
"swish_beta",
"knnc",
"wtac",
]

2
prototorch/functions/activations.py
View File

@ -61,4 +61,4 @@ def get_activation(funcname):
return funcname
if funcname in ACTIVATIONS:
return ACTIVATIONS.get(funcname)
raise NameError(f'Activation {funcname} was not found.')
raise NameError(f"Activation {funcname} was not found.")

2
prototorch/functions/competitions.py
View File

@ -12,7 +12,7 @@ def stratified_min(distances, labels):
return distances
batch_size = distances.size()[0]
winning_distances = torch.zeros(nclasses, batch_size)
inf = torch.full_like(distances.T, fill_value=float('inf'))
inf = torch.full_like(distances.T, fill_value=float("inf"))
# distances_to_wpluses = torch.where(matcher, distances, inf)
for i, cl in enumerate(clabels):
# cdists = distances.T[labels == cl]

10
prototorch/functions/distances.py
View File

@ -1,12 +1,10 @@
"""ProtoTorch distance functions."""
import torch
from prototorch.functions.helper import (
equal_int_shape,
_int_and_mixed_shape,
_check_shapes,
)
import numpy as np
import torch
from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
equal_int_shape)
def squared_euclidean_distance(x, y):

6
prototorch/functions/helper.py
View File

@ -23,7 +23,7 @@ def predict_label(y_pred, plabels):
def mixed_shape(inputs):
if not torch.is_tensor(inputs):
raise ValueError('Input must be a tensor.')
raise ValueError("Input must be a tensor.")
else:
int_shape = list(inputs.shape)
# sometimes int_shape returns mixed integer types
@ -39,11 +39,11 @@ def mixed_shape(inputs):
def equal_int_shape(shape_1, shape_2):
if not isinstance(shape_1,
(tuple, list)) or not isinstance(shape_2, (tuple, list)):
raise ValueError('Input shapes must list or tuple.')
raise ValueError("Input shapes must list or tuple.")
for shape in [shape_1, shape_2]:
if not all([isinstance(x, int) or x is None for x in shape]):
raise ValueError(
'Input shapes must be list or tuple of int and None values.')
"Input shapes must be list or tuple of int and None values.")
if len(shape_1) != len(shape_2):
return False

2
prototorch/functions/initializers.py
View File

@ -104,4 +104,4 @@ def get_initializer(funcname):
return funcname
if funcname in INITIALIZERS:
return INITIALIZERS.get(funcname)
raise NameError(f'Initializer {funcname} was not found.')
raise NameError(f"Initializer {funcname} was not found.")

2
prototorch/functions/losses.py
View File

@ -11,7 +11,7 @@ def _get_dp_dm(distances, targets, plabels):
matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
not_matcher = torch.bitwise_not(matcher)
inf = torch.full_like(distances, fill_value=float('inf'))
inf = torch.full_like(distances, fill_value=float("inf"))
d_matching = torch.where(matcher, distances, inf)
d_unmatching = torch.where(not_matcher, distances, inf)
dp = torch.min(d_matching, dim=1, keepdim=True).values

4
prototorch/functions/normalization.py
View File

@ -1,7 +1,5 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import absolute_import, division, print_function
import torch

2
prototorch/modules/__init__.py
View File

@ -3,5 +3,5 @@
from .prototypes import Prototypes1D
__all__ = [
'Prototypes1D',
"Prototypes1D",
]

2
prototorch/modules/losses.py
View File

@ -7,7 +7,7 @@ from prototorch.functions.losses import glvq_loss
class GLVQLoss(torch.nn.Module):
def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
super().__init__(**kwargs)
self.margin = margin
self.squashing = get_activation(squashing)

63
prototorch/modules/models.py
View File

@ -1,9 +1,11 @@
from torch import nn
import torch
from prototorch.modules.prototypes import Prototypes1D
from prototorch.functions.distances import tangent_distance, euclidean_distance_matrix
from prototorch.functions.normalization import orthogonalization
from torch import nn
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
class GTLVQ(nn.Module):
@ -71,7 +73,7 @@ class GTLVQ(nn.Module):
subspace_data=None,
prototype_data=None,
subspace_size=256,
tangent_projection_type='local',
tangent_projection_type="local",
prototypes_per_class=2,
feature_dim=256,
):
@ -82,37 +84,39 @@ class GTLVQ(nn.Module):
self.feature_dim = feature_dim
if subspace_data is None:
raise ValueError('Init Data must be specified!')
raise ValueError("Init Data must be specified!")
self.tpt = tangent_projection_type
with torch.no_grad():
if self.tpt == 'local' or self.tpt == 'local_proj':
if self.tpt == "local" or self.tpt == "local_proj":
self.init_local_subspace(subspace_data)
elif self.tpt == 'global':
elif self.tpt == "global":
self.init_gobal_subspace(subspace_data, subspace_size)
else:
self.subspaces = None
# Hypothesis-Margin-Classifier
self.cls = Prototypes1D(input_dim=feature_dim,
prototypes_per_class=prototypes_per_class,
nclasses=num_classes,
prototype_initializer='stratified_mean',
data=prototype_data)
self.cls = Prototypes1D(
input_dim=feature_dim,
prototypes_per_class=prototypes_per_class,
nclasses=num_classes,
prototype_initializer="stratified_mean",
data=prototype_data,
)
def forward(self, x):
# Tangent Projection
if self.tpt == 'local_proj':
x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2)
if self.tpt == "local_proj":
x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2))
dis, proj_x = self.local_tangent_projection(x_conform)
proj_x = proj_x.reshape(x.shape[0] * self.num_protos,
self.feature_dim)
return proj_x, dis
elif self.tpt == "local":
x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2)
x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2))
dis = tangent_distance(x_conform, self.cls.prototypes,
self.subspaces)
elif self.tpt == "gloabl":
@ -127,25 +131,27 @@ class GTLVQ(nn.Module):
_, _, v = torch.svd(data)
subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
subspaces = subspace[:, :num_subspaces]
self.subspaces = torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True)
self.subspaces = (torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True))
def init_local_subspace(self, data):
_, _, v = torch.svd(data)
inital_projector = (torch.eye(v.shape[0]) - (v @ v.T)).T
subspaces = inital_projector.unsqueeze(0).repeat_interleave(
self.num_protos, 0)
self.subspaces = torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True)
self.subspaces = (torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True))
def global_tangent_distances(self, x):
# Tangent Projection
x, projected_prototypes = x @ self.subspaces, self.cls.prototypes @ self.subspaces
x, projected_prototypes = (
x @ self.subspaces,
self.cls.prototypes @ self.subspaces,
)
# Euclidean Distance
return euclidean_distance_matrix(x, projected_prototypes)
def local_tangent_projection(self,
signals):
def local_tangent_projection(self, signals):
# Note: subspaces is always assumed as transposed and must be orthogonal!
# 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
@ -183,8 +189,7 @@ class GTLVQ(nn.Module):
def orthogonalize_subspace(self):
if self.subspaces is not None:
with torch.no_grad():
ortho_subpsaces = orthogonalization(
self.subspaces
) if self.tpt == 'global' else torch.nn.init.orthogonal_(
self.subspaces)
ortho_subpsaces = (orthogonalization(self.subspaces)
if self.tpt == "global" else
torch.nn.init.orthogonal_(self.subspaces))
self.subspaces.copy_(ortho_subpsaces)

18
prototorch/modules/prototypes.py
View File

@ -29,14 +29,16 @@ class Prototypes1D(_Prototypes):
TODO Complete this doc-string.
"""
def __init__(self,
prototypes_per_class=1,
prototype_initializer="ones",
prototype_distribution=None,
data=None,
dtype=torch.float32,
one_hot_labels=False,
**kwargs):
def __init__(
self,
prototypes_per_class=1,
prototype_initializer="ones",
prototype_distribution=None,
data=None,
dtype=torch.float32,
one_hot_labels=False,
**kwargs,
):
# Convert tensors to python lists before processing
if prototype_distribution is not None:

1
prototorch/utils/__init__.py
View File

@ -1 +0,0 @@
from .colors import color_scheme, get_legend_handles

10
prototorch/utils/celluloid.py
View File

@ -1,13 +1,13 @@
"""Easy matplotlib animation. From https://github.com/jwkvam/celluloid."""
from typing import Dict, List
from collections import defaultdict
from typing import Dict, List
from matplotlib.figure import Figure
from matplotlib.artist import Artist
from matplotlib.animation import ArtistAnimation
from matplotlib.artist import Artist
from matplotlib.figure import Figure
__version__ = '0.2.0'
__version__ = "0.2.0"
class Camera:
@ -19,7 +19,7 @@ class Camera:
self._offsets: Dict[str, Dict[int, int]] = {
k: defaultdict(int)
for k in
['collections', 'patches', 'lines', 'texts', 'artists', 'images']
["collections", "patches", "lines", "texts", "artists", "images"]
}
self._photos: List[List[Artist]] = []

28
prototorch/utils/colors.py
View File

@ -1,13 +1,14 @@
"""ProtoFlow color utilities."""
from matplotlib import cm
from matplotlib.colors import Normalize
from matplotlib.colors import to_hex
from matplotlib.colors import to_rgb
import matplotlib.lines as mlines
from matplotlib import cm
from matplotlib.colors import Normalize, to_hex, to_rgb
def color_scheme(n, cmap="viridis", form="hex", tikz=False,
def color_scheme(n,
cmap="viridis",
form="hex",
tikz=False,
zero_indexed=False):
"""Return *n* colors from the color scheme.
@ -57,13 +58,16 @@ def get_legend_handles(labels, marker="dots", zero_indexed=False):
zero_indexed=zero_indexed)
for label, color in zip(labels, colors.values()):
if marker == "dots":
handle = mlines.Line2D([], [],
color="white",
markerfacecolor=color,
marker="o",
markersize=10,
markeredgecolor="k",
label=label)
handle = mlines.Line2D(
[],
[],
color="white",
markerfacecolor=color,
marker="o",
markersize=10,
markeredgecolor="k",
label=label,
)
else:
handle = mlines.Line2D([], [],
color=color,

57
prototorch/utils/utils.py
View File

@ -11,17 +11,17 @@ import numpy as np
def progressbar(title, value, end, bar_width=20):
percent = float(value) / end
arrow = '=' * int(round(percent * bar_width) - 1) + '>'
spaces = '.' * (bar_width - len(arrow))
sys.stdout.write('\r{}: [{}] {}%'.format(title, arrow + spaces,
arrow = "=" * int(round(percent * bar_width) - 1) + ">"
spaces = "." * (bar_width - len(arrow))
sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
int(round(percent * 100))))
sys.stdout.flush()
if percent == 1.0:
print()
def prettify_string(inputs, start='', sep=' ', end='\n'):
outputs = start + ' '.join(inputs.split()) + end
def prettify_string(inputs, start="", sep=" ", end="\n"):
outputs = start + " ".join(inputs.split()) + end
return outputs
@ -29,22 +29,22 @@ def pretty_print(inputs):
print(prettify_string(inputs))
def writelog(self, *logs, logdir='./logs', logfile='run.txt'):
def writelog(self, *logs, logdir="./logs", logfile="run.txt"):
f = os.path.join(logdir, logfile)
with open(f, 'a+') as fh:
with open(f, "a+") as fh:
for log in logs:
fh.write(log)
fh.write('\n')
fh.write("\n")
def start_tensorboard(self, logdir='./logs'):
cmd = f'tensorboard --logdir={logdir} --port=6006'
def start_tensorboard(self, logdir="./logs"):
cmd = f"tensorboard --logdir={logdir} --port=6006"
os.system(cmd)
def make_directory(save_dir):
if not os.path.exists(save_dir):
print(f'Making directory {save_dir}.')
print(f"Making directory {save_dir}.")
os.mkdir(save_dir)
@ -52,36 +52,36 @@ def make_gif(filenames, duration, output_file=None):
try:
import imageio
except ModuleNotFoundError as e:
print('Please install Protoflow with [other] extra requirements.')
print("Please install Protoflow with [other] extra requirements.")
raise (e)
images = list()
for filename in filenames:
images.append(imageio.imread(filename))
if not output_file:
output_file = f'makegif.gif'
output_file = f"makegif.gif"
if images:
imageio.mimwrite(output_file, images, duration=duration)
def gif_from_dir(directory,
duration,
prefix='',
prefix="",
output_file=None,
verbose=True):
images = os.listdir(directory)
if verbose:
print(f'Making gif from {len(images)} images under {directory}.')
print(f"Making gif from {len(images)} images under {directory}.")
filenames = list()
# Sort images
images = sorted(
images,
key=lambda img: int(os.path.splitext(img)[0].replace(prefix, '')))
key=lambda img: int(os.path.splitext(img)[0].replace(prefix, "")))
for image in images:
fname = os.path.join(directory, image)
filenames.append(fname)
if not output_file:
output_file = os.path.join(directory, 'makegif.gif')
output_file = os.path.join(directory, "makegif.gif")
make_gif(filenames=filenames, duration=duration, output_file=output_file)
@ -95,12 +95,12 @@ def predict_and_score(clf,
x_test,
y_test,
verbose=False,
title='Test accuracy'):
title="Test accuracy"):
y_pred = clf.predict(x_test)
accuracy = np.sum(y_test == y_pred)
normalized_acc = accuracy / float(len(y_test))
if verbose:
print(f'{title}: {normalized_acc * 100:06.04f}%')
print(f"{title}: {normalized_acc * 100:06.04f}%")
return normalized_acc
@ -124,6 +124,7 @@ def replace_in(arr, replacement_dict, inplace=False):
new_arr = arr
else:
import copy
new_arr = copy.deepcopy(arr)
for k, v in replacement_dict.items():
new_arr[arr == k] = v
@ -135,7 +136,7 @@ def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
preserve the class distribution in subsamples of the dataset.
"""
if train + val > 1.0:
raise ValueError('Invalid split values for train and val.')
raise ValueError("Invalid split values for train and val.")
Y = data[:, -1]
labels = set(Y)
hist = dict()
@ -183,20 +184,20 @@ def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
return train_data, val_data, test_data
def class_histogram(data, title='Untitled'):
def class_histogram(data, title="Untitled"):
plt.figure(title)
plt.clf()
plt.title(title)
dist, counts = np.unique(data[:, -1], return_counts=True)
plt.bar(dist, counts)
plt.xticks(dist)
print('Call matplotlib.pyplot.show() to see the plot.')
print("Call matplotlib.pyplot.show() to see the plot.")
def ntimer(n=10):
"""Wraps a function which wraps another function to time it."""
if n < 1:
raise (Exception(f'Invalid n = {n} given.'))
raise (Exception(f"Invalid n = {n} given."))
def timer(func):
"""Wraps `func` with a timer and returns the wrapped `func`."""
@ -207,7 +208,7 @@ def ntimer(n=10):
rv = func(*args, **kwargs)
after = time()
elapsed = after - before
print(f'Elapsed: {elapsed*1e3:02.02f} ms')
print(f"Elapsed: {elapsed*1e3:02.02f} ms")
return rv
return wrapper
@ -228,13 +229,13 @@ def memoize(verbose=True):
t = (pickle.dumps(args), pickle.dumps(kwargs))
if t not in cache:
if verbose:
print(f'Adding NEW rv {func.__name__}{args}{kwargs} '
'to cache.')
print(f"Adding NEW rv {func.__name__}{args}{kwargs} "
"to cache.")
cache[t] = func(*args, **kwargs)
else:
if verbose:
print(f'Using OLD rv {func.__name__}{args}{kwargs} '
'from cache.')
print(f"Using OLD rv {func.__name__}{args}{kwargs} "
"from cache.")
return cache[t]
return wrapper

3
setup.py
View File

@ -8,8 +8,7 @@
ProtoTorch Core Package
"""
from setuptools import setup
from setuptools import find_packages
from setuptools import find_packages, setup
PROJECT_URL = "https://github.com/si-cim/prototorch"
DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"

28
tests/test_datasets.py
View File

@ -12,26 +12,26 @@ from prototorch.datasets import abstract, tecator
class TestAbstract(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.Dataset('./artifacts')[0]
abstract.Dataset("./artifacts")[0]
def test_len(self):
with self.assertRaises(NotImplementedError):
len(abstract.Dataset('./artifacts'))
len(abstract.Dataset("./artifacts"))
class TestProtoDataset(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts')[0]
abstract.ProtoDataset("./artifacts")[0]
def test_download(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts').download()
abstract.ProtoDataset("./artifacts").download()
class TestTecator(unittest.TestCase):
def setUp(self):
self.artifacts_dir = './artifacts/Tecator'
self.artifacts_dir = "./artifacts/Tecator"
self._remove_artifacts()
def _remove_artifacts(self):
@ -39,23 +39,23 @@ class TestTecator(unittest.TestCase):
shutil.rmtree(self.artifacts_dir)
def test_download_false(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
self._remove_artifacts()
with self.assertRaises(RuntimeError):
_ = tecator.Tecator(rootdir, download=False)
def test_download_caching(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
_ = tecator.Tecator(rootdir, download=True, verbose=False)
_ = tecator.Tecator(rootdir, download=False, verbose=False)
def test_repr(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(rootdir, download=True, verbose=True)
self.assertTrue('Split: Train' in train.__repr__())
self.assertTrue("Split: Train" in train.__repr__())
def test_download_train(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(root=rootdir,
train=True,
download=True,
@ -67,7 +67,7 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_train.shape[1], 100)
def test_download_test(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x_test, y_test = test.data, test.targets
self.assertEqual(x_test.shape[0], 71)
@ -75,19 +75,19 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_test.shape[1], 100)
def test_class_to_idx(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = test.class_to_idx
def test_getitem(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x, y = test[0]
self.assertEqual(x.shape[0], 100)
self.assertIsInstance(y, int)
def test_loadable_with_dataloader(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)

78
tests/test_functions.py
View File

@ -11,7 +11,7 @@ from prototorch.functions import (activations, competitions, distances,
class TestActivations(unittest.TestCase):
def setUp(self):
self.flist = ['identity', 'sigmoid_beta', 'swish_beta']
self.flist = ["identity", "sigmoid_beta", "swish_beta"]
self.x = torch.randn(1024, 1)
def test_registry(self):
@ -39,7 +39,7 @@ class TestActivations(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = activations.get_activation(funcname)
@ -76,7 +76,7 @@ class TestCompetitions(unittest.TestCase):
pass
def test_wtac(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.wtac(d, labels)
desired = torch.tensor([2, 0])
@ -86,7 +86,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_unequal_dist(self):
d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
labels = torch.tensor([0, 1, 1])
actual = competitions.wtac(d, labels)
desired = torch.tensor([0, 1])
@ -96,7 +96,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = competitions.wtac(d, labels)
desired = torch.tensor([[0, 1], [1, 0]])
@ -106,38 +106,38 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_stratified_min(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_one_hot(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels]
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_simple(self):
d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
labels = torch.tensor([0, 1, 2])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=torch.tensor([1]))
desired = torch.tensor([2, 0])
@ -194,12 +194,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -254,14 +254,14 @@ class TestDistances(unittest.TestCase):
self.assertIsNone(mismatch)
def test_lpnorm_pinf(self):
actual = distances.lpnorm_distance(self.x, self.y, p=float('inf'))
actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=float('inf'),
p=float("inf"),
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
@ -275,12 +275,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -293,12 +293,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -311,8 +311,12 @@ class TestDistances(unittest.TestCase):
class TestInitializers(unittest.TestCase):
def setUp(self):
self.flist = [
'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
'stratified_random'
"zeros",
"ones",
"rand",
"randn",
"stratified_mean",
"stratified_random",
]
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
@ -340,7 +344,7 @@ class TestInitializers(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = initializers.get_initializer(funcname)
@ -383,7 +387,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -393,7 +397,7 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -402,8 +406,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -413,8 +417,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -423,8 +427,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -434,8 +438,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -444,8 +448,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
@ -460,8 +464,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,

119
tests/test_modules.py
View File

@ -29,10 +29,12 @@ class TestPrototypes(unittest.TestCase):
_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer='ones')
p1 = prototypes.Prototypes1D(
input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer="ones",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.ones(8, 6)
@ -45,7 +47,7 @@ class TestPrototypes(unittest.TestCase):
pdist = [2, 2]
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -60,14 +62,15 @@ class TestPrototypes(unittest.TestCase):
input_dim=3,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None)
prototype_initializer="stratified_mean",
data=None,
)
def test_prototypes1d_init_torch_pdist(self):
pdist = torch.tensor([2, 2])
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -77,24 +80,30 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1], [0]], [1, 0]])
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
)
def test_prototypes1d_init_one_hot_without_data(self):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None,
one_hot_labels=True)
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=None,
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_false(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `False`
@ -105,9 +114,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0, 1], [1, 0]]),
one_hot_labels=False)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
one_hot_labels=False,
)
def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@ -118,9 +128,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [0, 1]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [0, 1]),
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@ -132,25 +143,27 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0], [1]]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0], [1]]),
one_hot_labels=True,
)
def test_prototypes1d_init_with_int_dtype(self):
with self.assertRaises(RuntimeError):
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
dtype=torch.int32)
dtype=torch.int32,
)
def test_prototypes1d_inputndim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=1,
prototypes_per_class=1,
data=[[1.], [1]])
data=[[1.0], [1]])
def test_prototypes1d_inputdim_with_data(self):
with self.assertRaises(ValueError):
@ -158,8 +171,9 @@ class TestPrototypes(unittest.TestCase):
input_dim=2,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_nclasses_with_data(self):
"""Test ValueError raise if provided `nclasses` is not the same
@ -170,13 +184,14 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=1,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [2.]], [1, 2]])
prototype_initializer="stratified_mean",
data=[[[1.0], [2.0]], [1, 2]],
)
def test_prototypes1d_init_with_ppc(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototypes_per_class=2,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -186,9 +201,11 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_with_pdist(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer='zeros')
p1 = prototypes.Prototypes1D(
data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer="zeros",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(15, 3)
@ -201,10 +218,12 @@ class TestPrototypes(unittest.TestCase):
def my_initializer(*args, **kwargs):
return torch.full((2, 99), 99.0), torch.tensor([0, 1])
p1 = prototypes.Prototypes1D(input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer)
p1 = prototypes.Prototypes1D(
input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer,
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = 99 * torch.ones(2, 99)
@ -231,7 +250,7 @@ class TestPrototypes(unittest.TestCase):
def test_prototypes1d_validate_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr()
self.assertNotEqual(rep, '')
self.assertNotEqual(rep, "")
def tearDown(self):
del self.x, self.y, self.gen
@ -243,11 +262,11 @@ class TestLosses(unittest.TestCase):
pass
def test_glvqloss_init(self):
_ = losses.GLVQLoss(0, 'swish_beta', beta=20)
_ = losses.GLVQLoss(0, "swish_beta", beta=20)
def test_glvqloss_forward_1ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
@ -259,7 +278,7 @@ class TestLosses(unittest.TestCase):
def test_glvqloss_forward_2ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([
torch.ones(100),