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Remove Prototypes1D and its tests

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This commit is contained in:
Alexander Engelsberger 2021-05-28 15:57:39 +02:00
parent 40ef3aeda2
commit 87334c11e6
3 changed files with 1 additions and 441 deletions
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8
prototorch/modules/__init__.py
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@ -1,7 +1 @@
"""ProtoTorch modules."""
from .prototypes import Prototypes1D
__all__ = [
"Prototypes1D",
]
"""ProtoTorch modules."""

137
prototorch/modules/prototypes.py
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@ -1,137 +0,0 @@
"""ProtoTorch prototype modules."""
import warnings
import torch
from prototorch.functions.initializers import get_initializer
class _Prototypes(torch.nn.Module):
"""Abstract prototypes class."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _validate_prototype_distribution(self):
if 0 in self.prototype_distribution:
warnings.warn("Are you sure about the `0` in "
"`prototype_distribution`?")
def extra_repr(self):
return f"prototypes.shape: {tuple(self.prototypes.shape)}"
def forward(self):
return self.prototypes, self.prototype_labels
class Prototypes1D(_Prototypes):
"""Create a learnable set of one-dimensional 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,
):
warnings.warn(
PendingDeprecationWarning(
"Prototypes1D will be replaced in future versions."))
# Convert tensors to python lists before processing
if prototype_distribution is not None:
if not isinstance(prototype_distribution, list):
prototype_distribution = prototype_distribution.tolist()
if data is None:
if "input_dim" not in kwargs:
raise NameError("`input_dim` required if "
"no `data` is provided.")
if prototype_distribution:
kwargs_num_classes = sum(prototype_distribution)
else:
if "num_classes" not in kwargs:
raise NameError("`prototype_distribution` required if "
"both `data` and `num_classes` are not "
"provided.")
kwargs_num_classes = kwargs.pop("num_classes")
input_dim = kwargs.pop("input_dim")
if prototype_initializer in [
"stratified_mean", "stratified_random"
]:
warnings.warn(
f"`prototype_initializer`: `{prototype_initializer}` "
"requires `data`, but `data` is not provided. "
"Using randomly generated data instead.")
x_train = torch.rand(kwargs_num_classes, input_dim)
y_train = torch.arange(kwargs_num_classes)
if one_hot_labels:
y_train = torch.eye(kwargs_num_classes)[y_train]
data = [x_train, y_train]
x_train, y_train = data
x_train = torch.as_tensor(x_train).type(dtype)
y_train = torch.as_tensor(y_train).type(torch.int)
num_classes = torch.unique(y_train, dim=-1).shape[-1]
if num_classes == 1:
warnings.warn("Are you sure about having one class only?")
if x_train.ndim != 2:
raise ValueError("`data[0].ndim != 2`.")
if y_train.ndim == 2:
if y_train.shape[1] == 1 and one_hot_labels:
raise ValueError("`one_hot_labels` is set to `True` "
"but target labels are not one-hot-encoded.")
if y_train.shape[1] != 1 and not one_hot_labels:
raise ValueError("`one_hot_labels` is set to `False` "
"but target labels in `data` "
"are one-hot-encoded.")
if y_train.ndim == 1 and one_hot_labels:
raise ValueError("`one_hot_labels` is set to `True` "
"but target labels are not one-hot-encoded.")
# Verify input dimension if `input_dim` is provided
if "input_dim" in kwargs:
input_dim = kwargs.pop("input_dim")
if input_dim != x_train.shape[1]:
raise ValueError(f"Provided `input_dim`={input_dim} does "
"not match data dimension "
f"`data[0].shape[1]`={x_train.shape[1]}")
# Verify the number of classes if `num_classes` is provided
if "num_classes" in kwargs:
kwargs_num_classes = kwargs.pop("num_classes")
if kwargs_num_classes != num_classes:
raise ValueError(
f"Provided `num_classes={kwargs_num_classes}` does "
"not match data labels "
"`torch.unique(data[1]).shape[0]`"
f"={num_classes}")
super().__init__(**kwargs)
if not prototype_distribution:
prototype_distribution = [prototypes_per_class] * num_classes
with torch.no_grad():
self.prototype_distribution = torch.tensor(prototype_distribution)
self._validate_prototype_distribution()
self.prototype_initializer = get_initializer(prototype_initializer)
prototypes, prototype_labels = self.prototype_initializer(
x_train,
y_train,
prototype_distribution=self.prototype_distribution,
one_hot=one_hot_labels,
)
# Register module parameters
self.prototypes = torch.nn.Parameter(prototypes)
self.prototype_labels = torch.nn.Parameter(
prototype_labels.type(dtype)).requires_grad_(False)

297
tests/test_modules.py
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@ -1,297 +0,0 @@
"""ProtoTorch modules test suite."""
import unittest
import numpy as np
import torch
from prototorch.modules import losses, prototypes
class TestPrototypes(unittest.TestCase):
def setUp(self):
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
dtype=torch.float32)
self.y = torch.tensor([0, 0, 1, 1])
self.gen = torch.manual_seed(42)
def test_prototypes1d_init_without_input_dim(self):
with self.assertRaises(NameError):
_ = prototypes.Prototypes1D(num_classes=2)
def test_prototypes1d_init_without_num_classes(self):
with self.assertRaises(NameError):
_ = prototypes.Prototypes1D(input_dim=1)
def test_prototypes1d_init_with_num_classes_1(self):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(num_classes=1, input_dim=1)
def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(
input_dim=6,
num_classes=2,
prototypes_per_class=4,
prototype_initializer="ones",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.ones(8, 6)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_data(self):
pdist = [2, 2]
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_proto_init_without_data(self):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(
input_dim=3,
num_classes=2,
prototypes_per_class=1,
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")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(
num_classes=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(
num_classes=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,
num_classes=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`
but the provided `data` has one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
num_classes=2,
prototypes_per_class=1,
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`
but the provided `data` does not contain one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
num_classes=2,
prototypes_per_class=1,
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`
but the provided `data` contains 2D targets but
does not contain one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
num_classes=2,
prototypes_per_class=1,
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(
num_classes=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
dtype=torch.int32,
)
def test_prototypes1d_inputndim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(input_dim=1,
num_classes=1,
prototypes_per_class=1,
data=[[1.0], [1]])
def test_prototypes1d_inputdim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=2,
num_classes=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_num_classes_with_data(self):
"""Test ValueError raise if provided `num_classes` is not the same
as the one computed from the provided `data`.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
num_classes=1,
prototypes_per_class=1,
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")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
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",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(15, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_func_initializer(self):
def my_initializer(*args, **kwargs):
return torch.full((2, 99), 99.0), torch.tensor([0, 1])
p1 = prototypes.Prototypes1D(
input_dim=99,
num_classes=2,
prototypes_per_class=1,
prototype_initializer=my_initializer,
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = 99 * torch.ones(2, 99)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_forward(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y])
protos, _ = p1()
actual = protos.detach().numpy()
desired = torch.ones(2, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_dist_validate(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
with self.assertWarns(UserWarning):
_ = p1._validate_prototype_distribution()
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, "")
def tearDown(self):
del self.x, self.y, self.gen
_ = torch.seed()
class TestLosses(unittest.TestCase):
def setUp(self):
pass
def test_glvqloss_init(self):
_ = losses.GLVQLoss(0, "swish_beta", beta=20)
def test_glvqloss_forward_1ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing="sigmoid_beta",
beta=100)
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
targets = torch.ones(100)
outputs = [d, labels]
loss = criterion(outputs, targets)
loss_value = loss.item()
self.assertAlmostEqual(loss_value, 0.0)
def test_glvqloss_forward_2ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing="sigmoid_beta",
beta=100)
d = torch.stack([
torch.ones(100),
torch.ones(100),
torch.zeros(100),
torch.ones(100)
],
dim=1)
labels = torch.tensor([0, 0, 1, 1])
targets = torch.ones(100)
outputs = [d, labels]
loss = criterion(outputs, targets)
loss_value = loss.item()
self.assertAlmostEqual(loss_value, 0.0)
def tearDown(self):
pass