Remove Prototypes1D and its tests
This commit is contained in:
Alexander Engelsberger
parent
40ef3aeda2
commit
87334c11e6
@ -1,7 +1 @@
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"""ProtoTorch modules."""
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from .prototypes import Prototypes1D
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__all__ = [
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"Prototypes1D",
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]
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@ -1,137 +0,0 @@
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"""ProtoTorch prototype modules."""
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import warnings
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import torch
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from prototorch.functions.initializers import get_initializer
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class _Prototypes(torch.nn.Module):
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"""Abstract prototypes class."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def _validate_prototype_distribution(self):
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if 0 in self.prototype_distribution:
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warnings.warn("Are you sure about the `0` in "
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"`prototype_distribution`?")
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def extra_repr(self):
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return f"prototypes.shape: {tuple(self.prototypes.shape)}"
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def forward(self):
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return self.prototypes, self.prototype_labels
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class Prototypes1D(_Prototypes):
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"""Create a learnable set of one-dimensional prototypes.
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TODO Complete this doc-string.
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"""
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def __init__(
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self,
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prototypes_per_class=1,
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prototype_initializer="ones",
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prototype_distribution=None,
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data=None,
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dtype=torch.float32,
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one_hot_labels=False,
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**kwargs,
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):
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warnings.warn(
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PendingDeprecationWarning(
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"Prototypes1D will be replaced in future versions."))
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# Convert tensors to python lists before processing
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if prototype_distribution is not None:
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if not isinstance(prototype_distribution, list):
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prototype_distribution = prototype_distribution.tolist()
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if data is None:
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if "input_dim" not in kwargs:
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raise NameError("`input_dim` required if "
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"no `data` is provided.")
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if prototype_distribution:
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kwargs_num_classes = sum(prototype_distribution)
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else:
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if "num_classes" not in kwargs:
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raise NameError("`prototype_distribution` required if "
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"both `data` and `num_classes` are not "
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"provided.")
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kwargs_num_classes = kwargs.pop("num_classes")
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input_dim = kwargs.pop("input_dim")
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if prototype_initializer in [
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"stratified_mean", "stratified_random"
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]:
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warnings.warn(
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f"`prototype_initializer`: `{prototype_initializer}` "
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"requires `data`, but `data` is not provided. "
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"Using randomly generated data instead.")
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x_train = torch.rand(kwargs_num_classes, input_dim)
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y_train = torch.arange(kwargs_num_classes)
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if one_hot_labels:
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y_train = torch.eye(kwargs_num_classes)[y_train]
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data = [x_train, y_train]
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x_train, y_train = data
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x_train = torch.as_tensor(x_train).type(dtype)
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y_train = torch.as_tensor(y_train).type(torch.int)
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num_classes = torch.unique(y_train, dim=-1).shape[-1]
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if num_classes == 1:
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warnings.warn("Are you sure about having one class only?")
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if x_train.ndim != 2:
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raise ValueError("`data[0].ndim != 2`.")
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if y_train.ndim == 2:
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if y_train.shape[1] == 1 and one_hot_labels:
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raise ValueError("`one_hot_labels` is set to `True` "
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"but target labels are not one-hot-encoded.")
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if y_train.shape[1] != 1 and not one_hot_labels:
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raise ValueError("`one_hot_labels` is set to `False` "
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"but target labels in `data` "
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"are one-hot-encoded.")
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if y_train.ndim == 1 and one_hot_labels:
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raise ValueError("`one_hot_labels` is set to `True` "
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"but target labels are not one-hot-encoded.")
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# Verify input dimension if `input_dim` is provided
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if "input_dim" in kwargs:
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input_dim = kwargs.pop("input_dim")
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if input_dim != x_train.shape[1]:
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raise ValueError(f"Provided `input_dim`={input_dim} does "
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"not match data dimension "
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f"`data[0].shape[1]`={x_train.shape[1]}")
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# Verify the number of classes if `num_classes` is provided
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if "num_classes" in kwargs:
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kwargs_num_classes = kwargs.pop("num_classes")
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if kwargs_num_classes != num_classes:
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raise ValueError(
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f"Provided `num_classes={kwargs_num_classes}` does "
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"not match data labels "
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"`torch.unique(data[1]).shape[0]`"
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f"={num_classes}")
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super().__init__(**kwargs)
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if not prototype_distribution:
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prototype_distribution = [prototypes_per_class] * num_classes
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with torch.no_grad():
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self.prototype_distribution = torch.tensor(prototype_distribution)
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self._validate_prototype_distribution()
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self.prototype_initializer = get_initializer(prototype_initializer)
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prototypes, prototype_labels = self.prototype_initializer(
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x_train,
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y_train,
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prototype_distribution=self.prototype_distribution,
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one_hot=one_hot_labels,
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)
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# Register module parameters
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self.prototypes = torch.nn.Parameter(prototypes)
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self.prototype_labels = torch.nn.Parameter(
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prototype_labels.type(dtype)).requires_grad_(False)
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@ -1,297 +0,0 @@
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"""ProtoTorch modules test suite."""
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import unittest
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import numpy as np
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import torch
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from prototorch.modules import losses, prototypes
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class TestPrototypes(unittest.TestCase):
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def setUp(self):
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self.x = torch.tensor(
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[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
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dtype=torch.float32)
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self.y = torch.tensor([0, 0, 1, 1])
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self.gen = torch.manual_seed(42)
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def test_prototypes1d_init_without_input_dim(self):
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with self.assertRaises(NameError):
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_ = prototypes.Prototypes1D(num_classes=2)
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def test_prototypes1d_init_without_num_classes(self):
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with self.assertRaises(NameError):
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_ = prototypes.Prototypes1D(input_dim=1)
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def test_prototypes1d_init_with_num_classes_1(self):
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with self.assertWarns(UserWarning):
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_ = prototypes.Prototypes1D(num_classes=1, input_dim=1)
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def test_prototypes1d_init_without_pdist(self):
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p1 = prototypes.Prototypes1D(
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input_dim=6,
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num_classes=2,
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prototypes_per_class=4,
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prototype_initializer="ones",
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.ones(8, 6)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_init_without_data(self):
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pdist = [2, 2]
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p1 = prototypes.Prototypes1D(input_dim=3,
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prototype_distribution=pdist,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_proto_init_without_data(self):
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with self.assertWarns(UserWarning):
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_ = prototypes.Prototypes1D(
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input_dim=3,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=None,
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)
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def test_prototypes1d_init_torch_pdist(self):
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pdist = torch.tensor([2, 2])
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p1 = prototypes.Prototypes1D(input_dim=3,
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prototype_distribution=pdist,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_init_without_inputdim_with_data(self):
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_ = prototypes.Prototypes1D(
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [0.0]], [1, 0]],
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)
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def test_prototypes1d_init_with_int_data(self):
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_ = prototypes.Prototypes1D(
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1], [0]], [1, 0]],
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)
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def test_prototypes1d_init_one_hot_without_data(self):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=None,
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one_hot_labels=True,
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)
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def test_prototypes1d_init_one_hot_labels_false(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `False`
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but the provided `data` has one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
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one_hot_labels=False,
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)
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def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `True`
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but the provided `data` does not contain one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [0, 1]),
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one_hot_labels=True,
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)
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def test_prototypes1d_init_one_hot_labels_true(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `True`
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but the provided `data` contains 2D targets but
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does not contain one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [[0], [1]]),
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one_hot_labels=True,
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)
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def test_prototypes1d_init_with_int_dtype(self):
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with self.assertRaises(RuntimeError):
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_ = prototypes.Prototypes1D(
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1], [0]], [1, 0]],
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dtype=torch.int32,
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)
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def test_prototypes1d_inputndim_with_data(self):
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(input_dim=1,
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num_classes=1,
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prototypes_per_class=1,
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data=[[1.0], [1]])
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def test_prototypes1d_inputdim_with_data(self):
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=2,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [0.0]], [1, 0]],
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)
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def test_prototypes1d_num_classes_with_data(self):
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"""Test ValueError raise if provided `num_classes` is not the same
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as the one computed from the provided `data`.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=1,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [2.0]], [1, 2]],
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)
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def test_prototypes1d_init_with_ppc(self):
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p1 = prototypes.Prototypes1D(data=[self.x, self.y],
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prototypes_per_class=2,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_init_with_pdist(self):
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p1 = prototypes.Prototypes1D(
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data=[self.x, self.y],
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prototype_distribution=[6, 9],
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prototype_initializer="zeros",
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(15, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_func_initializer(self):
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def my_initializer(*args, **kwargs):
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return torch.full((2, 99), 99.0), torch.tensor([0, 1])
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p1 = prototypes.Prototypes1D(
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input_dim=99,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer=my_initializer,
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = 99 * torch.ones(2, 99)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_forward(self):
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p1 = prototypes.Prototypes1D(data=[self.x, self.y])
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protos, _ = p1()
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actual = protos.detach().numpy()
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desired = torch.ones(2, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_dist_validate(self):
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p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
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with self.assertWarns(UserWarning):
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_ = p1._validate_prototype_distribution()
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def test_prototypes1d_validate_extra_repr_not_empty(self):
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p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
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rep = p1.extra_repr()
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self.assertNotEqual(rep, "")
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def tearDown(self):
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del self.x, self.y, self.gen
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_ = torch.seed()
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class TestLosses(unittest.TestCase):
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def setUp(self):
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pass
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def test_glvqloss_init(self):
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_ = losses.GLVQLoss(0, "swish_beta", beta=20)
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def test_glvqloss_forward_1ppc(self):
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criterion = losses.GLVQLoss(margin=0,
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squashing="sigmoid_beta",
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beta=100)
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d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
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labels = torch.tensor([0, 1])
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targets = torch.ones(100)
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outputs = [d, labels]
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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self.assertAlmostEqual(loss_value, 0.0)
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def test_glvqloss_forward_2ppc(self):
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criterion = losses.GLVQLoss(margin=0,
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squashing="sigmoid_beta",
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beta=100)
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d = torch.stack([
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torch.ones(100),
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torch.ones(100),
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torch.zeros(100),
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torch.ones(100)
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],
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dim=1)
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labels = torch.tensor([0, 0, 1, 1])
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targets = torch.ones(100)
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outputs = [d, labels]
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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self.assertAlmostEqual(loss_value, 0.0)
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def tearDown(self):
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pass
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