[BUGFIX] Fix reasonings initializer dimension bug
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@ -42,7 +42,6 @@ def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
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nk = (1 - A) * B
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numerator = (detections @ (pk - nk).T) + nk.sum(1)
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probs = numerator / (pk + nk).sum(1)
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# probs = probs.squeeze(0)
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return probs
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@ -13,6 +13,7 @@ from .initializers import (
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AbstractLabelsInitializer,
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AbstractReasoningsInitializer,
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LabelsInitializer,
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PurePositiveReasoningsInitializer,
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RandomReasoningsInitializer,
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)
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@ -308,9 +309,12 @@ class ReasoningComponents(AbstractComponents):
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three element probability distribution.
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"""
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def __init__(self, distribution: Union[dict, list, tuple],
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def __init__(
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self,
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distribution: Union[dict, list, tuple],
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components_initializer: AbstractComponentsInitializer,
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reasonings_initializer: AbstractReasoningsInitializer,
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reasonings_initializer:
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AbstractReasoningsInitializer = PurePositiveReasoningsInitializer(),
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**kwargs):
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super().__init__(**kwargs)
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self.add_components(distribution, components_initializer,
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@ -296,7 +296,7 @@ class OneHotLabelsInitializer(LabelsInitializer):
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# Reasonings
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class AbstractReasoningsInitializer(ABC):
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"""Abstract class for all reasonings initializers."""
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def __init__(self, components_first=True):
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def __init__(self, components_first: bool = True):
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self.components_first = components_first
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def compute_shape(self, distribution):
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@ -375,7 +375,7 @@ class PurePositiveReasoningsInitializer(AbstractReasoningsInitializer):
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num_components, num_classes, _ = self.compute_shape(distribution)
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A = OneHotLabelsInitializer().generate(distribution)
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B = torch.zeros(num_components, num_classes)
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reasonings = torch.stack([A, B]).permute(2, 1, 0)
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reasonings = torch.stack([A, B], dim=-1)
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reasonings = self.generate_end_hook(reasonings)
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return reasonings
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@ -220,13 +220,6 @@ def test_ones_reasonings_init():
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assert torch.allclose(reasonings, torch.zeros(6, 3, 2))
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def test_random_reasonings_init_channels_not_first():
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r = pt.initializers.RandomReasoningsInitializer(components_first=False)
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reasonings = r.generate(distribution=[1, 2])
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assert reasonings.shape[0] == 2
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assert reasonings.shape[-1] == 3
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def test_pure_positive_reasonings_init_one_per_class():
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r = pt.initializers.PurePositiveReasoningsInitializer(
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components_first=False)
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@ -234,13 +227,20 @@ def test_pure_positive_reasonings_init_one_per_class():
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assert torch.allclose(reasonings[0], torch.eye(4))
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def test_pure_positive_reasonings_init_unrepresented_class():
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r = pt.initializers.PurePositiveReasoningsInitializer(
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components_first=False)
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reasonings = r.generate(distribution=[1, 0, 1])
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def test_pure_positive_reasonings_init_unrepresented_classes():
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r = pt.initializers.PurePositiveReasoningsInitializer()
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reasonings = r.generate(distribution=[9, 0, 0, 0])
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assert reasonings.shape[0] == 9
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assert reasonings.shape[1] == 4
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assert reasonings.shape[2] == 2
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def test_random_reasonings_init_channels_not_first():
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r = pt.initializers.RandomReasoningsInitializer(components_first=False)
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reasonings = r.generate(distribution=[0, 0, 0, 1])
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assert reasonings.shape[0] == 2
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assert reasonings.shape[1] == 2
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assert reasonings.shape[2] == 3
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assert reasonings.shape[1] == 4
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assert reasonings.shape[2] == 1
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# Transform initializers
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