def __init__(self, validation_combinations, test_combinations, caption_size, words, language=None):
world_generator = GenericGenerator(
entity_counts=[1]
)
world_captioner = CaptionerMixer(
captioners=(
AttributesTypeCaptioner(
existing_attribute_ratio=0.0
),
ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(
hypernym_ratio=1.0,
existing_attribute_ratio=0.0
),
body_captioner=AttributesRelationCaptioner(
existing_attribute_ratio=0.0
)
)
),
trivial_acceptance_rate=1.0
)
super(OneshapeDataset, self).__init__(
world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language
)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations)
world_captioner = ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(),
body_captioner=SpatialRelationCaptioner(
# relations=('proximity-rel',)
))
super(SpatialDataset, self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
validation_combinations,
test_combinations,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=[2],
collision_tolerance=0.0,
boundary_tolerance=0.0,
validation_combinations=validation_combinations,
test_combinations=test_combinations,
max_provoke_collision_rate=0.0)
world_captioner = ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(
trivial_acceptance_rate=1.0),
body_captioner=SpatialRelationCaptioner(
reference_captioner=AttributesTypeCaptioner(),
relations=('x-rel', 'y-rel')))
super(SpatialSimpleDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
shapes_range,
colors_range,
textures_range,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
collision_tolerance=0.0,
boundary_tolerance=0.0,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations,
shapes_range=shapes_range,
colors_range=colors_range,
textures_range=textures_range,
max_provoke_collision_rate=0.0)
world_captioner = RelativeQuantifierCaptioner(
restrictor_captioner=AttributesTypeCaptioner(hypernym_ratio=1.0),
body_captioner=AttributesRelationCaptioner())
super(QuantificationDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
collision_tolerance=0.0,
boundary_tolerance=0.0,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations,
max_provoke_collision_rate=0.0)
world_captioner = AttributesTypeCaptioner()
super(MultishapeSimpleDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self, entity_counts, train_entity_counts, validation_entity_counts, test_entity_counts, validation_combinations, test_combinations, caption_size, words, language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations
)
world_captioner = CaptionerMixer(
captioners=(
AttributesTypeCaptioner(),
ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(
hypernym_ratio=1.0
),
body_captioner=AttributesRelationCaptioner()
)
)
)
super(MultishapeDataset, self).__init__(
world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language
)
def __init__(self,
validation_combinations,
test_combinations,
caption_size,
words,
incorrect_caption_modes=None,
hypernym_ratio=None,
correct_ratio=None,
train_correct_ratio=None,
validation_correct_ratio=None,
test_correct_ratio=None,
world_size=None,
world_color=None,
shapes=None,
colors=None,
textures=None,
rotation=None,
size_range=None,
distortion_range=None,
shade_range=None,
noise_range=None,
collision_tolerance=None,
boundary_tolerance=None,
realizer=None,
quantifier_tolerance=None,
**kwargs):
world_generator = GenericGenerator(
[2],
world_size,
world_color,
shapes,
colors,
textures,
rotation,
size_range,
distortion_range,
shade_range,
noise_range,
collision_tolerance,
boundary_tolerance,
validation_combinations=validation_combinations,
test_combinations=test_combinations)
world_captioner = SpatialCaptioner(
world_generator.shapes,
world_generator.colors,
world_generator.textures,
realizer=realizer,
quantifier_tolerance=quantifier_tolerance,
incorrect_modes=incorrect_caption_modes,
hypernym_ratio=hypernym_ratio)
super(SpatialDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
incorrect_world_ratio=0.0,
correct_ratio=correct_ratio,
train_correct_ratio=correct_ratio,
validation_correct_ratio=validation_correct_ratio,
test_correct_ratio=test_correct_ratio)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations)
verb_captioner = CaptionerMixer(
captioners=(SpatialRelationCaptioner(),
ComparisonRelationCaptioner()))
world_captioner = ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(),
body_captioner=verb_captioner,
)
super(RelationalDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
shapes_range,
colors_range,
textures_range,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations,
shapes_range=shapes_range,
colors_range=colors_range,
textures_range=textures_range)
oneshape = CaptionerMixer(
captioners=(AttributesTypeCaptioner(),
ExistentialCaptioner(
restrictor_captioner=AttributesTypeCaptioner(
hypernym_ratio=1.0),
body_captioner=AttributesRelationCaptioner())))
relational = CaptionerMixer(captioners=(SpatialRelationCaptioner(),
ComparisonRelationCaptioner()))
counting = AbsoluteQuantifierCaptioner(
restrictor_captioner=AttributesTypeCaptioner(),
body_captioner=CaptionerMixer(
captioners=(AttributesRelationCaptioner(),
SpatialRelationCaptioner(),
ComparisonRelationCaptioner())))
quantification = RelativeQuantifierCaptioner(
restrictor_captioner=AttributesTypeCaptioner(),
body_captioner=CaptionerMixer(
captioners=(AttributesRelationCaptioner(),
SpatialRelationCaptioner(),
ComparisonRelationCaptioner())))
world_captioner = CaptionerMixer(
captioners=(ConjunctionCaptioner(captioners=(oneshape, relational,
counting,
quantification)),
DisjunctionCaptioner(captioners=(oneshape, relational,
counting,
quantification))))
super(CombinationDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self, entity_counts, train_entity_counts,
validation_entity_counts, test_entity_counts):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts)
num_classes = len(world_generator.shapes) * len(
world_generator.colors) * len(world_generator.textures)
super(MultishapeDataset,
self).__init__(world_generator=world_generator,
num_classes=num_classes,
multi_class=True,
class_count=False)
def __init__(self,
validation_combinations,
test_combinations,
caption_size,
words,
language=None):
world_generator = GenericGenerator(entity_counts=[1],
collision_tolerance=0.0,
boundary_tolerance=0.0)
world_captioner = AttributesTypeCaptioner(existing_attribute_ratio=0.0,
trivial_acceptance_rate=1.0)
super(OneshapeSimpleDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
validation_combinations,
test_combinations,
shapes_range,
colors_range,
textures_range,
caption_size,
words,
language=None):
world_generator = GenericGenerator(
entity_counts=entity_counts,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
validation_combinations=validation_combinations,
test_combinations=test_combinations,
shapes_range=shapes_range,
colors_range=colors_range,
textures_range=textures_range)
body_captioner = CaptionerMixer(
captioners=(AttributesRelationCaptioner(),
SpatialRelationCaptioner(relations=('x-rel', 'y-rel',
'proximity-rel')),
ComparisonRelationCaptioner()))
world_captioner = RelativeQuantifierCaptioner(
restrictor_captioner=AttributesTypeCaptioner(),
body_captioner=body_captioner)
super(QuantificationDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
language=language)
def __init__(self,
world_size=None,
world_color=None,
shapes=None,
colors=None,
textures=None,
rotation=None,
size_range=None,
distortion_range=None,
shade_range=None,
noise_range=None,
collision_tolerance=None,
boundary_tolerance=None,
**kwargs):
world_generator = GenericGenerator([1], world_size, world_color,
shapes, colors, textures, rotation,
size_range, distortion_range,
shade_range, noise_range,
collision_tolerance,
boundary_tolerance)
num_classes = len(world_generator.shapes) * len(
world_generator.colors) * len(world_generator.textures)
super(OneShapeDataset, self).__init__(world_generator=world_generator,
num_classes=num_classes)
def __init__(self,
entity_counts,
train_entity_counts,
validation_entity_counts,
test_entity_counts,
shapes_range,
colors_range,
textures_range,
caption_size,
words,
caption_modes=None,
quantifiers=None,
correct_ratio=None,
train_correct_ratio=None,
validation_correct_ratio=None,
test_correct_ratio=None,
world_size=None,
world_color=None,
shapes=None,
colors=None,
textures=None,
rotation=None,
size_range=None,
distortion_range=None,
shade_range=None,
noise_range=None,
collision_tolerance=None,
boundary_tolerance=None,
realizer=None,
quantifier_tolerance=None,
**kwargs):
world_generator = GenericGenerator(
entity_counts,
world_size,
world_color,
shapes,
colors,
textures,
rotation,
size_range,
distortion_range,
shade_range,
noise_range,
collision_tolerance,
boundary_tolerance,
train_entity_counts=train_entity_counts,
validation_entity_counts=validation_entity_counts,
test_entity_counts=test_entity_counts,
shapes_range=shapes_range,
colors_range=colors_range,
textures_range=textures_range)
world_captioner = QuantificationCaptioner(
world_generator.shapes,
world_generator.colors,
world_generator.textures,
realizer=realizer,
quantifier_tolerance=quantifier_tolerance,
modes=caption_modes,
quantifiers=quantifiers)
super(QuantificationDataset,
self).__init__(world_generator=world_generator,
world_captioner=world_captioner,
caption_size=caption_size,
words=words,
incorrect_world_ratio=0.0,
correct_ratio=correct_ratio,
train_correct_ratio=correct_ratio,
validation_correct_ratio=validation_correct_ratio,
test_correct_ratio=test_correct_ratio)
def __init__(self):
world_generator = GenericGenerator(entity_counts=[1])
num_classes = len(world_generator.shapes) * len(
world_generator.colors) * len(world_generator.textures)
super(OneshapeDataset, self).__init__(world_generator=world_generator,
num_classes=num_classes)
