vision_unlearning.utils.parameter_attribution
Attributes
Classes
!!! abstract "Usage Documentation" |
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!!! abstract "Usage Documentation" |
Module Contents
- vision_unlearning.utils.parameter_attribution.logger
- class vision_unlearning.utils.parameter_attribution.ParameterAttributionMethod(/, **data: Any)[source]
Bases:
pydantic.BaseModel,abc.ABC- !!! abstract “Usage Documentation”
[Models](../concepts/models.md)
A base class for creating Pydantic models.
- __class_vars__
The names of the class variables defined on the model.
- __private_attributes__
Metadata about the private attributes of the model.
- __signature__
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__
The core schema of the model.
- __pydantic_custom_init__
Whether the model has a custom __init__ function.
- __pydantic_decorators__
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__
A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.
- __pydantic_parent_namespace__
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__
The name of the post-init method for the model, if defined.
- __pydantic_root_model__
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_fields__
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.
- __pydantic_computed_fields__
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_extra__
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__
The names of fields explicitly set during instantiation.
- __pydantic_private__
Values of private attributes set on the model instance.
- class vision_unlearning.utils.parameter_attribution.ParameterAttributionMethodSaliency(/, **data: Any)[source]
Bases:
ParameterAttributionMethod- !!! abstract “Usage Documentation”
[Models](../concepts/models.md)
A base class for creating Pydantic models.
- __class_vars__
The names of the class variables defined on the model.
- __private_attributes__
Metadata about the private attributes of the model.
- __signature__
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__
The core schema of the model.
- __pydantic_custom_init__
Whether the model has a custom __init__ function.
- __pydantic_decorators__
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__
A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.
- __pydantic_parent_namespace__
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__
The name of the post-init method for the model, if defined.
- __pydantic_root_model__
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_fields__
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.
- __pydantic_computed_fields__
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_extra__
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__
The names of fields explicitly set during instantiation.
- __pydantic_private__
Values of private attributes set on the model instance.
- attribute(noise_scheduler: Any, text_encoder: Any, vae: Any, unet: Any, dataloader: torch.utils.data.DataLoader, device: str, weight_dtype: torch.dtype) Dict[str, torch.Tensor][source]
@return saliency: keys like “down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.weight”, values are tensors of same shape as the parameter, containing the accumulated saliency values. Tensor are of type torch.float32.
Expected characteristics of the dataloader: * Batch size and number of workers are set according to the training arguments. * shuffled * collate behavior: Batches are created by stacking the per-example tensors (pixel_values stacked into contiguous FloatTensor) * Fields
pixel_values: preprocessed images, ready to be fed to the vae (i.e. resized, cropped, normalized…). Shape=[batch size, 3, resolution, resolution]
input_ids: tokenized captions, ready to be fed to the text encoder. Shape=[batch size, sequence length]
Expected characteristics of the model (scheduler, text encoder, vae, unet): * unet should have anabled gradients * All loaded in the same device (the one specified in the arguments) * All loaded in the same dtype (the one specified in the arguments) * Loadede from the same base model / working together coherently