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#!/usr/bin/env python3
from typing import Callable, Union
import torch
from torch.nn import Module
class Concept:
r"""
Concepts are human-friendly abstract representations that can be
numerically encoded into torch tensors. They can be illustrated as
images, text or any other form of representation. In case of images,
for example, "stripes" concept can be represented through a number
of example images resembling "stripes" in various different
contexts. In case of Natural Language Processing, the concept of
"happy", for instance, can be illustrated through a number of
adjectives and words that convey happiness.
"""
def __init__(
self, id: int, name: str, data_iter: Union[None, torch.utils.data.DataLoader]
) -> None:
r"""
Args:
id (int): The unique identifier of the concept.
name (str): A unique name of the concept.
data_iter (DataLoader): A pytorch DataLoader object that combines a dataset
and a sampler, and provides an iterable over a given
dataset. Only the input batches are provided by `data_iter`.
Concept ids can be used as labels if necessary.
For more information, please check:
https://pytorch.org/docs/stable/data.html
Example::
>>> # Creates a Concept object named "striped", with a data_iter
>>> # object to iterate over all files in "./concepts/striped"
>>> concept_name = "striped"
>>> concept_path = os.path.join("./concepts", concept_name) + "/"
>>> concept_iter = dataset_to_dataloader(
>>> get_tensor_from_filename, concepts_path=concept_path)
>>> concept_object = Concept(
id=0, name=concept_name, data_iter=concept_iter)
"""
self.id = id
self.name = name
self.data_iter = data_iter
@property
def identifier(self) -> str:
return "%s-%s" % (self.name, self.id)
def __repr__(self) -> str:
return "Concept(%r, %r)" % (self.id, self.name)
class ConceptInterpreter:
r"""
An abstract class that exposes an abstract interpret method
that has to be implemented by a specific algorithm for
concept-based model interpretability.
"""
def __init__(self, model: Module) -> None:
r"""
Args:
model (torch.nn.Module): An instance of pytorch model.
"""
self.model = model
interpret: Callable
r"""
An abstract interpret method that performs concept-based model interpretability
and returns the interpretation results in form of tensors, dictionaries or other
data structures.
Args:
inputs (tensor or tuple of tensors): Inputs for which concept-based
interpretation scores are computed. It can be provided as
a single tensor or a tuple of multiple tensors. If multiple
input tensors are provided, the batch size (the first
dimension of the tensors) must be aligned across all tensors.
"""
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