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embedding

sleap_io.model.embedding

Embedding data structure for per-detection appearance / re-ID vectors.

Classes:

Name Description
Embedding

A per-detection appearance / re-identification embedding vector.

Attributes:

Name Type Description
__cached__

str(object='') -> str
__doc__

str(object='') -> str
__file__

str(object='') -> str
__name__

str(object='') -> str
__package__

str(object='') -> str

__cached__ = '/home/runner/work/sleap-io/sleap-io/sleap_io/model/__pycache__/embedding.cpython-313.pyc' module-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__doc__ = 'Embedding data structure for per-detection appearance / re-ID vectors.' module-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__file__ = '/home/runner/work/sleap-io/sleap-io/sleap_io/model/embedding.py' module-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__name__ = 'sleap_io.model.embedding' module-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__package__ = 'sleap_io.model' module-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

Embedding

A per-detection appearance / re-identification embedding vector.

An Embedding wraps a single feature vector describing the visual appearance of one detection (e.g. the crop around an Instance, Centroid, SegmentationMask, or BoundingBox). What the vector represents is implied by the slot it fills on the detection -- currently identity_embedding on every detection modality, used for re-identification.

Attributes:

Name Type Description
vector

1-D feature vector of shape (D,). The dtype is preserved from the input when floating (e.g. float32 from a neural network); non-floating inputs are cast to float32.
Notes

Embedding uses value equality: two embeddings are equal when their vectors are element-wise equal. It is therefore unhashable (an Embedding is only ever a field value, never a set member or dict key). Detection objects use object-identity equality, so a (potentially large) embedding vector is never compared when two detections are compared.

Methods:

Name Description
__eq__

Method generated by attrs for class Embedding.
__init__

Method generated by attrs for class Embedding.
__repr__

Method generated by attrs for class Embedding.
__setattr__

Method generated by attrs for class Embedding.
Source code in sleap_io/model/embedding.py 
@define
class Embedding:
    """A per-detection appearance / re-identification embedding vector.

    An `Embedding` wraps a single feature vector describing the visual appearance
    of one detection (e.g. the crop around an `Instance`, `Centroid`,
    `SegmentationMask`, or `BoundingBox`). What the vector represents is implied by
    the slot it fills on the detection -- currently ``identity_embedding`` on every
    detection modality, used for re-identification.

    Attributes:
        vector: 1-D feature vector of shape ``(D,)``. The dtype is preserved from
            the input when floating (e.g. float32 from a neural network);
            non-floating inputs are cast to float32.

    Notes:
        `Embedding` uses value equality: two embeddings are equal when their
        vectors are element-wise equal. It is therefore unhashable (an `Embedding`
        is only ever a field value, never a set member or dict key). Detection
        objects use object-identity equality, so a (potentially large) embedding
        vector is never compared when two detections are compared.
    """

    vector: np.ndarray = field(
        converter=_as_vector,
        eq=attrs.cmp_using(eq=np.array_equal),
        repr=lambda v: f"<{v.shape[0]}-d {v.dtype}>",
    )

    @property
    def dim(self) -> int:
        """Dimensionality ``D`` of the embedding vector."""
        return int(self.vector.shape[0])

__annotations__ = {'vector': 'np.ndarray'} class-attribute

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)

__attrs_own_setattr__ = True class-attribute

Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f08471ce840>, field_transformer=None) class-attribute

Effective class properties as derived from parameters to attr.s() or define() decorators.

This is the same data structure that attrs uses internally to decide how to construct the final class.

Warning:

This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.

Attributes:

Name Type Description
is_exception bool

Whether the class is treated as an exception class.
is_slotted bool

Whether the class is slotted <slotted classes>.
has_weakref_slot bool

Whether the class has a slot for weak references.
is_frozen bool

Whether the class is frozen.
kw_only KeywordOnly

Whether / how the class enforces keyword-only arguments on the __init__ method.
collected_fields_by_mro bool

Whether the class fields were collected by method resolution order. That is, correctly but unlike dataclasses.
added_init bool

Whether the class has an attrs-generated __init__ method.
added_repr bool

Whether the class has an attrs-generated __repr__ method.
added_eq bool

Whether the class has attrs-generated equality methods.
added_ordering bool

Whether the class has attrs-generated ordering methods.
hashability Hashability

How hashable <hashing> the class is.
added_match_args bool

Whether the class supports positional match <match> over its fields.
added_str bool

Whether the class has an attrs-generated __str__ method.
added_pickling bool

Whether the class has attrs-generated __getstate__ and __setstate__ methods for pickle.
on_setattr_hook Callable[[Any, Attribute[Any], Any], Any] | None

The class's __setattr__ hook.
field_transformer Callable[[Attribute[Any]], Attribute[Any]] | None

The class's field transformers <transform-fields>.

.. versionadded:: 25.4.0

__doc__ = 'A per-detection appearance / re-identification embedding vector.\n\nAn `Embedding` wraps a single feature vector describing the visual appearance\nof one detection (e.g. the crop around an `Instance`, `Centroid`,\n`SegmentationMask`, or `BoundingBox`). What the vector represents is implied by\nthe slot it fills on the detection -- currently ``identity_embedding`` on every\ndetection modality, used for re-identification.\n\nAttributes:\n vector: 1-D feature vector of shape ``(D,)``. The dtype is preserved from\n the input when floating (e.g. float32 from a neural network);\n non-floating inputs are cast to float32.\n\nNotes:\n `Embedding` uses value equality: two embeddings are equal when their\n vectors are element-wise equal. It is therefore unhashable (an `Embedding`\n is only ever a field value, never a set member or dict key). Detection\n objects use object-identity equality, so a (potentially large) embedding\n vector is never compared when two detections are compared.\n' class-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__firstlineno__ = 26 class-attribute

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.

int('0b100', base=0) 4

__match_args__ = ('vector',) class-attribute

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.

If the argument is a tuple, the return value is the same object.

__module__ = 'sleap_io.model.embedding' class-attribute

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__slots__ = ('vector', '__weakref__') class-attribute

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.

If the argument is a tuple, the return value is the same object.

__static_attributes__ = () class-attribute

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.

If the argument is a tuple, the return value is the same object.

__weakref__ property

list of weak references to the object

dim property

Dimensionality D of the embedding vector.

__eq__(other)

Method generated by attrs for class Embedding.

Source code in sleap_io/model/embedding.py 
arr = np.asarray(value)
if not np.issubdtype(arr.dtype, np.floating):
    arr = arr.astype(np.float32)
if arr.ndim != 1:
    raise ValueError(
        f"Embedding vector must be 1-dimensional, got shape {arr.shape}."

__init__(vector)

Method generated by attrs for class Embedding.

Source code in sleap_io/model/embedding.py 
    )
return arr

__repr__()

Method generated by attrs for class Embedding.

Source code in sleap_io/model/embedding.py 
"""Embedding data structure for per-detection appearance / re-ID vectors."""

from __future__ import annotations

import attrs
import numpy as np
from attrs import define, field


def _as_vector(value) -> np.ndarray:
    """Coerce an input to a 1-D embedding vector, preserving floating dtype.

    Floating inputs (e.g. float32 from a neural network) keep their dtype;
    non-floating inputs are cast to float32.
    """

__setattr__(name, val)

Method generated by attrs for class Embedding.