배열

class Array(dtype: str, shape: tuple[int, ...], stype: str, data: bytes)

class Array(ndarray: ndarray[Any, dtype[Any]])

class Array(torch_tensor: torch.Tensor)

기반 클래스: InflatableObject

배열 유형.

A dataclass containing serialized data from an array-like or tensor-like object along with metadata about it. The class can be initialized in one of three ways:

1. By specifying explicit values for dtype, shape, stype, and data.

2. By providing a NumPy ndarray (via the ndarray argument).

3. By providing a PyTorch tensor (via the torch_tensor argument).

In scenarios (2)-(3), the dtype, shape, stype, and data are automatically derived from the input. In scenario (1), these fields must be specified manually.

매개변수:

• dtype (Optional[str] (default: None)) – A string representing the data type of the serialized object (e.g. “float32”). Only required if you are not passing in a ndarray or a tensor.

• shape (Optional[tuple[int, ...]] (default: None)) – A tuple representing the shape of the unserialized array-like object. Only required if you are not passing in a ndarray or a tensor.

• stype (Optional[str] (default: None)) – A string indicating the serialization mechanism used to generate the bytes in data from an array-like or tensor-like object. Only required if you are not passing in a ndarray or a tensor.

• data (Optional[bytes] (default: None)) – A buffer of bytes containing the data. Only required if you are not passing in a ndarray or a tensor.

• ndarray (Optional[NDArray] (default: None)) – A NumPy ndarray. If provided, the dtype, shape, stype, and data fields are derived automatically from it.

• torch_tensor (Optional[torch.Tensor] (default: None)) – A PyTorch tensor. If provided, it will be detached and moved to CPU before conversion, and the dtype, shape, stype, and data fields will be derived automatically from it.

예제

Initializing by specifying all fields directly:

arr1 = Array(
    dtype="float32",
    shape=[3, 3],
    stype="numpy.ndarray",
    data=b"serialized_data...",
)

Initializing with a NumPy ndarray:

import numpy as np
arr2 = Array(np.random.randn(3, 3))

Initializing with a PyTorch tensor:

import torch
arr3 = Array(torch.randn(3, 3))

메소드

deflate()	Deflate the Array.
from_numpy_ndarray(ndarray)	NumPy에서 배열을 만듭니다.
from_torch_tensor(tensor)	Create Array from PyTorch tensor.
inflate(object_content[, children])	Inflate an Array from bytes.
numpy()	배열을 NumPy 배열로 반환합니다.
slice_array()	Slice Array data and construct a list of ArrayChunks.

속성

children	Return a dictionary of ArrayChunks with their Object IDs as keys.
is_dirty	Check if the object is dirty after the last deflation.
object_id	Get object ID.
dtype
shape
stype
data

property children: dict[str, InflatableObject]

Return a dictionary of ArrayChunks with their Object IDs as keys.

deflate() → bytes

Deflate the Array.

classmethod from_numpy_ndarray(ndarray: ndarray[Any, dtype[Any]]) → Array

NumPy에서 배열을 만듭니다.

classmethod from_torch_tensor(tensor: torch.Tensor) → Array

Create Array from PyTorch tensor.

classmethod inflate(object_content: bytes, children: dict[str, InflatableObject] | None = None) → Array

Inflate an Array from bytes.

매개변수:

• object_content (bytes) – The deflated object content of the Array.

• children (Optional[dict[str, InflatableObject]] (default: None)) – Must be None. Array must have child objects. Providing no children will raise a ValueError.

반환:

The inflated Array.

반환 형식:

Array

property is_dirty: bool

Check if the object is dirty after the last deflation.

numpy() → ndarray[Any, dtype[Any]]

배열을 NumPy 배열로 반환합니다.

property object_id: str

Get object ID.

slice_array() → list[tuple[str, InflatableObject]]

Slice Array data and construct a list of ArrayChunks.