ArrayRecord

class ArrayRecord

class ArrayRecord(array_dict: OrderedDict[str, Array], *, keep_input: bool = True)

class ArrayRecord(numpy_ndarrays: list[ndarray[Any, dtype[Any]]], *, keep_input: bool = True)

class ArrayRecord(torch_state_dict: OrderedDict[str, torch.Tensor], *, keep_input: bool = True)

기반 클래스: TypedDict[str, Array]

Array record.

A typed dictionary (str to Array) that can store named arrays, including model parameters, gradients, embeddings or non-parameter arrays. Internally, this behaves similarly to an OrderedDict[str, Array]. An ArrayRecord can be viewed as an equivalent to PyTorch’s state_dict, but it holds arrays in a serialized form.

This object is one of the record types supported by RecordDict and can therefore be stored in the content of a Message or the state of a Context.

This class can be instantiated in multiple ways:

1. By providing nothing (empty container).

2. By providing a dictionary of Array (via the array_dict argument).

3. By providing a list of NumPy ndarray (via the numpy_ndarrays argument).

4. By providing a PyTorch state_dict (via the torch_state_dict argument).

매개변수:

• array_dict (Optional[OrderedDict[str, Array]] (default: None)) – An existing dictionary containing named Array instances. If provided, these entries will be used directly to populate the record.

• numpy_ndarrays (Optional[list[NDArray]] (default: None)) – A list of NumPy arrays. Each array will be automatically converted into an Array and stored in this record with generated keys.

• torch_state_dict (Optional[OrderedDict[str, torch.Tensor]] (default: None)) – A PyTorch state_dict (str keys to torch.Tensor values). Each tensor will be converted into an Array and stored in this record.

• keep_input (bool (default: True)) – If False, entries from the input are removed after being added to this record to free up memory. If True, the input remains unchanged. Regardless of this value, no duplicate memory is used if the input is a dictionary of Array, i.e., array_dict.

예제

Initializing an empty ArrayRecord:

>>> record = ArrayRecord()

Initializing with a dictionary of Array:

>>> arr = Array("float32", [5, 5], "numpy.ndarray", b"serialized_data...")
>>> record = ArrayRecord({"weight": arr})

Initializing with a list of NumPy arrays:

>>> import numpy as np
>>> arr1 = np.random.randn(3, 3)
>>> arr2 = np.random.randn(2, 2)
>>> record = ArrayRecord([arr1, arr2])

Initializing with a PyTorch model state_dict:

>>> import torch.nn as nn
>>> model = nn.Linear(10, 5)
>>> record = ArrayRecord(model.state_dict())

Initializing with a TensorFlow model weights (a list of NumPy arrays):

>>> import tensorflow as tf
>>> model = tf.keras.Sequential([tf.keras.layers.Dense(5, input_shape=(10,))])
>>> record = ArrayRecord(model.get_weights())

메소드

clear()
count_bytes()	이 객체에 저장된 바이트 수를 반환합니다.
from_array_dict(array_dict, *[, keep_input])	Create ArrayRecord from a dictionary of Array.
from_numpy_ndarrays(ndarrays, *[, keep_input])	Create ArrayRecord from a list of NumPy ndarray.
from_torch_state_dict(state_dict, *[, ...])	Create ArrayRecord from PyTorch state_dict.
get(k[,d])
items()
keys()
pop(k[,d])	키를 찾을 수 없으면 주어진 경우 d가 반환되고, 그렇지 않으면 KeyError가 발생합니다.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
to_numpy_ndarrays(*[, keep_input])	Return the ArrayRecord as a list of NumPy ndarray.
to_torch_state_dict(*[, keep_input])	Return the ArrayRecord as a PyTorch state_dict.
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()

clear() → None.  Remove all items from D.

count_bytes() → int

이 객체에 저장된 바이트 수를 반환합니다.

역직렬화에 필요한 직렬화된 객체의 메타데이터(예: NumPy 배열)에 해당하는 소량의 바이트도 이 카운팅에 포함될 수 있습니다.

classmethod from_array_dict(array_dict: OrderedDict[str, Array], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from a dictionary of Array.

classmethod from_numpy_ndarrays(ndarrays: list[ndarray[Any, dtype[Any]]], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from a list of NumPy ndarray.

classmethod from_torch_state_dict(state_dict: OrderedDict[str, torch.Tensor], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from PyTorch state_dict.

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

items() → a set-like object providing a view on D's items.

keys() → a set-like object providing a view on D's keys.

pop(k[, d]) → v, remove specified key and return the corresponding value.

키를 찾을 수 없으면 주어진 경우 d가 반환되고, 그렇지 않으면 KeyError가 발생합니다.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

to_numpy_ndarrays(*, keep_input: bool = True) → list[ndarray[Any, dtype[Any]]]

Return the ArrayRecord as a list of NumPy ndarray.

to_torch_state_dict(*, keep_input: bool = True) → OrderedDict[str, torch.Tensor]

Return the ArrayRecord as a PyTorch state_dict.

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values.