ParametersRecord

class ParametersRecord(*args: Any, **kwargs: Any)

Bases: ArrayRecord

Deprecated class ParametersRecord, use ArrayRecord instead.

Methods

clear()
copy()	Return a shallow copy of the dictionary.
count_bytes()	Return number of Bytes stored in this object.
deflate()	Deflate the ArrayRecord.
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])
inflate(object_content[, children])	Inflate an ArrayRecord from bytes.
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
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()

Attributes

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

property children: dict[str, InflatableObject]

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

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

copy() → Self

Return a shallow copy of the dictionary.

count_bytes() → int

Return number of Bytes stored in this object.

Note that a small amount of Bytes might also be included in this counting that correspond to metadata of the serialized object (e.g. of NumPy array) needed for deseralization.

deflate() → bytes

Deflate the ArrayRecord.

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

Create ArrayRecord from a dictionary of Array.

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

Create ArrayRecord from a list of NumPy ndarray.

classmethod from_torch_state_dict(state_dict: dict[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.

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

Inflate an ArrayRecord from bytes.

Parameters:

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

• children (Optional[dict[str, InflatableObject]] (default: None)) – Dictionary of children InflatableObjects mapped to their Object IDs. These children enable the full inflation of the ArrayRecord.

Returns:

The inflated ArrayRecord.

Return type:

ArrayRecord

property is_dirty: bool

Check if the object is dirty after the last deflation.

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

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

property object_id: str

Get object ID.

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

If key is not found, d is returned if given, otherwise KeyError is raised.

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[tuple[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.