Random

coola.random

Contain functionalities to manage randomness.

coola.random.BaseRandomManager

Bases: ABC

Implement the base class to manage randomness.

Each child class must implement the methods: - get_rng_state. - manual_seed. - set_rng_state.

Example usage:

>>> from coola.random import TorchRandomManager
>>> manager = TorchRandomManager()
>>> manager.manual_seed(42)

coola.random.BaseRandomManager.get_rng_state abstractmethod

get_rng_state() -> Any

Get the current RNG state.

Returns:

Type	Description
Any	The current RNG state.

Example usage:

>>> from coola.random import TorchRandomManager
>>> manager = TorchRandomManager()
>>> state = manager.get_rng_state()
>>> state
{'torch': tensor([...], dtype=torch.uint8), 'torch.cuda': ...}

coola.random.BaseRandomManager.manual_seed abstractmethod

manual_seed(seed: int) -> None

Set the seed for generating random numbers.

Parameters:

Name	Type	Description	Default
seed	int	The desired seed.	required

Example usage:

>>> from coola.random import TorchRandomManager
>>> manager = TorchRandomManager()
>>> manager.manual_seed(42)

coola.random.BaseRandomManager.set_rng_state abstractmethod

set_rng_state(state: Any) -> None

Set the RNG state.

Parameters:

Name	Type	Description	Default
state	Any	The new RNG state.	required

Example usage:

>>> import torch
>>> from coola.random import TorchRandomManager
>>> manager = TorchRandomManager()
>>> state = manager.get_rng_state()
>>> manager.set_rng_state(state)

coola.random.NumpyRandomManager

Bases: BaseRandomManager

Implement a random manager for the library numpy.

The seed must be between 0 and 2**32 - 1, so a modulo operator to convert an integer to an integer between 0 and 2**32 - 1.

Example usage:

>>> from coola.random import NumpyRandomManager
>>> manager = NumpyRandomManager()
>>> manager.manual_seed(42)

coola.random.RandomManager

Bases: BaseRandomManager

Implement the default random manager.

By default, it is initialized with the following random managers:

• 'random': RandomRandomManager
• 'numpy': NumpyRandomManager if numpy is available
• 'torch': TorchRandomManager if torch is available

Example usage:

>>> from coola.random import RandomManager
>>> manager = RandomManager()
>>> manager.manual_seed(42)

coola.random.RandomManager.add_manager classmethod

add_manager(
    name: str,
    manager: BaseRandomManager,
    exist_ok: bool = False,
) -> None

Add a random manager for a given name.

Parameters:

Name	Type	Description	Default
name	str	The name for the random manager to add.	required
manager	BaseRandomManager	The random manager to add.	required
exist_ok	bool	If False, ValueError is raised if the name already exists. This parameter should be set to True to overwrite the manager for a name.	False

Raises:

Type	Description
RuntimeError	if a random manager is already registered for the name and exist_ok=False.

Example usage:

>>> from coola.random import BaseRandomManager, RandomManager
>>> class OtherRandomManager(BaseRandomManager):
...     def get_rng_state(self) -> dict:
...         return {}
...     def manual_seed(self, seed: int) -> None:
...         pass
...     def set_rng_state(self, state: dict) -> dict:
...         pass
...
>>> RandomManager.add_manager("other", OtherRandomManager())
>>> # To overwrite an existing random manager
>>> RandomManager.add_manager("other", OtherRandomManager(), exist_ok=True)

coola.random.RandomManager.has_manager classmethod

has_manager(name: str) -> bool

Indicate if a random manager is registered for the given name.

Parameters:

Name	Type	Description	Default
name	str	The name to check.	required

Returns:

Type	Description
bool	True if a random manager is registered, otherwise False.

Example usage:

>>> from coola.random import RandomManager
>>> RandomManager.has_manager("random")
True
>>> RandomManager.has_manager("missing")
False

coola.random.RandomRandomManager

Bases: BaseRandomManager

Implement a random manager for the python standard library random.

Example usage:

>>> from coola.random import RandomRandomManager
>>> manager = RandomRandomManager()
>>> manager.manual_seed(42)

coola.random.TorchRandomManager

Bases: BaseRandomManager

Implements a random number generator for the library torch.

Example usage:

>>> from coola.random import TorchRandomManager
>>> manager = TorchRandomManager()
>>> manager.manual_seed(42)

coola.random.get_random_managers

get_random_managers() -> dict[str, BaseRandomManager]

Get the random managers and their default name.

Returns:

Type	Description
dict[str, BaseRandomManager]	The mapping between the name and random managers.

Example usage:

>>> from coola.random import get_random_managers
>>> get_random_managers()
{'random': RandomRandomManager()...}

coola.random.get_rng_state

get_rng_state() -> dict[str, Any]

Get the current RNG state.

Returns:

Type	Description
dict[str, Any]	The current RNG state.

Example usage:

>>> from coola.random import get_rng_state
>>> state = get_rng_state()
>>> state
{'random': ...}

coola.random.manual_seed

manual_seed(seed: int) -> None

Set the seed for generating random numbers.

Parameters:

Name	Type	Description	Default
seed	int	The desired random seed.	required

Example usage:

>>> import torch
>>> from coola.random import manual_seed
>>> manual_seed(42)
>>> torch.randn(3)
tensor([...])
>>> torch.randn(3)
tensor([...])
>>> manual_seed(42)
>>> torch.randn(3)
tensor([...])

coola.random.numpy_seed

numpy_seed(seed: int) -> Generator[None]

Implement a context manager to manage the NumPy random seed and random number generator (RNG) state.

The context manager sets the specified random seed and restores the original RNG state afterward.

Parameters:

Name	Type	Description	Default
seed	int	The random number generator seed to use while using this context manager.	required

Example usage:

>>> import numpy
>>> from coola.random import numpy_seed
>>> with numpy_seed(42):
...     print(numpy.random.randn(2, 4))
...
[[...]]
>>> with numpy_seed(42):
...     print(numpy.random.randn(2, 4))
...
[[...]]

coola.random.random_seed

random_seed(seed: int) -> Generator[None]

Implement a context manager to manage the random seed and random number generator (RNG) state.

The context manager sets the specified random seed and restores the original RNG state afterward.

Parameters:

Name	Type	Description	Default
seed	int	The random number generator seed to use while using this context manager.	required

Example usage:

>>> import numpy
>>> from coola.random import random_seed
>>> with random_seed(42):
...     print(numpy.random.randn(2, 4))
...
[[...]]
>>> with random_seed(42):
...     print(numpy.random.randn(2, 4))
...
[[...]]

coola.random.register_random_managers

register_random_managers() -> None

Register randomness managers to RandomManager.

Example usage:

>>> from coola.random import RandomManager
>>> from coola.random.default import register_random_managers
>>> register_random_managers()
>>> manager = RandomManager()
>>> manager
RandomManager(
  (random): RandomRandomManager()
  ...
)

coola.random.set_rng_state

set_rng_state(state: dict[str, Any]) -> None

Set the RNG state.

Parameters:

Name	Type	Description	Default
state	dict[str, Any]	The new RNG state.	required

Example usage:

>>> import torch
>>> from coola.random import get_rng_state, set_rng_state
>>> st = get_rng_state()
>>> set_rng_state(st)

coola.random.torch_seed

torch_seed(seed: int) -> Generator[None]

Implement a context manager to manage the PyTorch random seed and random number generator (RNG) state.

The context manager sets the specified random seed and restores the original RNG state afterward.

Parameters:

Name	Type	Description	Default
seed	int	The random number generator seed to use while using this context manager.	required

Example usage:

>>> import torch
>>> from coola.random import torch_seed
>>> with torch_seed(42):
...     print(torch.randn(2, 4))
...
tensor([[...]])
>>> with torch_seed(42):
...     print(torch.randn(2, 4))
...
tensor([[...]])