dacapo.store.weights_store

Classes

`Weights`	A class representing the weights of a model and optimizer.
`WeightsStore`	Base class for network weight stores.

Module Contents

class dacapo.store.weights_store.Weights(model_state_dict, optimizer_state_dict)

A class representing the weights of a model and optimizer.

optimizer

The optimizer’s state dictionary.

Type:: OrderedDict[str, torch.Tensor]

model

The model’s state dictionary.

Type:: OrderedDict[str, torch.Tensor]

__init__(model_state_dict, optimizer_state_dict): Initializes the Weights object with the given model and optimizer state dictionaries.

optimizer: collections.OrderedDict[str, torch.Tensor]

model: collections.OrderedDict[str, torch.Tensor]

class dacapo.store.weights_store.WeightsStore

Base class for network weight stores.

load_weights(run, iteration): Load the weights of the given iteration into the given run.

load_best(run, dataset, criterion): Load the best weights for the given run, dataset, and criterion into the given run.

latest_iteration(run): Return the latest iteration for which weights are available for the given run.

store_weights(run, iteration): Store the network weights of the given run.

retrieve_weights(run, iteration): Retrieve the network weights of the given run.

remove(run, iteration): Delete the weights associated with a specific run/iteration.

retrieve_best(run, dataset, criterion): Retrieve the best weights for the given run, dataset, and criterion.

load_weights(run: dacapo.experiments.run.Run, iteration: int) → None

Load this iterations weights into the given run. :param run: The run to load the weights into. :type run: Run :param iteration: The iteration to load the weights from. :type iteration: int

Raises:: ValueError – If the iteration is not available.

Examples

>>> store = WeightsStore()
>>> run = Run()
>>> iteration = 0
>>> store.load_weights(run, iteration)

load_best(run: dacapo.experiments.run.Run, dataset: str, criterion: str) → None

Load the best weights for this Run,dataset,criterion into Run.model

Parameters:

run (Run) – The run to load the weights into.
dataset (str) – The dataset to load the weights from.
criterion (str) – The criterion to load the weights from.

Raises:

ValueError – If the best iteration is not available.

Examples

>>> store = WeightsStore()
>>> run = Run()
>>> dataset = 'mnist'
>>> criterion = 'accuracy'
>>> store.load_best(run, dataset, criterion)

abstract latest_iteration(run: str) → int | None

Return the latest iteration for which weights are available for the given run.

Parameters:: run (str) – The name of the run.
Returns:: The latest iteration for which weights are available.
Return type:: int
Raises:: ValueError – If no weights are available for the given run.

Examples

>>> store = WeightsStore()
>>> run = 'run_0'
>>> store.latest_iteration(run)

abstract store_weights(run: dacapo.experiments.run.Run, iteration: int) → None

Store the network weights of the given run.

Parameters:

run (Run) – The run to store the weights of.
iteration (int) – The iteration to store the weights for.

Raises:

ValueError – If the iteration is already stored.

Examples

>>> store = WeightsStore()
>>> run = Run()
>>> iteration = 0
>>> store.store_weights(run, iteration)

abstract retrieve_weights(run: str, iteration: int) → Weights

Retrieve the network weights of the given run.

Parameters:

run (str) – The name of the run.
iteration (int) – The iteration to retrieve the weights for.

Returns:

The weights of the given run and iteration.

Return type:

Weights

Raises:

ValueError – If the weights are not available.

Examples

>>> store = WeightsStore()
>>> run = 'run_0'
>>> iteration = 0
>>> store.retrieve_weights(run, iteration)

abstract remove(run: str, iteration: int) → None

Delete the weights associated with a specific run/iteration

Parameters:

run (str) – The name of the run.
iteration (int) – The iteration to delete the weights for.

Raises:

ValueError – If the weights are not available.

Examples

>>> store = WeightsStore()
>>> run = 'run_0'
>>> iteration = 0
>>> store.remove(run, iteration)

abstract retrieve_best(run: str, dataset: str, criterion: str) → int

Retrieve the best weights for this run/dataset/criterion

Parameters:

run (str) – The name of the run.
dataset (str) – The dataset to retrieve the best weights for.
criterion (str) – The criterion to retrieve the best weights for.

Returns:

The iteration of the best weights.

Return type:

int

Raises:

ValueError – If the best weights are not available.

Examples

>>> store = WeightsStore()
>>> run = 'run_0'
>>> dataset = 'mnist'
>>> criterion = 'accuracy'
>>> store.retrieve_best(run, dataset, criterion)