Source code for monai.fl.client.client_algo

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from __future__ import annotations

from monai.fl.utils.exchange_object import ExchangeObject


[docs] class BaseClient: """ Provide an abstract base class to allow the client to return summary statistics of the data. To define a new stats script, subclass this class and implement the following abstract methods:: - self.get_data_stats() initialize(), abort(), and finalize() -- inherited from `ClientAlgoStats`; can be optionally be implemented to help with lifecycle management of the class object. """
[docs] def initialize(self, extra: dict | None = None) -> None: """ Call to initialize the ClientAlgo class. Args: extra: optional extra information, e.g. dict of `ExtraItems.CLIENT_NAME` and/or `ExtraItems.APP_ROOT`. """ pass
[docs] def finalize(self, extra: dict | None = None) -> None: """ Call to finalize the ClientAlgo class. Args: extra: Dict with additional information that can be provided by the FL system. """ pass
[docs] def abort(self, extra: dict | None = None) -> None: """ Call to abort the ClientAlgo training or evaluation. Args: extra: Dict with additional information that can be provided by the FL system. """ pass
[docs] class ClientAlgoStats(BaseClient):
[docs] def get_data_stats(self, extra: dict | None = None) -> ExchangeObject: """ Get summary statistics about the local data. Args: extra: Dict with additional information that can be provided by the FL system. For example, requested statistics. Returns: ExchangeObject: summary statistics. Extra dict example:: requested_stats = { FlStatistics.STATISTICS: metrics, FlStatistics.NUM_OF_BINS: num_of_bins, FlStatistics.BIN_RANGES: bin_ranges } Returned ExchangeObject example:: ExchangeObject( statistics = {...} ) """ raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
[docs] class ClientAlgo(ClientAlgoStats): """ Provide an abstract base class for defining algo to run on any platform. To define a new algo script, subclass this class and implement the following abstract methods: - self.train() - self.get_weights() - self.evaluate() - self.get_data_stats() (optional, inherited from `ClientAlgoStats`) initialize(), abort(), and finalize() - inherited from `ClientAlgoStats` - can be optionally be implemented to help with lifecycle management of the class object. """
[docs] def train(self, data: ExchangeObject, extra: dict | None = None) -> None: """ Train network and produce new network from train data. Args: data: ExchangeObject containing current network weights to base training on. extra: Dict with additional information that can be provided by the FL system. Returns: None """ raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
[docs] def get_weights(self, extra: dict | None = None) -> ExchangeObject: """ Get current local weights or weight differences. Args: extra: Dict with additional information that can be provided by the FL system. Returns: ExchangeObject: current local weights or weight differences. `ExchangeObject` example: .. code-block:: python ExchangeObject( weights = self.trainer.network.state_dict(), optim = None, # could be self.optimizer.state_dict() weight_type = WeightType.WEIGHTS ) """ raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
[docs] def evaluate(self, data: ExchangeObject, extra: dict | None = None) -> ExchangeObject: """ Get evaluation metrics on test data. Args: data: ExchangeObject with network weights to use for evaluation. extra: Dict with additional information that can be provided by the FL system. Returns: metrics: ExchangeObject with evaluation metrics. """ raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")