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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from monai.fl.utils.exchange_object import ExchangeObject
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.
"""
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
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
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
class ClientAlgoStats(BaseClient):
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.")
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.
"""
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.")
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.")
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.")
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