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| from __future__ import annotations |
|
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| from monai.fl.utils.constants import WeightType |
|
|
|
|
| class ExchangeObject(dict): |
| """ |
| Contains the information shared between client and server. |
| |
| Args: |
| weights: model weights. |
| optim: optimizer weights. |
| metrics: evaluation metrics. |
| weight_type: type of weights (see monai.fl.utils.constants.WeightType). |
| statistics: training statistics, i.e. number executed iterations. |
| """ |
|
|
| def __init__( |
| self, |
| weights: dict | None = None, |
| optim: dict | None = None, |
| metrics: dict | None = None, |
| weight_type: WeightType | None = None, |
| statistics: dict | None = None, |
| ): |
| super().__init__() |
| self.weights = weights |
| self.optim = optim |
| self.metrics = metrics |
| self.weight_type = weight_type |
| self.statistics = statistics |
| self._summary: dict = {} |
|
|
| @property |
| def metrics(self): |
| return self._metrics |
|
|
| @metrics.setter |
| def metrics(self, metrics): |
| if metrics is not None: |
| if not isinstance(metrics, dict): |
| raise ValueError(f"Expected metrics to be of type dict but received {type(metrics)}") |
| self._metrics = metrics |
|
|
| @property |
| def statistics(self): |
| return self._statistics |
|
|
| @statistics.setter |
| def statistics(self, statistics): |
| if statistics is not None: |
| if not isinstance(statistics, dict): |
| raise ValueError(f"Expected statistics to be of type dict but received {type(statistics)}") |
| self._statistics = statistics |
|
|
| @property |
| def weight_type(self): |
| return self._weight_type |
|
|
| @weight_type.setter |
| def weight_type(self, weight_type): |
| if weight_type is not None: |
| if weight_type not in [WeightType.WEIGHTS, WeightType.WEIGHT_DIFF]: |
| raise ValueError(f"Expected weight type to be either {WeightType.WEIGHTS} or {WeightType.WEIGHT_DIFF}") |
| self._weight_type = weight_type |
|
|
| def is_valid_weights(self): |
| if not self.weights: |
| return False |
| if not self.weight_type: |
| return False |
| return True |
|
|
| def _add_to_summary(self, key, value): |
| if value: |
| if isinstance(value, dict): |
| self._summary[key] = len(value) |
| elif isinstance(value, WeightType): |
| self._summary[key] = value |
| else: |
| self._summary[key] = type(value) |
|
|
| def summary(self): |
| self._summary.update(self) |
| for k, v in zip( |
| ["weights", "optim", "metrics", "weight_type", "statistics"], |
| [self.weights, self.optim, self.metrics, self.weight_type, self.statistics], |
| ): |
| self._add_to_summary(k, v) |
| return self._summary |
|
|
| def __repr__(self): |
| return str(self.summary()) |
|
|
| def __str__(self): |
| return str(self.summary()) |
|
|
