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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.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())
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