def __init__(self, data=None, estimator=None,
aggregation=None, transmitter=None) -> None:
from plato.config import Config
from plato.datasources import base
# set parameters
server = Config().server._asdict()
clients = Config().clients._asdict()
datastore = Config().data._asdict()
train = Config().trainer._asdict()
self.datasource = None
if data is not None:
if hasattr(data, "customized"):
if data.customized:
self.datasource = base.DataSource()
self.datasource.trainset = data.trainset
self.datasource.testset = data.testset
else:
datastore.update(data.parameters)
Config().data = Config.namedtuple_from_dict(datastore)
self.model = None
if estimator is not None:
self.model = estimator.model
train.update(estimator.hyperparameters)
Config().trainer = Config.namedtuple_from_dict(train)
if aggregation is not None:
Config().algorithm = Config.namedtuple_from_dict(
aggregation.parameters)
if aggregation.parameters["type"] == "mistnet":
clients["type"] = "mistnet"
server["type"] = "mistnet"
else:
clients["do_test"] = True
server["address"] = Context.get_parameters("AGG_IP")
server["port"] = Context.get_parameters("AGG_PORT")
if transmitter is not None:
server.update(transmitter.parameters)
Config().server = Config.namedtuple_from_dict(server)
Config().clients = Config.namedtuple_from_dict(clients)
from plato.clients import registry as client_registry
self.client = client_registry.get(model=self.model,
datasource=self.datasource)
self.client.configure()
def __init__(self, data=None, estimator=None,
aggregation=None, transmitter=None,
chooser=None) -> None:
from plato.config import Config
# set parameters
server = Config().server._asdict()
clients = Config().clients._asdict()
datastore = Config().data._asdict()
train = Config().trainer._asdict()
if data is not None:
datastore.update(data.parameters)
Config().data = Config.namedtuple_from_dict(datastore)
self.model = None
if estimator is not None:
self.model = estimator.model
if estimator.pretrained is not None:
Config().params['pretrained_model_dir'] = estimator.pretrained
if estimator.saved is not None:
Config().params['model_dir'] = estimator.saved
train.update(estimator.hyperparameters)
Config().trainer = Config.namedtuple_from_dict(train)
server["address"] = Context.get_parameters("AGG_BIND_IP", "0.0.0.0")
server["port"] = int(Context.get_parameters("AGG_BIND_PORT", 7363))
if transmitter is not None:
server.update(transmitter.parameters)
if aggregation is not None:
Config().algorithm = Config.namedtuple_from_dict(
aggregation.parameters)
if aggregation.parameters["type"] == "mistnet":
clients["type"] = "mistnet"
server["type"] = "mistnet"
else:
clients["do_test"] = True
if chooser is not None:
clients["per_round"] = chooser.parameters["per_round"]
LOGGER.info("address %s, port %s", server["address"], server["port"])
Config().server = Config.namedtuple_from_dict(server)
Config().clients = Config.namedtuple_from_dict(clients)
from plato.servers import registry as server_registry
self.server = server_registry.get(model=self.model)
def setup(self):
super().setup()
self.addTypeEqualityFunc(Config, 'assertConfigEqual')
self.defined_config = Config()
# define several example parameters
data_params_config = {
"downloader": {
"num_workers": 4
},
"multi_modal_pipeliner": {
"rgb": {
"rgb_data": {
"train": {
"type": "RawframeDataset"
}
}
},
"flow": {
"flow_data": {
"train": {
"type": "RawframeDataset"
}
}
},
"audio": {
"audio_data": {
"train": {
"type": "AudioFeatureDataset"
}
}
}
}
}
model_params_config = {
"model_name": "rgb_flow_audio_model",
"model_config": {
"rgb_model": {
"type": "Recognizer3D"
}
}
}
self.data_config = Config.namedtuple_from_dict(data_params_config)
self.model_config = Config.namedtuple_from_dict(model_params_config)
