Source code for nvflare.app_opt.pt.file_model_locator

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import List

from nvflare.apis.dxo import DXO
from nvflare.apis.event_type import EventType
from nvflare.apis.fl_context import FLContext
from nvflare.app_common.abstract.model import model_learnable_to_dxo
from nvflare.app_common.abstract.model_locator import ModelLocator
from nvflare.app_opt.pt.decomposers import TensorDecomposer
from nvflare.app_opt.pt.file_model_persistor import PTFileModelPersistor
from nvflare.fuel.utils import fobs


[docs] class PTFileModelLocator(ModelLocator): def __init__(self, pt_persistor_id: str): """The ModelLocator's job is to find and locate the models inventory saved during training. Args: pt_persistor_id (str): ModelPersistor component ID """ super().__init__() self.pt_persistor_id = pt_persistor_id self.model_persistor = None self.model_inventory = {}
[docs] def handle_event(self, event_type: str, fl_ctx: FLContext): if event_type == EventType.START_RUN: self._initialize(fl_ctx)
def _initialize(self, fl_ctx: FLContext): engine = fl_ctx.get_engine() self.model_persistor: PTFileModelPersistor = engine.get_component(self.pt_persistor_id) if self.model_persistor is None or not isinstance(self.model_persistor, PTFileModelPersistor): raise ValueError( f"pt_persistor_id component must be PTFileModelPersistor. " f"But got: {type(self.model_persistor)}" ) fobs.register(TensorDecomposer)
[docs] def get_model_names(self, fl_ctx: FLContext) -> List[str]: """Returns the list of model names that should be included from server in cross site validation.add(). Args: fl_ctx (FLContext): FL Context object. Returns: List[str]: List of model names. """ self.model_inventory: dict = self.model_persistor.get_model_inventory(fl_ctx) return list(self.model_inventory.keys())
[docs] def locate_model(self, model_name, fl_ctx: FLContext) -> DXO: """Call to locate and load the model weights of model_name. Args: model_name: name of the model fl_ctx: FLContext Returns: model_weight DXO """ if model_name not in list(self.model_inventory.keys()): raise ValueError(f"model inventory does not contain: {model_name}") model_learnable = self.model_persistor.get(model_name, fl_ctx) dxo = model_learnable_to_dxo(model_learnable) return dxo