# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""Decomposers for types from app_common and Machine Learning libraries."""
import os
from abc import ABC
from io import BytesIO
from typing import Any
import numpy as np
from nvflare.app_common.abstract.learnable import Learnable
from nvflare.app_common.abstract.model import ModelLearnable
from nvflare.app_common.widgets.event_recorder import _CtxPropReq, _EventReq, _EventStats
from nvflare.fuel.utils import fobs
from nvflare.fuel.utils.fobs import Decomposer
from nvflare.fuel.utils.fobs.decomposer import DictDecomposer
[docs]class ModelLearnableDecomposer(fobs.Decomposer):
[docs] def supported_type(self):
return ModelLearnable
[docs] def decompose(self, target: ModelLearnable) -> Any:
return target.copy()
[docs] def recompose(self, data: Any) -> ModelLearnable:
obj = ModelLearnable()
for k, v in data.items():
obj[k] = v
return obj
[docs]class NumpyScalarDecomposer(fobs.Decomposer, ABC):
"""Decomposer base class for all numpy types with item method."""
[docs] def decompose(self, target: Any) -> Any:
return target.item()
[docs] def recompose(self, data: Any) -> np.ndarray:
return self.supported_type()(data)
[docs]class Float64ScalarDecomposer(NumpyScalarDecomposer):
[docs] def supported_type(self):
return np.float64
[docs]class Float32ScalarDecomposer(NumpyScalarDecomposer):
[docs] def supported_type(self):
return np.float32
[docs]class Int64ScalarDecomposer(NumpyScalarDecomposer):
[docs] def supported_type(self):
return np.int64
[docs]class Int32ScalarDecomposer(NumpyScalarDecomposer):
[docs] def supported_type(self):
return np.int32
[docs]class NumpyArrayDecomposer(Decomposer):
[docs] def supported_type(self):
return np.ndarray
[docs] def decompose(self, target: np.ndarray) -> Any:
stream = BytesIO()
np.save(stream, target, allow_pickle=False)
return stream.getvalue()
[docs] def recompose(self, data: Any) -> np.ndarray:
stream = BytesIO(data)
return np.load(stream, allow_pickle=False)
[docs]def register():
if register.registered:
return
fobs.register(DictDecomposer(Learnable))
fobs.register(DictDecomposer(ModelLearnable))
fobs.register_data_classes(
_CtxPropReq,
_EventReq,
_EventStats,
)
fobs.register_folder(os.path.dirname(__file__), __package__)
register.registered = True
register.registered = False