Source code for deeppavlov.core.common.base

# Copyright 2021 Neural Networks and Deep Learning lab, MIPT
#
# 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
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# 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.
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from types import FunctionType
from typing import List, Optional, Union

from deeppavlov.core.common.chainer import Chainer
from deeppavlov.core.models.component import Component


[docs]class Element: """DeepPavlov model pipeline element."""
[docs] def __init__(self, component: Union[Component, FunctionType], x: Optional[Union[str, list]] = None, out: Optional[Union[str, list]] = None, y: Optional[Union[str, list]] = None, main: bool = False) -> None: """ Args: component: Pipeline component object. x: Names of the component inference inputs. Output from other pipeline elements with such names will be fed to the input of this component. out: Names of the component inference outputs. Component outputs can be fed to other pipeline elements using this names. y: Names of additional inputs (targets) for component training and evaluation. main: Set True if this is the main component. Main component is trained during model training process. """ self.component = component self.x = x self.y = y self.out = out self.main = main
[docs]class Model(Chainer): """Builds a component pipeline to train and infer models."""
[docs] def __init__(self, x: Optional[Union[str, list]] = None, out: Optional[Union[str, list]] = None, y: Optional[Union[str, list]] = None, pipe: Optional[List[Element]] = None) -> None: """ Args: x: Names of pipeline inference inputs. out: Names of pipeline inference outputs. y: Names of additional inputs (targets) for pipeline training and evaluation. pipe: List of pipeline elements. """ super().__init__(in_x=x, out_params=out, in_y=y) if pipe is not None: for element in pipe: self.append(element.component, element.x, element.out, element.y, element.main)