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"""This modules defines the interface which must be implemented in order to |
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utilize a pretrained model and its weights""" |
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from abc import ABC, abstractmethod |
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from typing import Tuple, Dict |
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from numpy.typing import NDArray |
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class PretrainedModelRoutines(ABC): |
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"""Set of routines that are required in order to use a pretrained model for nst.""" |
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@abstractmethod |
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def load_layers(self, file_path: str) -> NDArray: |
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"""Load a pretrained model from disk. |
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Loads the model parameters, given the path to a file in the disk, that |
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indicated where the pretrained model is. |
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Args: |
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file_path (str): the path corresponding to a file in the disk |
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Returns: |
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NDArray: the model parameters as a numpy array |
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""" |
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raise NotImplementedError |
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@abstractmethod |
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def get_id(self, layer: NDArray) -> str: |
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"""Get the id of a model's network layer. |
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The pretrained model being a neural network has a specific architecture |
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and each layer should a unique string id that one can reference it. |
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Args: |
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layer (NDArray): the layer of a pretrained neural network model |
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Returns: |
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str: the layer id |
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""" |
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raise NotImplementedError |
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@abstractmethod |
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def get_layers_dict(self, layers: NDArray) -> Dict[str, NDArray]: |
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"""Get a dict mapping strings to pretrained model layers. |
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Args: |
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layers (NDArray): the pretrained model layers |
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Returns: |
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Dict[str, NDArray]: the dictionary mapping strings to layers |
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""" |
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raise NotImplementedError |
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@abstractmethod |
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def get_weights(self, layer: NDArray) -> Tuple[NDArray, NDArray]: |
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"""Get the values of the weights of a given network layer. |
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Each pretrained model network layer has "learned" certain parameters in |
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the form of "weights" (ie weight matrices A and b in equation Ax + b). |
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Call this method to get a tuple of the A and b mathematical matrices. |
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Args: |
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layer (NDArray): the layer of a pretrained neural network model |
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Returns: |
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Tuple[NDArray, NDArray]: the weights in matrix A and b |
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""" |
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raise NotImplementedError |
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boromir674 /
neural-style-transfer
