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import abc |
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import torch |
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import torch.nn as nn |
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from typing import Union |
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class AbstractDetector(nn.Module, metaclass=abc.ABCMeta): |
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""" |
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All deepfake detectors should subclass this class. |
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""" |
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def __init__(self, config=None, load_param: Union[bool, str] = False): |
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""" |
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config: (dict) |
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configurations for the model |
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load_param: (False | True | Path(str)) |
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False Do not read; True Read the default path; Path Read the required path |
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""" |
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super().__init__() |
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@abc.abstractmethod |
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def features(self, data_dict: dict) -> torch.tensor: |
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""" |
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Returns the features from the backbone given the input data. |
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""" |
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pass |
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@abc.abstractmethod |
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def forward(self, data_dict: dict, inference=False) -> dict: |
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""" |
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Forward pass through the model, returning the prediction dictionary. |
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""" |
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pass |
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@abc.abstractmethod |
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def classifier(self, features: torch.tensor) -> torch.tensor: |
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""" |
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Classifies the features into classes. |
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""" |
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pass |
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@abc.abstractmethod |
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def build_backbone(self, config): |
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""" |
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Builds the backbone of the model. |
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""" |
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pass |
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@abc.abstractmethod |
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def build_loss(self, config): |
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""" |
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Builds the loss function for the model. |
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""" |
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pass |
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@abc.abstractmethod |
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def get_losses(self, data_dict: dict, pred_dict: dict) -> dict: |
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""" |
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Returns the losses for the model. |
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""" |
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pass |
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@abc.abstractmethod |
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def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict: |
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""" |
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Returns the training metrics for the model. |
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""" |
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pass |
