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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
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