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| """The generic parser interface."""
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| import abc
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| class Parser(object):
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| """Parses data and produces tensors to be consumed by models."""
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| __metaclass__ = abc.ABCMeta
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| @abc.abstractmethod
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| def _parse_train_data(self, decoded_tensors):
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| """Generates images and labels that are usable for model training.
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| Args:
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| decoded_tensors: a dict of Tensors produced by the decoder.
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| Returns:
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| images: the image tensor.
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| labels: a dict of Tensors that contains labels.
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| """
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| pass
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| @abc.abstractmethod
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| def _parse_eval_data(self, decoded_tensors):
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| """Generates images and labels that are usable for model evaluation.
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| Args:
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| decoded_tensors: a dict of Tensors produced by the decoder.
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| Returns:
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| images: the image tensor.
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| labels: a dict of Tensors that contains labels.
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| """
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| pass
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| def parse_fn(self, is_training):
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| """Returns a parse fn that reads and parses raw tensors from the decoder.
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| Args:
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| is_training: a `bool` to indicate whether it is in training mode.
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| Returns:
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| parse: a `callable` that takes the serialized example and generate the
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| images, labels tuple where labels is a dict of Tensors that contains
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| labels.
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| """
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| def parse(decoded_tensors):
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| """Parses the serialized example data."""
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| if is_training:
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| return self._parse_train_data(decoded_tensors)
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| else:
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| return self._parse_eval_data(decoded_tensors)
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| return parse
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| @classmethod
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| def inference_fn(cls, inputs):
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| """Parses inputs for predictions.
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| Args:
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| inputs: A Tensor, or dictionary of Tensors.
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| Returns:
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| processed_inputs: An input tensor to the model.
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| """
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| pass
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|
