data_decoders.py 文件源码

def decode(self, data, items=None):
        """Decodes the given serialized TF-example.
        Args:
          data: a serialized TF-example tensor.
          items: the list of items to decode. These must be a subset of the item
            keys in self._items_to_handlers. If `items` is left as None, then all
            of the items in self._items_to_handlers are decoded.
        Returns:
          the decoded items, a list of tensor.
        """
        context, sequence = tf.parse_single_sequence_example(
            data, self._context_keys_to_features, self._sequence_keys_to_features)

        # Merge context and sequence features
        example = {}
        example.update(context)
        example.update(sequence)

        all_features = {}
        all_features.update(self._context_keys_to_features)
        all_features.update(self._sequence_keys_to_features)

        # Reshape non-sparse elements just once:
        for k, value in all_features.items():
            if isinstance(value, tf.FixedLenFeature):
                example[k] = tf.reshape(example[k], value.shape)

        if not items:
            items = self._items_to_handlers.keys()

        outputs = []
        for item in items:
            handler = self._items_to_handlers[item]
            keys_to_tensors = {key: example[key] for key in handler.keys}
            outputs.append(handler.tensors_to_item(keys_to_tensors))
        return outputs