pipelines.py 文件源码

def make_data_provider(self, **kwargs):
        """Creates DataProvider instance for this input pipeline. Additional keyword arguments
        are passed to the DataProvider.
        """
        context_keys_to_features = {
            self.image_field: tf.FixedLenFeature(
                [], dtype=tf.string),
            "image/format": tf.FixedLenFeature(
                [], dtype=tf.string, default_value=self.image_format),
        }

        sequence_keys_to_features = {
            self.caption_ids_field: tf.FixedLenSequenceFeature(
                [], dtype=tf.int64),
            self.caption_tokens_field: tf.FixedLenSequenceFeature(
                [], dtype=tf.string)
        }

        items_to_handlers = {
            'image': tfslim.tfexample_decoder.Image(
                image_key=self.image_field,
                format_key="image/format",
                channels=3),
            'target_ids': tfslim.tfexample_decoder.Tensor(self.caption_ids_field),
            'target_token': tfslim.tfexample_decoder.Tensor(self.caption_tokens_field),
            'target_len': tfslim.tfexample_decoder.ItemHandlerCallback(
                keys=[self.caption_tokens_field],
                func=lambda x: tf.size(x[self.caption_tokens_field]))
        }

        decoder = TFSequenceExampleDecoder(
            context_keys_to_features, sequence_keys_to_features, items_to_handlers)

        dataset = Dataset(
            data_sources=self.files,
            reader=tf.TFRecordReader,
            decoder=decoder,
            num_samples=None,
            items_to_descriptions={})

        return DatasetDataProvider(
            dataset=dataset,
            shuffle=self.shuffle,
            num_epochs=self.num_epochs,
            **kwargs)