input_pipeline.py 文件源码

def make_data_provider(self, **kwargs):

    context_keys_to_features = {
        self.params["image_field"]: tf.FixedLenFeature(
            [], dtype=tf.string),
        "image/format": tf.FixedLenFeature(
            [], dtype=tf.string, default_value=self.params["image_format"]),
    }

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

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

    decoder = TFSEquenceExampleDecoder(
        context_keys_to_features, sequence_keys_to_features, items_to_handlers)

    dataset = tf.contrib.slim.dataset.Dataset(
        data_sources=self.params["files"],
        reader=tf.TFRecordReader,
        decoder=decoder,
        num_samples=None,
        items_to_descriptions={})

    return tf.contrib.slim.dataset_data_provider.DatasetDataProvider(
        dataset=dataset,
        shuffle=self.params["shuffle"],
        num_epochs=self.params["num_epochs"],
        **kwargs)