How to pass a *serialized* tensor to a TensorFlow Model Serving server? - tensorflow

Question

I implemented a simple TF model. The model received a serialized tensor of a gray image (simply a 2d ndarray), and restored it to a 2-d tensor. After then, some inference is applied on this 2-d tensor.
I deployed the mode with TensorFlow Model Serving, and tried to send a JSON string to the REST port as follows:
{
"instances": [
{"b64": bin_str},
]
}
I tried something like tf.io.serialize_tensor etc to convert input image into a serialized tensor and to pass it to the server, but all failed.
I would like to know how to send a serialized tensor to the serving server.
And my saved model has following signature:
signatures = {
"serving_default": _get_serve_tf_examples_fn(
model,
transform_output).get_concrete_function(
# explicitly specify input signature of serve_tf_examples_fn
tf.TensorSpec(
shape=[None],
dtype=tf.string,
name="examples")),
}
and the definition of _get_serve_tf_examples_fn is,
def _get_serve_tf_examples_fn(model: tf.keras.models.Model,
transform_output: tft.TFTransformOutput):
# get the Transform graph from the component
model.tft_layer = transform_output.transform_features_layer()
#tf.function
def serve_tf_examples_fn(serialized: str) -> Dict:
''' Args: serialized: is serialized image tensor.
'''
feature_spec = transform_output.raw_feature_spec()
# remove label spec.
feature_spec.pop("label")
# Deserialize the image tensor.
parsed_features = tf.io.parse_example(
serialized,
feature_spec)
# Preprocess the example using outputs of Transform pipeline.
transformed_features = model.tft_layer(parsed_features)
outputs = model(transformed_features)
return {"outputs": outputs}
return serve_tf_examples_fn
The above code segment received a serialized tensor of a gray image (simply a 2d ndarray), and restored it to a 2-d tensor. After then, the model is doing inference on this 2-d tensor.
I would like to know how to send a serialized tensor to the REST port of the serving server.
Any help would be appreciated.