I am following the blog post below to run TFX’s RunInference API on images:

  
      

      blog.tensorflow.org
  

  
    

Using TFX inference with Dataflow for large scale ML inference patterns 2

  Walk-through of TensorFlow Extended ( TFX ) RunInference API with Google Cloud Dataflow. Abstracting you away from the manual toil of implementing inf


  

  
    
    
  

  


While I was able to export the model so that it can directly consume TFRecord strings I am unable to parse the Beam outputs which look like so:
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Restoring parameters from tfx-inference-exported/model/image-demo/v1/variables/variables
INFO:tensorflow:Restoring parameters from tfx-inference-exported/model/image-demo/v1/variables/variables
predict_log {
  request {
    model_spec {
      signature_name: "serving_default"
    }
    inputs {
      key: "bytes_inputs"
      value {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: "..."
      }
    }
  }
  response {
    outputs {
      key: "probabilities"
      value {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 10
          }
        }
        tensor_content: "\000\000\000\000\000\000\200?\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000"
      }
    }
    model_spec {
      signature_name: "serving_default"
    }
  }
}

predict_log {
  request {
    model_spec {
      signature_name: "serving_default"
    }
    inputs {
      key: "bytes_inputs"
      value {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: "..."
      }
    }
  }
  response {
    outputs {
      key: "probabilities"
      value {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 10
          }
        }
        tensor_content: "\000\000\000\000\000\000\200?\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000"
      }
    }
    model_spec {
      signature_name: "serving_default"
    }
  }
}

I am able to go till the tensor_content key (with the help of the blog post) :
class PredictionProcessor(beam.DoFn):

   def process(
           self,
           element: prediction_log_pb2.PredictionLog):
       predict_log = element.predict_log
       output_value = predict_log.response.outputs
       # yield tf.train.Example.FromString(output_value['output_0'].tensor_content)
       # yield (f"output is {output_value['output_0'].tensor_content[0]}")
       yield (f"output is {str(output_value['probabilities'].tensor_content, 'utf-8')}")

But this does not decode the content in the expected manner. This is the underlying model:
def serialize_model():
    image_inputs = tf.keras.Input((224, 224, 3), name="image")
    x = tf.keras.layers.Conv2D(32, 9)(image_inputs)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    outputs = tf.keras.layers.Dense(10, activation="softmax", dtype="float32")(x)

    image_model = tf.keras.Model(image_inputs, outputs)
    tf.keras.models.save_model(image_model, save_model_dir_image)   
    return image_model

And this is how I have exported it (following this book):
_CONCRETE_INPUT = "numpy_inputs"

def _preprocess(bytes_input):
    decoded = tf.io.decode_jpeg(bytes_input, channels=3)
    resized = tf.image.resize(decoded, size=(224, 224))
    return resized


@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def preprocess_fn(bytes_inputs):
    decoded_images = tf.map_fn(
        _preprocess, bytes_inputs, dtype=tf.float32, back_prop=False
    )
    return {_CONCRETE_INPUT: decoded_images}


def _model_exporter(model: tf.keras.Model):
    m_call = tf.function(model.call).get_concrete_function(
        [
            tf.TensorSpec(
                shape=[None, 224, 224, 3], dtype=tf.float32, name=_CONCRETE_INPUT
            )
        ]
    )

    @tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
    def serving_fn(bytes_inputs):
        features = tf.io.parse_example(bytes_inputs, image_feature_description)   
        images = preprocess_fn(features["image_raw"])
        probs = m_call(**images)
        return {"probabilities": probs}

    return serving_fn


save_model_dir_image_new = 'tfx-inference-exported/model/image-demo/v1/'
tf.keras.models.save_model(image_model, 
                           save_model_dir_image_new, 
                           signatures={"serving_default": _model_exporter(image_model)})

Here’s the Colab notebook for full details: Google Colab.
The above-mentioned blog post exports the model in a simpler manner. However, I couldn’t follow that process since images are needed to be decoded properly from the TFRecord strings. The following serving function does not work:
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(bytes_inputs):
    features = tf.io.parse_example(bytes_inputs, image_feature_description)   
    image = _preprocess(features["image_raw"])
    features.update({"image": image})
    return model(features, training=False)

Is it possible to simplify the model export method I am following and align it with that example has shown?
Ccing @Robert_Crowe @zhitaoli @martin_gorner_tf