old_contents: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """A script to export TF-Hub SavedModel."""
  
  from __future__ import absolute_import
  from __future__ import division
  # from __future__ import google_type_annotations
  from __future__ import print_function
  
  from absl import app
  from absl import flags
  
  import tensorflow as tf
  
  from official.vision.image_classification import resnet_model
  from official.vision.image_classification import imagenet_preprocessing
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string("model_path", None,
                      "File path to TF model checkpoint or H5 file.")
  flags.DEFINE_string("export_path", None,
                      "TF-Hub SavedModel destination path to export.")
  
  
  def export_tfhub(model_path, hub_destination):
    """Restores a tf.keras.Model and saves for TF-Hub."""
    model = resnet_model.resnet50(num_classes=imagenet_preprocessing.NUM_CLASSES)
    model.load_weights(model_path)
  
    # Extracts a sub-model to use pooling feature vector as model output.
    image_input = model.get_layer(index=0).get_output_at(0)
    feature_vector_output = model.get_layer(name='reduce_mean').get_output_at(0)
    hub_model = tf.keras.Model(image_input, feature_vector_output)
  
    # Exports a SavedModel.
    hub_model.save(hub_destination, include_optimizer=False)
  
  
  def main(argv):
    if len(argv) > 1:
      raise app.UsageError("Too many command-line arguments.")
  
    assert tf.version.VERSION.startswith('2.')
    export_tfhub(FLAGS.model_path, FLAGS.export_path)
  
  
  if __name__ == "__main__":
    app.run(main)
  
new_contents: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """A script to export TF-Hub SavedModel."""
  
  from __future__ import absolute_import
  from __future__ import division
  # from __future__ import google_type_annotations
  from __future__ import print_function
  
  import os
  
  from absl import app
  from absl import flags
  
  import tensorflow as tf
  
  from official.vision.image_classification import resnet_model
  from official.vision.image_classification import imagenet_preprocessing
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string("model_path", None,
                      "File path to TF model checkpoint or H5 file.")
  flags.DEFINE_string("export_path", None,
                      "TF-Hub SavedModel destination path to export.")
  
  
  def export_tfhub(model_path, hub_destination):
    """Restores a tf.keras.Model and saves for TF-Hub."""
    model = resnet_model.resnet50(num_classes=imagenet_preprocessing.NUM_CLASSES)
    model.load_weights(model_path)
    model.save(os.path.join(hub_destination, "classification"), include_optimizer=False)
  
    # Extracts a sub-model to use pooling feature vector as model output.
    image_input = model.get_layer(index=0).get_output_at(0)
    feature_vector_output = model.get_layer(name="reduce_mean").get_output_at(0)
    hub_model = tf.keras.Model(image_input, feature_vector_output)
  
    # Exports a SavedModel.
    hub_model.save(
        os.path.join(hub_destination, "feature-vector"),
        include_optimizer=False)
  
  
  def main(argv):
    if len(argv) > 1:
      raise app.UsageError("Too many command-line arguments.")
  
    assert tf.version.VERSION.startswith('2.')
    export_tfhub(FLAGS.model_path, FLAGS.export_path)
  
  
  if __name__ == "__main__":
    app.run(main)
  
subject: Update resnet hub export