# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Export an RNN cell in SavedModel format."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl import app
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from absl import flags
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import numpy as np
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import tensorflow.compat.v2 as tf
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FLAGS = flags.FLAGS
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flags.DEFINE_string("export_dir", None, "Directory to export SavedModel.")
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def main(argv):
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del argv
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root = tf.train.Checkpoint()
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# Create a cell and attach to our trackable.
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root.rnn_cell = tf.keras.layers.LSTMCell(units=10, recurrent_initializer=None)
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# Wrap the rnn_cell.__call__ function and assign to next_state.
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root.next_state = tf.function(root.rnn_cell.__call__, autograph=False)
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# Wrap the rnn_cell.get_initial_function using a decorator and assign to an
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# attribute with the same name.
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@tf.function(input_signature=[tf.TensorSpec([None, None], tf.float32)])
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def get_initial_state(tensor):
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return root.rnn_cell.get_initial_state(tensor, None, None)
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root.get_initial_state = get_initial_state
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# Construct an initial_state, then call next_state explicitly to trigger a
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# trace for serialization (we need an explicit call, because next_state has
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# not been annotated with an input_signature).
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initial_state = root.get_initial_state(
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tf.constant(np.random.uniform(size=[3, 10]).astype(np.float32)))
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root.next_state(
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tf.constant(np.random.uniform(size=[3, 19]).astype(np.float32)),
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initial_state)
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tf.saved_model.save(root, FLAGS.export_dir)
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if __name__ == "__main__":
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tf.enable_v2_behavior()
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app.run(main)
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