Add SavedModel export to Resnet (#3759)

* Adding export_dir and model saving for Resnet

* Moving to utils for tests

* Adding batch_size

* Adding multi-gpu export warning

* Responding to CR

* Py3 compliance

Author: karmel

official/mnist/mnist.py

@@ -246,14 +246,9 @@ class MNISTArgParser(argparse.ArgumentParser):
   def __init__(self):
     super(MNISTArgParser, self).__init__(parents=[
         parsers.BaseParser(),
-        parsers.ImageModelParser()])
-
-    self.add_argument(
-        '--export_dir',
-        type=str,
-        help='[default: %(default)s] If set, a SavedModel serialization of the '
-             'model will be exported to this directory at the end of training. '
-             'See the README for more details and relevant links.')
+        parsers.ImageModelParser(),
+        parsers.ExportParser(),
+    ])
 
     self.set_defaults(
         data_dir='/tmp/mnist_data',

official/resnet/cifar10_main.py

@@ -228,7 +228,10 @@ def main(argv):
   flags = parser.parse_args(args=argv[1:])
 
   input_function = flags.use_synthetic_data and get_synth_input_fn() or input_fn
-  resnet_run_loop.resnet_main(flags, cifar10_model_fn, input_function)
+
+  resnet_run_loop.resnet_main(
+      flags, cifar10_model_fn, input_function,
+      shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
 
 
 if __name__ == '__main__':

official/resnet/imagenet_main.py

@@ -305,7 +305,10 @@ def main(argv):
   flags = parser.parse_args(args=argv[1:])
 
   input_function = flags.use_synthetic_data and get_synth_input_fn() or input_fn
-  resnet_run_loop.resnet_main(flags, imagenet_model_fn, input_function)
+
+  resnet_run_loop.resnet_main(
+      flags, imagenet_model_fn, input_function,
+      shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
 
 
 if __name__ == '__main__':

official/resnet/resnet_run_loop.py

@@ -30,6 +30,7 @@
 
 from official.resnet import resnet_model
 from official.utils.arg_parsers import parsers
+from official.utils.export import export
 from official.utils.logging import hooks_helper
 from official.utils.logging import logger
 
@@ -219,7 +220,13 @@ def resnet_model_fn(features, labels, mode, model_class,
   }
 
   if mode == tf.estimator.ModeKeys.PREDICT:
-    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+    # Return the predictions and the specification for serving a SavedModel
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions=predictions,
+        export_outputs={
+            'predict': tf.estimator.export.PredictOutput(predictions)
+        })
 
   # Calculate loss, which includes softmax cross entropy and L2 regularization.
   cross_entropy = tf.losses.softmax_cross_entropy(
@@ -310,8 +317,20 @@ def validate_batch_size_for_multi_gpu(batch_size):
     raise ValueError(err)
 
 
-def resnet_main(flags, model_function, input_function):
-  """Shared main loop for ResNet Models."""
+def resnet_main(flags, model_function, input_function, shape=None):
+  """Shared main loop for ResNet Models.
+
+  Args:
+    flags: FLAGS object that contains the params for running. See
+      ResnetArgParser for created flags.
+    model_function: the function that instantiates the Model and builds the
+      ops for train/eval. This will be passed directly into the estimator.
+    input_function: the function that processes the dataset and returns a
+      dataset that the estimator can train on. This will be wrapped with
+      all the relevant flags for running and passed to estimator.
+    shape: list of ints representing the shape of the images used for training.
+      This is only used if flags.export_dir is passed.
+  """
 
   # Using the Winograd non-fused algorithms provides a small performance boost.
   os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
@@ -389,16 +408,34 @@ def input_fn_eval():
     if benchmark_logger:
       benchmark_logger.log_estimator_evaluation_result(eval_results)
 
+  if flags.export_dir is not None:
+    warn_on_multi_gpu_export(flags.multi_gpu)
+
+    # Exports a saved model for the given classifier.
+    input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
+        shape, batch_size=flags.batch_size)
+    classifier.export_savedmodel(flags.export_dir, input_receiver_fn)
+
+
+def warn_on_multi_gpu_export(multi_gpu=False):
+  """For the time being, multi-GPU mode does not play nicely with exporting."""
+  if multi_gpu:
+    tf.logging.warning(
+        'You are exporting a SavedModel while in multi-GPU mode. Note that '
+        'the resulting SavedModel will require the same GPUs be available.'
+        'If you wish to serve the SavedModel from a different device, '
+        'try exporting the SavedModel with multi-GPU mode turned off.')
+
 
 class ResnetArgParser(argparse.ArgumentParser):
-  """Arguments for configuring and running a Resnet Model.
-  """
+  """Arguments for configuring and running a Resnet Model."""
 
   def __init__(self, resnet_size_choices=None):
     super(ResnetArgParser, self).__init__(parents=[
         parsers.BaseParser(),
         parsers.PerformanceParser(),
         parsers.ImageModelParser(),
+        parsers.ExportParser(),
         parsers.BenchmarkParser(),
     ])
 

official/utils/arg_parsers/parsers.py

@@ -226,6 +226,29 @@ def __init__(self, add_help=False, data_format=True):
       )
 
 
+class ExportParser(argparse.ArgumentParser):
+  """Parsing options for exporting saved models or other graph defs.
+
+  This is a separate parser for now, but should be made part of BaseParser
+  once all models are brought up to speed.
+
+  Args:
+    add_help: Create the "--help" flag. False if class instance is a parent.
+    export_dir: Create a flag to specify where a SavedModel should be exported.
+  """
+
+  def __init__(self, add_help=False, export_dir=True):
+    super(ExportParser, self).__init__(add_help=add_help)
+    if export_dir:
+      self.add_argument(
+          "--export_dir", "-ed",
+          help="[default: %(default)s] If set, a SavedModel serialization of "
+               "the model will be exported to this directory at the end of "
+               "training. See the README for more details and relevant links.",
+          metavar="<ED>"
+      )
+
+
 class BenchmarkParser(argparse.ArgumentParser):
   """Default parser for benchmark logging.
 

official/utils/export/__init__.py

@@ -0,0 +1,49 @@
+# 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.
+# ==============================================================================
+"""Convenience functions for exporting models as SavedModels or other types."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+
+def build_tensor_serving_input_receiver_fn(shape, dtype=tf.float32,
+                                           batch_size=1):
+  """Returns a input_receiver_fn that can be used during serving.
+
+  This expects examples to come through as float tensors, and simply
+  wraps them as TensorServingInputReceivers.
+
+  Arguably, this should live in tf.estimator.export. Testing here first.
+
+  Args:
+    shape: list representing target size of a single example.
+    dtype: the expected datatype for the input example
+    batch_size: number of input tensors that will be passed for prediction
+
+  Returns:
+    A function that itself returns a TensorServingInputReceiver.
+  """
+  def serving_input_receiver_fn():
+    # Prep a placeholder where the input example will be fed in
+    features = tf.placeholder(
+        dtype=dtype, shape=[batch_size] + shape, name='input_tensor')
+
+    return tf.estimator.export.TensorServingInputReceiver(
+        features=features, receiver_tensors=features)
+
+  return serving_input_receiver_fn

official/utils/export/export.py

@@ -0,0 +1,63 @@
+# 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.
+# ==============================================================================
+"""Tests for exporting utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+
+from official.utils.export import export
+
+
+class ExportUtilsTest(tf.test.TestCase):
+  """Tests for the ExportUtils."""
+
+  def test_build_tensor_serving_input_receiver_fn(self):
+    receiver_fn = export.build_tensor_serving_input_receiver_fn(shape=[4, 5])
+    with tf.Graph().as_default():
+      receiver = receiver_fn()
+      self.assertIsInstance(
+          receiver, tf.estimator.export.TensorServingInputReceiver)
+
+      self.assertIsInstance(receiver.features, tf.Tensor)
+      self.assertEqual(receiver.features.shape, tf.TensorShape([1, 4, 5]))
+      self.assertEqual(receiver.features.dtype, tf.float32)
+      self.assertIsInstance(receiver.receiver_tensors, dict)
+      # Note that Python 3 can no longer index .values() directly; cast to list.
+      self.assertEqual(list(receiver.receiver_tensors.values())[0].shape,
+                       tf.TensorShape([1, 4, 5]))
+
+  def test_build_tensor_serving_input_receiver_fn_batch_dtype(self):
+    receiver_fn = export.build_tensor_serving_input_receiver_fn(
+        shape=[4, 5], dtype=tf.int8, batch_size=10)
+
+    with tf.Graph().as_default():
+      receiver = receiver_fn()
+      self.assertIsInstance(
+          receiver, tf.estimator.export.TensorServingInputReceiver)
+
+      self.assertIsInstance(receiver.features, tf.Tensor)
+      self.assertEqual(receiver.features.shape, tf.TensorShape([10, 4, 5]))
+      self.assertEqual(receiver.features.dtype, tf.int8)
+      self.assertIsInstance(receiver.receiver_tensors, dict)
+      # Note that Python 3 can no longer index .values() directly; cast to list.
+      self.assertEqual(list(receiver.receiver_tensors.values())[0].shape,
+                       tf.TensorShape([10, 4, 5]))
+
+
+if __name__ == "__main__":
+  tf.test.main()