Use util functions hooks_helper and parser in mnist and wide_deep, and rename epochs_between_eval (from epochs_per_eval) (tensorflow#3650)

Author: k-w-w

official/mnist/README.md

@@ -11,7 +11,10 @@ APIs.
 
 ## Setup
 
-To begin, you'll simply need the latest version of TensorFlow installed.
+To begin, you'll simply need the latest version of TensorFlow installed,
+and make sure to run the command to export the `/models` folder to the
+python path: https://github.com/tensorflow/models/tree/master/official#running-the-models
+
 Then to train the model, run the following:
 
 ```

official/mnist/mnist.py

@@ -18,12 +18,15 @@
 from __future__ import print_function
 
 import argparse
-import os
 import sys
 
 import tensorflow as tf
+
 from official.mnist import dataset
+from official.utils.arg_parsers import parsers
+from official.utils.logging import hooks_helper
 
+LEARNING_RATE = 1e-4
 
 class Model(tf.keras.Model):
   """Model to recognize digits in the MNIST dataset.
@@ -104,7 +107,7 @@ def model_fn(features, labels, mode, params):
             'classify': tf.estimator.export.PredictOutput(predictions)
         })
   if mode == tf.estimator.ModeKeys.TRAIN:
-    optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
+    optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
 
     # If we are running multi-GPU, we need to wrap the optimizer.
     if params.get('multi_gpu'):
@@ -114,10 +117,15 @@ def model_fn(features, labels, mode, params):
     loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
     accuracy = tf.metrics.accuracy(
         labels=labels, predictions=tf.argmax(logits, axis=1))
-    # Name the accuracy tensor 'train_accuracy' to demonstrate the
-    # LoggingTensorHook.
+
+    # Name tensors to be logged with LoggingTensorHook.
+    tf.identity(LEARNING_RATE, 'learning_rate')
+    tf.identity(loss, 'cross_entropy')
     tf.identity(accuracy[1], name='train_accuracy')
+
+    # Save accuracy scalar to Tensorboard output.
     tf.summary.scalar('train_accuracy', accuracy[1])
+
     return tf.estimator.EstimatorSpec(
         mode=tf.estimator.ModeKeys.TRAIN,
         loss=loss,
@@ -185,30 +193,32 @@ def main(unused_argv):
           'multi_gpu': FLAGS.multi_gpu
       })
 
-  # Train the model
+  # Set up training and evaluation input functions.
   def train_input_fn():
     # When choosing shuffle buffer sizes, larger sizes result in better
     # randomness, while smaller sizes use less memory. MNIST is a small
     # enough dataset that we can easily shuffle the full epoch.
     ds = dataset.train(FLAGS.data_dir)
-    ds = ds.cache().shuffle(buffer_size=50000).batch(FLAGS.batch_size).repeat(
-        FLAGS.train_epochs)
-    return ds
+    ds = ds.cache().shuffle(buffer_size=50000).batch(FLAGS.batch_size)
 
-  # Set up training hook that logs the training accuracy every 100 steps.
-  tensors_to_log = {'train_accuracy': 'train_accuracy'}
-  logging_hook = tf.train.LoggingTensorHook(
-      tensors=tensors_to_log, every_n_iter=100)
-  mnist_classifier.train(input_fn=train_input_fn, hooks=[logging_hook])
+    # Iterate through the dataset a set number (`epochs_between_evals`) of times
+    # during each training session.
+    ds = ds.repeat(FLAGS.epochs_between_evals)
+    return ds
 
-  # Evaluate the model and print results
   def eval_input_fn():
     return dataset.test(FLAGS.data_dir).batch(
         FLAGS.batch_size).make_one_shot_iterator().get_next()
 
-  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
-  print()
-  print('Evaluation results:\n\t%s' % eval_results)
+  # Set up hook that outputs training logs every 100 steps.
+  train_hooks = hooks_helper.get_train_hooks(
+      FLAGS.hooks, batch_size=FLAGS.batch_size)
+
+  # Train and evaluate model.
+  for n in range(FLAGS.train_epochs // FLAGS.epochs_between_evals):
+    mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
+    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
+    print('\nEvaluation results:\n\t%s\n' % eval_results)
 
   # Export the model
   if FLAGS.export_dir is not None:
@@ -220,51 +230,28 @@ def eval_input_fn():
 
 
 class MNISTArgParser(argparse.ArgumentParser):
-
+  """Argument parser for running MNIST model."""
   def __init__(self):
-    super(MNISTArgParser, self).__init__()
+    super(MNISTArgParser, self).__init__(parents=[
+      parsers.BaseParser(),
+      parsers.ImageModelParser()])
 
-    self.add_argument(
-        '--multi_gpu', action='store_true',
-        help='If set, run across all available GPUs.')
-    self.add_argument(
-        '--batch_size',
-        type=int,
-        default=100,
-        help='Number of images to process in a batch')
-    self.add_argument(
-        '--data_dir',
-        type=str,
-        default='/tmp/mnist_data',
-        help='Path to directory containing the MNIST dataset')
-    self.add_argument(
-        '--model_dir',
-        type=str,
-        default='/tmp/mnist_model',
-        help='The directory where the model will be stored.')
-    self.add_argument(
-        '--train_epochs',
-        type=int,
-        default=40,
-        help='Number of epochs to train.')
-    self.add_argument(
-        '--data_format',
-        type=str,
-        default=None,
-        choices=['channels_first', 'channels_last'],
-        help='A flag to override the data format used in the model. '
-        'channels_first provides a performance boost on GPU but is not always '
-        'compatible with CPU. If left unspecified, the data format will be '
-        'chosen automatically based on whether TensorFlow was built for CPU or '
-        'GPU.')
     self.add_argument(
         '--export_dir',
         type=str,
-        help='The directory where the exported SavedModel will be stored.')
+        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.')
+
+    self.set_defaults(
+        data_dir='/tmp/mnist_data',
+        model_dir='/tmp/mnist_model',
+        batch_size=100,
+        train_epochs=40)
 
 
 if __name__ == '__main__':
-  parser = MNISTArgParser()
   tf.logging.set_verbosity(tf.logging.INFO)
+  parser = MNISTArgParser()
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

official/mnist/mnist_eager.py

@@ -33,8 +33,10 @@
 
 import tensorflow as tf
 import tensorflow.contrib.eager as tfe
+
 from official.mnist import mnist
 from official.mnist import dataset
+from official.utils.arg_parsers import parsers
 
 FLAGS = None
 
@@ -98,9 +100,13 @@ def test(model, dataset):
 def main(_):
   tfe.enable_eager_execution()
 
+  # Automatically determine device and data_format
   (device, data_format) = ('/gpu:0', 'channels_first')
   if FLAGS.no_gpu or tfe.num_gpus() <= 0:
     (device, data_format) = ('/cpu:0', 'channels_last')
+  # If data_format is defined in FLAGS, overwrite automatically set value.
+  if FLAGS.data_format is not None:
+    data_format = data_format
   print('Using device %s, and data format %s.' % (device, data_format))
 
   # Load the datasets
@@ -112,6 +118,7 @@ def main(_):
   model = mnist.Model(data_format)
   optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
 
+  # Create file writers for writing TensorBoard summaries.
   if FLAGS.output_dir:
     # Create directories to which summaries will be written
     # tensorboard --logdir=<output_dir>
@@ -126,15 +133,18 @@ def main(_):
       train_dir, flush_millis=10000)
   test_summary_writer = tf.contrib.summary.create_file_writer(
       test_dir, flush_millis=10000, name='test')
-  checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
+
+  # Create and restore checkpoint (if one exists on the path)
+  checkpoint_prefix = os.path.join(FLAGS.model_dir, 'ckpt')
   step_counter = tf.train.get_or_create_global_step()
   checkpoint = tfe.Checkpoint(
       model=model, optimizer=optimizer, step_counter=step_counter)
   # Restore variables on creation if a checkpoint exists.
-  checkpoint.restore(tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
-  # Train and evaluate for 10 epochs.
+  checkpoint.restore(tf.train.latest_checkpoint(FLAGS.model_dir))
+
+  # Train and evaluate for a set number of epochs.
   with tf.device(device):
-    for _ in range(10):
+    for _ in range(FLAGS.train_epochs):
       start = time.time()
       with summary_writer.as_default():
         train(model, optimizer, train_ds, step_counter, FLAGS.log_interval)
@@ -148,54 +158,52 @@ def main(_):
       checkpoint.save(checkpoint_prefix)
 
 
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument(
-      '--data_dir',
-      type=str,
-      default='/tmp/tensorflow/mnist/input_data',
-      help='Directory for storing input data')
-  parser.add_argument(
-      '--batch_size',
-      type=int,
-      default=100,
-      metavar='N',
-      help='input batch size for training (default: 100)')
-  parser.add_argument(
-      '--log_interval',
-      type=int,
-      default=10,
-      metavar='N',
-      help='how many batches to wait before logging training status')
-  parser.add_argument(
-      '--output_dir',
-      type=str,
-      default=None,
-      metavar='N',
-      help='Directory to write TensorBoard summaries')
-  parser.add_argument(
-      '--checkpoint_dir',
-      type=str,
-      default='/tmp/tensorflow/mnist/checkpoints/',
-      metavar='N',
-      help='Directory to save checkpoints in (once per epoch)')
-  parser.add_argument(
-      '--lr',
-      type=float,
-      default=0.01,
-      metavar='LR',
-      help='learning rate (default: 0.01)')
-  parser.add_argument(
-      '--momentum',
-      type=float,
-      default=0.5,
-      metavar='M',
-      help='SGD momentum (default: 0.5)')
-  parser.add_argument(
-      '--no_gpu',
-      action='store_true',
-      default=False,
-      help='disables GPU usage even if a GPU is available')
+class MNISTEagerArgParser(argparse.ArgumentParser):
+  """Argument parser for running MNIST model with eager trainng loop."""
+  def __init__(self):
+    super(MNISTEagerArgParser, self).__init__(parents=[
+      parsers.BaseParser(epochs_between_evals=False, multi_gpu=False,
+                         hooks=False),
+      parsers.ImageModelParser()])
+
+    self.add_argument(
+        '--log_interval', '-li',
+        type=int,
+        default=10,
+        metavar='N',
+        help='[default: %(default)s] batches between logging training status')
+    self.add_argument(
+        '--output_dir', '-od',
+        type=str,
+        default=None,
+        metavar='<OD>',
+        help='[default: %(default)s] Directory to write TensorBoard summaries')
+    self.add_argument(
+        '--lr', '-lr',
+        type=float,
+        default=0.01,
+        metavar='<LR>',
+        help='[default: %(default)s] learning rate')
+    self.add_argument(
+        '--momentum', '-m',
+        type=float,
+        default=0.5,
+        metavar='<M>',
+        help='[default: %(default)s] SGD momentum')
+    self.add_argument(
+        '--no_gpu', '-nogpu',
+        action='store_true',
+        default=False,
+        help='disables GPU usage even if a GPU is available')
+
+    self.set_defaults(
+        data_dir='/tmp/tensorflow/mnist/input_data',
+        model_dir='/tmp/tensorflow/mnist/checkpoints/',
+        batch_size=100,
+        train_epochs=10,
+    )
 
+if __name__ == '__main__':
+  parser = MNISTEagerArgParser()
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

official/resnet/cifar10_main.py

@@ -216,7 +216,7 @@ def main(argv):
                       model_dir='/tmp/cifar10_model',
                       resnet_size=32,
                       train_epochs=250,
-                      epochs_per_eval=10,
+                      epochs_between_evals=10,
                       batch_size=128)
 
   flags = parser.parse_args(args=argv[1:])

official/resnet/resnet_run_loop.py

@@ -339,15 +339,16 @@ def resnet_main(flags, model_function, input_function):
           'version': flags.version,
       })
 
-  for _ in range(flags.train_epochs // flags.epochs_per_eval):
-    train_hooks = hooks_helper.get_train_hooks(flags.hooks, batch_size=flags.batch_size)
+  for _ in range(flags.train_epochs // flags.epochs_between_evals):
+    train_hooks = hooks_helper.get_train_hooks(flags.hooks,
+                                               batch_size=flags.batch_size)
 
     print('Starting a training cycle.')
 
     def input_fn_train():
       return input_function(True, flags.data_dir, flags.batch_size,
-                            flags.epochs_per_eval, flags.num_parallel_calls,
-                            flags.multi_gpu)
+                            flags.epochs_between_evals,
+                            flags.num_parallel_calls, flags.multi_gpu)
 
     classifier.train(input_fn=input_fn_train, hooks=train_hooks,
                      max_steps=flags.max_train_steps)