bug fixes and clean ups

official/resnet/cifar10_main.py

@@ -39,7 +39,7 @@ NUM_CLASSES = 10
 _NUM_DATA_FILES = 5
 
 # TODO(tobyboyd): Change to best practice 45K(train)/5K(val)/10K(test) splits.
-_NUM_IMAGES = {
+NUM_IMAGES = {
     'train': 50000,
     'validation': 10000,
 }
@@ -134,7 +134,7 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1,
       dataset=dataset,
       is_training=is_training,
       batch_size=batch_size,
-      shuffle_buffer=_NUM_IMAGES['train'],
+      shuffle_buffer=NUM_IMAGES['train'],
       parse_record_fn=parse_record_fn,
       num_epochs=num_epochs,
       dtype=dtype,
@@ -200,7 +200,7 @@ def cifar10_model_fn(features, labels, mode, params):
   # Learning rate schedule follows arXiv:1512.03385 for ResNet-56 and under.
   learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
       batch_size=params['batch_size'], batch_denom=128,
-      num_images=_NUM_IMAGES['train'], boundary_epochs=[91, 136, 182],
+      num_images=NUM_IMAGES['train'], boundary_epochs=[91, 136, 182],
       decay_rates=[1, 0.1, 0.01, 0.001])
 
   # Weight decay of 2e-4 diverges from 1e-4 decay used in the ResNet paper

official/resnet/keras/keras_cifar_main.py

@@ -152,18 +152,24 @@ def run(flags_obj):
   model.compile(loss='categorical_crossentropy',
                 optimizer=optimizer,
                 metrics=['categorical_accuracy'],
-                strategy=strategy)
+                distribute=strategy)
 
   time_callback, tensorboard_callback, lr_callback = keras_common.get_callbacks(
       learning_rate_schedule, cifar_main.NUM_IMAGES['train'])
 
-  steps_per_epoch = cifar_main.NUM_IMAGES['train'] // flags_obj.batch_size
+  train_steps = cifar_main.NUM_IMAGES['train'] // flags_obj.batch_size
+  train_epochs = flags_obj.train_epochs
+
+  if flags_obj.train_steps:
+    train_steps = min(flags_obj.train_steps, train_steps)
+    train_epochs = 1
+
   num_eval_steps = (cifar_main.NUM_IMAGES['validation'] //
                     flags_obj.batch_size)
 
   history = model.fit(train_input_dataset,
-                      epochs=flags_obj.train_epochs,
-                      steps_per_epoch=steps_per_epoch,
+                      epochs=train_epochs,
+                      steps_per_epoch=train_steps,
                       callbacks=[
                           time_callback,
                           lr_callback,
@@ -190,4 +196,5 @@ def main(_):
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
   cifar_main.define_cifar_flags()
+  keras_common.define_keras_flags()
   absl_app.run(main)

official/resnet/keras/keras_common.py

@@ -56,8 +56,7 @@ class TimeHistory(tf.keras.callbacks.Callback):
   def on_batch_end(self, batch, logs=None):
     if batch % self.log_batch_size == 0:
       last_n_batches = time.time() - self.batch_time_start
-      examples_per_second = 
-        (self._batch_size * self.log_batch_size) / last_n_batches
+      examples_per_second = (self._batch_size * self.log_batch_size) / last_n_batches
       self.batch_times_secs.append(last_n_batches)
       self.record_batch = True
       # TODO(anjalisridhar): add timestamp as well.
@@ -131,8 +130,14 @@ def analyze_fit_and_eval_result(history, eval_output):
   stats['training_loss'] = history.history['loss'][-1]
   stats['training_accuracy_top_1'] = history.history['categorical_accuracy'][-1]
 
-  print('Test loss:{}'.format(stats['']))
+  print('Test loss:{}'.format(stats['eval_loss']))
   print('top_1 accuracy:{}'.format(stats['accuracy_top_1']))
   print('top_1_training_accuracy:{}'.format(stats['training_accuracy_top_1']))
 
   return stats
+
+def define_keras_flags():
+  flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
+  flags.DEFINE_integer(
+      name="train_steps", default=None,
+      help="The number of steps to run for training")

official/resnet/keras/keras_imagenet_main.py

@@ -68,12 +68,12 @@ def learning_rate_schedule(current_epoch, current_batch, batches_per_epoch, batc
 
 
 def parse_record_keras(raw_record, is_training, dtype):
-	"""Adjust the shape of label."""
+  """Adjust the shape of label."""
   image, label = imagenet_main.parse_record(raw_record, is_training, dtype)
   # Subtract one so that labels are in [0, 1000), and cast to float32 for
   # Keras model.
   label = tf.cast(tf.cast(tf.reshape(label, shape=[1]), dtype=tf.int32) - 1,
-                  dtype=tf.float32)
+      dtype=tf.float32)
   return image, label
 
 
@@ -153,9 +153,16 @@ def run_imagenet_with_keras(flags_obj):
   num_eval_steps = (imagenet_main.NUM_IMAGES['validation'] //
                   flags_obj.batch_size)
 
+  train_steps = imagenet_main.NUM_IMAGES['train'] // flags_obj.batch_size
+  train_epochs = flags_obj.train_epochs
+
+  if flags_obj.train_steps:
+    train_steps = min(flags_obj.train_steps, train_steps)
+    train_epochs = 1
+
   history = model.fit(train_input_dataset,
-                      epochs=flags_obj.train_epochs,
-                      steps_per_epoch=steps_per_epoch,
+                      epochs=train_epochs,
+                      steps_per_epoch=train_steps,
                       callbacks=[
                         time_callback,
                         lr_callback,
@@ -182,4 +189,5 @@ def main(_):
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
   imagenet_main.define_imagenet_flags()
+  keras_common.define_keras_flags()
   absl_app.run(main)

official/resnet/resnet_run_loop.py

@@ -632,7 +632,6 @@ def define_resnet_flags(resnet_size_choices=None):
       name='use_one_device_strategy', default=True,
       help=flags_core.help_wrap('Set to False to not use distribution '
                                 'strategies.'))
-  flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
   flags.DEFINE_boolean(name='use_tf_momentum_optimizer', default=False,
           help='Use tf MomentumOptimizer.')
 

official/utils/misc/distribution_utils.py

@@ -22,7 +22,7 @@ import tensorflow as tf
 
 
 def get_distribution_strategy(
-  num_gpus, all_reduce_alg=None, use_one_device_strategy):
+  num_gpus, all_reduce_alg=None, use_one_device_strategy=True):
   """Return a DistributionStrategy for running the model.
 
   Args:
@@ -31,8 +31,8 @@ def get_distribution_strategy(
       See tf.contrib.distribute.AllReduceCrossDeviceOps for available
       algorithms. If None, DistributionStrategy will choose based on device
       topology.
-    use_one_device_strategy: Should only be set to Truen when num_gpus is 1. 
-      If True, then use OneDeviceStrategy; otherwise, do not use any 
+    use_one_device_strategy: Should only be set to Truen when num_gpus is 1.
+      If True, then use OneDeviceStrategy; otherwise, do not use any
       distribution strategy.
 
   Returns:
@@ -47,7 +47,7 @@ def get_distribution_strategy(
   elif num_gpus == 1:
     return None
   elif use_one_device_strategy:
-    rase ValueError("When %d GPUs are specified, use_one_device_strategy"
+    raise ValueError("When %d GPUs are specified, use_one_device_strategy"
         " flag cannot be set to True.".format(num_gpus))
   else: # num_gpus > 1 and not use_one_device_strategy
     if all_reduce_alg: