Changed to params

tutorials/image/cifar10_estimator/cifar10_main.py

@@ -44,8 +44,9 @@ import tensorflow as tf
 tf.logging.set_verbosity(tf.logging.INFO)
 
 
-def get_model_fn(num_gpus, variable_strategy, data_format, num_workers):
+def get_model_fn(num_gpus, variable_strategy, num_workers):
   """Returns a function that will build the resnet model."""
+
   def _resnet_model_fn(features, labels, mode, params):
     """Resnet model body.
 
@@ -73,6 +74,16 @@ def get_model_fn(num_gpus, variable_strategy, data_format, num_workers):
     tower_gradvars = []
     tower_preds = []
 
+    # channels first (NCHW) is normally optimal on GPU and channels last (NHWC)
+    # on CPU. The exception is Intel MKL on CPU which is optimal with
+    # channels_last.
+    data_format = params.data_format
+    if not data_format:
+      if num_gpus == 0:
+        data_format = 'channels_last'
+      else:
+        data_format = 'channels_first'
+
     if num_gpus == 0:
       num_devices = 1
       device_type = 'cpu'
@@ -276,7 +287,6 @@ def input_fn(data_dir,
 def get_experiment_fn(data_dir,
                       num_gpus,
                       variable_strategy,
-                      data_format,
                       use_distortion_for_training=True):
   """Returns an Experiment function.
 
@@ -291,7 +301,6 @@ def get_experiment_fn(data_dir,
       num_gpus: int. Number of GPUs on each worker.
       variable_strategy: String. CPU to use CPU as the parameter server
       and GPU to use the GPUs as the parameter server.
-      data_format: String. channels_first or channels_last.
       use_distortion_for_training: bool. See cifar10.Cifar10DataSet.
   Returns:
       A function (tf.estimator.RunConfig, tf.contrib.training.HParams) ->
@@ -338,11 +347,10 @@ def get_experiment_fn(data_dir,
     hooks = [logging_hook, examples_sec_hook]
 
     classifier = tf.estimator.Estimator(
-        model_fn=get_model_fn(num_gpus, variable_strategy, data_format,
+        model_fn=get_model_fn(num_gpus, variable_strategy,
                               run_config.num_worker_replicas or 1),
         config=run_config,
-        params=hparams
-    )
+        params=hparams)
 
     # Create experiment.
     experiment = tf.contrib.learn.Experiment(
@@ -354,25 +362,17 @@ def get_experiment_fn(data_dir,
     # Adding hooks to be used by the estimator on training modes
     experiment.extend_train_hooks(hooks)
     return experiment
+
   return _experiment_fn
 
 
-def main(job_dir, data_dir, num_gpus, variable_strategy, data_format,
+def main(job_dir, data_dir, num_gpus, variable_strategy,
          use_distortion_for_training, log_device_placement, num_intra_threads,
          **hparams):
   # The env variable is on deprecation path, default is set to off.
   os.environ['TF_SYNC_ON_FINISH'] = '0'
   os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
 
-  # channels first (NCHW) is normally optimal on GPU and channels last (NHWC)
-  # on CPU. The exception is Intel MKL on CPU which is optimal with
-  # channels_last.
-  if not data_format:
-    if num_gpus == 0:
-      data_format = 'channels_last'
-    else:
-      data_format = 'channels_first'
-
   # Session configuration.
   sess_config = tf.ConfigProto(
       allow_soft_placement=True,
@@ -383,7 +383,7 @@ def main(job_dir, data_dir, num_gpus, variable_strategy, data_format,
   config = cifar10_utils.RunConfig(
       session_config=sess_config, model_dir=job_dir)
   tf.contrib.learn.learn_runner.run(
-      get_experiment_fn(data_dir, num_gpus, variable_strategy, data_format,
+      get_experiment_fn(data_dir, num_gpus, variable_strategy,
                         use_distortion_for_training),
       run_config=config,
       hparams=tf.contrib.training.HParams(**hparams))
@@ -505,13 +505,12 @@ if __name__ == '__main__':
       help='Epsilon for batch norm.')
   args = parser.parse_args()
 
-    if args.num_gpus < 0:
+  if args.num_gpus < 0:
     raise ValueError(
         'Invalid GPU count: \"--num-gpus\" must be 0 or a positive integer.')
   if args.num_gpus == 0 and args.variable_strategy == 'GPU':
-    raise ValueError(
-        'num-gpus=0, CPU must be used as parameter server. Set'
-        '--variable-strategy=CPU.')
+    raise ValueError('num-gpus=0, CPU must be used as parameter server. Set'
+                     '--variable-strategy=CPU.')
   if (args.num_layers - 2) % 6 != 0:
     raise ValueError('Invalid --num-layers parameter.')
   if args.num_gpus != 0 and args.train_batch_size % args.num_gpus != 0: