Rename --version or --resnet_version (tensorflow#4165)

* rename --version flag and fix tests to correctly specify version rather than verbosity

* rename version to resnet_version throughout

* fix bugs

* delint

* missed layer_test

* fix indent

official/resnet/cifar10_main.py

@@ -140,7 +140,7 @@ class Cifar10Model(resnet_model.Model):
   """Model class with appropriate defaults for CIFAR-10 data."""
 
   def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
-               version=resnet_model.DEFAULT_VERSION,
+               resnet_version=resnet_model.DEFAULT_VERSION,
                dtype=resnet_model.DEFAULT_DTYPE):
     """These are the parameters that work for CIFAR-10 data.
 
@@ -150,8 +150,8 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
         data format to use when setting up the model.
       num_classes: The number of output classes needed from the model. This
         enables users to extend the same model to their own datasets.
-      version: Integer representing which version of the ResNet network to use.
-        See README for details. Valid values: [1, 2]
+      resnet_version: Integer representing which version of the ResNet network
+      to use. See README for details. Valid values: [1, 2]
       dtype: The TensorFlow dtype to use for calculations.
 
     Raises:
@@ -174,7 +174,7 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
         block_sizes=[num_blocks] * 3,
         block_strides=[1, 2, 2],
         final_size=64,
-        version=version,
+        resnet_version=resnet_version,
         data_format=data_format,
         dtype=dtype
     )
@@ -211,7 +211,7 @@ def loss_filter_fn(_):
       learning_rate_fn=learning_rate_fn,
       momentum=0.9,
       data_format=params['data_format'],
-      version=params['version'],
+      resnet_version=params['resnet_version'],
       loss_scale=params['loss_scale'],
       loss_filter_fn=loss_filter_fn,
       dtype=params['dtype']

official/resnet/cifar10_test.py

@@ -76,7 +76,7 @@ def test_dataset_input_fn(self):
         for pixel in row:
           self.assertAllClose(pixel, np.array([-1.225, 0., 1.225]), rtol=1e-3)
 
-  def cifar10_model_fn_helper(self, mode, version, dtype):
+  def cifar10_model_fn_helper(self, mode, resnet_version, dtype):
     input_fn = cifar10_main.get_synth_input_fn()
     dataset = input_fn(True, '', _BATCH_SIZE)
     iterator = dataset.make_one_shot_iterator()
@@ -87,7 +87,7 @@ def cifar10_model_fn_helper(self, mode, version, dtype):
             'resnet_size': 32,
             'data_format': 'channels_last',
             'batch_size': _BATCH_SIZE,
-            'version': version,
+            'resnet_version': resnet_version,
             'loss_scale': 128 if dtype == tf.float16 else 1,
         })
 
@@ -111,56 +111,57 @@ def cifar10_model_fn_helper(self, mode, version, dtype):
       self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
 
   def test_cifar10_model_fn_train_mode_v1(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, version=1,
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, resnet_version=1,
                                  dtype=tf.float32)
 
   def test_cifar10_model_fn_trainmode__v2(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, version=2,
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, resnet_version=2,
                                  dtype=tf.float32)
 
   def test_cifar10_model_fn_eval_mode_v1(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, version=1,
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, resnet_version=1,
                                  dtype=tf.float32)
 
   def test_cifar10_model_fn_eval_mode_v2(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, version=2,
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, resnet_version=2,
                                  dtype=tf.float32)
 
   def test_cifar10_model_fn_predict_mode_v1(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT, version=1,
-                                 dtype=tf.float32)
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT,
+                                 resnet_version=1, dtype=tf.float32)
 
   def test_cifar10_model_fn_predict_mode_v2(self):
-    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT, version=2,
-                                 dtype=tf.float32)
+    self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT,
+                                 resnet_version=2, dtype=tf.float32)
 
-  def _test_cifar10model_shape(self, version):
+  def _test_cifar10model_shape(self, resnet_version):
     batch_size = 135
     num_classes = 246
 
     model = cifar10_main.Cifar10Model(32, data_format='channels_last',
-                                      num_classes=num_classes, version=version)
+                                      num_classes=num_classes,
+                                      resnet_version=resnet_version)
     fake_input = tf.random_uniform([batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS])
     output = model(fake_input, training=True)
 
     self.assertAllEqual(output.shape, (batch_size, num_classes))
 
   def test_cifar10model_shape_v1(self):
-    self._test_cifar10model_shape(version=1)
+    self._test_cifar10model_shape(resnet_version=1)
 
   def test_cifar10model_shape_v2(self):
-    self._test_cifar10model_shape(version=2)
+    self._test_cifar10model_shape(resnet_version=2)
 
   def test_cifar10_end_to_end_synthetic_v1(self):
     integration.run_synthetic(
         main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
-        extra_flags=['-v', '1']
+        extra_flags=['-resnet_version', '1']
     )
 
   def test_cifar10_end_to_end_synthetic_v2(self):
     integration.run_synthetic(
         main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
-        extra_flags=['-v', '2']
+        extra_flags=['-resnet_version', '2']
     )
 
 

official/resnet/imagenet_main.py

@@ -197,7 +197,7 @@ class ImagenetModel(resnet_model.Model):
   """Model class with appropriate defaults for Imagenet data."""
 
   def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
-               version=resnet_model.DEFAULT_VERSION,
+               resnet_version=resnet_model.DEFAULT_VERSION,
                dtype=resnet_model.DEFAULT_DTYPE):
     """These are the parameters that work for Imagenet data.
 
@@ -207,8 +207,8 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
         data format to use when setting up the model.
       num_classes: The number of output classes needed from the model. This
         enables users to extend the same model to their own datasets.
-      version: Integer representing which version of the ResNet network to use.
-        See README for details. Valid values: [1, 2]
+      resnet_version: Integer representing which version of the ResNet network
+        to use. See README for details. Valid values: [1, 2]
       dtype: The TensorFlow dtype to use for calculations.
     """
 
@@ -232,7 +232,7 @@ def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
         block_sizes=_get_block_sizes(resnet_size),
         block_strides=[1, 2, 2, 2],
         final_size=final_size,
-        version=version,
+        resnet_version=resnet_version,
         data_format=data_format,
         dtype=dtype
     )
@@ -289,7 +289,7 @@ def imagenet_model_fn(features, labels, mode, params):
       learning_rate_fn=learning_rate_fn,
       momentum=0.9,
       data_format=params['data_format'],
-      version=params['version'],
+      resnet_version=params['resnet_version'],
       loss_scale=params['loss_scale'],
       loss_filter_fn=None,
       dtype=params['dtype']