bug fixes

official/resnet/keras/keras_cifar_main.py

@@ -136,8 +136,7 @@ def run(flags_obj):
   strategy = distribution_utils.get_distribution_strategy(
       flags_obj.num_gpus, flags_obj.turn_off_distribution_strategy)
 
-  model = resnet_cifar_model.resnet56(input_shape=(32, 32, 3),
-                                      classes=cifar_main.NUM_CLASSES)
+  model = resnet_cifar_model.resnet56(classes=cifar_main.NUM_CLASSES)
 
   model.compile(loss='categorical_crossentropy',
                 optimizer=optimizer,

official/resnet/keras/keras_common.py

@@ -163,7 +163,7 @@ def define_keras_flags():
   flags.DEFINE_integer(
       name='train_steps', default=None,
       help='The number of steps to run for training. If it is larger than '
-      '# batches per epoch, then use # bathes per epoch. When this flag is '
+      '# batches per epoch, then use # batches per epoch. When this flag is '
       'set, only one epoch is going to run for training.')
 
 

official/resnet/keras/resnet_cifar_model.py

@@ -33,42 +33,6 @@ BATCH_NORM_EPSILON = 1e-5
 L2_WEIGHT_DECAY = 2e-4
 
 
-def _obtain_input_shape(input_shape,
-                        default_size,
-                        data_format):
-  """Internal utility to compute/validate a model's input shape.
-
-  Arguments:
-    input_shape: Either None (will return the default network input shape),
-        or a user-provided shape to be validated.
-    default_size: Default input width/height for the model.
-    data_format: Image data format to use.
-
-  Returns:
-    An integer shape tuple (may include None entries).
-
-  Raises:
-    ValueError: In case of invalid argument values.
-  """
-  if input_shape and len(input_shape) == 3:
-    if data_format == 'channels_first':
-      if input_shape[0] not in {1, 3}:
-        warnings.warn(
-            'This model usually expects 1 or 3 input channels. '
-            'However, it was passed an input_shape with ' +
-            str(input_shape[0]) + ' input channels.')
-      default_shape = (input_shape[0], default_size, default_size)
-    else:
-      if input_shape[-1] not in {1, 3}:
-        warnings.warn(
-            'This model usually expects 1 or 3 input channels. '
-            'However, it was passed an input_shape with ' +
-            str(input_shape[-1]) + ' input channels.')
-      default_shape = (default_size, default_size, input_shape[-1])
-
-  return input_shape
-
-
 def identity_building_block(input_tensor,
                             kernel_size,
                             filters,
@@ -212,7 +176,7 @@ def conv_building_block(input_tensor,
   return x
 
 
-def resnet56(input_shape=None, classes=100, training=None):
+def resnet56(classes=100, training=None):
   """Instantiates the ResNet56 architecture.
 
   Arguments:
@@ -225,16 +189,12 @@ def resnet56(input_shape=None, classes=100, training=None):
       A Keras model instance.
   """
   # Determine proper input shape
-  input_shape = _obtain_input_shape(
-      input_shape,
-      default_size=32,
-      data_format=tf.keras.backend.image_data_format())
-
-  img_input = tf.keras.layers.Input(shape=input_shape)
-  if tf.keras.backend.image_data_format() == 'channels_last':
-    bn_axis = 3
-  else:
+  if backend.image_data_format() == 'channels_first':
+    input_shape = (3, 32, 32)
     bn_axis = 1
+  else: # channel_last
+    input_shape = (32, 32, 3)
+    bn_axis = 3
 
   x = tf.keras.layers.ZeroPadding2D(padding=(1, 1), name='conv1_pad')(img_input)
   x = tf.keras.layers.Conv2D(16, (3, 3),

official/resnet/keras/resnet_model.py

@@ -181,6 +181,7 @@ def conv_block(input_tensor,
 
 
 def resnet50(num_classes):
+  # TODO(tfboyd): add training argument, just lik resnet56.
   """Instantiates the ResNet50 architecture.
 
   Args: