Export RandomZoom after its odd behavior was fixed.

PiperOrigin-RevId: 311417546
Change-Id: Idb5bcff8b97a1bba1ab054a19ad0a701cf04cc00

tensorflow/python/keras/layers/preprocessing/image_preprocessing.py

@@ -827,6 +827,7 @@ class RandomRotation(Layer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@keras_export('keras.layers.experimental.preprocessing.RandomZoom')
 class RandomZoom(Layer):
   """Randomly zoom each image during training.
 
@@ -847,7 +848,8 @@ class RandomZoom(Layer):
       For instance, `width_factor=(0.2, 0.3)` result in an output zooming out
       between 20% to 30%.
       `width_factor=(-0.3, -0.2)` result in an output zooming in between 20%
-      to 30%.
+      to 30%. Defaults to `None`, i.e., zooming vertical and horizontal
+      directions by preserving the aspect ratio.
     fill_mode: Points outside the boundaries of the input are filled according
       to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
       - *reflect*: `(d c b a | a b c d | d c b a)`
@@ -860,6 +862,14 @@ class RandomZoom(Layer):
     seed: Integer. Used to create a random seed.
     name: A string, the name of the layer.
 
+  Example:
+
+  >>> input_img = np.random.random((32, 224, 224, 3))
+  >>> layer = tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2)
+  >>> out_img = layer(input_img)
+  >>> out_img.shape
+  TensorShape([32, 224, 224, 3])
+
   Input shape:
     4D tensor with shape:
     `(samples, height, width, channels)`, data_format='channels_last'.
@@ -873,9 +883,10 @@ class RandomZoom(Layer):
       negative.
   """
 
+  # TODO(b/156526279): Add `fill_value` argument.
   def __init__(self,
                height_factor,
-               width_factor,
+               width_factor=None,
                fill_mode='reflect',
                interpolation='bilinear',
                seed=None,
@@ -894,16 +905,17 @@ class RandomZoom(Layer):
                        'got {}'.format(height_factor))
 
     self.width_factor = width_factor
-    if isinstance(width_factor, (tuple, list)):
+    if width_factor is not None:
-      self.width_lower = width_factor[0]
+      if isinstance(width_factor, (tuple, list)):
-      self.width_upper = width_factor[1]
+        self.width_lower = width_factor[0]
-    else:
+        self.width_upper = width_factor[1]
-      self.width_lower = -width_factor
+      else:
-      self.width_upper = width_factor
+        self.width_lower = -width_factor  # pylint: disable=invalid-unary-operand-type
+        self.width_upper = width_factor
 
-    if self.width_lower < -1. or self.width_upper < -1.:
+      if self.width_lower < -1. or self.width_upper < -1.:
-      raise ValueError('`width_factor` must have values larger than -1, '
+        raise ValueError('`width_factor` must have values larger than -1, '
-                       'got {}'.format(width_factor))
+                         'got {}'.format(width_factor))
 
     check_fill_mode_and_interpolation(fill_mode, interpolation)
 
@@ -928,10 +940,13 @@ class RandomZoom(Layer):
           shape=[batch_size, 1],
           minval=1. + self.height_lower,
           maxval=1. + self.height_upper)
-      width_zoom = self._rng.uniform(
+      if self.width_factor is not None:
-          shape=[batch_size, 1],
+        width_zoom = self._rng.uniform(
-          minval=1. + self.width_lower,
+            shape=[batch_size, 1],
-          maxval=1. + self.width_upper)
+            minval=1. + self.width_lower,
+            maxval=1. + self.width_upper)
+      else:
+        width_zoom = height_zoom
       zooms = math_ops.cast(
           array_ops.concat([width_zoom, height_zoom], axis=1),
           dtype=dtypes.float32)

tensorflow/python/keras/layers/preprocessing/image_preprocessing_test.py

@@ -1021,7 +1021,27 @@ class RandomZoomTest(keras_parameterized.TestCase):
     for dtype in (np.int64, np.float32):
       with tf_test_util.use_gpu():
         input_image = np.reshape(np.arange(0, 25), (5, 5, 1)).astype(dtype)
-        layer = image_preprocessing.RandomZoom((.5, .5), (.5, .5),
+        layer = image_preprocessing.RandomZoom((.5, .5), (.8, .8),
+                                               fill_mode='constant',
+                                               interpolation='nearest')
+        output_image = layer(np.expand_dims(input_image, axis=0))
+        # pyformat: disable
+        expected_output = np.asarray([
+            [0, 0, 0, 0, 0],
+            [0, 5, 7, 9, 0],
+            [0, 10, 12, 14, 0],
+            [0, 20, 22, 24, 0],
+            [0, 0, 0, 0, 0]
+        ]).astype(dtype)
+        # pyformat: enable
+        expected_output = np.reshape(expected_output, (1, 5, 5, 1))
+        self.assertAllEqual(expected_output, output_image)
+
+  def test_random_zoom_out_numeric_preserve_aspect_ratio(self):
+    for dtype in (np.int64, np.float32):
+      with tf_test_util.use_gpu():
+        input_image = np.reshape(np.arange(0, 25), (5, 5, 1)).astype(dtype)
+        layer = image_preprocessing.RandomZoom((.5, .5),
                                                fill_mode='constant',
                                                interpolation='nearest')
         output_image = layer(np.expand_dims(input_image, axis=0))

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-random-zoom.pbtxt

@@ -0,0 +1,218 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.RandomZoom"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.RandomZoom\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'bilinear\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.pbtxt

@@ -44,6 +44,10 @@ tf_module {
     name: "RandomWidth"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "RandomZoom"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Rescaling"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-random-zoom.pbtxt

@@ -0,0 +1,218 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.RandomZoom"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.RandomZoom\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'height_factor\', \'width_factor\', \'fill_mode\', \'interpolation\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'reflect\', \'bilinear\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.pbtxt

@@ -44,6 +44,10 @@ tf_module {
     name: "RandomWidth"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "RandomZoom"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Rescaling"
     mtype: "<type \'type\'>"