Merge remote-tracking branch 'upstream/master'

1 files changed, 0 insertions, 106 deletions

diff --git a/tensorflow/python/keras/applications/imagenet_utils_test.py b/tensorflow/python/keras/applications/imagenet_utils_test.py
index 3493393090..037e939ac5 100644
--- a/tensorflow/python/keras/applications/imagenet_utils_test.py
+++ b/tensorflow/python/keras/applications/imagenet_utils_test.py
@@ -88,112 +88,6 @@ class ImageNetUtilsTest(test.TestCase):
     out2 = model2.predict(x2[np.newaxis])[0]
     self.assertAllClose(out1, out2.transpose(1, 2, 0))
 
-  def test_obtain_input_shape(self):
-    # input_shape and default_size are not identical.
-    with self.assertRaises(ValueError):
-      keras.applications.imagenet_utils._obtain_input_shape(
-          input_shape=(224, 224, 3),
-          default_size=299,
-          min_size=139,
-          data_format='channels_last',
-          require_flatten=True,
-          weights='imagenet')
-
-    # Test invalid use cases
-    for data_format in ['channels_last', 'channels_first']:
-      # input_shape is smaller than min_size.
-      shape = (100, 100)
-      if data_format == 'channels_last':
-        input_shape = shape + (3,)
-      else:
-        input_shape = (3,) + shape
-      with self.assertRaises(ValueError):
-        keras.applications.imagenet_utils._obtain_input_shape(
-            input_shape=input_shape,
-            default_size=None,
-            min_size=139,
-            data_format=data_format,
-            require_flatten=False)
-
-      # shape is 1D.
-      shape = (100,)
-      if data_format == 'channels_last':
-        input_shape = shape + (3,)
-      else:
-        input_shape = (3,) + shape
-      with self.assertRaises(ValueError):
-        keras.applications.imagenet_utils._obtain_input_shape(
-            input_shape=input_shape,
-            default_size=None,
-            min_size=139,
-            data_format=data_format,
-            require_flatten=False)
-
-      # the number of channels is 5 not 3.
-      shape = (100, 100)
-      if data_format == 'channels_last':
-        input_shape = shape + (5,)
-      else:
-        input_shape = (5,) + shape
-      with self.assertRaises(ValueError):
-        keras.applications.imagenet_utils._obtain_input_shape(
-            input_shape=input_shape,
-            default_size=None,
-            min_size=139,
-            data_format=data_format,
-            require_flatten=False)
-
-      # require_flatten=True with dynamic input shape.
-      with self.assertRaises(ValueError):
-        keras.applications.imagenet_utils._obtain_input_shape(
-            input_shape=None,
-            default_size=None,
-            min_size=139,
-            data_format='channels_first',
-            require_flatten=True)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=(3, 200, 200),
-        default_size=None,
-        min_size=139,
-        data_format='channels_first',
-        require_flatten=True) == (3, 200, 200)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=None,
-        default_size=None,
-        min_size=139,
-        data_format='channels_last',
-        require_flatten=False) == (None, None, 3)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=None,
-        default_size=None,
-        min_size=139,
-        data_format='channels_first',
-        require_flatten=False) == (3, None, None)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=None,
-        default_size=None,
-        min_size=139,
-        data_format='channels_last',
-        require_flatten=False) == (None, None, 3)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=(150, 150, 3),
-        default_size=None,
-        min_size=139,
-        data_format='channels_last',
-        require_flatten=False) == (150, 150, 3)
-
-    assert keras.applications.imagenet_utils._obtain_input_shape(
-        input_shape=(3, None, None),
-        default_size=None,
-        min_size=139,
-        data_format='channels_first',
-        require_flatten=False) == (3, None, None)
-
 
 if __name__ == '__main__':
   test.main()