Fix bilinear_op Python API (#11117)

* fix conflict

* code clean

doc/fluid/api/layers.rst

@@ -1003,10 +1003,10 @@ dice_loss
 ..  autofunction:: paddle.fluid.layers.dice_loss
     :noindex:
 
-upsampling_bilinear2d
+resize_bilinear
 ____
 
-..  autofunction:: paddle.fluid.layers.upsampling_bilinear2d
+..  autofunction:: paddle.fluid.layers.resize_bilinear
     :noindex:
 
 gather

python/paddle/fluid/layers/nn.py

@@ -81,7 +81,7 @@ __all__ = [
     'label_smooth',
     'roi_pool',
     'dice_loss',
-    'upsampling_bilinear2d',
+    'resize_bilinear',
     'gather',
     'random_crop',
 ]
@@ -3929,9 +3929,9 @@ def dice_loss(input, label, epsilon=0.00001):
     return reduce_mean(dice_score)
 
 
-def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
+def resize_bilinear(input, out_shape=None, scale=None, name=None):
     """
-    The mathematical meaning of upsampling_bilinear2d is also called
+    The mathematical meaning of resize bilinear layer is
     Bilinear interpolation.
     Bilinear interpolation is an extension of linear interpolation for
     interpolating functions of two variables (e.g. H-direction and
@@ -3941,13 +3941,13 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     https://en.wikipedia.org/wiki/Bilinear_interpolation
 
     Args:
-        input (Variable): The input tensor of bilinear interpolation,
+        input (Variable): The input tensor of resize bilinear layer,
                           This is a 4-D tensor of the shape
                           (num_batches, channels, in_h, in_w).
-        out_shape(list|tuple|Variable|None): Output shape of bilinear interpolation
+        out_shape(list|tuple|Variable|None): Output shape of resize bilinear
                                     layer, the shape is (out_h, out_w).
                                     Default: None
-        scale(int|None): The multiplier for the input height or width.
+        scale(float|None): The multiplier for the input height or width.
                          At least one of out_shape or scale must be set.
                          And out_shape has a higher priority than scale.
                          Default: None
@@ -3961,7 +3961,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.bilinear_interp(input, out_shape=[12, 12])
+            out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
     """
     if out_shape is None and scale is None:
         raise ValueError("One of out_shape and scale must not be None")
@@ -3975,10 +3975,9 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     out_w = 0
     inputs = {"X": input}
     if out_shape is not None:
-        if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2) and (
-                out_shape is not Variable):
-            raise ValueError('out_shape should be a list or tuple ',
-                             'with length 2, (out_h, out_w).')
+        if not (_is_list_or_turple_(out_shape) and
+                len(out_shape) == 2) and not isinstance(out_shape, Variable):
+            raise ValueError('out_shape should be a list or tuple or variable')
         if _is_list_or_turple_(out_shape):
             out_shape = list(map(int, out_shape))
             out_h = out_shape[0]

python/paddle/fluid/tests/unittests/test_layers.py

@@ -369,13 +369,13 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
-    def test_upsampling_bilinear2d(self):
+    def test_resize_bilinear(self):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
-            output = layers.upsampling_bilinear2d(x, out_shape=[12, 12])
+            output = layers.resize_bilinear(x, out_shape=[12, 12])
             self.assertIsNotNone(output)
-            output = layers.upsampling_bilinear2d(x, scale=3)
+            output = layers.resize_bilinear(x, scale=3)
             self.assertIsNotNone(output)
         print(str(program))