Revert params in paddle.nn.SpectralNorm and paddle.nnFlatten.forward

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

@@ -942,7 +942,7 @@ def test_spectral_norm(self):
                 lod_level=1,
                 append_batch_size=False,
             )
-            spectralNorm = paddle.nn.SpectralNorm(shape, axis=1, power_iters=2)
+            spectralNorm = paddle.nn.SpectralNorm(shape, dim=1, power_iters=2)
             ret = spectralNorm(Weight)
             static_ret2 = self.get_static_graph_result(
                 feed={
@@ -955,7 +955,7 @@ def test_spectral_norm(self):
             )[0]
 
         with self.dynamic_graph():
-            spectralNorm = paddle.nn.SpectralNorm(shape, axis=1, power_iters=2)
+            spectralNorm = paddle.nn.SpectralNorm(shape, dim=1, power_iters=2)
             dy_ret = spectralNorm(base.to_variable(input))
             dy_rlt_value = dy_ret.numpy()
 

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

@@ -154,7 +154,7 @@ class TestDygraphSpectralNormOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
             shape = (2, 4, 3, 3)
-            spectralNorm = paddle.nn.SpectralNorm(shape, axis=1, power_iters=2)
+            spectralNorm = paddle.nn.SpectralNorm(shape, dim=1, power_iters=2)
 
             def test_Variable():
                 weight_1 = np.random.random((2, 4)).astype("float32")

python/paddle/nn/layer/common.py

@@ -1737,8 +1737,8 @@ def __init__(self, start_axis=1, stop_axis=-1):
         self.start_axis = start_axis
         self.stop_axis = stop_axis
 
-    def forward(self, x):
+    def forward(self, input):
         out = paddle.flatten(
-            x, start_axis=self.start_axis, stop_axis=self.stop_axis
+            input, start_axis=self.start_axis, stop_axis=self.stop_axis
         )
         return out

python/paddle/nn/layer/norm.py

@@ -1812,7 +1812,7 @@ class SpectralNorm(Layer):
 
     Step 1:
     Generate vector U in shape of [H], and V in shape of [W].
-    While H is the :attr:`axis` th dimension of the input weights,
+    While H is the :attr:`dim` th dimension of the input weights,
     and W is the product result of remaining dimensions.
 
     Step 2:
@@ -1839,9 +1839,9 @@ class SpectralNorm(Layer):
 
     Parameters:
         weight_shape(list or tuple): The shape of weight parameter.
-        axis(int, optional): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer. Default: 0.
+        dim(int, optional): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer. Default: 0.
         power_iters(int, optional): The number of power iterations to calculate spectral norm. Default: 1.
-        epsilon(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
+        eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
         name (str, optional): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name` .
         dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
 
@@ -1854,7 +1854,7 @@ class SpectralNorm(Layer):
             import paddle
             x = paddle.rand((2,8,32,32))
 
-            spectral_norm = paddle.nn.SpectralNorm(x.shape, axis=1, power_iters=2)
+            spectral_norm = paddle.nn.SpectralNorm(x.shape, dim=1, power_iters=2)
             spectral_norm_out = spectral_norm(x)
 
             print(spectral_norm_out.shape) # [2, 8, 32, 32]
@@ -1864,25 +1864,25 @@ class SpectralNorm(Layer):
     def __init__(
         self,
         weight_shape,
-        axis=0,
+        dim=0,
         power_iters=1,
-        epsilon=1e-12,
+        eps=1e-12,
         dtype='float32',
     ):
         super().__init__()
         self._power_iters = power_iters
-        self._epsilon = epsilon
-        self._dim = axis
+        self._epsilon = eps
+        self._dim = dim
         self._dtype = dtype
 
         self._weight_shape = list(weight_shape)
         assert (
             np.prod(self._weight_shape) > 0
         ), "Any dimension of `weight_shape` cannot be equal to 0."
-        assert axis < len(self._weight_shape), (
-            "The input `axis` should be less than the "
-            "length of `weight_shape`, but received axis="
-            "{}".format(axis)
+        assert dim < len(self._weight_shape), (
+            "The input `dim` should be less than the "
+            "length of `weight_shape`, but received dim="
+            "{}".format(dim)
         )
         h = self._weight_shape[self._dim]
         w = np.prod(self._weight_shape) // h