update PixelShuffle and PRelu api in vision.py and activation.py；test=document_fix (#45423)

* update PixelShuffle and PRelu api in vision.py and activation.py

* update activation.py, using paddle replace numpy

* update format issue

* update foramt issue

python/paddle/nn/functional/activation.py

@@ -445,7 +445,9 @@ def leaky_relu(x, negative_slope=0.01, name=None):
             import paddle.nn.functional as F
 
             x = paddle.to_tensor([-2., 0., 1.])
-            out = F.leaky_relu(x) # [-0.02, 0., 1.]
+            out = F.leaky_relu(x)
+            print(out)
+            # [-0.02, 0., 1.]
 
     """
     if in_dygraph_mode():
@@ -490,17 +492,17 @@ def prelu(x, weight, data_format="NCHW", name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            data = np.array([[[[-2.0,  3.0, -4.0,  5.0],
+            data = paddle.to_tensor([[[[-2.0,  3.0, -4.0,  5.0],
                                [ 3.0, -4.0,  5.0, -6.0],
                                [-7.0, -8.0,  8.0,  9.0]],
                               [[ 1.0, -2.0, -3.0,  4.0],
                                [-5.0,  6.0,  7.0, -8.0],
-                               [ 6.0,  7.0,  8.0,  9.0]]]], 'float32')
-            x = paddle.to_tensor(data)
-            w = paddle.to_tensor(np.array([0.25]).astype('float32'))
-            out = F.prelu(x, w)
+                               [ 6.0,  7.0,  8.0,  9.0]]]], dtype='float32')
+
+            w = paddle.to_tensor([0.25], dtype='float32')
+            out = F.prelu(data, w)
+            print(out)
             # [[[[-0.5 ,  3.  , -1.  ,  5.  ],
             #    [ 3.  , -1.  ,  5.  , -1.5 ],
             #    [-1.75, -2.  ,  8.  ,  9.  ]],
@@ -625,6 +627,7 @@ def rrelu(x, lower=1. / 8., upper=1. / 3., training=True, name=None):
                                             [ 6.0,  7.0,  8.0,  9.0]]]], dtype='float32')
 
             out = F.rrelu(input_tensor, 0.1, 0.3)
+            print(out)
             #[[[[-0.20000899  3.         -0.8810822   5.        ]
             #   [ 3.         -0.55175185  5.         -1.0776101 ]
             #   [-1.0680687  -1.9896201   8.          9.        ]]
@@ -699,10 +702,11 @@ def relu(x, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([-2, 0, 1]).astype('float32'))
-            out = F.relu(x) # [0., 0., 1.]
+            x = paddle.to_tensor([-2, 0, 1], dtype='float32')
+            out = F.relu(x)
+            print(out)
+            # [0., 0., 1.]
     """
 
     if in_dygraph_mode():
@@ -868,10 +872,11 @@ def relu6(x, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
-            out = F.relu6(x) # [0, 0.3, 6]
+            x = paddle.to_tensor([-1, 0.3, 6.5])
+            out = F.relu6(x)
+            print(out)
+            # [0, 0.3, 6]
     """
     threshold = 6.0
     if in_dygraph_mode():
@@ -921,10 +926,11 @@ def selu(x,
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([[0.0, 1.0],[2.0, 3.0]]))
-            out = F.selu(x) # [[0, 1.050701],[2.101402, 3.152103]]
+            x = paddle.to_tensor([[0.0, 1.0],[2.0, 3.0]])
+            out = F.selu(x)
+            print(out)
+            # [[0, 1.050701],[2.101402, 3.152103]]
     """
     if scale <= 1.0:
         raise ValueError(

python/paddle/nn/functional/vision.py

@@ -305,25 +305,27 @@ def pixel_shuffle(x, upscale_factor, data_format="NCHW", name=None):
     """
     This API implements pixel shuffle operation.
     See more details in :ref:`api_nn_vision_PixelShuffle` .
+
+
     Parameters:
         x(Tensor): 4-D tensor, the data type should be float32 or float64.
         upscale_factor(int): factor to increase spatial resolution.
-        data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
+        data_format (str, optional): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
         name (str, optional): The default value is None.  Normally there is no need for user to set this property.
+
     Returns:
         Out(tensor): Reshaped tensor according to the new dimension.
-    Raises:
-        ValueError: If the square of upscale_factor cannot divide the channels of input.
+
     Examples:
         .. code-block:: python
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
-            x = np.random.randn(2, 9, 4, 4).astype(np.float32)
-            x_var = paddle.to_tensor(x)
-            out_var = F.pixel_shuffle(x_var, 3)
+
+            x = paddle.randn(shape=[2,9,4,4])
+            out_var = F.pixel_shuffle(x, 3)
             out = out_var.numpy()
+            print(out.shape)
             # (2, 1, 12, 12)
     """
     if not isinstance(upscale_factor, int):
@@ -361,7 +363,7 @@ def pixel_unshuffle(x, downscale_factor, data_format="NCHW", name=None):
     Parameters:
         x (Tensor): 4-D tensor, the data type should be float32 or float64.
         downscale_factor (int): Factor to decrease spatial resolution.
-        data_format (str): The data format of the input and output data. An optional string of NCHW or NHWC. The default is NCHW. When it is NCHW, the data is stored in the order of [batch_size, input_channels, input_height, input_width].
+        data_format (str, optional): The data format of the input and output data. An optional string of NCHW or NHWC. The default is NCHW. When it is NCHW, the data is stored in the order of [batch_size, input_channels, input_height, input_width].
         name (str, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -374,7 +376,8 @@ def pixel_unshuffle(x, downscale_factor, data_format="NCHW", name=None):
             import paddle.nn.functional as F
             x = paddle.randn([2, 1, 12, 12])
             out = F.pixel_unshuffle(x, 3)
-            # out.shape = [2, 9, 4, 4]
+            print(out.shape)
+            # [2, 9, 4, 4]
     """
     if len(x.shape) != 4:
         raise ValueError(

python/paddle/nn/layer/activation.py

@@ -384,19 +384,18 @@ class PReLU(Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-
             paddle.set_default_dtype("float64")
 
-            data = np.array([[[[-2.0,  3.0, -4.0,  5.0],
-                            [ 3.0, -4.0,  5.0, -6.0],
-                            [-7.0, -8.0,  8.0,  9.0]],
-                            [[ 1.0, -2.0, -3.0,  4.0],
-                            [-5.0,  6.0,  7.0, -8.0],
-                            [ 6.0,  7.0,  8.0,  9.0]]]], 'float64')
-            x = paddle.to_tensor(data)
+            data = paddle.to_tensor([[[[-2.0,  3.0, -4.0,  5.0],
+                                    [ 3.0, -4.0,  5.0, -6.0],
+                                    [-7.0, -8.0,  8.0,  9.0]],
+                                    [[ 1.0, -2.0, -3.0,  4.0],
+                                    [-5.0,  6.0,  7.0, -8.0],
+                                    [ 6.0,  7.0,  8.0,  9.0]]]])
+
             m = paddle.nn.PReLU(1, 0.25)
-            out = m(x)
+            out = m(data)
+            print(out)
             # [[[[-0.5 ,  3.  , -1.  ,  5.  ],
             #    [ 3.  , -1.  ,  5.  , -1.5 ],
             #    [-1.75, -2.  ,  8.  ,  9.  ]],
@@ -496,7 +495,8 @@ class RReLU(Layer):
                                             [ 6.0,  7.0,  8.0,  9.0]]]], dtype='float32')
 
             rrelu_layer = paddle.nn.RReLU(0.1, 0.3)
-            output = rrelu_layer(input_tensor)
+            out = rrelu_layer(input_tensor)
+            print(out)
             #[[[[-0.20000899  3.         -0.88108218  5.        ]
             #   [ 3.         -0.55175185  5.         -1.07761011]
             #   [-1.06806871 -1.98962009  8.          9.        ]]
@@ -546,7 +546,9 @@ class ReLU(Layer):
 
             x = paddle.to_tensor([-2., 0., 1.])
             m = paddle.nn.ReLU()
-            out = m(x) # [0., 0., 1.]
+            out = m(x)
+            print(out)
+            # [0., 0., 1.]
     """
 
     def __init__(self, name=None):
@@ -581,11 +583,12 @@ class ReLU6(Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
+            x = paddle.to_tensor([-1., 0.3, 6.5])
             m = paddle.nn.ReLU6()
-            out = m(x) # [0, 0.3, 6]
+            out = m(x)
+            print(out)
+            # [0, 0.3, 6]
     """
 
     def __init__(self, name=None):
@@ -628,11 +631,12 @@ class SELU(Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([[0.0, 1.0],[2.0, 3.0]]))
+            x = paddle.to_tensor([[0.0, 1.0],[2.0, 3.0]])
             m = paddle.nn.SELU()
-            out = m(x) # [[0, 1.050701],[2.101402, 3.152103]]
+            out = m(x)
+            print(out)
+            # [[0, 1.050701],[2.101402, 3.152103]]
     """
 
     def __init__(self,

python/paddle/nn/layer/vision.py

@@ -25,9 +25,9 @@ class PixelShuffle(Layer):
     
     PixelShuffle Layer    
 
-    This operator rearranges elements in a tensor of shape [N, C, H, W]
-    to a tensor of shape [N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor],
-    or from shape [N, H, W, C] to [N, H*upscale_factor, W*upscale_factor, C/upscale_factor**2].
+    Rearranges elements in a tensor of shape :math:`[N, C, H, W]`
+    to a tensor of shape :math:`[N, C/upscale_factor^2, H*upscale_factor, W \times upscale_factor]`,
+    or from shape :math:`[N, H, W, C]` to :math:`[N, H \times upscale_factor, W \times upscale_factor, C/upscale_factor^2]`.
     This is useful for implementing efficient sub-pixel convolution
     with a stride of 1/upscale_factor.
     Please refer to the paper: `Real-Time Single Image and Video Super-Resolution
@@ -37,12 +37,12 @@ class PixelShuffle(Layer):
     Parameters:
 
         upscale_factor(int): factor to increase spatial resolution.
-        data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
+        data_format (str, optional): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Shape:
-        - x: 4-D tensor with shape: (N, C, H, W) or (N, H, W, C).
-        - out: 4-D tensor with shape: (N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor) or (N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2).
+        - x: 4-D tensor with shape of :math:`(N, C, H, W)` or :math:`(N, H, W, C)`.
+        - out: 4-D tensor with shape of :math:`(N, C/upscale_factor^2, H \times upscale_factor, W \times upscale_factor)` or :math:`(N, H \times upscale_factor, W \times upscale_factor, C/upscale_factor^2)`.
 
 
     Examples:
@@ -50,14 +50,12 @@ class PixelShuffle(Layer):
             
             import paddle
             import paddle.nn as nn
-            import numpy as np
 
-            x = np.random.randn(2, 9, 4, 4).astype(np.float32)
-            x_var = paddle.to_tensor(x)
+            x = paddle.randn(shape=[2,9,4,4])
             pixel_shuffle = nn.PixelShuffle(3)
-            out_var = pixel_shuffle(x_var)
+            out_var = pixel_shuffle(x)
             out = out_var.numpy()
-            print(out.shape) 
+            print(out.shape)
             # (2, 1, 12, 12)
 
     """
@@ -91,7 +89,7 @@ class PixelShuffle(Layer):
 
 class PixelUnshuffle(Layer):
     """
-    This operator rearranges elements in a tensor of shape :math:`[N, C, H, W]` 
+    Rearranges elements in a tensor of shape :math:`[N, C, H, W]`
     to a tensor of shape :math:`[N, r^2C, H/r, W/r]`, or from shape 
     :math:`[N, H, W, C]` to :math:`[N, H/r, W/r, r^2C]`, where :math:`r` is the 
     downscale factor. This operation is the reversion of PixelShuffle operation.
@@ -101,7 +99,7 @@ class PixelUnshuffle(Layer):
 
     Parameters:
         downscale_factor (int): Factor to decrease spatial resolution.
-        data_format (str): The data format of the input and output data. An optional string of NCHW or NHWC. The default is NCHW. When it is NCHW, the data is stored in the order of [batch_size, input_channels, input_height, input_width].
+        data_format (str, optional): The data format of the input and output data. An optional string of NCHW or NHWC. The default is NCHW. When it is NCHW, the data is stored in the order of [batch_size, input_channels, input_height, input_width].
         name (str, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Shape:
@@ -117,7 +115,8 @@ class PixelUnshuffle(Layer):
             x = paddle.randn([2, 1, 12, 12])
             pixel_unshuffle = nn.PixelUnshuffle(3)
             out = pixel_unshuffle(x)
-            # out.shape = [2, 9, 4, 4]
+            print(out.shape)
+            # [2, 9, 4, 4]
 
     """