Fix docs in 2.0 API (#29081)

* 1. grid_sample
1.1 fix has_print
2. conv1d_transpose
2.1 fix code_example error
3. conv1d
4. affine_grid
4.1 has_print
4.2 has_disable_static
5. Conv1DTranspose
5.1 fix code_example error
5.2 has_disable_static
6. Conv1d
6.1 code_example
6.2 has_disable_static

python/paddle/nn/functional/conv.py

@@ -182,7 +182,7 @@ def conv1d(x,
 
     .. math::
 
-        Out = \sigma (W \\ast X + b)
+        Out = \sigma (W \ast X + b)
 
     Where:
 
@@ -209,7 +209,7 @@ def conv1d(x,
 
         .. math::
 
-            L_{out}&= \\frac{(L_{in} + 2 * padding - (dilation * (L_f - 1) + 1))}{stride} + 1
+            L_{out} = \frac{(L_{in} + 2 * padding - (dilation * (L_f - 1) + 1))}{stride} + 1
 
     Args:
         x (Tensor): The input is 3-D Tensor with shape [N, C, L], the data type 
@@ -583,7 +583,7 @@ def conv1d_transpose(x,
 
     .. math::
 
-        Out = \sigma (W \\ast X + b)
+        Out = \sigma (W \ast X + b)
 
     Where:
 
@@ -693,13 +693,12 @@ def conv1d_transpose(x,
           x=np.array([[[4, 0, 9, 7],
                        [8, 0, 9, 2,]]]).astype(np.float32)
           # shape: (2, 1, 2)
-          y=np.array([[[7, 0]],
+          w=np.array([[[7, 0]],
                       [[4, 2]]]).astype(np.float32)
           x_var = paddle.to_tensor(x)
           w_var = paddle.to_tensor(w)
           y_var = F.conv1d_transpose(x_var, w_var)
-          y_np = y_var.numpy()
-          print y_np
+          print(y_var)
           
           # [[[60. 16. 99. 75.  4.]]]
     """

python/paddle/nn/functional/vision.py

@@ -86,7 +86,6 @@ def affine_grid(theta, out_shape, align_corners=True, name=None):
             import paddle
             import paddle.nn.functional as F
             import numpy as np
-            paddle.disable_static()
             # theta shape = [1, 2, 3]
             theta = np.array([[[-0.7, -0.4, 0.3],
                                [ 0.6,  0.5, 1.5]]]).astype("float32")
@@ -95,7 +94,7 @@ def affine_grid(theta, out_shape, align_corners=True, name=None):
                     theta_t,
                     [1, 2, 3, 3],
                     align_corners=False)
-            print(y_t.numpy())
+            print(y_t)
             
             #[[[[ 1.0333333   0.76666665]
             #   [ 0.76666665  1.0999999 ]
@@ -270,7 +269,7 @@ def grid_sample(x,
                 mode='bilinear',
                 padding_mode='border',
                 align_corners=True)
-            print(y_t.numpy())
+            print(y_t)
             
             # output shape = [1, 1, 3, 4]
             # [[[[ 0.34   0.016  0.086 -0.448]

python/paddle/nn/layer/conv.py

@@ -163,25 +163,40 @@ class Conv1D(_ConvNd):
     If bias attribution and activation type are provided, bias is added to the
     output of the convolution, and the corresponding activation function is
     applied to the final result.
-    For each input :math:`X`, the equation is:
+
+    For each input :math:`X` , the equation is:
+
     .. math::
-        Out = \\sigma (W \\ast X + b)
+
+        Out = \sigma (W \\ast X + b)
+
     Where:
+
     * :math:`X`: Input value, a ``Tensor`` with 'NCL' format or 'NLC' format.
     * :math:`W`: Filter value, a ``Tensor`` with shape [MCK] .
     * :math:`\\ast`: Convolution operation.
     * :math:`b`: Bias value, a 2-D ``Tensor`` with shape [M, 1].
     * :math:`\\sigma`: Activation function.
     * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
     Example:
+
         - Input:
+
           Input shape: :math:`(N, C_{in}, L_{in})`
+
           Kernel shape: :math:`(C_{out}, C_{in}, K)`
+
         - Output:
+
           Output shape: :math:`(N, C_{out}, L_{out})`
+
         Where
+
         .. math::
+
             L_{out}&= \\frac{(L_{in} + 2 * padding - (dilation * (L_f - 1) + 1))}{stride} + 1
+
     Parameters:
         in_channels(int): The number of channels in the input image.
         out_channels(int): The number of filter. It is as same as the output
@@ -218,17 +233,21 @@ class Conv1D(_ConvNd):
             If it is set to None or one attribute of ParamAttr, conv1d
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
+
     Attribute:
         **weight** (Parameter): the learnable weights of filter of this layer.
         **bias** (Parameter or None): the learnable bias of this layer.
+
     Shape:
         - x: 3-D tensor with shape: (batch, in_channels, length) or (batch, length, in_channels).
         - output: 3-D tensor with same shape as input x.
     
     Raises:
         None
+
     Examples:
         .. code-block:: python
+
           import paddle
           from paddle.nn import Conv1D
           import numpy as np
@@ -242,13 +261,11 @@ class Conv1D(_ConvNd):
            [[0, 3, 4],
             [2, 9, 7],
             [5, 6, 8]]]).astype(np.float32)
-          paddle.disable_static()
           x_t = paddle.to_tensor(x)
           conv = Conv1D(3, 2, 3)
           conv.weight.set_value(w)
           y_t = conv(x_t)
-          y_np = y_t.numpy()
-          print(y_np)
+          print(y_t)
           # [[[133. 238.]
           #   [160. 211.]]]
     """
@@ -401,14 +418,9 @@ class Conv1DTranspose(_ConvNd):
         **bias** (Parameter or None): the learnable bias of this layer.
 
     Shape:
-        - x(Tensor): 3-D tensor with shape (batch, in_channels, length) when data_format is
-            "NCL" or shape (batch, length, in_channels) when data_format is "NLC".
-        - output_size(int|tuple|list, optional): The output image size. If output size is a
-            tuple, it must contain one integer, (feature_length). None if use
-            kernel_size, padding, output_padding and stride to calculate output_size.
-            If output_size and kernel_size are specified at the same time, They
-            should follow the formula above. Default: None. output_size and kernel_size
-            should not be None at the same time.
+
+        - x(Tensor): 3-D tensor with shape (batch, in_channels, length) when data_format is "NCL" or shape (batch, length, in_channels) when data_format is "NLC".
+        - output_size(int|tuple|list, optional): The output image size. If output size is a tuple, it must contain one integer, (feature_length). None if use kernel_size, padding, output_padding and stride to calculate output_size. If output_size and kernel_size are specified at the same time, They should follow the formula above. Default: None. output_size and kernel_size should not be None at the same time.
         - output(Tensor): 3-D tensor with same shape as input x.
 
     Examples:
@@ -418,7 +430,6 @@ class Conv1DTranspose(_ConvNd):
           from paddle.nn import Conv1DTranspose
           import numpy as np
           
-          paddle.disable_static()
           # shape: (1, 2, 4)
           x=np.array([[[4, 0, 9, 7],
                        [8, 0, 9, 2]]]).astype(np.float32)
@@ -429,8 +440,7 @@ class Conv1DTranspose(_ConvNd):
           conv = Conv1DTranspose(2, 1, 2)
           conv.weight.set_value(y)
           y_t = conv(x_t)
-          y_np = y_t.numpy()
-          print y_np
+          print(y_t)
           
           # [[[60. 16. 99. 75.  4.]]]
     """