[cherry-pick] Fix code examples (#43904)

* Update api docs (#42725)

* Fix max_pool3d doc, test=document_fix (#42715)

* fix pooling doc

* fix typo test=document_fix

* fix doc typo, test=document_fix

* fix adaptive_avg_pool1d doc bug (#42721)

* fix adaptive_avg_pool1d doc bug

* fix adaptive_avg_pool1d doc bug

* fix spectral_norm en doc (#42728)

* Fix example code bugs (#42739)

* update readme test=document_fix

* fix api docs bugs test=document_fix

* fix code example bugs;test=document_fix
Co-authored-by: NLinjie Chen <40840292+linjieccc@users.noreply.github.com>
Co-authored-by: NWei Shengyu <weisy11@163.com>
Co-authored-by: NWalter <dongshl1226@hotmail.com>
Co-authored-by: Nwangna11BD <79366697+wangna11BD@users.noreply.github.com>

python/paddle/nn/functional/loss.py

@@ -389,20 +389,24 @@ def hsigmoid_loss(input,
 
             paddle.set_device('cpu')
 
-            input = paddle.uniform([2, 3])
-            # [[-0.8018668   0.8736385  -0.9064771 ] # random
-            #  [-0.10228515 -0.87188244 -0.8783718 ]] # random
+            input = paddle.uniform([4, 3])
+            # [[0.45424712  -0.77296764  0.82943869] # random
+            #  [0.85062802  0.63303483  0.35312140] # random
+            #  [0.57170701  0.16627562  0.21588242] # random
+            #  [0.27610803  -0.99303514  -0.17114788]] # random
             label = paddle.to_tensor([0, 1, 4, 5])
             num_classes = 5
             weight=paddle.uniform([num_classes-1, 3])
-            # [[-0.24148715  0.8449961  -0.7399121 ] # random
-            #  [-0.9800559   0.43509364  0.9091208 ] # random
-            #  [ 0.60194826  0.10430074 -0.4521166 ] # random
-            #  [-0.4469818  -0.01536179 -0.604454  ]] # random
+            # [[-0.64477652  0.24821866  -0.17456549] # random
+            #  [-0.04635394  0.07473493  -0.25081766] # random
+            #  [ 0.05986035  -0.12185556  0.45153677] # random
+            #  [-0.66236806  0.91271877  -0.88088769]] # random
 
             out=F.hsigmoid_loss(input, label, num_classes, weight)
-            # [[3.0159328]
-            #  [2.2407534]]
+            # [[1.96709502]
+            #  [2.40019274]
+            #  [2.11009121]
+            #  [1.92374969]]
     """
 
     if in_dynamic_mode():

python/paddle/nn/functional/pooling.py

@@ -1160,22 +1160,21 @@ def max_pool3d(x,
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
             # max pool3d
-            x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
-            output = F.max_pool2d(x,
+            x = paddle.uniform([1, 3, 32, 32, 32])
+            output = F.max_pool3d(x,
                                   kernel_size=2,
                                   stride=2, padding=0)
-            output.shape [1, 3, 16, 16, 16]
+            # output.shape [1, 3, 16, 16, 16]
             # for return_mask=True
-            x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
+            x = paddle.uniform([1, 3, 32, 32, 32])
             output, max_indices = paddle.nn.functional.max_pool3d(x,
                                           kernel_size = 2,
                                           stride = 2,
                                           padding=0,
                                           return_mask=True)
-            # output.shape [None, 3, 16, 16, 16], max_indices.shape [None, 3, 16, 16, 16],
+            # output.shape [1, 3, 16, 16, 16], max_indices.shape [1, 3, 16, 16, 16]
     """
     kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
     if stride is None:
@@ -1267,10 +1266,9 @@ def adaptive_avg_pool1d(x, output_size, name=None):
     Returns:
             Tensor: The output tensor of adaptive average pooling result. The data type is same
                       as input tensor.
-    Raises:
-            ValueError: 'output_size' should be an integer.
     Examples:
         .. code-block:: python
+          :name: code-example1
 
               # average adaptive pool1d
               # suppose input data in shape of [N, C, L], `output_size` is m or [m],
@@ -1286,10 +1284,9 @@ def adaptive_avg_pool1d(x, output_size, name=None):
               #
               import paddle
               import paddle.nn.functional as F
-              import numpy as np
 
-              data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
-              pool_out = F.adaptive_average_pool1d(data, output_size=16)
+              data = paddle.uniform([1, 3, 32])
+              pool_out = F.adaptive_avg_pool1d(data, output_size=16)
               # pool_out shape: [1, 3, 16])
     """
     pool_type = 'avg'

python/paddle/nn/layer/loss.py

@@ -465,14 +465,18 @@ class HSigmoidLoss(Layer):
             import paddle
             paddle.set_device('cpu')
 
-            input = paddle.uniform([2, 3])
-            # [[-0.2820413   0.9528898  -0.81638825] # random
-            #  [-0.6733154  -0.33866507  0.25770962]] # random
+            input = paddle.uniform([4, 3])
+            # [[0.56194401  -0.22450298  -0.10741806] # random
+            #  [0.36136317  0.23556745  0.88748658] # random
+            #  [0.18151939  0.80947340  -0.31078976] # random
+            #  [0.68886101  -0.14239830  -0.41297770]] # random
             label = paddle.to_tensor([0, 1, 4, 5])
             m = paddle.nn.HSigmoidLoss(3, 5)
             out = m(input, label)
-            # [[2.4543471]
-            #  [1.9359267]]
+            # [[2.42524505]
+            #  [1.74917245]
+            #  [3.14571381]
+            #  [2.34564662]]
     """
 
     def __init__(self,

python/paddle/nn/utils/spectral_norm_hook.py

@@ -178,7 +178,7 @@ def spectral_norm(layer,
        .. code-block:: python
 
             from paddle.nn import Conv2D
-            from paddle.nn.utils import Spectralnorm
+            from paddle.nn.utils import spectral_norm
 
             conv = Conv2D(3, 1, 3)
             sn_conv = spectral_norm(conv)

python/paddle/nn/utils/weight_norm_hook.py

@@ -213,15 +213,21 @@ def remove_weight_norm(layer, name='weight'):
     Examples:
         .. code-block:: python
           
-          import paddle
-          from paddle.nn import Conv2D
-          from paddle.nn.utils import weight_norm, remove_weight_norm
-
-          conv = Conv2D(3, 5, 3)
-          wn = weight_norm(conv)
-          remove_weight_norm(conv)
-          print(conv.weight_g)
-          # AttributeError: 'Conv2D' object has no attribute 'weight_g'
+            import paddle
+            from paddle.nn import Conv2D
+            from paddle.nn.utils import weight_norm, remove_weight_norm
+
+            conv = Conv2D(3, 5, 3)
+            wn = weight_norm(conv)
+            print(conv.weight_g)
+            # Parameter containing:
+            # Tensor(shape=[5], dtype=float32, place=Place(gpu:0), stop_gradient=False,
+            #        [0., 0., 0., 0., 0.])
+            # Conv2D(3, 5, kernel_size=[3, 3], data_format=NCHW)
+
+            remove_weight_norm(conv)
+            # print(conv.weight_g)
+            # AttributeError: 'Conv2D' object has no attribute 'weight_g'
     """
     for k, hook in layer._forward_pre_hooks.items():
         if isinstance(hook, WeightNorm) and hook.name == name:

python/paddle/vision/ops.py

@@ -851,15 +851,14 @@ def read_file(filename, name=None):
             import cv2
             import paddle
 
-            fake_img = (np.random.random(
-                        (400, 300, 3)) * 255).astype('uint8')
-
+            fake_img = (paddle.rand((400, 300, 3)).numpy() * 255).astype('uint8')
+            
             cv2.imwrite('fake.jpg', fake_img)
 
             img_bytes = paddle.vision.ops.read_file('fake.jpg')
             
             print(img_bytes.shape)
-
+            # [142915]
     """
 
     if _non_static_mode():