[xdoctest] reformat example code with google style in No.95-99

python/paddle/nn/quant/quant_layers.py

@@ -611,19 +611,21 @@ class QuantizedConv2DTranspose(Layer):
     The only difference is that its inputs are all fake quantized.
 
     Examples:
-       .. code-block:: python
-
-          import paddle
-          import paddle.nn as nn
-          from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
-
-          x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
-          conv = nn.Conv2DTranspose(4, 6, (3, 3))
-          conv_quantized = QuantizedConv2DTranspose(conv)
-          y_quantized = conv_quantized(x_var)
-          y_var = conv(x_var)
-          print(y_var.shape, y_quantized.shape)
-          # [2, 6, 10, 10], [2, 6, 10, 10]
+        .. code-block:: python
+
+            >>> import paddle
+            >>> import paddle.nn as nn
+            >>> from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
+
+            >>> x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
+            >>> conv = nn.Conv2DTranspose(4, 6, (3, 3))
+            >>> conv_quantized = QuantizedConv2DTranspose(conv)
+            >>> y_quantized = conv_quantized(x_var)
+            >>> y_var = conv(x_var)
+            >>> print(y_var.shape)
+            [2, 6, 10, 10]
+            >>> print(y_quantized.shape)
+            [2, 6, 10, 10]
 
     """
 

python/paddle/nn/quant/stub.py

@@ -28,26 +28,35 @@ class Stub(Layer):
         It will use a global configuration to create the observers if the 'observer' is none.
     Examples:
         .. code-block:: python
-            import paddle
-            from paddle.nn.quant import Stub
-            from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
-            from paddle.nn import Conv2D
-            from paddle.quantization import QAT, QuantConfig
-            quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
-            class Model(paddle.nn.Layer):
-                def __init__(self, num_classes=10):
-                    super().__init__()
-                    self.conv = Conv2D(3, 6, 3, stride=1, padding=1)
-                    self.quant = Stub(quanter)
-                def forward(self, inputs):
-                    out = self.conv(inputs)
-                    out = self.quant(out)
-                    return paddle.nn.functional.relu(out)
-            model = Model()
-            q_config = QuantConfig(activation=quanter, weight=quanter)
-            qat = QAT(q_config)
-            quant_model = qat.quantize(model)
-            print(quant_model)
+            >>> import paddle
+            >>> from paddle.nn.quant import Stub
+            >>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
+            >>> from paddle.nn import Conv2D
+            >>> from paddle.quantization import QAT, QuantConfig
+            >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
+            >>> class Model(paddle.nn.Layer):
+            ...     def __init__(self, num_classes=10):
+            ...         super().__init__()
+            ...         self.conv = Conv2D(3, 6, 3, stride=1, padding=1)
+            ...         self.quant = Stub(quanter)
+            ...     def forward(self, inputs):
+            ...         out = self.conv(inputs)
+            ...         out = self.quant(out)
+            ...         return paddle.nn.functional.relu(out)
+            >>> model = Model()
+            >>> q_config = QuantConfig(activation=quanter, weight=quanter)
+            >>> qat = QAT(q_config)
+            >>> quant_model = qat.quantize(model)
+            >>> print(quant_model)
+            Model(
+                (conv): QuantedConv2D(
+                  (weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
+                  (activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
+                )
+                (quant): QuanterStub(
+                  (_observer): FakeQuanterWithAbsMaxObserverLayer()
+                )
+            )
     """
 
     def __init__(self, observer=None):

python/paddle/nn/utils/clip_grad_norm_.py

@@ -45,19 +45,19 @@ def clip_grad_norm_(
         Total norm of the parameter gradients (treated as a single vector).
     Example:
         .. code-block:: python
-            import paddle
+            >>> import paddle
 
-            x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
-            max_norm = float(5.0)
-            linear = paddle.nn.Linear(in_features=10, out_features=10)
-            out = linear(x)
-            loss = paddle.mean(out)
-            loss.backward()
+            >>> x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
+            >>> max_norm = float(5.0)
+            >>> linear = paddle.nn.Linear(in_features=10, out_features=10)
+            >>> out = linear(x)
+            >>> loss = paddle.mean(out)
+            >>> loss.backward()
 
-            paddle.nn.utils.clip_grad_norm_(linear.parameters(), max_norm)
+            >>> paddle.nn.utils.clip_grad_norm_(linear.parameters(), max_norm)
 
-            sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
-            sdg.step()
+            >>> sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
+            >>> sdg.step()
     """
     if not paddle.in_dynamic_mode():
         raise RuntimeError('this API can only run in dynamic mode.')

python/paddle/nn/utils/clip_grad_value_.py

@@ -34,16 +34,16 @@ def clip_grad_value_(
     Example:
         .. code-block:: python
 
-            import paddle
-            x = paddle.uniform([10, 10], min=-10.0, max=10.0, dtype='float32')
-            clip_value = float(5.0)
-            linear = paddle.nn.Linear(in_features=10, out_features=10)
-            out = linear(x)
-            loss = paddle.mean(out)
-            loss.backward()
-            paddle.nn.utils.clip_grad_value_(linear.parameters(), clip_value)
-            sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
-            sdg.step()
+            >>> import paddle
+            >>> x = paddle.uniform([10, 10], min=-10.0, max=10.0, dtype='float32')
+            >>> clip_value = float(5.0)
+            >>> linear = paddle.nn.Linear(in_features=10, out_features=10)
+            >>> out = linear(x)
+            >>> loss = paddle.mean(out)
+            >>> loss.backward()
+            >>> paddle.nn.utils.clip_grad_value_(linear.parameters(), clip_value)
+            >>> sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
+            >>> sdg.step()
     """
     if not paddle.in_dynamic_mode():
         raise RuntimeError('this API can only run in dynamic mode.')

python/paddle/nn/utils/spectral_norm_hook.py

@@ -182,28 +182,27 @@ def spectral_norm(
         Layer, the original layer with the spectral norm hook.
 
     Examples:
-       .. code-block:: python
-
-            from paddle.nn import Conv2D
-            from paddle.nn.utils import spectral_norm
-
-            conv = Conv2D(3, 1, 3)
-            sn_conv = spectral_norm(conv)
-            print(sn_conv)
-            # Conv2D(3, 1, kernel_size=[3, 3], data_format=NCHW)
-            print(sn_conv.weight)
-            # Tensor(shape=[1, 3, 3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-            #        [[[[-0.21090528,  0.18563725, -0.14127982],
-            #           [-0.02310637,  0.03197737,  0.34353802],
-            #           [-0.17117859,  0.33152047, -0.28408015]],
-            #
-            #          [[-0.13336606, -0.01862637,  0.06959272],
-            #           [-0.02236020, -0.27091628, -0.24532901],
-            #           [ 0.27254242,  0.15516677,  0.09036587]],
-            #
-            #          [[ 0.30169338, -0.28146112, -0.11768346],
-            #           [-0.45765871, -0.12504843, -0.17482486],
-            #           [-0.36866254, -0.19969313,  0.08783543]]]])
+        .. code-block:: python
+
+            >>> from paddle.nn import Conv2D
+            >>> from paddle.nn.utils import spectral_norm
+            >>> paddle.seed(2023)
+            >>> conv = Conv2D(3, 1, 3)
+            >>> sn_conv = spectral_norm(conv)
+            >>> print(sn_conv)
+            Conv2D(3, 1, kernel_size=[3, 3], data_format=NCHW)
+            >>> # Conv2D(3, 1, kernel_size=[3, 3], data_format=NCHW)
+            >>> print(sn_conv.weight)
+            Tensor(shape=[1, 3, 3, 3], dtype=float32, place=Place(cpu), stop_gradient=False,
+            [[[[ 0.01668976,  0.30305523,  0.11405435],
+            [-0.06765547, -0.50396705, -0.40925547],
+            [ 0.47344422,  0.03628403,  0.45277366]],
+            [[-0.15177251, -0.16305730, -0.15723954],
+            [-0.28081197, -0.09183260, -0.08081978],
+            [-0.40895155,  0.18298769, -0.29325116]],
+            [[ 0.21819633, -0.01822380, -0.50351536],
+            [-0.06262003,  0.17713565,  0.20517939],
+            [ 0.16659889, -0.14333329,  0.05228264]]]])
 
     """