[xdoctest][task 376~381] reformat example code with google style (#58173

) * xdoctest 365-370 * fix
PaddlePaddle · Oct 18, 2023 · 9690055 · 9690055
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commit 9690055
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Showing 5 changed files with 11 additions and 26 deletions.
diff --git a/python/paddle/distribution/bernoulli.py b/python/paddle/distribution/bernoulli.py
@@ -212,6 +212,7 @@ def rsample(self, shape, temperature=1.0):
             .. code-block:: python
 
                 >>> import paddle
+                >>> paddle.seed(1)
                 >>> from paddle.distribution import Bernoulli
 
                 >>> rv = Bernoulli(paddle.full((1), 0.3))
@@ -231,28 +232,26 @@ def rsample(self, shape, temperature=1.0):
                 [100, 2, 2]
 
                 >>> # `rsample` has to be followed by a `sigmoid`
-                >>> # doctest: +SKIP
                 >>> rv = Bernoulli(0.3)
                 >>> rsample = rv.rsample([3, ])
                 >>> rsample_sigmoid = paddle.nn.functional.sigmoid(rsample)
-                >>> print(rsample, rsample_sigmoid)
-                Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
-                [[-0.88315082],
-                [-0.62347704],
-                [-0.31513220]])
-                Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
-                [[0.29252526],
-                [0.34899110],
-                [0.42186251]])
+                >>> print(rsample)
+                Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
+                [-1.46112013, -0.01239836, -1.32765460])
+                >>> print(rsample_sigmoid)
+                Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
+                [0.18829606, 0.49690047, 0.20954758])
 
                 >>> # The smaller the `temperature`, the distribution of `rsample` closer to `sample`, with `probs` of 0.3.
                 >>> print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=1.0)).sum())
+                >>> # doctest: +SKIP('output will be different')
                 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
-                361.06829834)
+                365.63122559)
+                >>> # doctest: -SKIP
 
                 >>> print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=0.1)).sum())
                 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
-                288.66418457)
+                320.15057373)
         """
         name = self.name + '_rsample'
         if not in_dynamic_mode():

diff --git a/python/paddle/distribution/categorical.py b/python/paddle/distribution/categorical.py
@@ -64,14 +64,12 @@ class Categorical(distribution.Distribution):
             >>> cat = Categorical(x)
             >>> cat2 = Categorical(y)
 
-            >>> # doctest: +SKIP
             >>> paddle.seed(1000) # on CPU device
             >>> print(cat.sample([2,3]))
             Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
             [[0, 1, 5],
             [3, 4, 5]])
 
-            >>> # doctest: -SKIP
             >>> print(cat.entropy())
             Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
             1.77528250)

diff --git a/python/paddle/hapi/dynamic_flops.py b/python/paddle/hapi/dynamic_flops.py
@@ -85,7 +85,6 @@ def flops(net, input_size, custom_ops=None, print_detail=False):
             ...                      [1, 1, 28, 28],
             ...                      custom_ops= {nn.LeakyReLU: count_leaky_relu},
             ...                      print_detail=True)
-            >>> # doctest: +SKIP
             >>> print(FLOPs)
             <class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted
             <class 'paddle.nn.layer.activation.ReLU'>'s flops has been counted
@@ -106,7 +105,6 @@ def flops(net, input_size, custom_ops=None, print_detail=False):
             +--------------+-----------------+-----------------+--------+--------+
             Total Flops: 347560     Total Params: 61610
             347560
-            >>> # doctest: -SKIP
     """
     if isinstance(net, nn.Layer):
         # If net is a dy2stat model, net.forward is StaticFunction instance,

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
@@ -2399,7 +2399,6 @@ def summary(self, input_size=None, dtype=None):
                 >>> optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
                 >>> model.prepare(optim, paddle.nn.CrossEntropyLoss())
                 >>> params_info = model.summary()
-                >>> # doctest: +SKIP
                 >>> print(params_info)
                 ---------------------------------------------------------------------------
                 Layer (type)       Input Shape          Output Shape         Param #
@@ -2424,7 +2423,6 @@ def summary(self, input_size=None, dtype=None):
                 Estimated Total Size (MB): 0.35
                 ---------------------------------------------------------------------------
                 {'total_params': 61610, 'trainable_params': 61610}
-                >>> # doctest: -SKIP
 
         """
         assert (

diff --git a/python/paddle/hapi/model_summary.py b/python/paddle/hapi/model_summary.py
@@ -78,7 +78,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             >>> lenet = LeNet()
 
             >>> params_info = paddle.summary(lenet, (1, 1, 28, 28))
-            >>> # doctest: +SKIP
             >>> print(params_info)
             ---------------------------------------------------------------------------
             Layer (type)       Input Shape          Output Shape         Param #
@@ -103,7 +102,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
             {'total_params': 61610, 'trainable_params': 61610}
-            >>> # doctest: -SKIP
             >>> # multi input demo
             >>> class LeNetMultiInput(LeNet):
             ...     def forward(self, inputs, y):
@@ -119,7 +117,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             >>> params_info = paddle.summary(lenet_multi_input,
             ...                              [(1, 1, 28, 28), (1, 400)],
             ...                              dtypes=['float32', 'float32'])
-            >>> # doctest: +SKIP
             >>> print(params_info)
             ---------------------------------------------------------------------------
             Layer (type)       Input Shape          Output Shape         Param #
@@ -144,7 +141,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
             {'total_params': 61610, 'trainable_params': 61610}
-            >>> # doctest: -SKIP
             >>> # list input demo
             >>> class LeNetListInput(LeNet):
             ...     def forward(self, inputs):
@@ -158,7 +154,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             >>> lenet_list_input = LeNetListInput()
             >>> input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
             >>> params_info = paddle.summary(lenet_list_input, input=input_data)
-            >>> # doctest: +SKIP
             >>> print(params_info)
             ---------------------------------------------------------------------------
             Layer (type)       Input Shape          Output Shape         Param #
@@ -183,7 +178,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
             {'total_params': 61610, 'trainable_params': 61610}
-            >>> # doctest: -SKIP
             >>> # dict input demo
             >>> class LeNetDictInput(LeNet):
             ...     def forward(self, inputs):
@@ -198,7 +192,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             >>> input_data = {'x1': paddle.rand([1, 1, 28, 28]),
             ...               'x2': paddle.rand([1, 400])}
             >>> params_info = paddle.summary(lenet_dict_input, input=input_data)
-            >>> # doctest: +SKIP
             >>> print(params_info)
             ---------------------------------------------------------------------------
             Layer (type)       Input Shape          Output Shape         Param #
@@ -223,7 +216,6 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
             {'total_params': 61610, 'trainable_params': 61610}
-            >>> # doctest: -SKIP
 
     """
     if input_size is None and input is None: