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[xdoctest][task cleanup 376~381] reformat example code with google style #57853

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[xdoctest][task cleanup 376~381] reformat example code with google style #57853

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8 changes: 8 additions & 0 deletions python/paddle/distribution/bernoulli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@ class Bernoulli(exponential_family.ExponentialFamily):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
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376-381 是添加 SKIP reason，这些额外修改的原因是？

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那我恢复？

>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -156,6 +157,7 @@ def sample(self, shape):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -211,6 +213,7 @@ def rsample(self, shape, temperature=1.0):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -308,6 +311,7 @@ def cdf(self, value):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -346,6 +350,7 @@ def log_prob(self, value):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -385,6 +390,7 @@ def prob(self, value):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -415,6 +421,7 @@ def entropy(self):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down Expand Up @@ -448,6 +455,7 @@ def kl_divergence(self, other):

.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Bernoulli

Expand Down
8 changes: 7 additions & 1 deletion python/paddle/distribution/categorical.py
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Expand Up @@ -46,6 +46,7 @@ class Categorical(distribution.Distribution):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand All @@ -64,7 +65,7 @@ class Categorical(distribution.Distribution):
>>> cat = Categorical(x)
>>> cat2 = Categorical(y)

>>> # doctest: +SKIP
>>> # doctest: +SKIP('`paddle.distribution` can not run in xdoctest')
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并不是说直接 xxx can not run in xdoctest 就可以了，这样和直接空着没啥区别

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咋说呢，其实我是照着
#57676
来做的，不知道可否进一步指导一下？

>>> paddle.seed(1000) # on CPU device
>>> print(cat.sample([2,3]))
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
Expand Down Expand Up @@ -134,6 +135,7 @@ def sample(self, shape):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand Down Expand Up @@ -191,6 +193,7 @@ def kl_divergence(self, other):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand Down Expand Up @@ -245,6 +248,7 @@ def entropy(self):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand Down Expand Up @@ -289,6 +293,7 @@ def probs(self, value):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand Down Expand Up @@ -336,6 +341,7 @@ def log_prob(self, value):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution import Categorical

Expand Down
5 changes: 3 additions & 2 deletions python/paddle/distribution/gumbel.py
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Expand Up @@ -46,13 +46,14 @@ class Gumbel(TransformedDistribution):
Examples:
.. code-block:: python

>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> import paddle
>>> from paddle.distribution.gumbel import Gumbel

>>> # Gumbel distributed with loc=0, scale=1
>>> dist = Gumbel(paddle.full([1], 0.0), paddle.full([1], 1.0))

>>> # doctest: +SKIP
>>> # doctest: +SKIP('`paddle.distribution` can not run in xdoctest')
>>> print(dist.sample([2]))
Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.40484068],
Expand All @@ -63,7 +64,7 @@ class Gumbel(TransformedDistribution):
[[-0.95093185],
[ 0.32422572]])

>>> # doctest: -SKIP
>>> # doctest: -SKIP('`paddle.distribution` can not run in xdoctest')
>>> value = paddle.full([1], 0.5)
>>> print(dist.prob(value))
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
Expand Down
6 changes: 3 additions & 3 deletions python/paddle/hapi/dynamic_flops.py
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Expand Up @@ -59,7 +59,7 @@ def flops(net, input_size, custom_ops=None, print_detail=False):
... nn.Conv2D(6, 16, 5, stride=1, padding=0),
... nn.ReLU(),
... nn.MaxPool2D(2, 2))
...

... if num_classes > 0:
... self.fc = nn.Sequential(
... nn.Linear(400, 120),
Expand All @@ -73,14 +73,14 @@ def flops(net, input_size, custom_ops=None, print_detail=False):
... x = paddle.flatten(x, 1)
... x = self.fc(x)
... return x
...

>>> lenet = LeNet()
>>> # m is the instance of nn.Layer, x is the intput of layer, y is the output of layer.
>>> def count_leaky_relu(m, x, y):
... x = x[0]
... nelements = x.numel()
... m.total_ops += int(nelements)
...

>>> FLOPs = paddle.flops(lenet,
... [1, 1, 28, 28],
... custom_ops= {nn.LeakyReLU: count_leaky_relu},
Expand Down
34 changes: 17 additions & 17 deletions python/paddle/hapi/model.py
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Expand Up @@ -1140,7 +1140,7 @@ class Model:
...
... model = paddle.Model(net)
... optim = paddle.optimizer.SGD(learning_rate=1e-3, parameters=model.parameters())
...

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为什么要删？

... amp_configs = {
... "level": "O1",
... "custom_white_list": {'conv2d'},
Expand All @@ -1150,15 +1150,15 @@ class Model:
... paddle.nn.CrossEntropyLoss(),
... paddle.metric.Accuracy(),
... amp_configs=amp_configs)
...

... transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
... data = paddle.vision.datasets.MNIST(mode='train', transform=transform)
... model.fit(data, epochs=2, batch_size=32, verbose=1)
...
>>> # mixed precision training is only supported on GPU now.
>>> if paddle.is_compiled_with_cuda():
... run_example_code()
...

"""

def __init__(self, network, inputs=None, labels=None):
Expand Down Expand Up @@ -1228,7 +1228,7 @@ def train_batch(self, inputs, labels=None, update=True):
... nn.Linear(784, 200),
... nn.Tanh(),
... nn.Linear(200, 10))
...

>>> input = InputSpec([None, 784], 'float32', 'x')
>>> label = InputSpec([None, 1], 'int64', 'label')
>>> model = paddle.Model(net, input, label)
Expand Down Expand Up @@ -1282,7 +1282,7 @@ def eval_batch(self, inputs, labels=None):
... nn.Linear(784, 200),
... nn.Tanh(),
... nn.Linear(200, 10))
...

>>> input = InputSpec([None, 784], 'float32', 'x')
>>> label = InputSpec([None, 1], 'int64', 'label')
>>> model = paddle.Model(net, input, label)
Expand Down Expand Up @@ -1337,7 +1337,7 @@ def predict_batch(self, inputs):
... nn.Tanh(),
... nn.Linear(200, 10),
... nn.Softmax())
...

>>> model = paddle.Model(net, input, label)
>>> model.prepare()
>>> data = paddle.rand((1, 784), dtype="float32")
Expand Down Expand Up @@ -1467,7 +1467,7 @@ def load(self, path, skip_mismatch=False, reset_optimizer=False):
... nn.Tanh(),
... nn.Linear(200, 10),
... nn.Softmax()), input)
...

>>> model.save('checkpoint/test')
>>> model.load('checkpoint/test')

Expand Down Expand Up @@ -1558,7 +1558,7 @@ def parameters(self, *args, **kwargs):
... nn.Linear(784, 200),
... nn.Tanh(),
... nn.Linear(200, 10)), input)
...

>>> params = model.parameters()
>>> print(params)
[Parameter containing:
Expand Down Expand Up @@ -1835,7 +1835,7 @@ def fit(
>>> dynamic = True
>>> if not dynamic:
... paddle.enable_static()
...

>>> transform = T.Compose([T.Transpose(),
... T.Normalize([127.5], [127.5])])
>>> train_dataset = MNIST(mode='train', transform=transform)
Expand All @@ -1858,7 +1858,7 @@ def fit(
... epochs=2,
... batch_size=64,
... save_dir='mnist_checkpoint')
...

2. An example use DataLoader, batch size and shuffle is set in
DataLoader.

Expand All @@ -1873,7 +1873,7 @@ def fit(
>>> dynamic = True
>>> if not dynamic:
... paddle.enable_static()
...

>>> transform = T.Compose([T.Transpose(),
... T.Normalize([127.5], [127.5])])
>>> train_dataset = MNIST(mode='train', transform=transform)
Expand All @@ -1882,7 +1882,7 @@ def fit(
>>> val_dataset = MNIST(mode='test', transform=transform)
>>> val_loader = paddle.io.DataLoader(val_dataset,
... batch_size=64)
...

>>> input = InputSpec([None, 1, 28, 28], 'float32', 'image')
>>> label = InputSpec([None, 1], 'int64', 'label')

Expand All @@ -1898,7 +1898,7 @@ def fit(
... val_loader,
... epochs=2,
... save_dir='mnist_checkpoint')
...

"""
assert train_data is not None, "train_data must be given!"

Expand Down Expand Up @@ -2154,16 +2154,16 @@ def predict(
... def __init__(self, mode, return_label=True):
... super().__init__(mode=mode)
... self.return_label = return_label
...

... def __getitem__(self, idx):
... img = np.reshape(self.images[idx], [1, 28, 28])
... if self.return_label:
... return img, np.array(self.labels[idx]).astype('int64')
... return img
...

... def __len__(self):
... return len(self.images)
...

>>> test_dataset = MnistDataset(mode='test', return_label=False)

>>> # imperative mode
Expand Down Expand Up @@ -2399,7 +2399,7 @@ 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
>>> # doctest: +SKIP('`paddle.static` can not run in xdoctest')
>>> print(params_info)
---------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
Expand Down
2 changes: 1 addition & 1 deletion python/paddle/hapi/model_summary.py
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Expand Up @@ -60,7 +60,7 @@ def summary(net, input_size=None, dtypes=None, input=None):
... nn.Conv2D(6, 16, 5, stride=1, padding=0),
... nn.ReLU(),
... nn.MaxPool2D(2, 2))
...

... if num_classes > 0:
... self.fc = nn.Sequential(
... nn.Linear(400, 120),
Expand Down