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未验证 提交 9084a034 编写于 作者: L LoneRanger 提交者： GitHub
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[xdoctest] reformat example code with google style in No.1-No.5 (#55804)

上级 7ce0f9e1
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Showing with 469 addition and 463 deletion
python/paddle/batch.py
浏览文件 @ 9084a034
......@@ -35,21 +35,20 @@ def batch(reader, batch_size, drop_last=False):
Examples:
.. code-block:: python
import paddle
def reader():
for i in range(10):
yield i
batch_reader = paddle.batch(reader, batch_size=2)
>>> import paddle
>>> def reader():
... for i in range(10):
... yield i
>>> batch_reader = paddle.batch(reader, batch_size=2)
for data in batch_reader():
print(data)
# Output is
# [0, 1]
# [2, 3]
# [4, 5]
# [6, 7]
# [8, 9]
>>> for data in batch_reader():
... print(data)
...
[0, 1]
[2, 3]
[4, 5]
[6, 7]
[8, 9]
"""
def batch_reader():
......
python/paddle/dataset/image.py
浏览文件 @ 9084a034
......@@ -123,9 +123,9 @@ def load_image_bytes(bytes, is_color=True):
.. code-block:: python
with open('cat.jpg') as f:
im = load_image_bytes(f.read())
>>> with open('cat.jpg') as f:
... im = load_image_bytes(f.read())
...
:param bytes: the input image bytes array.
:type bytes: str
:param is_color: If set is_color True, it will load and
......@@ -148,7 +148,7 @@ def load_image(file, is_color=True):
.. code-block:: python
im = load_image('cat.jpg')
>>> im = load_image('cat.jpg')
:param file: the input image path.
:type file: string
......@@ -178,8 +178,8 @@ def resize_short(im, size):
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
>>> im = load_image('cat.jpg')
>>> im = resize_short(im, 256)
:param im: the input image with HWC layout.
:type im: ndarray
......@@ -208,9 +208,9 @@ def to_chw(im, order=(2, 0, 1)):
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
im = to_chw(im)
>>> im = load_image('cat.jpg')
>>> im = resize_short(im, 256)
>>> im = to_chw(im)
:param im: the input image with HWC layout.
:type im: ndarray
......@@ -230,7 +230,8 @@ def center_crop(im, size, is_color=True):
.. code-block:: python
im = center_crop(im, 224)
>>> im = load_image('cat.jpg')
>>> im = center_crop(im, 224)
:param im: the input image with HWC layout.
:type im: ndarray
......@@ -258,7 +259,8 @@ def random_crop(im, size, is_color=True):
.. code-block:: python
im = random_crop(im, 224)
>>> im = load_image('cat.jpg')
>>> im = random_crop(im, 224)
:param im: the input image with HWC layout.
:type im: ndarray
......@@ -287,7 +289,8 @@ def left_right_flip(im, is_color=True):
.. code-block:: python
im = left_right_flip(im)
>>> im = load_image('cat.jpg')
>>> im = left_right_flip(im)
:param im: input image with HWC layout or HW layout for gray image
:type im: ndarray
......@@ -311,7 +314,8 @@ def simple_transform(
.. code-block:: python
im = simple_transform(im, 256, 224, True)
>>> im = load_image('cat.jpg')
>>> im = simple_transform(im, 256, 224, True)
:param im: The input image with HWC layout.
:type im: ndarray
......@@ -365,7 +369,7 @@ def load_and_transform(
.. code-block:: python
im = load_and_transform('cat.jpg', 256, 224, True)
>>> im = load_and_transform('cat.jpg', 256, 224, True)
:param filename: The file name of input image.
:type filename: string
......
python/paddle/fft.py
浏览文件 @ 9084a034
此差异已折叠。
python/paddle/metric/metrics.py
浏览文件 @ 9084a034
......@@ -85,13 +85,13 @@ class Metric(metaclass=abc.ABCMeta):
.. code-block:: python
:name: code-compute-example
def compute(pred, label):
# sort prediction and slice the top-5 scores
pred = paddle.argsort(pred, descending=True)[:, :5]
# calculate whether the predictions are correct
correct = pred == label
return paddle.cast(correct, dtype='float32')
>>> def compute(pred, label):
... # sort prediction and slice the top-5 scores
... pred = paddle.argsort(pred, descending=True)[:, :5]
... # calculate whether the predictions are correct
... correct = pred == label
... return paddle.cast(correct, dtype='float32')
...
With the :code:`compute`, we split some calculations to OPs (which
may run on GPU devices, will be faster), and only fetch 1 tensor with
shape as [N, 5] instead of 2 tensors with shapes as [N, 10] and [N, 1].
......@@ -100,15 +100,15 @@ class Metric(metaclass=abc.ABCMeta):
.. code-block:: python
:name: code-update-example
def update(self, correct):
accs = []
for i, k in enumerate(self.topk):
num_corrects = correct[:, :k].sum()
num_samples = len(correct)
accs.append(float(num_corrects) / num_samples)
self.total[i] += num_corrects
self.count[i] += num_samples
return accs
>>> def update(self, correct):
... accs = []
... for i, k in enumerate(self.topk):
... num_corrects = correct[:, :k].sum()
... num_samples = len(correct)
... accs.append(float(num_corrects) / num_samples)
... self.total[i] += num_corrects
... self.count[i] += num_samples
... return accs
"""
def __init__(self):
......@@ -201,44 +201,45 @@ class Accuracy(Metric):
.. code-block:: python
:name: code-standalone-example
import numpy as np
import paddle
>>> import numpy as np
>>> import paddle
x = paddle.to_tensor(np.array([
[0.1, 0.2, 0.3, 0.4],
[0.1, 0.4, 0.3, 0.2],
[0.1, 0.2, 0.4, 0.3],
[0.1, 0.2, 0.3, 0.4]]))
y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
>>> x = paddle.to_tensor(np.array([
... [0.1, 0.2, 0.3, 0.4],
... [0.1, 0.4, 0.3, 0.2],
... [0.1, 0.2, 0.4, 0.3],
... [0.1, 0.2, 0.3, 0.4]]))
>>> y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
m = paddle.metric.Accuracy()
correct = m.compute(x, y)
m.update(correct)
res = m.accumulate()
print(res) # 0.75
>>> m = paddle.metric.Accuracy()
>>> correct = m.compute(x, y)
>>> m.update(correct)
>>> res = m.accumulate()
>>> print(res)
0.75
.. code-block:: python
:name: code-model-api-example
import paddle
from paddle.static import InputSpec
import paddle.vision.transforms as T
from paddle.vision.datasets import MNIST
input = InputSpec([None, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = MNIST(mode='train', transform=transform)
model = paddle.Model(paddle.vision.models.LeNet(), input, label)
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=paddle.nn.CrossEntropyLoss(),
metrics=paddle.metric.Accuracy())
model.fit(train_dataset, batch_size=64)
>>> import paddle
>>> from paddle.static import InputSpec
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import MNIST
>>> input = InputSpec([None, 1, 28, 28], 'float32', 'image')
>>> label = InputSpec([None, 1], 'int64', 'label')
>>> transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
>>> train_dataset = MNIST(mode='train', transform=transform)
>>> model = paddle.Model(paddle.vision.models.LeNet(), input, label)
>>> optim = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> model.prepare(
... optim,
... loss=paddle.nn.CrossEntropyLoss(),
... metrics=paddle.metric.Accuracy())
...
>>> model.fit(train_dataset, batch_size=64)
"""
......@@ -353,51 +354,52 @@ class Precision(Metric):
.. code-block:: python
:name: code-standalone-example
import numpy as np
import paddle
>>> import numpy as np
>>> import paddle
x = np.array([0.1, 0.5, 0.6, 0.7])
y = np.array([0, 1, 1, 1])
>>> x = np.array([0.1, 0.5, 0.6, 0.7])
>>> y = np.array([0, 1, 1, 1])
m = paddle.metric.Precision()
m.update(x, y)
res = m.accumulate()
print(res) # 1.0
>>> m = paddle.metric.Precision()
>>> m.update(x, y)
>>> res = m.accumulate()
>>> print(res)
1.0
.. code-block:: python
:name: code-model-api-example
import numpy as np
import paddle
import paddle.nn as nn
class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
def __len__(self):
return self.n
model = paddle.Model(nn.Sequential(
nn.Linear(10, 1),
nn.Sigmoid()
))
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=nn.BCELoss(),
metrics=paddle.metric.Precision())
data = Data()
model.fit(data, batch_size=16)
>>> import numpy as np
>>> import paddle
>>> import paddle.nn as nn
>>> class Data(paddle.io.Dataset):
... def __init__(self):
... super().__init__()
... self.n = 1024
... self.x = np.random.randn(self.n, 10).astype('float32')
... self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
...
... def __getitem__(self, idx):
... return self.x[idx], self.y[idx]
...
... def __len__(self):
... return self.n
...
>>> model = paddle.Model(nn.Sequential(
... nn.Linear(10, 1),
... nn.Sigmoid()
... ))
>>> optim = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> model.prepare(
... optim,
... loss=nn.BCELoss(),
... metrics=paddle.metric.Precision())
...
>>> data = Data()
>>> model.fit(data, batch_size=16)
"""
def __init__(self, name='precision', *args, **kwargs):
......@@ -484,51 +486,52 @@ class Recall(Metric):
.. code-block:: python
:name: code-standalone-example
import numpy as np
import paddle
>>> import numpy as np
>>> import paddle
x = np.array([0.1, 0.5, 0.6, 0.7])
y = np.array([1, 0, 1, 1])
>>> x = np.array([0.1, 0.5, 0.6, 0.7])
>>> y = np.array([1, 0, 1, 1])
m = paddle.metric.Recall()
m.update(x, y)
res = m.accumulate()
print(res) # 2.0 / 3.0
>>> m = paddle.metric.Recall()
>>> m.update(x, y)
>>> res = m.accumulate()
>>> print(res)
0.6666666666666666
.. code-block:: python
:name: code-model-api-example
import numpy as np
import paddle
import paddle.nn as nn
class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
def __len__(self):
return self.n
model = paddle.Model(nn.Sequential(
nn.Linear(10, 1),
nn.Sigmoid()
))
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=nn.BCELoss(),
metrics=[paddle.metric.Precision(), paddle.metric.Recall()])
data = Data()
model.fit(data, batch_size=16)
>>> import numpy as np
>>> import paddle
>>> import paddle.nn as nn
>>> class Data(paddle.io.Dataset):
... def __init__(self):
... super().__init__()
... self.n = 1024
... self.x = np.random.randn(self.n, 10).astype('float32')
... self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
...
... def __getitem__(self, idx):
... return self.x[idx], self.y[idx]
...
... def __len__(self):
... return self.n
...
>>> model = paddle.Model(nn.Sequential(
... nn.Linear(10, 1),
... nn.Sigmoid()
... ))
>>> optim = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> model.prepare(
... optim,
... loss=nn.BCELoss(),
... metrics=[paddle.metric.Precision(), paddle.metric.Recall()])
...
>>> data = Data()
>>> model.fit(data, batch_size=16)
"""
def __init__(self, name='recall', *args, **kwargs):
......@@ -624,56 +627,56 @@ class Auc(Metric):
.. code-block:: python
:name: code-standalone-example
import numpy as np
import paddle
>>> import numpy as np
>>> import paddle
m = paddle.metric.Auc()
>>> m = paddle.metric.Auc()
n = 8
class0_preds = np.random.random(size = (n, 1))
class1_preds = 1 - class0_preds
>>> n = 8
>>> class0_preds = np.random.random(size = (n, 1))
>>> class1_preds = 1 - class0_preds
preds = np.concatenate((class0_preds, class1_preds), axis=1)
labels = np.random.randint(2, size = (n, 1))
>>> preds = np.concatenate((class0_preds, class1_preds), axis=1)
>>> labels = np.random.randint(2, size = (n, 1))
m.update(preds=preds, labels=labels)
res = m.accumulate()
>>> m.update(preds=preds, labels=labels)
>>> res = m.accumulate()
.. code-block:: python
:name: code-model-api-example
import numpy as np
import paddle
import paddle.nn as nn
class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('int64')
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
def __len__(self):
return self.n
model = paddle.Model(nn.Sequential(
nn.Linear(10, 2), nn.Softmax())
)
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
def loss(x, y):
return nn.functional.nll_loss(paddle.log(x), y)
model.prepare(
optim,
loss=loss,
metrics=paddle.metric.Auc())
data = Data()
model.fit(data, batch_size=16)
>>> import numpy as np
>>> import paddle
>>> import paddle.nn as nn
>>> class Data(paddle.io.Dataset):
... def __init__(self):
... super().__init__()
... self.n = 1024
... self.x = np.random.randn(self.n, 10).astype('float32')
... self.y = np.random.randint(2, size=(self.n, 1)).astype('int64')
...
... def __getitem__(self, idx):
... return self.x[idx], self.y[idx]
...
... def __len__(self):
... return self.n
...
>>> model = paddle.Model(nn.Sequential(
... nn.Linear(10, 2), nn.Softmax())
... )
>>> optim = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
...
>>> def loss(x, y):
... return nn.functional.nll_loss(paddle.log(x), y)
...
>>> model.prepare(
... optim,
... loss=loss,
... metrics=paddle.metric.Auc())
>>> data = Data()
>>> model.fit(data, batch_size=16)
"""
def __init__(
......@@ -789,12 +792,14 @@ def accuracy(input, label, k=1, correct=None, total=None, name=None):
Examples:
.. code-block:: python
import paddle
>>> import paddle
predictions = paddle.to_tensor([[0.2, 0.1, 0.4, 0.1, 0.1], [0.2, 0.3, 0.1, 0.15, 0.25]], dtype='float32')
label = paddle.to_tensor([[2], [0]], dtype="int64")
result = paddle.metric.accuracy(input=predictions, label=label, k=1)
# 0.5
>>> predictions = paddle.to_tensor([[0.2, 0.1, 0.4, 0.1, 0.1], [0.2, 0.3, 0.1, 0.15, 0.25]], dtype='float32')
>>> label = paddle.to_tensor([[2], [0]], dtype="int64")
>>> result = paddle.metric.accuracy(input=predictions, label=label, k=1)
>>> print(result)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.50000000)
"""
if label.dtype == paddle.int32:
label = paddle.cast(label, paddle.int64)
......
python/paddle/onnx/export.py
浏览文件 @ 9084a034
......@@ -46,45 +46,46 @@ def export(layer, path, input_spec=None, opset_version=9, **configs):
Examples:
.. code-block:: python
import paddle
class LinearNet(paddle.nn.Layer):
def __init__(self):
super().__init__()
self._linear = paddle.nn.Linear(128, 10)
def forward(self, x):
return self._linear(x)
# Export model with 'InputSpec' to support dynamic input shape.
def export_linear_net():
model = LinearNet()
x_spec = paddle.static.InputSpec(shape=[None, 128], dtype='float32')
paddle.onnx.export(model, 'linear_net', input_spec=[x_spec])
export_linear_net()
class Logic(paddle.nn.Layer):
def __init__(self):
super().__init__()
def forward(self, x, y, z):
if z:
return x
else:
return y
# Export model with 'Tensor' to support pruned model by set 'output_spec'.
def export_logic():
model = Logic()
x = paddle.to_tensor([1])
y = paddle.to_tensor([2])
# Static and run model.
paddle.jit.to_static(model)
out = model(x, y, z=True)
paddle.onnx.export(model, 'pruned', input_spec=[x], output_spec=[out])
export_logic()
>>> import paddle
>>> class LinearNet(paddle.nn.Layer):
... def __init__(self):
... super().__init__()
... self._linear = paddle.nn.Linear(128, 10)
...
... def forward(self, x):
... return self._linear(x)
...
>>> # Export model with 'InputSpec' to support dynamic input shape.
>>> def export_linear_net():
... model = LinearNet()
... x_spec = paddle.static.InputSpec(shape=[None, 128], dtype='float32')
... paddle.onnx.export(model, 'linear_net', input_spec=[x_spec])
...
>>> # doctest: +SKIP('raise ImportError')
>>> export_linear_net()
>>> class Logic(paddle.nn.Layer):
... def __init__(self):
... super().__init__()
...
... def forward(self, x, y, z):
... if z:
... return x
... else:
... return y
...
>>> # Export model with 'Tensor' to support pruned model by set 'output_spec'.
>>> def export_logic():
... model = Logic()
... x = paddle.to_tensor([1])
... y = paddle.to_tensor([2])
... # Static and run model.
... paddle.jit.to_static(model)
... out = model(x, y, z=True)
... paddle.onnx.export(model, 'pruned', input_spec=[x], output_spec=[out])
...
>>> export_logic()
"""
p2o = try_import('paddle2onnx')
......
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