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未验证 提交 14b1374f 编写于 作者: C cyberslack_lee 提交者： GitHub
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[xdoctest] reformat example code with google style in No.65-68 (#55953) 

* test=docs_preview

* test=docs_preview

* test=docs_preview

* test=docs_preview

* fix indent in math.py

* test=docs_preview

* test=docs_preview

* test=docs_preview

* test=docs_preview

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>
上级 f16e1869
展开全部 隐藏空白更改
内联 并排
Showing with 663 addition and 544 deletion
python/paddle/hapi/dynamic_flops.py
浏览文件 @ 14b1374f
......@@ -45,63 +45,68 @@ def flops(net, input_size, custom_ops=None, print_detail=False):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
class LeNet(nn.Layer):
def __init__(self, num_classes=10):
super().__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2D(2, 2),
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),
nn.Linear(120, 84),
nn.Linear(
84, 10))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
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},
print_detail=True)
print(FLOPs)
#+--------------+-----------------+-----------------+--------+--------+
#| Layer Name | Input Shape | Output Shape | Params | Flops |
#+--------------+-----------------+-----------------+--------+--------+
#| conv2d_2 | [1, 1, 28, 28] | [1, 6, 28, 28] | 60 | 47040 |
#| re_lu_2 | [1, 6, 28, 28] | [1, 6, 28, 28] | 0 | 0 |
#| max_pool2d_2 | [1, 6, 28, 28] | [1, 6, 14, 14] | 0 | 0 |
#| conv2d_3 | [1, 6, 14, 14] | [1, 16, 10, 10] | 2416 | 241600 |
#| re_lu_3 | [1, 16, 10, 10] | [1, 16, 10, 10] | 0 | 0 |
#| max_pool2d_3 | [1, 16, 10, 10] | [1, 16, 5, 5] | 0 | 0 |
#| linear_0 | [1, 400] | [1, 120] | 48120 | 48000 |
#| linear_1 | [1, 120] | [1, 84] | 10164 | 10080 |
#| linear_2 | [1, 84] | [1, 10] | 850 | 840 |
#+--------------+-----------------+-----------------+--------+--------+
#Total Flops: 347560 Total Params: 61610
>>> import paddle
>>> import paddle.nn as nn
>>> class LeNet(nn.Layer):
... def __init__(self, num_classes=10):
... super().__init__()
... self.num_classes = num_classes
... self.features = nn.Sequential(
... nn.Conv2D(1, 6, 3, stride=1, padding=1),
... nn.ReLU(),
... nn.MaxPool2D(2, 2),
... 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),
... nn.Linear(120, 84),
... nn.Linear(84, 10))
...
... def forward(self, inputs):
... x = self.features(inputs)
...
... if self.num_classes > 0:
... 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},
... 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
Cannot find suitable count function for <class 'paddle.nn.layer.pooling.MaxPool2D'>. Treat it as zero FLOPs.
<class 'paddle.nn.layer.common.Linear'>'s flops has been counted
+--------------+-----------------+-----------------+--------+--------+
| Layer Name | Input Shape | Output Shape | Params | Flops |
+--------------+-----------------+-----------------+--------+--------+
| conv2d_0 | [1, 1, 28, 28] | [1, 6, 28, 28] | 60 | 47040 |
| re_lu_0 | [1, 6, 28, 28] | [1, 6, 28, 28] | 0 | 0 |
| max_pool2d_0 | [1, 6, 28, 28] | [1, 6, 14, 14] | 0 | 0 |
| conv2d_1 | [1, 6, 14, 14] | [1, 16, 10, 10] | 2416 | 241600 |
| re_lu_1 | [1, 16, 10, 10] | [1, 16, 10, 10] | 0 | 0 |
| max_pool2d_1 | [1, 16, 10, 10] | [1, 16, 5, 5] | 0 | 0 |
| linear_0 | [1, 400] | [1, 120] | 48120 | 48000 |
| linear_1 | [1, 120] | [1, 84] | 10164 | 10080 |
| linear_2 | [1, 84] | [1, 10] | 850 | 840 |
+--------------+-----------------+-----------------+--------+--------+
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,
......
python/paddle/hapi/hub.py
浏览文件 @ 14b1374f
......@@ -130,7 +130,7 @@ def _get_cache_or_reload(repo, force_reload, verbose=True, source='github'):
_remove_if_exists(cached_file)
_remove_if_exists(repo_dir)
# rename the repo
# Rename the repo
shutil.move(extracted_repo, repo_dir)
return repo_dir
......@@ -177,24 +177,24 @@ def list(repo_dir, source='github', force_reload=False):
List all entrypoints available in `github` hubconf.
Args:
repo_dir(str): github or local path.
repo_dir(str): Github or local path.
github path (str): a str with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
- github path (str): A string with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
- local path (str): Local repo path.
local path (str): local repo path
source (str): `github` | `gitee` | `local`. Default is `github`.
force_reload (bool, optional): Whether to discard the existing cache and force a fresh download. Default is `False`.
source (str): `github` | `gitee` | `local`, default is `github`.
force_reload (bool, optional): whether to discard the existing cache and force a fresh download, default is `False`.
Returns:
entrypoints: a list of available entrypoint names
entrypoints: A list of available entrypoint names.
Example:
Examples:
.. code-block:: python
import paddle
>>> import paddle
paddle.hub.list('lyuwenyu/paddlehub_demo:main', source='github', force_reload=False)
>>> paddle.hub.list('lyuwenyu/paddlehub_demo:main', source='github', force_reload=False)
"""
if source not in ('github', 'gitee', 'local'):
......@@ -225,25 +225,25 @@ def help(repo_dir, model, source='github', force_reload=False):
Show help information of model
Args:
repo_dir(str): github or local path.
repo_dir(str): Github or local path.
github path (str): a str with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
- github path (str): A string with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
local path (str): Local repo path.
local path (str): local repo path.
model (str): Model name.
source (str): `github` | `gitee` | `local`. Default is `github`.
force_reload (bool, optional): Default is `False`.
model (str): model name.
source (str): `github` | `gitee` | `local`, default is `github`.
force_reload (bool, optional): default is `False`.
Return:
Returns:
docs
Example:
Examples:
.. code-block:: python
import paddle
>>> import paddle
paddle.hub.help('lyuwenyu/paddlehub_demo:main', model='MM', source='github')
>>> paddle.hub.help('lyuwenyu/paddlehub_demo:main', model='MM', source='github')
"""
if source not in ('github', 'gitee', 'local'):
......@@ -270,24 +270,25 @@ def load(repo_dir, model, source='github', force_reload=False, **kwargs):
Load model
Args:
repo_dir(str): github or local path.
repo_dir(str): Github or local path.
- github path (str): A string with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
- local path (str): Local repo path.
github path (str): a str with format "repo_owner/repo_name[:tag_name]" with an optional
tag/branch. The default branch is `main` if not specified.
model (str): Model name.
source (str): `github` | `gitee` | `local`. Default is `github`.
force_reload (bool, optional): Default is `False`.
**kwargs: Parameters using for model.
local path (str): local repo path.
Returns:
paddle model.
model (str): model name.
source (str): `github` | `gitee` | `local`, default is `github`.
force_reload (bool, optional): default is `False`.
**kwargs: parameters using for model
Return:
paddle model
Example:
Examples:
.. code-block:: python
import paddle
paddle.hub.load('lyuwenyu/paddlehub_demo:main', model='MM', source='github')
>>> import paddle
>>> paddle.hub.load('lyuwenyu/paddlehub_demo:main', model='MM', source='github')
"""
if source not in ('github', 'gitee', 'local'):
......
python/paddle/hapi/model.py
浏览文件 @ 14b1374f
此差异已折叠。
python/paddle/hapi/model_summary.py
浏览文件 @ 14b1374f
......@@ -30,108 +30,200 @@ def summary(net, input_size=None, dtypes=None, input=None):
"""Prints a string summary of the network.
Args:
net (Layer): the network which must be a subinstance of Layer.
input_size (tuple|InputSpec|list[tuple|InputSpec], optional): size of input tensor. if model only
net (Layer): The network which must be a subinstance of Layer.
input_size (tuple|InputSpec|list[tuple|InputSpec], optional): Size of input tensor. if model only
have one input, input_size can be tuple or InputSpec. if model
have multiple input, input_size must be a list which contain
every input's shape. Note that input_size only dim of
batch_size can be None or -1. Default: None. Note that
input_size and input cannot be None at the same time.
dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
input: the input tensor. if input is given, input_size and dtype will be ignored, Default: None.
dtypes (str, optional): If dtypes is None, 'float32' will be used, Default: None.
input (Tensor, optional): If input is given, input_size and dtype will be ignored, Default: None.
Returns:
Dict: a summary of the network including total params and total trainable params.
Dict: A summary of the network including total params and total trainable params.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
class LeNet(nn.Layer):
def __init__(self, num_classes=10):
super().__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2D(2, 2),
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),
nn.Linear(120, 84),
nn.Linear(
84, 10))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
lenet = LeNet()
params_info = paddle.summary(lenet, (1, 1, 28, 28))
print(params_info)
# multi input demo
class LeNetMultiInput(LeNet):
def forward(self, inputs, y):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + y)
return x
lenet_multi_input = LeNetMultiInput()
params_info = paddle.summary(lenet_multi_input, [(1, 1, 28, 28), (1, 400)],
dtypes=['float32', 'float32'])
print(params_info)
# list input demo
class LeNetListInput(LeNet):
def forward(self, inputs):
x = self.features(inputs[0])
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + inputs[1])
return x
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)
print(params_info)
# dict input demo
class LeNetDictInput(LeNet):
def forward(self, inputs):
x = self.features(inputs['x1'])
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + inputs['x2'])
return x
lenet_dict_input = LeNetDictInput()
input_data = {'x1': paddle.rand([1, 1, 28, 28]),
'x2': paddle.rand([1, 400])}
params_info = paddle.summary(lenet_dict_input, input=input_data)
print(params_info)
>>> import paddle
>>> import paddle.nn as nn
>>> paddle.seed(2023)
>>> class LeNet(nn.Layer):
... def __init__(self, num_classes=10):
... super().__init__()
... self.num_classes = num_classes
... self.features = nn.Sequential(
... nn.Conv2D(1, 6, 3, stride=1, padding=1),
... nn.ReLU(),
... nn.MaxPool2D(2, 2),
... 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),
... nn.Linear(120, 84),
... nn.Linear(84, 10))
...
... def forward(self, inputs):
... x = self.features(inputs)
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x)
... return x
...
>>> lenet = LeNet()
>>> params_info = paddle.summary(lenet, (1, 1, 28, 28))
>>> # doctest: +SKIP
>>> print(params_info)
---------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===========================================================================
Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
Linear-1 [[1, 400]] [1, 120] 48,120
Linear-2 [[1, 120]] [1, 84] 10,164
Linear-3 [[1, 84]] [1, 10] 850
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
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):
... x = self.features(inputs)
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + y)
... return x
...
>>> lenet_multi_input = LeNetMultiInput()
>>> 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 #
===========================================================================
Conv2D-3 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
ReLU-3 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
MaxPool2D-3 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
Conv2D-4 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
ReLU-4 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
MaxPool2D-4 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
Linear-4 [[1, 400]] [1, 120] 48,120
Linear-5 [[1, 120]] [1, 84] 10,164
Linear-6 [[1, 84]] [1, 10] 850
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # doctest: -SKIP
>>> # list input demo
>>> class LeNetListInput(LeNet):
... def forward(self, inputs):
... x = self.features(inputs[0])
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + inputs[1])
... return x
...
>>> 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 #
===========================================================================
Conv2D-5 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
ReLU-5 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
MaxPool2D-5 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
Conv2D-6 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
ReLU-6 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
MaxPool2D-6 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
Linear-7 [[1, 400]] [1, 120] 48,120
Linear-8 [[1, 120]] [1, 84] 10,164
Linear-9 [[1, 84]] [1, 10] 850
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # doctest: -SKIP
>>> # dict input demo
>>> class LeNetDictInput(LeNet):
... def forward(self, inputs):
... x = self.features(inputs['x1'])
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + inputs['x2'])
... return x
...
>>> lenet_dict_input = LeNetDictInput()
>>> 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 #
===========================================================================
Conv2D-7 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
ReLU-7 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
MaxPool2D-7 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
Conv2D-8 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
ReLU-8 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
MaxPool2D-8 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
Linear-10 [[1, 400]] [1, 120] 48,120
Linear-11 [[1, 120]] [1, 84] 10,164
Linear-12 [[1, 84]] [1, 10] 850
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # doctest: -SKIP
"""
if input_size is None and input is None:
......
python/paddle/tensor/math.py
浏览文件 @ 14b1374f
......@@ -1617,7 +1617,7 @@ def nansum(x, axis=None, dtype=None, keepdim=False, name=None):
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle
>>> # x is a Tensor with following elements:
>>> # [[nan, 0.3, 0.5, 0.9]
......
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