From b23d41ca5e711356ce4892da1ac6333456d62d67 Mon Sep 17 00:00:00 2001
From: LielinJiang <50691816+LielinJiang@users.noreply.github.com>
Date: Fri, 11 Sep 2020 20:46:14 +0800
Subject: [PATCH] Add some hapi cn docs (#2588)

* add some hapi cn docs
---
 .../api/paddle/hapi/callbacks/Callback_cn.rst | 26 +++++++++
 .../hapi/callbacks/ModelCheckpoint_cn.rst     | 36 ++++++++++++
 .../hapi/callbacks/ProgBarLogger_cn.rst       | 36 ++++++++++++
 doc/paddle/api/paddle/hapi/summary_cn.rst     | 58 +++++++++++++++++++
 .../utils/get_weights_path_from_url_cn.rst    | 24 ++++++++
 .../vision/datasets/DatasetFolder_cn.rst      | 54 +++++++++++++++++
 .../paddle/vision/datasets/ImageFolder_cn.rst | 50 ++++++++++++++++
 .../api/paddle/vision/models/LeNet_cn.rst     | 26 +++++++++
 .../paddle/vision/models/MobileNetV1_cn.rst   | 29 ++++++++++
 .../paddle/vision/models/MobileNetV2_cn.rst   | 29 ++++++++++
 .../api/paddle/vision/models/ResNet_cn.rst    | 33 +++++++++++
 .../api/paddle/vision/models/VGG_cn.rst       | 33 +++++++++++
 .../paddle/vision/models/mobilenet_v1_cn.rst  | 35 +++++++++++
 .../paddle/vision/models/mobilenet_v2_cn.rst  | 35 +++++++++++
 .../api/paddle/vision/models/resnet101_cn.rst | 31 ++++++++++
 .../api/paddle/vision/models/resnet152_cn.rst | 31 ++++++++++
 .../api/paddle/vision/models/resnet18_cn.rst  | 31 ++++++++++
 .../api/paddle/vision/models/resnet34_cn.rst  | 31 ++++++++++
 .../api/paddle/vision/models/resnet50_cn.rst  | 31 ++++++++++
 .../api/paddle/vision/models/vgg11_cn.rst     | 32 ++++++++++
 .../api/paddle/vision/models/vgg13_cn.rst     | 32 ++++++++++
 .../api/paddle/vision/models/vgg16_cn.rst     | 32 ++++++++++
 .../api/paddle/vision/models/vgg19_cn.rst     | 32 ++++++++++
 23 files changed, 787 insertions(+)
 create mode 100644 doc/paddle/api/paddle/hapi/callbacks/Callback_cn.rst
 create mode 100644 doc/paddle/api/paddle/hapi/callbacks/ModelCheckpoint_cn.rst
 create mode 100644 doc/paddle/api/paddle/hapi/callbacks/ProgBarLogger_cn.rst
 create mode 100644 doc/paddle/api/paddle/hapi/summary_cn.rst
 create mode 100644 doc/paddle/api/paddle/utils/get_weights_path_from_url_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/datasets/DatasetFolder_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/datasets/ImageFolder_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/LeNet_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/MobileNetV1_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/MobileNetV2_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/ResNet_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/VGG_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/mobilenet_v1_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/mobilenet_v2_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/resnet101_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/resnet152_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/resnet18_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/resnet34_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/resnet50_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/vgg11_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/vgg13_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/vgg16_cn.rst
 create mode 100644 doc/paddle/api/paddle/vision/models/vgg19_cn.rst

diff --git a/doc/paddle/api/paddle/hapi/callbacks/Callback_cn.rst b/doc/paddle/api/paddle/hapi/callbacks/Callback_cn.rst
new file mode 100644
index 000000000..45eadc4c6
--- /dev/null
+++ b/doc/paddle/api/paddle/hapi/callbacks/Callback_cn.rst
@@ -0,0 +1,26 @@
+.. _cn_api_paddle_callbacks_Callback:
+
+Callback
+-------------------------------
+
+.. py:class:: paddle.callbacks.Callback()
+
+ ``Callback`` 是一个基类，用于实现用户自定义的callback。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+
+    # build a simple model checkpoint callback
+    class ModelCheckpoint(paddle.callbacks.Callback):
+        def __init__(self, save_freq=1, save_dir=None):
+            self.save_freq = save_freq
+            self.save_dir = save_dir
+
+        def on_epoch_end(self, epoch, logs=None):
+            if self.model is not None and epoch % self.save_freq == 0:
+                path = '{}/{}'.format(self.save_dir, epoch)
+                print('save checkpoint at {}'.format(path))
+                self.model.save(path)
\ No newline at end of file
diff --git a/doc/paddle/api/paddle/hapi/callbacks/ModelCheckpoint_cn.rst b/doc/paddle/api/paddle/hapi/callbacks/ModelCheckpoint_cn.rst
new file mode 100644
index 000000000..a8b304a6d
--- /dev/null
+++ b/doc/paddle/api/paddle/hapi/callbacks/ModelCheckpoint_cn.rst
@@ -0,0 +1,36 @@
+.. _cn_api_paddle_callbacks_ModelCheckpoint:
+
+ModelCheckpoint
+-------------------------------
+
+.. py:class:: paddle.callbacks.ModelCheckpoint(save_freq=1, save_dir=None)
+
+ ``ModelCheckpoint`` 是一个日志回调类。
+
+参数：
+  - **save_freq** (int，可选) - 间隔多少个epoch保存模型。默认值：1。 
+  - **save_dir** (int，可选) - 保存模型的文件夹。如果不设定，将不会保存模型。默认值：None。 
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.static import InputSpec
+
+    inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+    labels = [InputSpec([None, 1], 'int64', 'label')]
+
+    train_dataset = paddle.vision.datasets.MNIST(mode='train')
+
+    model = paddle.Model(paddle.vision.LeNet(classifier_activation=None),
+        inputs, labels)
+
+    optim = paddle.optimizer.Adam(0.001)
+    model.prepare(optimizer=optim,
+                loss=paddle.nn.CrossEntropyLoss(),
+                metrics=paddle.metric.Accuracy())
+
+    callback = paddle.callbacks.ModelCheckpoint(save_dir='./temp')
+    model.fit(train_dataset, batch_size=64, callbacks=callback)
\ No newline at end of file
diff --git a/doc/paddle/api/paddle/hapi/callbacks/ProgBarLogger_cn.rst b/doc/paddle/api/paddle/hapi/callbacks/ProgBarLogger_cn.rst
new file mode 100644
index 000000000..e90cc940f
--- /dev/null
+++ b/doc/paddle/api/paddle/hapi/callbacks/ProgBarLogger_cn.rst
@@ -0,0 +1,36 @@
+.. _cn_api_paddle_callbacks_ProgBarLogger:
+
+ProgBarLogger
+-------------------------------
+
+.. py:class:: paddle.callbacks.ProgBarLogger(log_freq=1, verbose=2)
+
+ ``ProgBarLogger`` 是一个日志回调类。
+
+参数：
+  - **log_freq** (int，可选) - 损失值和指标打印的频率。默认值：1。
+  - **verbose** (int，可选) - 打印信息的模式。设置为0时，不打印信息；设置为1时，使用进度条的形式打印信息；是指为2时，使用行的形式打印信息。默认值：2。
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.static import InputSpec
+
+    inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+    labels = [InputSpec([None, 1], 'int64', 'label')]
+
+    train_dataset = paddle.vision.datasets.MNIST(mode='train')
+
+    model = paddle.Model(paddle.vision.LeNet(classifier_activation=None),
+        inputs, labels)
+
+    optim = paddle.optimizer.Adam(0.001)
+    model.prepare(optimizer=optim,
+                loss=paddle.nn.CrossEntropyLoss(),
+                metrics=paddle.metric.Accuracy())
+
+    callback = paddle.callbacks.ProgBarLogger(log_freq=10)
+    model.fit(train_dataset, batch_size=64, callbacks=callback)
\ No newline at end of file
diff --git a/doc/paddle/api/paddle/hapi/summary_cn.rst b/doc/paddle/api/paddle/hapi/summary_cn.rst
new file mode 100644
index 000000000..1505fba3c
--- /dev/null
+++ b/doc/paddle/api/paddle/hapi/summary_cn.rst
@@ -0,0 +1,58 @@
+.. _cn_api_paddle_summary:
+
+summary
+-------------------------------
+
+.. py:function:: paddle.summary(net, input_size, batch_size=None, dtypes=None)
+
+ ``summary`` 函数能够打印网络的基础结构和参数信息。
+
+参数：
+  - **net** (Layer) - 网络实例，必须是 ``Layer`` 的子类。
+  - **input_size** (tuple|InputSpec|list[tuple|InputSpec) - 输入张量的大小。如果网络只有一个输入，那么该值需要设定为tuple或InputSpec。如果模型有多个输入。那么该值需要设定为list[tuple|InputSpec]，包含每个输入的shape。
+  - **batch_size** (int，可选) - 输入张量的批大小。默认值：None。
+  - **dtypes** (str，可选) - 输入张量的数据类型，如果没有给定，默认使用 ``float32`` 类型。默认值：None。
+
+返回：字典，包含了总的参数量和总的可训练的参数量。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    import paddle.nn as nn
+
+    class LeNet(nn.Layer):
+        def __init__(self, num_classes=10):
+            super(LeNet, self).__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, 28, 28))
+    print(params_info)
+
diff --git a/doc/paddle/api/paddle/utils/get_weights_path_from_url_cn.rst b/doc/paddle/api/paddle/utils/get_weights_path_from_url_cn.rst
new file mode 100644
index 000000000..ce9fd2e01
--- /dev/null
+++ b/doc/paddle/api/paddle/utils/get_weights_path_from_url_cn.rst
@@ -0,0 +1,24 @@
+.. _cn_api_paddle_utils_download_get_weights_path_from_url:
+
+get_weights_path_from_url
+-------------------------------
+
+.. py:function:: paddle.utils.download.get_weights_path_from_url(url, md5sum=None)
+
+ 从 ``WEIGHT_HOME`` 文件夹获取权重，如果不存在，就从url下载
+
+参数：
+  - **url** (str) - 下载的链接。
+  - **md5sum** (str，可选) - 下载文件的md5值。默认值：None。
+
+返回：权重的本地路径。
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    from paddle.utils.download import get_weights_path_from_url
+
+    resnet18_pretrained_weight_url = 'https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams'
+    local_weight_path = get_weights_path_from_url(resnet18_pretrained_weight_url)
diff --git a/doc/paddle/api/paddle/vision/datasets/DatasetFolder_cn.rst b/doc/paddle/api/paddle/vision/datasets/DatasetFolder_cn.rst
new file mode 100644
index 000000000..89259a14d
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/datasets/DatasetFolder_cn.rst
@@ -0,0 +1,54 @@
+.. _cn_api_paddle_vision_datasets_DatasetFolder:
+
+DatasetFolder
+-------------------------------
+
+.. py:class:: paddle.vision.datasets.DatasetFolder(root, loader=None, extensions=None, transform=None, is_valid_file=None)
+
+ 一种通用的数据加载方式，当输入以如下的格式存放时：
+ root/class_a/1.ext
+ root/class_a/2.ext
+ root/class_a/3.ext
+
+ root/class_b/123.ext
+ root/class_b/456.ext
+ root/class_b/789.ext
+
+参数：
+  - **root** (str) - 根目录路径。
+  - **loader** (callable，可选) - 可以加载数据路径的一个函数，如果该值没有设定，默认使用 ``cv2.imread``  。默认值：None。
+  - **extensions** (tuple[str]，可选) - 允许的数据后缀列表，如果该值没有设定，默认使用 ``('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')`` 。默认值：None。
+  - **transform** (callable，可选) - 数据增强函数。默认值：None。
+  - **is_valid_file** (callable，可选) - 根据每条数据的路径来判断是否合法的一个函数。默认值：None。
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    import os
+    import cv2
+    import tempfile
+    import shutil
+    import numpy as np
+    from paddle.vision.datasets import DatasetFolder
+
+    def make_fake_dir():
+        data_dir = tempfile.mkdtemp()
+
+        for i in range(2):
+            sub_dir = os.path.join(data_dir, 'class_' + str(i))
+            if not os.path.exists(sub_dir):
+                os.makedirs(sub_dir)
+            for j in range(2):
+                fake_img = (np.random.random((32, 32, 3)) * 255).astype('uint8')
+                cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
+        return data_dir
+
+    temp_dir = make_fake_dir()
+    data_folder = DatasetFolder(temp_dir)
+
+    for items in data_folder:
+        break
+        
+    shutil.rmtree(temp_dir)
diff --git a/doc/paddle/api/paddle/vision/datasets/ImageFolder_cn.rst b/doc/paddle/api/paddle/vision/datasets/ImageFolder_cn.rst
new file mode 100644
index 000000000..37571e124
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/datasets/ImageFolder_cn.rst
@@ -0,0 +1,50 @@
+.. _cn_api_paddle_vision_datasets_ImageFolder:
+
+ImageFolder
+-------------------------------
+
+.. py:class:: paddle.vision.datasets.ImageFolder(root, loader=None, extensions=None, transform=None, is_valid_file=None)
+
+ 一种通用的数据加载方式，当输入以如下的格式存放时：
+ root/1.ext
+ root/2.ext
+ root/sub_dir/3.ext
+
+参数：
+  - **root** (str) - 根目录路径。
+  - **loader** (callable，可选) - 可以加载数据路径的一个函数，如果该值没有设定，默认使用 ``cv2.imread``  。默认值：None。
+  - **extensions** (tuple[str]，可选) - 允许的数据后缀列表，如果该值没有设定，默认使用 ``('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')`` 。默认值：None。
+  - **transform** (callable，可选) - 数据增强函数。默认值：None。
+  - **is_valid_file** (callable，可选) - 根据每条数据的路径来判断是否合法的一个函数。默认值：None。
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    import os
+    import cv2
+    import tempfile
+    import shutil
+    import numpy as np
+    from paddle.vision.datasets import ImageFolder
+
+    def make_fake_dir():
+        data_dir = tempfile.mkdtemp()
+
+        for i in range(2):
+            sub_dir = os.path.join(data_dir, 'class_' + str(i))
+            if not os.path.exists(sub_dir):
+                os.makedirs(sub_dir)
+            for j in range(2):
+                fake_img = (np.random.random((32, 32, 3)) * 255).astype('uint8')
+                cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
+        return data_dir
+
+    temp_dir = make_fake_dir()
+    data_folder = ImageFolder(temp_dir)
+
+    for items in data_folder:
+        break
+        
+    shutil.rmtree(temp_dir)
diff --git a/doc/paddle/api/paddle/vision/models/LeNet_cn.rst b/doc/paddle/api/paddle/vision/models/LeNet_cn.rst
new file mode 100644
index 000000000..2aa28af12
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/LeNet_cn.rst
@@ -0,0 +1,26 @@
+.. _cn_api_paddle_vision_models_LeNet:
+
+LeNet
+-------------------------------
+
+.. py:class:: paddle.vision.models.LeNet(num_classes=10)
+
+ LeNet模型，来自论文`"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_。
+
+参数：
+  - **num_classes** (int，可选) - 最后一个全连接层输出的维度。默认值：10。
+
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import LeNet
+
+    model = LeNet()
+
+    x = paddle.rand([1, 1, 28, 28])
+    out = model(x)
+
+    print(out.shape)
\ No newline at end of file
diff --git a/doc/paddle/api/paddle/vision/models/MobileNetV1_cn.rst b/doc/paddle/api/paddle/vision/models/MobileNetV1_cn.rst
new file mode 100644
index 000000000..67a4cae63
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/MobileNetV1_cn.rst
@@ -0,0 +1,29 @@
+.. _cn_api_paddle_vision_models_MobileNetV1:
+
+MobileNetV1
+-------------------------------
+
+.. py:class:: paddle.vision.models.MobileNetV1(scale=1.0, num_classes=1000, with_pool=True)
+
+ MobileNetV1模型，来自论文`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_。
+
+参数：
+  - **scale** (float，可选) - 模型通道数的缩放比例。默认值：1.0。
+  - **num_classes** (int, 可选) - 最后一个全连接层输出的维度。如果该值小于0，则不定义最后一个全连接层。默认值：1000。
+  - **with_pool** (bool，可选) - 是否定义最后一个全连接层之前的池化层。默认值：True。
+
+返回：mobilenetv1模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import MobileNetV1
+
+    model = MobileNetV1()
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/MobileNetV2_cn.rst b/doc/paddle/api/paddle/vision/models/MobileNetV2_cn.rst
new file mode 100644
index 000000000..56c4066fa
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/MobileNetV2_cn.rst
@@ -0,0 +1,29 @@
+.. _cn_api_paddle_vision_models_MobileNetV2:
+
+MobileNetV2
+-------------------------------
+
+.. py:class:: paddle.vision.models.MobileNetV2(scale=1.0, num_classes=1000, with_pool=True)
+
+ MobileNetV2模型，来自论文`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_。
+
+参数：
+  - **scale** (float，可选) - 模型通道数的缩放比例。默认值：1.0。
+  - **num_classes** (int, 可选) - 最后一个全连接层输出的维度。如果该值小于0，则不定义最后一个全连接层。默认值：1000。
+  - **with_pool** (bool，可选) - 是否定义最后一个全连接层之前的池化层。默认值：True。
+
+返回：mobilenetv2模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import MobileNetV2
+
+    model = MobileNetV2()
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/ResNet_cn.rst b/doc/paddle/api/paddle/vision/models/ResNet_cn.rst
new file mode 100644
index 000000000..dde8845e5
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/ResNet_cn.rst
@@ -0,0 +1,33 @@
+.. _cn_api_paddle_vision_models_ResNet:
+
+ResNet
+-------------------------------
+
+.. py:class:: paddle.vision.models.ResNet(Block, depth=50, num_classes=1000, with_pool=True)
+
+ ResNet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **Block** (BasicBlock|BottleneckBlock) - 模型的残差模块。
+  - **depth** (int，可选) - resnet模型的深度。默认值：50
+  - **num_classes** (int, 可选) - 最后一个全连接层输出的维度。如果该值小于0，则不定义最后一个全连接层。默认值：1000。
+  - **with_pool** (bool，可选) - 是否定义最后一个全连接层之前的池化层。默认值：True。
+
+返回：ResNet模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import ResNet
+    from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
+
+    resnet50 = ResNet(BottleneckBlock, 50)
+
+    resnet18 = ResNet(BasicBlock, 18)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = resnet18(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/VGG_cn.rst b/doc/paddle/api/paddle/vision/models/VGG_cn.rst
new file mode 100644
index 000000000..21147c244
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/VGG_cn.rst
@@ -0,0 +1,33 @@
+.. _cn_api_paddle_vision_models_VGG:
+
+VGG
+-------------------------------
+
+.. py:class:: paddle.vision.models.VGG(features, num_classes=1000)
+
+ VGG模型，来自论文`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_。
+
+参数：
+  - **features** (Layer) - vgg模型的特征层。由函数make_layers产生。
+  - **num_classes** (int, 可选) - 最后一个全连接层输出的维度。如果该值小于0，则不定义最后一个全连接层。默认值：1000。
+
+返回：vgg模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import VGG
+    from paddle.vision.models.vgg import make_layers
+
+    vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
+
+    features = make_layers(vgg11_cfg)
+
+    vgg11 = VGG(features)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = vgg11(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/mobilenet_v1_cn.rst b/doc/paddle/api/paddle/vision/models/mobilenet_v1_cn.rst
new file mode 100644
index 000000000..83a49508c
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/mobilenet_v1_cn.rst
@@ -0,0 +1,35 @@
+.. _cn_api_paddle_vision_models_mobilenet_v1:
+
+mobilenet_v1
+-------------------------------
+
+.. py:function:: paddle.vision.models.mobilenet_v1(pretrained=False, scale=1.0, **kwargs)
+
+ MobileNetV1模型，来自论文`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **scale** (float，可选) - 模型通道数的缩放比例。默认值：1.0。
+
+返回：mobilenetv1模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import mobilenet_v1
+
+    # build model
+    model = mobilenet_v1()
+
+    # build model and load imagenet pretrained weight
+    # model = mobilenet_v1(pretrained=True)
+
+    # build mobilenet v1 with scale=0.5
+    model_scale = mobilenet_v1(scale=0.5)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/mobilenet_v2_cn.rst b/doc/paddle/api/paddle/vision/models/mobilenet_v2_cn.rst
new file mode 100644
index 000000000..93bb11560
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/mobilenet_v2_cn.rst
@@ -0,0 +1,35 @@
+.. _cn_api_paddle_vision_models_mobilenet_v2:
+
+mobilenet_v2
+-------------------------------
+
+.. py:function:: paddle.vision.models.mobilenet_v2(pretrained=False, scale=1.0, **kwargs)
+
+ MobileNetV2模型，来自论文`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **scale** (float，可选) - 模型通道数的缩放比例。默认值：1.0。
+
+返回：mobilenetv2模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import mobilenet_v2
+
+    # build model
+    model = mobilenet_v2()
+
+    # build model and load imagenet pretrained weight
+    # model = mobilenet_v2(pretrained=True)
+
+    # build mobilenet v2 with scale=0.5
+    model = mobilenet_v2(scale=0.5)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/resnet101_cn.rst b/doc/paddle/api/paddle/vision/models/resnet101_cn.rst
new file mode 100644
index 000000000..0baf12db9
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/resnet101_cn.rst
@@ -0,0 +1,31 @@
+.. _cn_api_paddle_vision_models_resnet101:
+
+resnet101
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnet101(pretrained=False, **kwargs)
+
+ 101层的resnet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回：resnet101模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnet101
+
+    # build model
+    model = resnet101()
+
+    # build model and load imagenet pretrained weight
+    # model = resnet101(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/resnet152_cn.rst b/doc/paddle/api/paddle/vision/models/resnet152_cn.rst
new file mode 100644
index 000000000..b0de35f85
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/resnet152_cn.rst
@@ -0,0 +1,31 @@
+.. _cn_api_paddle_vision_models_resnet152:
+
+resnet152
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnet152(pretrained=False, **kwargs)
+
+ 152层的resnet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回：resnet152模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnet152
+
+    # build model
+    model = resnet152()
+
+    # build model and load imagenet pretrained weight
+    # model = resnet152(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/resnet18_cn.rst b/doc/paddle/api/paddle/vision/models/resnet18_cn.rst
new file mode 100644
index 000000000..05f3a158c
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/resnet18_cn.rst
@@ -0,0 +1,31 @@
+.. _cn_api_paddle_vision_models_resnet18:
+
+resnet18
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnet18(pretrained=False, **kwargs)
+
+ 18层的resnet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回：resnet18模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnet18
+
+    # build model
+    model = resnet18()
+
+    # build model and load imagenet pretrained weight
+    # model = resnet18(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/resnet34_cn.rst b/doc/paddle/api/paddle/vision/models/resnet34_cn.rst
new file mode 100644
index 000000000..9f3aaaff9
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/resnet34_cn.rst
@@ -0,0 +1,31 @@
+.. _cn_api_paddle_vision_models_resnet34:
+
+resnet34
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnet34(pretrained=False, **kwargs)
+
+ 34层的resnet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回：resnet34模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnet34
+
+    # build model
+    model = resnet34()
+
+    # build model and load imagenet pretrained weight
+    # model = resnet34(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/resnet50_cn.rst b/doc/paddle/api/paddle/vision/models/resnet50_cn.rst
new file mode 100644
index 000000000..1035193e0
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/resnet50_cn.rst
@@ -0,0 +1,31 @@
+.. _cn_api_paddle_vision_models_resnet50:
+
+resnet50
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnet50(pretrained=False, **kwargs)
+
+ 50层的resnet模型，来自论文`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回：resnet50模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnet50
+
+    # build model
+    model = resnet50()
+
+    # build model and load imagenet pretrained weight
+    # model = resnet50(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/vgg11_cn.rst b/doc/paddle/api/paddle/vision/models/vgg11_cn.rst
new file mode 100644
index 000000000..35ee24863
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/vgg11_cn.rst
@@ -0,0 +1,32 @@
+.. _cn_api_paddle_vision_models_vgg11:
+
+vgg11
+-------------------------------
+
+.. py:function:: paddle.vision.models.vgg11(pretrained=False, batch_norm=False, **kwargs)
+
+ vgg11模型，来自论文`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **batch_norm** (bool, 可选) - 是否在每个卷积层后添加批归一化层。默认值：False。
+
+返回：vgg11模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import vgg11
+
+    # build model
+    model = vgg11()
+
+    # build vgg11 model with batch_norm
+    model = vgg11(batch_norm=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
\ No newline at end of file
diff --git a/doc/paddle/api/paddle/vision/models/vgg13_cn.rst b/doc/paddle/api/paddle/vision/models/vgg13_cn.rst
new file mode 100644
index 000000000..d96204947
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/vgg13_cn.rst
@@ -0,0 +1,32 @@
+.. _cn_api_paddle_vision_models_vgg13:
+
+vgg13
+-------------------------------
+
+.. py:function:: paddle.vision.models.vgg13(pretrained=False, batch_norm=False, **kwargs)
+
+ vgg13模型，来自论文`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **batch_norm** (bool, 可选) - 是否在每个卷积层后添加批归一化层。默认值：False。
+
+返回：vgg13模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import vgg13
+
+    # build model
+    model = vgg13()
+
+    # build vgg13 model with batch_norm
+    model = vgg13(batch_norm=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/vgg16_cn.rst b/doc/paddle/api/paddle/vision/models/vgg16_cn.rst
new file mode 100644
index 000000000..4fb42f17b
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/vgg16_cn.rst
@@ -0,0 +1,32 @@
+.. _cn_api_paddle_vision_models_vgg16:
+
+vgg16
+-------------------------------
+
+.. py:function:: paddle.vision.models.vgg16(pretrained=False, batch_norm=False, **kwargs)
+
+ vgg16模型，来自论文`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **batch_norm** (bool, 可选) - 是否在每个卷积层后添加批归一化层。默认值：False。
+
+返回：vgg16模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import vgg16
+
+    # build model
+    model = vgg16()
+
+    # build vgg16 model with batch_norm
+    model = vgg16(batch_norm=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/doc/paddle/api/paddle/vision/models/vgg19_cn.rst b/doc/paddle/api/paddle/vision/models/vgg19_cn.rst
new file mode 100644
index 000000000..3dadfa165
--- /dev/null
+++ b/doc/paddle/api/paddle/vision/models/vgg19_cn.rst
@@ -0,0 +1,32 @@
+.. _cn_api_paddle_vision_models_vgg19:
+
+vgg19
+-------------------------------
+
+.. py:function:: paddle.vision.models.vgg19(pretrained=False, batch_norm=False, **kwargs)
+
+ vgg19模型，来自论文`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_。
+
+参数：
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+  - **batch_norm** (bool, 可选) - 是否在每个卷积层后添加批归一化层。默认值：False。
+
+返回：vgg19模型，Layer的实例。
+
+**代码示例**：
+
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import vgg19
+
+    # build model
+    model = vgg19()
+
+    # build vgg19 model with batch_norm
+    model = vgg19(batch_norm=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
-- 
GitLab