[PaddlePaddle Hackathon] add ResNeXt zh doc

docs/api/paddle/vision/Overview_cn.rst

@@ -49,6 +49,13 @@ paddle.vision 目录是飞桨在视觉领域的高层API。具体如下：
     " :ref:`resnet50 <cn_api_paddle_vision_models_resnet50>` ", "50层的ResNet模型"
     " :ref:`resnet101 <cn_api_paddle_vision_models_resnet101>` ", "101层的ResNet模型"
     " :ref:`resnet152 <cn_api_paddle_vision_models_resnet152>` ", "152层的ResNet模型"
+    " :ref:`ResNeXt <cn_api_paddle_vision_models_ResNeXt>` ", "ResNeXt模型"
+    " :ref:`resnext50_32x4d <cn_api_paddle_vision_models_resnext50_32x4d>` ", "ResNeXt-50 32x4d模型"
+    " :ref:`resnext50_64x4d <cn_api_paddle_vision_models_resnext50_64x4d>` ", "ResNeXt-50 64x4d模型"
+    " :ref:`resnext101_32x4d <cn_api_paddle_vision_models_resnext101_32x4d>` ", "ResNeXt-101 32x4d模型"
+    " :ref:`resnext101_64x4d <cn_api_paddle_vision_models_resnext101_64x4d>` ", "ResNeXt-101 64x4d模型"
+    " :ref:`resnext152_32x4d <cn_api_paddle_vision_models_resnext152_32x4d>` ", "ResNeXt-152 32x4d模型"
+    " :ref:`resnext152_64x4d <cn_api_paddle_vision_models_resnext152_64x4d>` ", "ResNeXt-152 64x4d模型"
     " :ref:`VGG <cn_api_paddle_vision_models_VGG>` ", "VGG模型"
     " :ref:`vgg11 <cn_api_paddle_vision_models_vgg11>` ", "11层的VGG模型"
     " :ref:`vgg13 <cn_api_paddle_vision_models_vgg13>` ", "13层的VGG模型"
@@ -57,7 +64,6 @@ paddle.vision 目录是飞桨在视觉领域的高层API。具体如下：
     " :ref:`InceptionV3 <cn_api_paddle_vision_models_InceptionV3>` ", "InceptionV3模型"
     " :ref:`inception_v3 <cn_api_paddle_vision_models_inception_v3>` ", "InceptionV3模型"
 
-
 .. _about_ops:
 
 视觉操作相关API

docs/api/paddle/vision/models/ResNeXt_cn.rst

@@ -0,0 +1,33 @@
+.. _cn_api_paddle_vision_models_ResNeXt:
+
+ResNeXt
+-------------------------------
+
+.. py:class:: paddle.vision.models.ResNeXt(layers=50, cardinality=32, num_classes=1000, with_pool=True)
+
+ ResNeXt模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
+
+参数
+:::::::::
+  - **layers** (int，可选) - ResNeXt 模型的深度。默认值：50
+  - **cardinality** (int，可选) - 模型基数，也即划分组的数量。默认值：32
+  - **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
+:::::::::
+ResNeXt模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import ResNeXt
+
+    resnext50_32x4d = ResNeXt(depth=50, cardinality=32)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = resnext50_32x4d(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext101_32x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext101_32x4d:
+
+resnext101_32x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext101_32x4d(pretrained=False, **kwargs)
+
+ ResNeXt-101 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext101_32x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext101_32x4d
+
+    # build model
+    model = resnext101_32x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext101_32x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext101_64x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext101_64x4d:
+
+resnext101_64x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext101_64x4d(pretrained=False, **kwargs)
+
+ ResNeXt-101 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext101_64x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext101_64x4d
+
+    # build model
+    model = resnext101_64x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext101_64x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext152_32x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext152_32x4d:
+
+resnext152_32x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext152_32x4d(pretrained=False, **kwargs)
+
+ ResNeXt-152 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext152_32x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext152_32x4d
+
+    # build model
+    model = resnext152_32x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext152_32x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext152_64x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext152_64x4d:
+
+resnext152_64x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext152_64x4d(pretrained=False, **kwargs)
+
+ ResNeXt-152 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext152_64x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext152_64x4d
+
+    # build model
+    model = resnext152_64x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext152_64x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext50_32x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext50_32x4d:
+
+resnext50_32x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext50_32x4d(pretrained=False, **kwargs)
+
+ ResNeXt-50 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext50_32x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext50_32x4d
+
+    # build model
+    model = resnext50_32x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext50_32x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)

docs/api/paddle/vision/models/resnext50_64x4d_cn.rst

@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_resnext50_64x4d:
+
+resnext50_64x4d
+-------------------------------
+
+.. py:function:: paddle.vision.models.resnext50_64x4d(pretrained=False, **kwargs)
+
+ ResNeXt-50 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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
+:::::::::
+resnext50_64x4d模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import resnext50_64x4d
+
+    # build model
+    model = resnext50_64x4d()
+
+    # build model and load imagenet pretrained weight
+    # model = resnext50_64x4d(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)