From 7fbe34245c698fe4f79346259b4a264e2ba1e78e Mon Sep 17 00:00:00 2001
From: David Nicolas <37790151+liyongchao911@users.noreply.github.com>
Date: Mon, 21 Aug 2023 11:58:28 +0800
Subject: [PATCH]  [xdoctest] No.161-162 update models docstring for doc
 xdoctest (#56422)

---
 python/paddle/vision/models/resnet.py       | 290 ++++++++++----------
 python/paddle/vision/models/shufflenetv2.py | 156 +++++------
 2 files changed, 223 insertions(+), 223 deletions(-)

diff --git a/python/paddle/vision/models/resnet.py b/python/paddle/vision/models/resnet.py
index 00f49201aea..d91cd66f04f 100644
--- a/python/paddle/vision/models/resnet.py
+++ b/python/paddle/vision/models/resnet.py
@@ -210,27 +210,27 @@ class ResNet(nn.Layer):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import ResNet
-            from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
+            >>> import paddle
+            >>> from paddle.vision.models import ResNet
+            >>> from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
 
-            # build ResNet with 18 layers
-            resnet18 = ResNet(BasicBlock, 18)
+            >>> # build ResNet with 18 layers
+            >>> resnet18 = ResNet(BasicBlock, 18)
 
-            # build ResNet with 50 layers
-            resnet50 = ResNet(BottleneckBlock, 50)
+            >>> # build ResNet with 50 layers
+            >>> resnet50 = ResNet(BottleneckBlock, 50)
 
-            # build Wide ResNet model
-            wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)
+            >>> # build Wide ResNet model
+            >>> wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)
 
-            # build ResNeXt model
-            resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)
+            >>> # build ResNeXt model
+            >>> resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = resnet18(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = resnet18(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
 
     def __init__(
@@ -380,20 +380,20 @@ def resnet18(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnet18
+            >>> import paddle
+            >>> from paddle.vision.models import resnet18
 
-            # build model
-            model = resnet18()
+            >>> # build model
+            >>> model = resnet18()
 
-            # build model and load imagenet pretrained weight
-            # model = resnet18(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnet18(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
 
@@ -413,20 +413,20 @@ def resnet34(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnet34
+            >>> import paddle
+            >>> from paddle.vision.models import resnet34
 
-            # build model
-            model = resnet34()
+            >>> # build model
+            >>> model = resnet34()
 
-            # build model and load imagenet pretrained weight
-            # model = resnet34(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnet34(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
 
@@ -446,20 +446,20 @@ def resnet50(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnet50
+            >>> import paddle
+            >>> from paddle.vision.models import resnet50
 
-            # build model
-            model = resnet50()
+            >>> # build model
+            >>> model = resnet50()
 
-            # build model and load imagenet pretrained weight
-            # model = resnet50(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnet50(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
 
@@ -479,20 +479,20 @@ def resnet101(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnet101
+            >>> import paddle
+            >>> from paddle.vision.models import resnet101
 
-            # build model
-            model = resnet101()
+            >>> # build model
+            >>> model = resnet101()
 
-            # build model and load imagenet pretrained weight
-            # model = resnet101(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnet101(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
 
@@ -512,20 +512,20 @@ def resnet152(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnet152
+            >>> import paddle
+            >>> from paddle.vision.models import resnet152
 
-            # build model
-            model = resnet152()
+            >>> # build model
+            >>> model = resnet152()
 
-            # build model and load imagenet pretrained weight
-            # model = resnet152(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnet152(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
 
@@ -545,20 +545,20 @@ def resnext50_32x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext50_32x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext50_32x4d
 
-            # build model
-            model = resnext50_32x4d()
+            >>> # build model
+            >>> model = resnext50_32x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext50_32x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext50_32x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 32
     kwargs['width'] = 4
@@ -580,20 +580,20 @@ def resnext50_64x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext50_64x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext50_64x4d
 
-            # build model
-            model = resnext50_64x4d()
+            >>> # build model
+            >>> model = resnext50_64x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext50_64x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext50_64x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 64
     kwargs['width'] = 4
@@ -615,20 +615,20 @@ def resnext101_32x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext101_32x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext101_32x4d
 
-            # build model
-            model = resnext101_32x4d()
+            >>> # build model
+            >>> model = resnext101_32x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext101_32x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext101_32x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 32
     kwargs['width'] = 4
@@ -652,20 +652,20 @@ def resnext101_64x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext101_64x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext101_64x4d
 
-            # build model
-            model = resnext101_64x4d()
+            >>> # build model
+            >>> model = resnext101_64x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext101_64x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext101_64x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 64
     kwargs['width'] = 4
@@ -689,20 +689,20 @@ def resnext152_32x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext152_32x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext152_32x4d
 
-            # build model
-            model = resnext152_32x4d()
+            >>> # build model
+            >>> model = resnext152_32x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext152_32x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext152_32x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 32
     kwargs['width'] = 4
@@ -726,20 +726,20 @@ def resnext152_64x4d(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import resnext152_64x4d
+            >>> import paddle
+            >>> from paddle.vision.models import resnext152_64x4d
 
-            # build model
-            model = resnext152_64x4d()
+            >>> # build model
+            >>> model = resnext152_64x4d()
 
-            # build model and load imagenet pretrained weight
-            # model = resnext152_64x4d(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = resnext152_64x4d(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['groups'] = 64
     kwargs['width'] = 4
@@ -763,20 +763,20 @@ def wide_resnet50_2(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import wide_resnet50_2
+            >>> import paddle
+            >>> from paddle.vision.models import wide_resnet50_2
 
-            # build model
-            model = wide_resnet50_2()
+            >>> # build model
+            >>> model = wide_resnet50_2()
 
-            # build model and load imagenet pretrained weight
-            # model = wide_resnet50_2(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = wide_resnet50_2(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['width'] = 64 * 2
     return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
@@ -797,20 +797,20 @@ def wide_resnet101_2(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import wide_resnet101_2
+            >>> import paddle
+            >>> from paddle.vision.models import wide_resnet101_2
 
-            # build model
-            model = wide_resnet101_2()
+            >>> # build model
+            >>> model = wide_resnet101_2()
 
-            # build model and load imagenet pretrained weight
-            # model = wide_resnet101_2(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = wide_resnet101_2(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     kwargs['width'] = 64 * 2
     return _resnet(
diff --git a/python/paddle/vision/models/shufflenetv2.py b/python/paddle/vision/models/shufflenetv2.py
index c146bb88ddc..4725798b1e6 100644
--- a/python/paddle/vision/models/shufflenetv2.py
+++ b/python/paddle/vision/models/shufflenetv2.py
@@ -209,14 +209,14 @@ class ShuffleNetV2(nn.Layer):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import ShuffleNetV2
-
-            shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish")
-            x = paddle.rand([1, 3, 224, 224])
-            out = shufflenet_v2_swish(x)
-            print(out.shape)
-            # [1, 1000]
+            >>> import paddle
+            >>> from paddle.vision.models import ShuffleNetV2
+
+            >>> shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish")
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = shufflenet_v2_swish(x)
+            >>> print(out.shape)
+            [1, 1000]
     """
 
     def __init__(self, scale=1.0, act="relu", num_classes=1000, with_pool=True):
@@ -345,20 +345,20 @@ def shufflenet_v2_x0_25(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x0_25
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x0_25
 
-            # build model
-            model = shufflenet_v2_x0_25()
+            >>> # build model
+            >>> model = shufflenet_v2_x0_25()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x0_25(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x0_25(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x0_25", scale=0.25, pretrained=pretrained, **kwargs
@@ -380,20 +380,20 @@ def shufflenet_v2_x0_33(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x0_33
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x0_33
 
-            # build model
-            model = shufflenet_v2_x0_33()
+            >>> # build model
+            >>> model = shufflenet_v2_x0_33()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x0_33(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x0_33(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x0_33", scale=0.33, pretrained=pretrained, **kwargs
@@ -415,20 +415,20 @@ def shufflenet_v2_x0_5(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x0_5
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x0_5
 
-            # build model
-            model = shufflenet_v2_x0_5()
+            >>> # build model
+            >>> model = shufflenet_v2_x0_5()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x0_5(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x0_5(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs
@@ -450,20 +450,20 @@ def shufflenet_v2_x1_0(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x1_0
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x1_0
 
-            # build model
-            model = shufflenet_v2_x1_0()
+            >>> # build model
+            >>> model = shufflenet_v2_x1_0()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x1_0(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x1_0(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x1_0", scale=1.0, pretrained=pretrained, **kwargs
@@ -485,20 +485,20 @@ def shufflenet_v2_x1_5(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x1_5
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x1_5
 
-            # build model
-            model = shufflenet_v2_x1_5()
+            >>> # build model
+            >>> model = shufflenet_v2_x1_5()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x1_5(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x1_5(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x1_5", scale=1.5, pretrained=pretrained, **kwargs
@@ -520,20 +520,20 @@ def shufflenet_v2_x2_0(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_x2_0
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_x2_0
 
-            # build model
-            model = shufflenet_v2_x2_0()
+            >>> # build model
+            >>> model = shufflenet_v2_x2_0()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_x2_0(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_x2_0(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_x2_0", scale=2.0, pretrained=pretrained, **kwargs
@@ -555,20 +555,20 @@ def shufflenet_v2_swish(pretrained=False, **kwargs):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.vision.models import shufflenet_v2_swish
+            >>> import paddle
+            >>> from paddle.vision.models import shufflenet_v2_swish
 
-            # build model
-            model = shufflenet_v2_swish()
+            >>> # build model
+            >>> model = shufflenet_v2_swish()
 
-            # build model and load imagenet pretrained weight
-            # model = shufflenet_v2_swish(pretrained=True)
+            >>> # build model and load imagenet pretrained weight
+            >>> # model = shufflenet_v2_swish(pretrained=True)
 
-            x = paddle.rand([1, 3, 224, 224])
-            out = model(x)
+            >>> x = paddle.rand([1, 3, 224, 224])
+            >>> out = model(x)
 
-            print(out.shape)
-            # [1, 1000]
+            >>> print(out.shape)
+            [1, 1000]
     """
     return _shufflenet_v2(
         "shufflenet_v2_swish",
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