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未验证 提交 7fbe3424 编写于 作者: D David Nicolas 提交者： GitHub
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[xdoctest] No.161-162 update models docstring for doc xdoctest (#56422)

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python/paddle/vision/models/resnet.py
浏览文件 @ 7fbe3424
...@@ -210,27 +210,27 @@ class ResNet(nn.Layer): ...@@ -210,27 +210,27 @@ class ResNet(nn.Layer):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import ResNet >>> from paddle.vision.models import ResNet
from paddle.vision.models.resnet import BottleneckBlock, BasicBlock >>> from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
# build ResNet with 18 layers >>> # build ResNet with 18 layers
resnet18 = ResNet(BasicBlock, 18) >>> resnet18 = ResNet(BasicBlock, 18)
# build ResNet with 50 layers >>> # build ResNet with 50 layers
resnet50 = ResNet(BottleneckBlock, 50) >>> resnet50 = ResNet(BottleneckBlock, 50)
# build Wide ResNet model >>> # build Wide ResNet model
wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2) >>> wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)
# build ResNeXt model >>> # build ResNeXt model
resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32) >>> resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = resnet18(x) >>> out = resnet18(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
def __init__( def __init__(
...@@ -380,20 +380,20 @@ def resnet18(pretrained=False, **kwargs): ...@@ -380,20 +380,20 @@ def resnet18(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnet18 >>> from paddle.vision.models import resnet18
# build model >>> # build model
model = resnet18() >>> model = resnet18()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnet18(pretrained=True) >>> # model = resnet18(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs) return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
...@@ -413,20 +413,20 @@ def resnet34(pretrained=False, **kwargs): ...@@ -413,20 +413,20 @@ def resnet34(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnet34 >>> from paddle.vision.models import resnet34
# build model >>> # build model
model = resnet34() >>> model = resnet34()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnet34(pretrained=True) >>> # model = resnet34(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs) return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
...@@ -446,20 +446,20 @@ def resnet50(pretrained=False, **kwargs): ...@@ -446,20 +446,20 @@ def resnet50(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnet50 >>> from paddle.vision.models import resnet50
# build model >>> # build model
model = resnet50() >>> model = resnet50()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnet50(pretrained=True) >>> # model = resnet50(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs) return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
...@@ -479,20 +479,20 @@ def resnet101(pretrained=False, **kwargs): ...@@ -479,20 +479,20 @@ def resnet101(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnet101 >>> from paddle.vision.models import resnet101
# build model >>> # build model
model = resnet101() >>> model = resnet101()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnet101(pretrained=True) >>> # model = resnet101(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs) return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
...@@ -512,20 +512,20 @@ def resnet152(pretrained=False, **kwargs): ...@@ -512,20 +512,20 @@ def resnet152(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnet152 >>> from paddle.vision.models import resnet152
# build model >>> # build model
model = resnet152() >>> model = resnet152()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnet152(pretrained=True) >>> # model = resnet152(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs) return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
...@@ -545,20 +545,20 @@ def resnext50_32x4d(pretrained=False, **kwargs): ...@@ -545,20 +545,20 @@ def resnext50_32x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext50_32x4d >>> from paddle.vision.models import resnext50_32x4d
# build model >>> # build model
model = resnext50_32x4d() >>> model = resnext50_32x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext50_32x4d(pretrained=True) >>> # model = resnext50_32x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 32 kwargs['groups'] = 32
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -580,20 +580,20 @@ def resnext50_64x4d(pretrained=False, **kwargs): ...@@ -580,20 +580,20 @@ def resnext50_64x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext50_64x4d >>> from paddle.vision.models import resnext50_64x4d
# build model >>> # build model
model = resnext50_64x4d() >>> model = resnext50_64x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext50_64x4d(pretrained=True) >>> # model = resnext50_64x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 64 kwargs['groups'] = 64
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -615,20 +615,20 @@ def resnext101_32x4d(pretrained=False, **kwargs): ...@@ -615,20 +615,20 @@ def resnext101_32x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext101_32x4d >>> from paddle.vision.models import resnext101_32x4d
# build model >>> # build model
model = resnext101_32x4d() >>> model = resnext101_32x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext101_32x4d(pretrained=True) >>> # model = resnext101_32x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 32 kwargs['groups'] = 32
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -652,20 +652,20 @@ def resnext101_64x4d(pretrained=False, **kwargs): ...@@ -652,20 +652,20 @@ def resnext101_64x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext101_64x4d >>> from paddle.vision.models import resnext101_64x4d
# build model >>> # build model
model = resnext101_64x4d() >>> model = resnext101_64x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext101_64x4d(pretrained=True) >>> # model = resnext101_64x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 64 kwargs['groups'] = 64
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -689,20 +689,20 @@ def resnext152_32x4d(pretrained=False, **kwargs): ...@@ -689,20 +689,20 @@ def resnext152_32x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext152_32x4d >>> from paddle.vision.models import resnext152_32x4d
# build model >>> # build model
model = resnext152_32x4d() >>> model = resnext152_32x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext152_32x4d(pretrained=True) >>> # model = resnext152_32x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 32 kwargs['groups'] = 32
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -726,20 +726,20 @@ def resnext152_64x4d(pretrained=False, **kwargs): ...@@ -726,20 +726,20 @@ def resnext152_64x4d(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import resnext152_64x4d >>> from paddle.vision.models import resnext152_64x4d
# build model >>> # build model
model = resnext152_64x4d() >>> model = resnext152_64x4d()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = resnext152_64x4d(pretrained=True) >>> # model = resnext152_64x4d(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['groups'] = 64 kwargs['groups'] = 64
kwargs['width'] = 4 kwargs['width'] = 4
...@@ -763,20 +763,20 @@ def wide_resnet50_2(pretrained=False, **kwargs): ...@@ -763,20 +763,20 @@ def wide_resnet50_2(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import wide_resnet50_2 >>> from paddle.vision.models import wide_resnet50_2
# build model >>> # build model
model = wide_resnet50_2() >>> model = wide_resnet50_2()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = wide_resnet50_2(pretrained=True) >>> # model = wide_resnet50_2(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['width'] = 64 * 2 kwargs['width'] = 64 * 2
return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs) return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
...@@ -797,20 +797,20 @@ def wide_resnet101_2(pretrained=False, **kwargs): ...@@ -797,20 +797,20 @@ def wide_resnet101_2(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import wide_resnet101_2 >>> from paddle.vision.models import wide_resnet101_2
# build model >>> # build model
model = wide_resnet101_2() >>> model = wide_resnet101_2()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = wide_resnet101_2(pretrained=True) >>> # model = wide_resnet101_2(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
kwargs['width'] = 64 * 2 kwargs['width'] = 64 * 2
return _resnet( return _resnet(
......
python/paddle/vision/models/shufflenetv2.py
浏览文件 @ 7fbe3424
...@@ -209,14 +209,14 @@ class ShuffleNetV2(nn.Layer): ...@@ -209,14 +209,14 @@ class ShuffleNetV2(nn.Layer):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import ShuffleNetV2 >>> from paddle.vision.models import ShuffleNetV2
shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish") >>> shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish")
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = shufflenet_v2_swish(x) >>> out = shufflenet_v2_swish(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
def __init__(self, scale=1.0, act="relu", num_classes=1000, with_pool=True): 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): ...@@ -345,20 +345,20 @@ def shufflenet_v2_x0_25(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x0_25 >>> from paddle.vision.models import shufflenet_v2_x0_25
# build model >>> # build model
model = shufflenet_v2_x0_25() >>> model = shufflenet_v2_x0_25()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x0_25(pretrained=True) >>> # model = shufflenet_v2_x0_25(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x0_25", scale=0.25, pretrained=pretrained, **kwargs "shufflenet_v2_x0_25", scale=0.25, pretrained=pretrained, **kwargs
...@@ -380,20 +380,20 @@ def shufflenet_v2_x0_33(pretrained=False, **kwargs): ...@@ -380,20 +380,20 @@ def shufflenet_v2_x0_33(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x0_33 >>> from paddle.vision.models import shufflenet_v2_x0_33
# build model >>> # build model
model = shufflenet_v2_x0_33() >>> model = shufflenet_v2_x0_33()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x0_33(pretrained=True) >>> # model = shufflenet_v2_x0_33(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x0_33", scale=0.33, pretrained=pretrained, **kwargs "shufflenet_v2_x0_33", scale=0.33, pretrained=pretrained, **kwargs
...@@ -415,20 +415,20 @@ def shufflenet_v2_x0_5(pretrained=False, **kwargs): ...@@ -415,20 +415,20 @@ def shufflenet_v2_x0_5(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x0_5 >>> from paddle.vision.models import shufflenet_v2_x0_5
# build model >>> # build model
model = shufflenet_v2_x0_5() >>> model = shufflenet_v2_x0_5()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x0_5(pretrained=True) >>> # model = shufflenet_v2_x0_5(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs "shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs
...@@ -450,20 +450,20 @@ def shufflenet_v2_x1_0(pretrained=False, **kwargs): ...@@ -450,20 +450,20 @@ def shufflenet_v2_x1_0(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x1_0 >>> from paddle.vision.models import shufflenet_v2_x1_0
# build model >>> # build model
model = shufflenet_v2_x1_0() >>> model = shufflenet_v2_x1_0()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x1_0(pretrained=True) >>> # model = shufflenet_v2_x1_0(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x1_0", scale=1.0, pretrained=pretrained, **kwargs "shufflenet_v2_x1_0", scale=1.0, pretrained=pretrained, **kwargs
...@@ -485,20 +485,20 @@ def shufflenet_v2_x1_5(pretrained=False, **kwargs): ...@@ -485,20 +485,20 @@ def shufflenet_v2_x1_5(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x1_5 >>> from paddle.vision.models import shufflenet_v2_x1_5
# build model >>> # build model
model = shufflenet_v2_x1_5() >>> model = shufflenet_v2_x1_5()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x1_5(pretrained=True) >>> # model = shufflenet_v2_x1_5(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x1_5", scale=1.5, pretrained=pretrained, **kwargs "shufflenet_v2_x1_5", scale=1.5, pretrained=pretrained, **kwargs
...@@ -520,20 +520,20 @@ def shufflenet_v2_x2_0(pretrained=False, **kwargs): ...@@ -520,20 +520,20 @@ def shufflenet_v2_x2_0(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_x2_0 >>> from paddle.vision.models import shufflenet_v2_x2_0
# build model >>> # build model
model = shufflenet_v2_x2_0() >>> model = shufflenet_v2_x2_0()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_x2_0(pretrained=True) >>> # model = shufflenet_v2_x2_0(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_x2_0", scale=2.0, pretrained=pretrained, **kwargs "shufflenet_v2_x2_0", scale=2.0, pretrained=pretrained, **kwargs
...@@ -555,20 +555,20 @@ def shufflenet_v2_swish(pretrained=False, **kwargs): ...@@ -555,20 +555,20 @@ def shufflenet_v2_swish(pretrained=False, **kwargs):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
from paddle.vision.models import shufflenet_v2_swish >>> from paddle.vision.models import shufflenet_v2_swish
# build model >>> # build model
model = shufflenet_v2_swish() >>> model = shufflenet_v2_swish()
# build model and load imagenet pretrained weight >>> # build model and load imagenet pretrained weight
# model = shufflenet_v2_swish(pretrained=True) >>> # model = shufflenet_v2_swish(pretrained=True)
x = paddle.rand([1, 3, 224, 224]) >>> x = paddle.rand([1, 3, 224, 224])
out = model(x) >>> out = model(x)
print(out.shape) >>> print(out.shape)
# [1, 1000] [1, 1000]
""" """
return _shufflenet_v2( return _shufflenet_v2(
"shufflenet_v2_swish", "shufflenet_v2_swish",
......
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