LayerDict¶

class paddle.nn. LayerDict ( sublayers=None ) [源代码] ¶

LayerDict 用于保存子层到有序字典中，它包含的子层将被正确地注册和添加。列表中的子层可以像常规 python 有序字典一样被访问。

参数¶

sublayers (LayerDict|OrderedDict|list[(key, Layer)]，可选) - 键值对的可迭代对象，值的类型为 paddle.nn.Layer 。

代码示例¶

          >>> import paddle
>>> import numpy as np
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layers_dict = paddle.nn.LayerDict(sublayers=sublayers)

>>> l = layers_dict['conv1d']

>>> for k in layers_dict:
...     l = layers_dict[k]
...
>>> print(len(layers_dict))
3

>>> del layers_dict['conv2d']
>>> print(len(layers_dict))
2

>>> conv1d = layers_dict.pop('conv1d')
>>> print(len(layers_dict))
1

>>> layers_dict.clear()
>>> print(len(layers_dict))
0

         

方法¶

clear()¶

清除 LayerDict 中所有的子层。

参数

无。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)
>>> len(layer_dict)
3

>>> layer_dict.clear()
>>> len(layer_dict)
0

          

pop()¶

移除 LayerDict 中的键并且返回该键对应的子层。

参数

key (str) - 要移除的 key。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)
>>> len(layer_dict)
3

>>> layer_dict.pop('conv2d')
>>> len(layer_dict)
2

          

keys()¶

返回 LayerDict 中键的可迭代对象。

参数

无。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)
>>> for k in layer_dict.keys():
...     print(k)
conv1d
conv2d
conv3d

          

items()¶

返回 LayerDict 中键/值对的可迭代对象。

参数

无。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)
>>> for k, v in layer_dict.items():
...     print(f"{k}:", v)
conv1d : Conv1D(3, 2, kernel_size=[3], data_format=NCL)
conv2d : Conv2D(3, 2, kernel_size=[3, 3], data_format=NCHW)
conv3d : Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)

          

values()¶

返回 LayerDict 中值的可迭代对象。

参数

无。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)
>>> for v in layer_dict.values():
...     print(v)
Conv1D(3, 2, kernel_size=[3], data_format=NCL)
Conv2D(3, 2, kernel_size=[3, 3], data_format=NCHW)
Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)

          

update()¶

更新子层中的键/值对到 LayerDict 中，会覆盖已经存在的键。

参数

sublayers (LayerDict|OrderedDict|list[(key, Layer)]) - 键值对的可迭代对象，值的类型为 paddle.nn.Layer 。

代码示例

           >>> import paddle
>>> from collections import OrderedDict

>>> sublayers = OrderedDict([
...     ('conv1d', paddle.nn.Conv1D(3, 2, 3)),
...     ('conv2d', paddle.nn.Conv2D(3, 2, 3)),
...     ('conv3d', paddle.nn.Conv3D(4, 6, (3, 3, 3))),
>>> ])

>>> new_sublayers = OrderedDict([
...     ('relu', paddle.nn.ReLU()),
...     ('conv2d', paddle.nn.Conv2D(4, 2, 4)),
>>> ])
>>> layer_dict = paddle.nn.LayerDict(sublayers=sublayers)

>>> layer_dict.update(new_sublayers)

>>> for k, v in layer_dict.items():
...     print(f"{k}:", v)
conv1d : Conv1D(3, 2, kernel_size=[3], data_format=NCL)
conv2d : Conv2D(4, 2, kernel_size=[4, 4], data_format=NCHW)
conv3d : Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)
relu : ReLU()

          