forked from huawei/mindspore2022
88 lines
2.6 KiB
ReStructuredText
88 lines
2.6 KiB
ReStructuredText
mindspore.nn.SequentialCell
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============================
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.. py:class:: mindspore.nn.SequentialCell(*args)
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构造Cell顺序容器。关于Cell的介绍，可参考 `<https://www.mindspore.cn/docs/api/zh-CN/master/api_python/nn/mindspore.nn.Cell.html#mindspore.nn.Cell>`_。
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SequentialCell将按照传入List的顺序依次将Cell添加。此外，也支持OrderedDict作为构造器传入。
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.. note:: SequentialCell 和 torch.nn.ModuleList 是不同的，ModuleList是一个用于存储模块的列表，但SequentialCell中的Cell是以级联方式连接的，不是单纯的存储。
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**参数：**
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**args** (list, OrderedDict) - 仅包含Cell子类的列表或有序字典。
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**输入：**
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**x** (Tensor) - Tensor，其shape取决于序列中的第一个Cell。
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**输出：**
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Tensor，输出Tensor，其shape取决于输入 `x` 和定义的Cell序列。
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**异常：**
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**TypeError** - `args` 的类型不是列表或有序字典。
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**支持平台：**
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``Ascend`` ``GPU`` ``CPU``
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**样例：**
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>>> from mindspore import Tensor
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>>> import mindspore
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>>> import mindspore.nn as nn
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>>> import numpy as np
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>>>
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>>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid', weight_init="ones")
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>>> relu = nn.ReLU()
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>>> seq = nn.SequentialCell([conv, relu])
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>>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
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>>> output = seq(x)
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>>> print(output)
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[[[[27. 27.]
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[27. 27.]]
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[[27. 27.]
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[27. 27.]]]]
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>>> from collections import OrderedDict
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>>> d = OrderedDict()
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>>> d["conv"] = conv
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>>> d["relu"] = relu
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>>> seq = nn.SequentialCell(d)
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>>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
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>>> output = seq(x)
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>>> print(output)
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[[[[27. 27.]
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[27. 27.]]
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[[27. 27.]
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[27. 27.]]]]
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.. py:method:: append(cell)
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在容器末尾添加一个Cell。
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**参数：**
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**cell** (Cell) - 要添加的Cell。
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**样例：**
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>>> from mindspore import Tensor
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>>> import mindspore
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>>> import mindspore.nn as nn
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>>> import numpy as np
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>>>
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>>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid', weight_init="ones")
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>>> bn = nn.BatchNorm2d(2)
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>>> relu = nn.ReLU()
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>>> seq = nn.SequentialCell([conv, bn])
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>>> seq.append(relu)
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>>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
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>>> output = seq(x)
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>>> print(output)
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[[[[26.999863 26.999863]
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[26.999863 26.999863]]
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[[26.999863 26.999863]
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[26.999863 26.999863]]]] |