深入浅出Pytorch函数——torch.nn.Module

von Neumann2023-07-28 12:38

torch.nn.Module是所有Pytorch中所有神经网络模型的基类，我们的神经网络模型也应该继承这个类。Modules可以包含其它Modules，也允许使用树结构嵌入他们，还可以将子模块赋值给模型属性。

实例

复制代码

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)       # submodule: Conv2d
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
       x = F.relu(self.conv1(x))
       return F.relu(self.conv2(x))

通过上面方式赋值的submodule会被注册。当调用.cuda()的时候，submodule的参数也会转换为cuda Tensor。

函数

`eval()`

将模块设置为evaluation模式，相当于self.train(False)。这个函数仅当模型中有Dropout和BatchNorm时才会有影响。

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def eval(self: T) -> T:
        r"""Sets the module in evaluation mode.

        This has any effect only on certain modules. See documentations of
        particular modules for details of their behaviors in training/evaluation
        mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
        etc.

        This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.

        See :ref:`locally-disable-grad-doc` for a comparison between
        `.eval()` and several similar mechanisms that may be confused with it.

        Returns:
            Module: self
        """
        return self.train(False)