这篇文章主要讲解了“Pytorch中怎么调用forward()函数”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“Pytorch中怎么调用forward()函数”吧！
Pytorch调用forward()函数
Module类是nn模块里提供的一个模型构造类，是所有神经网络模块的基类，我们可以继承它来定义我们想要的模型。
下面继承Module类构造本节开头提到的多层感知机。
这里定义的MLP类重载了Module类的__init__函数和forward函数。
它们分别用于创建模型参数和定义前向计算。
前向计算也即正向传播。
import torch
from torch import nn
class MLP(nn.Module):
# 声明带有模型参数的层，这里声明了两个全连接层
def __init__(self, **kwargs):
# 调用MLP父类Module的构造函数来进行必要的初始化。这样在构造实例时还可以指定其他函数
# 参数，如“模型参数的访问、初始化和共享”一节将介绍的模型参数params
super(MLP, self).__init__(**kwargs)
self.hidden = nn.Linear(784, 256) # 隐藏层
self.act = nn.ReLU()
self.output = nn.Linear(256, 10) # 输出层
# 定义模型的前向计算，即如何根据输入x计算返回所需要的模型输出
def forward(self, x):
a = self.act(self.hidden(x))
return self.output(a)
X = torch.rand(2, 784)
net = MLP()
print(net)
net(X)输出：
MLP( (hidden): Linear(in_features=784, out_features=256, bias=True) (act): ReLU() (output): Linear(in_features=256, out_features=10, bias=True) ) tensor([[-0.1798, -0.2253, 0.0206, -0.1067, -0.0889, 0.1818, -0.1474, 0.1845, -0.1870, 0.1970], [-0.1843, -0.1562, -0.0090, 0.0351, -0.1538, 0.0992, -0.0883, 0.0911, -0.2293, 0.2360]], grad_fn=<ThAddmmBackward>)
为什么会调用forward()呢，是因为Module中定义了__call__()函数，该函数调用了forward()函数，当执行net(x)的时候，会自动调用__call__()函数
Pytorch函数调用的问题和源码解读
最近用到 softmax 函数，但是发现 softmax 的写法五花八门，记录如下
# torch._C._VariableFunctions
torch.softmax(x, dim=-1)# class
softmax = torch.nn.Softmax(dim=-1)
x=softmax(x)# function
x = torch.nn.functional.softmax(x, dim=-1)简单测试了一下，用 torch.nn.Softmax 类是最慢的，另外两个差不多
torch.nn.Softmax 源码如下，可以看到这是个类，而他这里的 return F.softmax(input, self.dim, _stacklevel=5) 调用的是 torch.nn.functional.softmax
class Softmax(Module):
r"""Applies the Softmax function to an n-dimensional input Tensor
rescaling them so that the elements of the n-dimensional output Tensor
lie in the range [0,1] and sum to 1.
Softmax is defined as:
.. math::
ext{Softmax}(x_{i}) = frac{exp(x_i)}{sum_j exp(x_j)}
When the input Tensor is a sparse tensor then the unspecifed
values are treated as ``-inf``.
Shape:
- Input: :math:`(*)` where `*` means, any number of additional
dimensions
- Output: :math:`(*)`, same shape as the input
Returns:
a Tensor of the same dimension and shape as the input with
values in the range [0, 1]
Args:
dim (int): A dimension along which Softmax will be computed (so every slice
along dim will sum to 1).
.. note::
This module doesn't work directly with NLLLoss,
which expects the Log to be computed between the Softmax and itself.
Use `LogSoftmax` instead (it's faster and has better numerical properties).
Examples::
>>> m = nn.Softmax(dim=1)
>>> input = torch.randn(2, 3)
>>> output = m(input)
"""
__constants__ = ['dim']
dim: Optional[int]
def __init__(self, dim: Optional[int] = None) -> None:
super(Softmax, self).__init__()
self.dim = dim
def __setstate__(self, state):
self.__dict__.update(state)
if not hasattr(self, 'dim'):
self.dim = None
def forward(self, input: Tensor) -> Tensor:
return F.softmax(input, self.dim, _stacklevel=5)
def extra_repr(self) -> str:
return 'dim={dim}'.format(dim=self.dim)torch.nn.functional.softmax 函数源码如下，可以看到 ret = input.softmax(dim) 实际上调用了 torch._C._VariableFunctions 中的 softmax 函数
def softmax(input: Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[DType] = None) -> Tensor:
r"""Applies a softmax function.
Softmax is defined as:
:math:` ext{Softmax}(x_{i}) = frac{exp(x_i)}{sum_j exp(x_j)}`
It is applied to all slices along dim, and will re-scale them so that the elements
lie in the range `[0, 1]` and sum to 1.
See :class:`~torch.nn.Softmax` for more details.
Args:
input (Tensor): input
dim (int): A dimension along which softmax will be computed.
dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
If specified, the input tensor is casted to :attr:`dtype` before the operation
is performed. This is useful for preventing data type overflows. Default: None.
.. note::
This function doesn't work directly with NLLLoss,
which expects the Log to be computed between the Softmax and itself.
Use log_softmax instead (it's faster and has better numerical properties).
"""
if has_torch_function_unary(input):
return handle_torch_function(softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
if dim is None:
dim = _get_softmax_dim("softmax", input.dim(), _stacklevel)
if dtype is None:
ret = input.softmax(dim)
else:
ret = input.softmax(dim, dtype=dtype)
return ret那么不如直接调用 built-in C 的函数？
但是有个博客 A selective excursion into the internals of PyTorch 里说
Note: That bilinear is exported as torch.bilinear is somewhat accidental. Do use the documented interfaces, here torch.nn.functional.bilinear whenever you can!
意思是说 built-in C 能被 torch.xxx 直接调用是意外的，强烈建议使用 torch.nn.functional.xxx 这样的接口
看到最新的 transformer 官方代码里也用的是 torch.nn.functional.softmax，还是和他们一致更好。