在深度学习模型中，卷积层绝对是最常用的基本操作，因此学习好卷积操作至关重要。卷积运算是线性变换的一种，而且属于一种稀疏连接的线性变换（不同与全连接的线性变换层，其是稠密连接的线性变换）。

卷积操作的运算涉及两个张量

• 第一个张量是输入张量
• 第二个是线性变换的权重张量（也称为卷积核 or 滤波器）

在 Pytorch 中，卷积操作主要可以分为两类，第一类是正常的卷积操作，第二类为转置卷积。这两类卷积分别有三个子类，即一维卷积、二维卷积 & 三维卷积。卷积核 & 转置卷积 都有一个公共的父类，即 _ConvNd 类，这个类是隐藏的，具体代码在 torch/nn/modules/conv.py 文件夹下。

_ConvNd 父类

# _ConvNd 父类

class _ConvNd(in_channels, out_channels, kernel_size, stride, padding, 
			  dilation,transposed, out_channels, output_padding, 
			  groups, bias, padding_mode)

• stride: controls the stride for the cross-correlation.
• padding: controls the amount of implicit zero-paddings on both sides for d i l a t i o n ∗ − ( k e r n e l s i z e − 1 ) − p a d d i n g dilation * - (kernel_size - 1) - padding dilation∗−(kernels​ize−1)−padding number of points. See note below for details.
• output_padding: controls the additional size added to one side of the output shape. See note below for details.
• dilation: controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what dilation does.
• groups: controls the connections between inputs and outputs. in_channels and out_channels must both be divisible by groups.

卷积操作

# nn.Conv2d 卷积

class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=True)

转置卷积操作

# nn.ConvTranspose2d 反卷积

torch.nn.ConvTranspose2d(
		in_channels   	: int, 
		out_channels  	: int, 
		kernel_size	  	: Union[T, Tuple[T, T]], 
		stride		  	: Union[T, Tuple[T, T]] = 1, 
		padding		  	: Union[T, Tuple[T, T]] = 0, 
		output_padding	: Union[T, Tuple[T, T]] = 0, 
		groups		  	: int = 1, 
		bias		  	: bool = True, 
		dilation  	  	: int = 1, 
		padding_mode  	: str = 'zeros')