Pytorch卷积和反卷积计算方法

torch.nn.Conv2d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1,
                 bias=True, padding_mode='zeros'):

Parameters

• in_channels (int) – Number of channels in the input image
• out_channels (int) – Number of channels produced by the convolution
• kernel_size (int or tuple) – Size of the convolving kernel
• stride (int or tuple, optional) – Stride of the convolution. Default: 1
• padding (int or tuple, optional) – Zero-padding added to both sides of the input. Default: 0
• padding_mode (string, optional) – ‘zeros’, ‘reflect’, ‘replicate’ or ‘circular’. Default: ‘zeros’
• dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
• groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
• bias (bool, optional) – If True, adds a learnable bias to the output. Default: True

Example

torch.nn.ConvTranspose2d

看了这篇，使用里面的公式，发现计算出来的不对，不过还是有助于理解。

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, output_padding=0, groups=1, bias=True,
                 dilation=1, padding_mode='zeros'):

Parameters

• in_channels (int) – Number of channels in the input image
• out_channels (int) – Number of channels produced by the convolution
• kernel_size (int or tuple) – Size of the convolving kernel
• stride (int or tuple, optional) – Stride of the convolution. Default: 1
• padding (int or tuple, optional) – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Default: 0
• output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0
• groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
• bias (bool, optional) – If True, adds a learnable bias to the output. Default: True
  dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1

Example

H_out=(H_in−1)×stride[0]−2×padding[0]+dilation[0]×(kernel_size[0]−1)+output_padding[0]+1

W_out=(W_in−1)×stride[1]−2×padding[1]+dilation[1]×(kernel_size[1]−1)+output_padding[1]+1

本文地址：https://blog.csdn.net/Doraemon_Zzn/article/details/107930752