欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页  >  IT编程

Pytorch卷积和反卷积计算方法

程序员文章站 2022-03-03 19:42:25
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'):Parametersin_channels (int) – Number of channels in the input i...

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

Pytorch卷积和反卷积计算方法

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

Pytorch卷积和反卷积计算方法

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

相关标签: Pytorch