pytorch卷积的输入输出以及计算公式

1、nn.Conv2d
class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)
二维卷积层, 输入的尺度是(N, C_in, H, W)，输出尺度（N, C_out, H_out, W_out）。

\[out = （input + 2 * padding - kernel\_size) / stride + 1 \]

2、nn.ConvTranspose2d
class torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)

\[out = （input - 1) * stride - 2 * padding + kernel\_size \]

3、nn.MaxPool2d
class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

\[out = (input + 2 * padding - kernel\_size) / stride + 1 \]

4、nn.Conv3d
class torch.nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

\[out = (imput + 2 * padding - kernel\_size) / stride + 1 \]

5、nn.ConvTranspose3d
class torch.nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True)

\[out = (input - 1) * stride - 2 * padding + kernel\_size \]