默认情况下，conv1D执行二维卷积

Question

因此，大多数CNN指南将一维卷积解释为一系列1D核与输入序列卷积（类似于传统的FIR滤波器）。然而，据我所知，conv1d的默认值在每个输出的所有通道上实现卷积（基本上是2D卷积）。 如果需要传统的FIR滤波器实现，则应在信道中指定groups=组

检查重量似乎验证了这一点：

from torch import nn

C1 = nn.Conv1d(in_channels=3, out_channels=6, kernel_size=7)
C2 = nn.Conv1d(in_channels=3, out_channels=6, kernel_size=7,groups=3)
C3 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)
C4 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7, groups=3)

print(C1.weight.shape, '<-- 6 filters which convolve across two dimensions')
print(C2.weight.shape, '<-- 6 filters which convolve across one dimensions')
print(C3.weight.shape, '<-- 6 filters which convolve across three dimensions')
print(C4.weight.shape, '<-- 6 filters which convolve across two dimensions')

提供以下输出：

torch.Size([6, 3, 7]) <-- 6 filters which convolve across two dimensions
torch.Size([6, 1, 7]) <-- 6 filters which convolve across one dimensions
torch.Size([6, 3, 7, 7]) <-- 6 filters which convolve across three dimensions
torch.Size([6, 1, 7, 7]) <-- 6 filters which convolve across two dimensions

我的看法错了吗

如果a是正确的，我相信conv1d的命名相当混乱，因为它意味着1d卷积

Answer 1

需要考虑的几件事：

1）Conv1d使用一维（向量）核运行卷积。C1和C2的内核大小为（7）

2）Conv2d使用2维核（矩阵）运行卷积。C3和C4的内核大小为（7,7）

3）分组是控制输入和输出通道之间连接的一种方法，如果可以同时产生多个同时卷积

更多信息here