gsconv 代码 pytorch

以下是一个简单的 PyTorch 实现 gsconv 的示例代码：

import torch
import torch.nn as nn


class GSConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=None):
        super(GSConv, self).__init__()

        assert kernel_size % 2 == 1, "Kernel size must be odd."

        if groups is None:
            groups = out_channels

        self.groups = groups

        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups=1)

    def forward(self, x):
        batch_size, channels, height, width = x.size()

        # Split the input feature map along the channel dimension
        x = x.view(batch_size, self.groups, channels // self.groups, height, width)

        # Apply the convolution operation to each group separately
        x = self.conv(x)

        # Concatenate the output feature maps from each group along the channel dimension
        x = x.view(batch_size, -1, height, width)

        return x

在这个实现中，我们使用了 PyTorch 自带的 `nn.Conv2d` 模块，但是我们将 `groups` 参数设置为 1，这样每个 group 中的卷积核都是相同的。我们手动将输入特征图分成多个 group，在每个 group 中执行卷积操作，最后将每个 group 的输出特征图按通道维度拼接在一起。这样就实现了 gsconv 的效果。