Can you help me in figuring out how do I modify the Depthwise convolution to full convolution? I have them as follows -
class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True)
        )


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, kernel, stride, expand_ratio, res_connect):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]
        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = res_connect
        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            # depth-wise
            ConvBNReLU(hidden_dim, hidden_dim, kernel_size=kernel, stride=stride, groups=hidden_dim),
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        ])
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)