MixStyle layers for DigitsDG dataset

[Wenyu Zhang]

Hi, for the CNN used in DigitsDG experiments in the paper, on which layers is MixStyle applied? Is the network also trained for 150 epochs? Thanks.

[Kaiyang]

The network architecture looks like this

class Convolution(nn.Module):

    def __init__(self, c_in, c_out):
        super().__init__()
        self.conv = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1)
        self.relu = nn.ReLU(True)

    def forward(self, x):
        return self.relu(self.conv(x))


class ConvNet(Backbone):
    """CNN + MixStyle."""

    def __init__(self, c_hidden=64, mixstyle_layers=[]):
        super().__init__()
        self.conv1 = Convolution(3, c_hidden)
        self.conv2 = Convolution(c_hidden, c_hidden)
        self.conv3 = Convolution(c_hidden, c_hidden)
        self.conv4 = Convolution(c_hidden, c_hidden)

        self.mixstyle = MixStyle(p=0.5, alpha=0.1)
        self.mixstyle_layers = mixstyle_layers
        print('Insert MixStyle after the following layers: {}'.format(mixstyle_layers))

        self._out_features = 2**2 * c_hidden

    def _check_input(self, x):
        H, W = x.shape[2:]
        assert H == 32 and W == 32, \
            'Input to network must be 32x32, ' \
            'but got {}x{}'.format(H, W)

    def forward(self, x):
        self._check_input(x)
        x = self.conv1(x)
        x = F.max_pool2d(x, 2)
        if 'conv1' in self.mixstyle_layers:
            x = self.mixstyle(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2)
        if 'conv2' in self.mixstyle_layers:
            x = self.mixstyle(x)
        x = self.conv3(x)
        x = F.max_pool2d(x, 2)
        if 'conv3' in self.mixstyle_layers:
            x = self.mixstyle(x)
        x = self.conv4(x)
        x = F.max_pool2d(x, 2)
        return x.view(x.size(0), -1)

and the mixstyle layer is inserted only after conv1.

The training parameters follow this https://github.com/KaiyangZhou/Dassl.pytorch/blob/master/configs/trainers/dg/vanilla/digits_dg.yaml

[Wenyu Zhang]

Thanks!