Initialize weights of this model

1

Hello please, iam using this model for segmentation and i need to initialize weights by torch.nn.init. xavier_uniform_ can someone show me please how can i do it on this model ?

def double_conv(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, 3, padding=1),
        torch.nn.BatchNorm2d(out_channels),
        nn.ReLU(inplace=True),
        nn.Conv2d(out_channels, out_channels, 3, padding=1),
        torch.nn.BatchNorm2d(out_channels),
        nn.ReLU(inplace=True)
    )

class Model(nn.Module):
    def __init__(self, n_class=5):
        super(Model).__init__()

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.dconv_down1 = double_conv(3, 64)
        self.dconv_down2 = double_conv(64, 128)
        self.dconv_down3 = double_conv(128, 256)
        self.dconv_down4 = double_conv(256, 512)

        self.maxool = nn.MaxPool2d(2)
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

        self.dconv_up3 = double_conv(256 + 512, 256)
        self.dconv_up2 = double_conv(128 + 256, 128)
        self.dconv_up1 = double_conv(128 + 64, 64)

        self.conv_last = nn.Conv2d(64, n_class, 1)

    def forward(self, x):
        conv1 = self.dconv_down1(x)
        x = self.maxpool(conv1)

        conv2 = self.dconv_down2(x)
        x = self.maxpool(conv2)

        conv3 = self.dconv_down3(x)
        x = self.maxpool(conv3)

        x = self.dconv_down4(x)

        x = self.upsample(x)
        x = torch.cat([x, conv3], dim=1)

        x = self.dconv_up3(x)
        x = self.upsample(x)
        x = torch.cat([x, conv2], dim=1)

        x = self.dconv_up2(x)
        x = self.upsample(x)
        x = torch.cat([x, conv1], dim=1)

        x = self.dconv_up1(x)

        out = self.conv_last(x)

        return out
2

You could write a weight_init method and apply it on the model:

def weight_init(m):
    if isinstance(m, nn.Conv2d):
        print('initializing conv2d weight')
        torch.nn.init.xavier_uniform_(m.weight)

model.apply(weight_init)
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