3333bab612229b68566a1643f7b22b44f5c4f739,mmcv/cnn/vgg.py,VGG,init_weights,#VGG#Any#,108

Before Change

            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(
                        m.weight, mode="fan_out", nonlinearity="relu")
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    nn.init.constant_(m.bias, 0)
        else:
            raise TypeError("pretrained must be a str or None")

After Change

        elif pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    kaiming_init(m)
                elif isinstance(m, nn.BatchNorm2d):
                    constant_init(m, 1)
                elif isinstance(m, nn.Linear):
                    normal_init(m, std=0.01)
        else:
            raise TypeError("pretrained must be a str or None")

Instances