def init_weights()

in segmentation/util/util.py [0:0]

• 39 lines of code
• 19 McCabe index (conditional complexity)

def init_weights(model, conv='kaiming', batchnorm='normal', linear='kaiming', lstm='kaiming'):
    """
    :param model: Pytorch Model which is nn.Module
    :param conv:  'kaiming' or 'xavier'
    :param batchnorm: 'normal' or 'constant'
    :param linear: 'kaiming' or 'xavier'
    :param lstm: 'kaiming' or 'xavier'
    """
    for m in model.modules():
        if isinstance(m, (nn.modules.conv._ConvNd)):
            if conv == 'kaiming':
                initer.kaiming_normal_(m.weight)
            elif conv == 'xavier':
                initer.xavier_normal_(m.weight)
            else:
                raise ValueError("init type of conv error.\n")
            if m.bias is not None:
                initer.constant_(m.bias, 0)

        elif isinstance(m, (nn.modules.batchnorm._BatchNorm)):
            if batchnorm == 'normal':
                initer.normal_(m.weight, 1.0, 0.02)
            elif batchnorm == 'constant':
                initer.constant_(m.weight, 1.0)
            else:
                raise ValueError("init type of batchnorm error.\n")
            initer.constant_(m.bias, 0.0)

        elif isinstance(m, nn.Linear):
            if linear == 'kaiming':
                initer.kaiming_normal_(m.weight)
            elif linear == 'xavier':
                initer.xavier_normal_(m.weight)
            else:
                raise ValueError("init type of linear error.\n")
            if m.bias is not None:
                initer.constant_(m.bias, 0)

        elif isinstance(m, nn.LSTM):
            for name, param in m.named_parameters():
                if 'weight' in name:
                    if lstm == 'kaiming':
                        initer.kaiming_normal_(param)
                    elif lstm == 'xavier':
                        initer.xavier_normal_(param)
                    else:
                        raise ValueError("init type of lstm error.\n")
                elif 'bias' in name:
                    initer.constant_(param, 0)