64bba81a6867ad72aad461e27221ea278d25f5c1,models/networks.py,,define_G,#Any#Any#Any#Any#Any#Any#Any#Any#,101

Before Change

def define_G(input_nc, output_nc, ngf, which_model_netG, norm="batch", use_dropout=False, init_type="normal", gpu_ids=[]):
    netG = None
    use_gpu = len(gpu_ids) > 0
    norm_layer = get_norm_layer(norm_type=norm)

    if which_model_netG == "resnet_9blocks":
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
    elif which_model_netG == "resnet_6blocks":
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
    elif which_model_netG == "unet_128":
        netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    elif which_model_netG == "unet_256":
        netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    else:
        raise NotImplementedError("Generator model name [%s] is not recognized" % which_model_netG)
    if use_gpu:
        assert(torch.cuda.is_available())
        netG.cuda(gpu_ids[0])
        if len(gpu_ids) > 1:
            netG = torch.nn.DataParallel(netG, gpu_ids)
    init_weights(netG, init_type=init_type)
    return netG


def define_D(input_nc, ndf, which_model_netD,
             n_layers_D=3, norm="batch", use_sigmoid=False, init_type="normal", gpu_ids=[]):

After Change

def define_G(input_nc, output_nc, ngf, which_model_netG, norm="batch", use_dropout=False, init_type="normal", gpu_ids=[]):
    netG = None
    norm_layer = get_norm_layer(norm_type=norm)

    if which_model_netG == "resnet_9blocks":
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
    elif which_model_netG == "resnet_6blocks":
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
    elif which_model_netG == "unet_128":
        netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    elif which_model_netG == "unet_256":
        netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    else:
        raise NotImplementedError("Generator model name [%s] is not recognized" % which_model_netG)
    return init_net(netG, init_type, gpu_ids)


def define_D(input_nc, ndf, which_model_netD,
             n_layers_D=3, norm="batch", use_sigmoid=False, init_type="normal", gpu_ids=[]):

Instances