07eeeac726b1b7f771e10909426663cc6bd0c477,models/networks.py,,define_D,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,153

Before Change

             norm="batch", nl="lrelu",
             use_sigmoid=False, init_type="xavier", num_Ds=1, gpu_ids=[]):
    netD = None
    use_gpu = len(gpu_ids) > 0
    norm_layer = get_norm_layer(layer_type=norm)
    nl = "lrelu"  // use leaky relu for D
    nl_layer = get_non_linearity(layer_type=nl)
    if use_gpu:
        assert(torch.cuda.is_available())
    if which_model_netD == "basic_128":
        netD = D_NLayers(input_nc, ndf, n_layers=2, norm_layer=norm_layer,
                         nl_layer=nl_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    elif which_model_netD == "basic_256":
        netD = D_NLayers(input_nc, ndf, n_layers=3, norm_layer=norm_layer,
                         nl_layer=nl_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    elif which_model_netD == "basic_128_multi":
        netD = D_NLayersMulti(input_nc=input_nc, ndf=ndf, n_layers=2, norm_layer=norm_layer,
                              use_sigmoid=use_sigmoid, gpu_ids=gpu_ids, num_D=num_Ds)
    elif which_model_netD == "basic_256_multi":
        netD = D_NLayersMulti(input_nc=input_nc, ndf=ndf, n_layers=3, norm_layer=norm_layer,
                              use_sigmoid=use_sigmoid, gpu_ids=gpu_ids, num_D=num_Ds)
    else:
        raise NotImplementedError(
            "Discriminator model name [%s] is not recognized" % which_model_netD)
    if use_gpu:
        netD.cuda(gpu_ids[0])
    init_weights(netD, init_type=init_type)
    return netD


def define_E(input_nc, output_nc, ndf, which_model_netE,
             norm="batch", nl="lrelu",

After Change

def define_D(input_nc, ndf, which_model_netD,
             norm="batch", nl="lrelu",
             use_sigmoid=False, init_type="xavier", num_Ds=1, gpu_ids=[]):
    netD = None
    norm_layer = get_norm_layer(layer_type=norm)
    nl = "lrelu"  // use leaky relu for D
    nl_layer = get_non_linearity(layer_type=nl)

    if which_model_netD == "basic_128":
        netD = D_NLayers(input_nc, ndf, n_layers=2, norm_layer=norm_layer,
                         nl_layer=nl_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    elif which_model_netD == "basic_256":
        netD = D_NLayers(input_nc, ndf, n_layers=3, norm_layer=norm_layer,
                         nl_layer=nl_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    elif which_model_netD == "basic_128_multi":
        netD = D_NLayersMulti(input_nc=input_nc, ndf=ndf, n_layers=2, norm_layer=norm_layer,
                              use_sigmoid=use_sigmoid, gpu_ids=gpu_ids, num_D=num_Ds)
    elif which_model_netD == "basic_256_multi":
        netD = D_NLayersMulti(input_nc=input_nc, ndf=ndf, n_layers=3, norm_layer=norm_layer,
                              use_sigmoid=use_sigmoid, gpu_ids=gpu_ids, num_D=num_Ds)
    else:
        raise NotImplementedError(
            "Discriminator model name [%s] is not recognized" % which_model_netD)
    return init_net(netD, init_type, gpu_ids)


def define_E(input_nc, output_nc, ndf, which_model_netE,
             norm="batch", nl="lrelu",

Instances