风格迁移0-07：stylegan-源码无死角解读(3)-generate网络框架总览

以下链接是个人关于stylegan所有见解，如有错误欢迎大家指出，我会第一时间纠正，如有兴趣可以加微信：17575010159 相互讨论技术。若是帮助到了你什么，一定要记得点赞奥！因为这是对我最大的鼓励。 风格迁移0-00：stylegan-目录-史上最全：https://blog.csdn.net/weixin_43013761/article/details/100895333

generate net流程

其实，之前做项目的时候，我都不是很在意网络的结构，因为给我的感觉，搞来搞去，无非就是卷积，池化 ，反卷积，跳跃链接等等组合而已。但是这次的网络结构吗，可能是相对来说比较复杂，也考虑到源码的难阅读性，打算好好的深入理解一番，那么我们就开始吧。

根据：风格迁移0-05：stylegan-源码无死角解读(1)-框架总览我们可以知道，其网络实现是从：

def training_loop(
	 G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
	 D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)

开始搭建网络的,对于生成网络，其最终会调用到training/networks_stylegan.py文件中的def G_style函数，对于该函数的注释如下：

#----------------------------------------------------------------------------
# Style-based generator used in the StyleGAN paper.
# Composed of two sub-networks (G_mapping and G_synthesis) that are defined below.

def G_style(
    latents_in,                                     # First input: Latent vectors (Z) [minibatch, latent_size].
    labels_in,                                      # Second input: Conditioning labels [minibatch, label_size].
    truncation_psi          = 0.7,                  # Style strength multiplier for the truncation trick. None = disable.
    truncation_cutoff       = 8,                    # Number of layers for which to apply the truncation trick. None = disable.
    truncation_psi_val      = None,                 # Value for truncation_psi to use during validation.
    truncation_cutoff_val   = None,                 # Value for truncation_cutoff to use during validation.
    dlatent_avg_beta        = 0.995,                # Decay for tracking the moving average of W during training. None = disable.
    style_mixing_prob       = 0.9,                  # Probability of mixing styles during training. None = disable.
    is_training             = False,                # Network is under training? Enables and disables specific features.
    is_validation           = False,                # Network is under validation? Chooses which value to use for truncation_psi.
    is_template_graph       = False,                # True = template graph constructed by the Network class, False = actual evaluation.
    components              = dnnlib.EasyDict(),    # Container for sub-networks. Retained between calls.
    **kwargs):                                      # Arguments for sub-networks (G_mapping and G_synthesis).

    # Validate arguments.
    assert not is_training or not is_validation
    assert isinstance(components, dnnlib.EasyDict)
    if is_validation:
        truncation_psi = truncation_psi_val
        truncation_cutoff = truncation_cutoff_val
    if is_training or (truncation_psi is not None and not tflib.is_tf_expression(truncation_psi) and truncation_psi == 1):
        truncation_psi = None
    if is_training or (truncation_cutoff is not None and not tflib.is_tf_expression(truncation_cutoff) and truncation_cutoff