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 <= 0):
truncation_cutoff = None
if not is_training or (dlatent_avg_beta is not None
and not tflib.is_tf_expression(dlatent_avg_beta)
and dlatent_avg_beta == 1):
dlatent_avg_beta = None
if not is_training or (style_mixing_prob is not None
and not tflib.is_tf_expression(style_mixing_prob)
and style_mixing_prob <= 0):
style_mixing_prob = None
# Setup components.
if 'synthesis' not in components:
components.synthesis = tflib.Network('G_synthesis',
func_name=G_synthesis,
**kwargs)
num_layers = components.synthesis.input_shape[1]
dlatent_size = components.synthesis.input_shape[2]
if 'mapping' not in components:
components.mapping = tflib.Network('G_mapping',
func_name=G_mapping,
dlatent_broadcast=num_layers,
**kwargs)
# Setup variables.
lod_in = tf.get_variable('lod', initializer=np.float32(0), trainable=False)
dlatent_avg = tf.get_variable('dlatent_avg',
shape=[dlatent_size],
initializer=tf.initializers.zeros(),
trainable=False)
# Evaluate mapping network.
dlatents = components.mapping.get_output_for(latents_in, labels_in,
**kwargs)
# Update moving average of W.
if dlatent_avg_beta is not None:
with tf.variable_scope('DlatentAvg'):
batch_avg = tf.reduce_mean(dlatents[:, 0], axis=0)
update_op = tf.assign(
dlatent_avg,
tflib.lerp(batch_avg, dlatent_avg, dlatent_avg_beta))
with tf.control_dependencies([update_op]):
dlatents = tf.identity(dlatents)
# Perform style mixing regularization.
if style_mixing_prob is not None:
with tf.name_scope('StyleMix'):
latents2 = tf.random_normal(tf.shape(latents_in))
dlatents2 = components.mapping.get_output_for(
latents2, labels_in, **kwargs)
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis]
cur_layers = num_layers - tf.cast(lod_in, tf.int32) * 2
mixing_cutoff = tf.cond(
tf.random_uniform([], 0.0, 1.0) < style_mixing_prob,
lambda: tf.random_uniform([], 1, cur_layers, dtype=tf.int32),
lambda: cur_layers)
dlatents = tf.where(
tf.broadcast_to(layer_idx < mixing_cutoff, tf.shape(dlatents)),
dlatents, dlatents2)
# Apply truncation trick.
if truncation_psi is not None and truncation_cutoff is not None:
with tf.variable_scope('Truncation'):
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis]
ones = np.ones(layer_idx.shape, dtype=np.float32)
coefs = tf.where(layer_idx < truncation_cutoff,
truncation_psi * ones, ones)
dlatents = tflib.lerp(dlatent_avg, dlatents, coefs)
# Evaluate synthesis network.
with tf.control_dependencies(
[tf.assign(components.synthesis.find_var('lod'), lod_in)]):
images_out = components.synthesis.get_output_for(
dlatents, force_clean_graph=is_template_graph, **kwargs)
return tf.identity(images_out, name='images_out')
def G_style(
latents_in, # 第一个输入：Z码向量 [minibatch, latent_size].
labels_in, # 第二个输入：条件标签 [minibatch, label_size].
truncation_psi = 0.7, # 截断技巧的样式强度乘数。 None = disable.
truncation_cutoff = 8, # 要应用截断技巧的层数。 None = disable.
truncation_psi_val = None, # 验证期间要使用的truncation_psi的值。
truncation_cutoff_val = None, # 验证期间要使用的truncation_cutoff的值。
dlatent_avg_beta = 0.995, # 在训练期间跟踪W的移动平均值的衰减率。 None = disable.
style_mixing_prob = 0.9, # 训练期间混合样式的概率。 None = disable.
is_training = False, # 网络正在接受训练？ 这个选择可以启用和禁用特定特征。
is_validation = False, # 网络正在验证中？ 这个选择用于确定truncation_psi的值。
is_template_graph = False, # True表示由Network类构造的模板图，False表示实际评估。
components = dnnlib.EasyDict(), # 子网络的容器。调用时候保留。
**kwargs): # 子网络的参数们 (G_mapping 和 G_synthesis)。
# 参数验证。
assert not is_training or not is_validation # 不能同时出现训练/验证状态
assert isinstance(components, dnnlib.EasyDict) # components作为EasyDict类，后续被用来装下synthesis和mapping两个网络
if is_validation: # 把验证期间要使用的truncation_psi_val和truncation_cutoff_val值赋过来（默认是None），也就是验证期不使用截断
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 # 训练期间或截断率为1时，不使用截断
if is_training or (truncation_cutoff is not None and not tflib.is_tf_expression(truncation_cutoff) and truncation_cutoff <= 0):
truncation_cutoff = None # 训练期间或截断层为0时，不使用截断
if not is_training or (dlatent_avg_beta is not None and not tflib.is_tf_expression(dlatent_avg_beta) and dlatent_avg_beta == 1):
dlatent_avg_beta = None # 非训练期间或计算平均W时的衰减率为1时，不使用衰减
if not is_training or (style_mixing_prob is not None and not tflib.is_tf_expression(style_mixing_prob) and style_mixing_prob <= 0):
style_mixing_prob = None # 非训练期间或样式混合的概率小于等于0时，不使用样式混合
# 设置子网络。
if 'synthesis' not in components: # 载入合成网络
components.synthesis = tflib.Network('G_synthesis', func_name=G_synthesis, **kwargs)
num_layers = components.synthesis.input_shape[1] # num_layers = 18
dlatent_size = components.synthesis.input_shape[2] # dlatent_size = (18,512)
if 'mapping' not in components: # 载入映射网络
components.mapping = tflib.Network('G_mapping', func_name=G_mapping, dlatent_broadcast=num_layers, **kwargs)
# 设置变量。
lod_in = tf.get_variable('lod', initializer=np.float32(0), trainable=False)
# 初始化为0。lod的定义式为：lod = resolution_log2 - res，其中resolution_log2（=10）表示最终分辨率级别，res表示当前层对应的分辨率级别（2-10）。
dlatent_avg = tf.get_variable('dlatent_avg', shape=[dlatent_size], initializer=tf.initializers.zeros(), trainable=False) # 人脸平均值
# 计算映射网络输出。
dlatents = components.mapping.get_output_for(latents_in, labels_in, **kwargs)
# 更新W的移动平均值。
if dlatent_avg_beta is not None:
with tf.variable_scope('DlatentAvg'):
batch_avg = tf.reduce_mean(dlatents[:, 0], axis=0) # 找到新batch的dlatent平均值
# ？？？
update_op = tf.assign(dlatent_avg, tflib.lerp(batch_avg, dlatent_avg, dlatent_avg_beta))
# 把batch的dlatent平均值朝着总dlatent平均值以dlatent_avg_beta步幅靠近，作为新的人脸dlatent平均值
# update_op 是一个用于sess的变量
with tf.control_dependencies([update_op]):
dlatents = tf.identity(dlatents) # 确保update_op操作完成
# with tf.control_dependencies: 在with包含的操作operation执行前先执行op列表即[update_up]:
# tf.identity(x)是一个operation。所以会确保update_op操作完成。
# tf.identity是返回一个一模一样新的tensor的op，这会增加一个新节点到gragh中
# 执行样式混合正则化。
if style_mixing_prob is not None:
with tf.name_scope('StyleMix'):
latents2 = tf.random_normal(tf.shape(latents_in))
dlatents2 = components.mapping.get_output_for(latents2, labels_in, **kwargs) # 用来做样式混合的随机中间向量
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis] # 层的索引：[[[0],[1],[2],[3],[4]...[17]]]
cur_layers = num_layers - tf.cast(lod_in, tf.int32) * 2 # 当前层等于总层数减去lod_in的两倍,因为每个分辨率对应两层
mixing_cutoff = tf.cond(
tf.random_uniform([], 0.0, 1.0) < style_mixing_prob, # 如果随机值小于样式混合的概率，则从1到当前层随机选一个层，否则保留原层
lambda: tf.random_uniform([], 1, cur_layers, dtype=tf.int32),
lambda: cur_layers)
dlatents = tf.where(tf.broadcast_to(layer_idx < mixing_cutoff, tf.shape(dlatents)), dlatents, dlatents2)
# 对于1到mixing_cutoff层保留dlatents的值，其余层采用dlatents2的值替换
# 应用截断技巧。
if truncation_psi is not None and truncation_cutoff is not None:
with tf.variable_scope('Truncation'):
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis] # 层的索引：[[[0],[1],[2],[3],[4]...[17]]]
ones = np.ones(layer_idx.shape, dtype=np.float32) # ones:[[[1],[1],[1],[1],[1]...[1]]]
coefs = tf.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones)
# 截断的步幅，需要截断的层步幅为truncation_psi，否则为1
dlatents = tflib.lerp(dlatent_avg, dlatents, coefs)
# 截断，用平均脸dlatent_avg朝着当前脸dlatents以coefs步幅靠近，最后得到的结果取代当前脸dlatents
# 计算合成网络输出。
with tf.control_dependencies([tf.assign(components.synthesis.find_var('lod'), lod_in)]):
images_out = components.synthesis.get_output_for(dlatents, force_clean_graph=is_template_graph, **kwargs)
return tf.identity(images_out, name='images_out') # 返回生成的图片
def G_main(
latents_in, # First input: Latent vectors (Z) [minibatch, latent_size].
labels_in, # Second input: Conditioning labels [minibatch, label_size].
latmask, # mask for split-frame latents blending
dconst, # initial (const) layer displacement
truncation_psi=0.5, # Style strength multiplier for the truncation trick. None = disable.
truncation_cutoff=None, # 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.
return_dlatents=False, # Return dlatents in addition to the images?
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.
mapping_func='G_mapping', # Build func name for the mapping network.
synthesis_func='G_synthesis_stylegan2', # Build func name for the synthesis network.
**kwargs): # Arguments for sub-networks (mapping and 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:
truncation_cutoff = None
if not is_training or (dlatent_avg_beta is not None
and not tflib.is_tf_expression(dlatent_avg_beta)
and dlatent_avg_beta == 1):
dlatent_avg_beta = None
if not is_training or (style_mixing_prob is not None
and not tflib.is_tf_expression(style_mixing_prob)
and style_mixing_prob <= 0):
style_mixing_prob = None
# Setup components.
if 'synthesis' not in components:
components.synthesis = tflib.Network(
'G_synthesis', func_name=globals()[synthesis_func], **kwargs)
num_layers = components.synthesis.input_shape[1]
dlatent_size = components.synthesis.input_shape[2]
if 'mapping' not in components:
components.mapping = tflib.Network('G_mapping',
func_name=globals()[mapping_func],
dlatent_broadcast=num_layers,
**kwargs)
# Setup variables.
lod_in = tf.get_variable('lod', initializer=np.float32(0), trainable=False)
dlatent_avg = tf.get_variable('dlatent_avg',
shape=[dlatent_size],
initializer=tf.initializers.zeros(),
trainable=False)
# Evaluate mapping network.
dlatents = components.mapping.get_output_for(latents_in,
labels_in,
is_training=is_training,
**kwargs)
dlatents = tf.cast(dlatents, tf.float32)
# Update moving average of W.
if dlatent_avg_beta is not None:
with tf.variable_scope('DlatentAvg'):
batch_avg = tf.reduce_mean(dlatents[:, 0], axis=0)
update_op = tf.assign(
dlatent_avg,
tflib.lerp(batch_avg, dlatent_avg, dlatent_avg_beta))
with tf.control_dependencies([update_op]):
dlatents = tf.identity(dlatents)
# Perform style mixing regularization.
if style_mixing_prob is not None:
with tf.variable_scope('StyleMix'):
latents2 = tf.random_normal(tf.shape(latents_in))
dlatents2 = components.mapping.get_output_for(
latents2, labels_in, is_training=is_training, **kwargs)
dlatents2 = tf.cast(dlatents2, tf.float32)
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis]
cur_layers = num_layers - tf.cast(lod_in, tf.int32) * 2
# original version
mixing_cutoff = tf.cond(
tf.random_uniform([], 0.0, 1.0) < style_mixing_prob,
lambda: tf.random_uniform([], 1, cur_layers, dtype=tf.int32),
lambda: cur_layers)
""" # Diff Augment version
mixing_cutoff = tf.where_v2(
tf.random_uniform([tf.shape(dlatents)[0]], 0.0, 1.0) < style_mixing_prob,
tf.random_uniform([tf.shape(dlatents)[0]], 1, cur_layers, dtype=tf.int32),
cur_layers[np.newaxis])[:, np.newaxis, np.newaxis]
dlatents = tf.where(tf.broadcast_to(layer_idx < mixing_cutoff, tf.shape(dlatents)), dlatents, dlatents2)
"""
# Apply truncation trick.
if truncation_psi is not None:
with tf.variable_scope('Truncation'):
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis]
layer_psi = np.ones(layer_idx.shape, dtype=np.float32)
if truncation_cutoff is None:
layer_psi *= truncation_psi
else:
layer_psi = tf.where(layer_idx < truncation_cutoff,
layer_psi * truncation_psi, layer_psi)
dlatents = tflib.lerp(dlatent_avg, dlatents, layer_psi)
# Evaluate synthesis network.
deps = []
if 'lod' in components.synthesis.vars:
deps.append(tf.assign(components.synthesis.vars['lod'], lod_in))
with tf.control_dependencies(deps):
images_out = components.synthesis.get_output_for(
dlatents,
latmask,
dconst,
is_training=is_training,
force_clean_graph=is_template_graph,
**kwargs)
# Return requested outputs.
images_out = tf.identity(images_out, name='images_out')
if return_dlatents:
return images_out, dlatents
return images_out
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.
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.
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 <= 0):
truncation_cutoff = None
if not is_training or (dlatent_avg_beta is not None
and not tflib.is_tf_expression(dlatent_avg_beta)
and dlatent_avg_beta == 1):
dlatent_avg_beta = None
# Setup components.
if "synthesis" not in components:
components.synthesis = tflib.Network(num_inputs=1,
name="G_synthesis",
func_name=G_synthesis,
**kwargs)
num_layers = components.synthesis.input_shape[1]
dlatent_size = components.synthesis.input_shape[2]
if "mapping" not in components:
components.mapping = tflib.Network(num_inputs=2,
name="G_mapping",
func_name=G_mapping,
dlatent_broadcast=num_layers,
**kwargs)
# Setup variables.
dlatent_avg = tf.get_variable(
"dlatent_avg",
shape=[dlatent_size],
initializer=tf.initializers.zeros(),
trainable=False,
)
# Evaluate mapping network.
dlatents = components.mapping.get_output_for(latents_in, labels_in,
**kwargs)
# Update moving average of W.
if dlatent_avg_beta is not None:
with tf.variable_scope("DlatentAvg"):
batch_avg = tf.reduce_mean(dlatents[:, 0], axis=0)
update_op = tf.assign(
dlatent_avg,
tflib.lerp(batch_avg, dlatent_avg, dlatent_avg_beta))
with tf.control_dependencies([update_op]):
dlatents = tf.identity(dlatents)
# Apply truncation trick.
if truncation_psi is not None and truncation_cutoff is not None:
with tf.variable_scope("Truncation"):
layer_idx = np.arange(num_layers)[np.newaxis, :, np.newaxis]
ones = np.ones(layer_idx.shape, dtype=np.float32)
coefs = tf.where(layer_idx < truncation_cutoff,
truncation_psi * ones, ones)
dlatents = tflib.lerp(dlatent_avg, dlatents, coefs)
# Evaluate synthesis network.
images_out = components.synthesis.get_output_for(dlatents, **kwargs)
return images_out
