mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
61472cdaf7
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
69 lines
No EOL
2.8 KiB
Python
69 lines
No EOL
2.8 KiB
Python
from tensorflow.python.ops import control_flow_ops, state_ops
|
|
from core.leras import nn
|
|
tf = nn.tf
|
|
|
|
class RMSprop(nn.OptimizerBase):
|
|
def __init__(self, lr=0.001, rho=0.9, lr_dropout=1.0, epsilon=1e-7, clipnorm=0.0, name=None):
|
|
super().__init__(name=name)
|
|
|
|
if name is None:
|
|
raise ValueError('name must be defined.')
|
|
|
|
self.lr_dropout = lr_dropout
|
|
self.clipnorm = clipnorm
|
|
|
|
with tf.device('/CPU:0') :
|
|
with tf.variable_scope(self.name):
|
|
self.lr = tf.Variable (lr, name="lr")
|
|
self.rho = tf.Variable (rho, name="rho")
|
|
self.epsilon = tf.Variable (epsilon, name="epsilon")
|
|
self.iterations = tf.Variable(0, dtype=tf.int64, name='iters')
|
|
|
|
self.accumulators_dict = {}
|
|
self.lr_rnds_dict = {}
|
|
|
|
def get_weights(self):
|
|
return [self.lr, self.rho, self.epsilon, self.iterations] + list(self.accumulators_dict.values())
|
|
|
|
def initialize_variables(self, trainable_weights, vars_on_cpu=True):
|
|
# Initialize here all trainable variables used in training
|
|
e = tf.device('/CPU:0') if vars_on_cpu else None
|
|
if e: e.__enter__()
|
|
with tf.variable_scope(self.name):
|
|
accumulators = { v.name : tf.get_variable ( f'acc_{v.name}'.replace(':','_'), v.shape, dtype=v.dtype, initializer=tf.initializers.constant(0.0), trainable=False) for v in trainable_weights }
|
|
self.accumulators_dict.update ( accumulators)
|
|
|
|
if self.lr_dropout != 1.0:
|
|
lr_rnds = [ nn.random_binomial( v.shape, p=self.lr_dropout, dtype=v.dtype) for v in trainable_weights ]
|
|
self.lr_rnds_dict.update ( { v.name : rnd for v,rnd in zip(trainable_weights,lr_rnds) } )
|
|
if e: e.__exit__(None, None, None)
|
|
|
|
def get_update_op(self, grads_vars):
|
|
updates = []
|
|
|
|
if self.clipnorm > 0.0:
|
|
norm = tf.sqrt( sum([tf.reduce_sum(tf.square(g)) for g,v in grads_vars]))
|
|
updates += [ state_ops.assign_add( self.iterations, 1) ]
|
|
for i, (g,v) in enumerate(grads_vars):
|
|
if self.clipnorm > 0.0:
|
|
g = self.tf_clip_norm(g, self.clipnorm, norm)
|
|
|
|
a = self.accumulators_dict[ v.name ]
|
|
|
|
rho = tf.cast(self.rho, a.dtype)
|
|
new_a = rho * a + (1. - rho) * tf.square(g)
|
|
|
|
lr = tf.cast(self.lr, a.dtype)
|
|
epsilon = tf.cast(self.epsilon, a.dtype)
|
|
|
|
v_diff = - lr * g / (tf.sqrt(new_a) + epsilon)
|
|
if self.lr_dropout != 1.0:
|
|
lr_rnd = self.lr_rnds_dict[v.name]
|
|
v_diff *= lr_rnd
|
|
new_v = v + v_diff
|
|
|
|
updates.append (state_ops.assign(a, new_a))
|
|
updates.append (state_ops.assign(v, new_v))
|
|
|
|
return control_flow_ops.group ( *updates, name=self.name+'_updates')
|
|
nn.RMSprop = RMSprop |