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Upgraded to TF version 1.13.2

Browse source 
Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
This commit is contained in:
Colombo 2020-01-25 21:58:19 +04:00
parent a3dfcb91b9
commit 76ca79216e
49 changed files with 1320 additions and 1297 deletions
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16
core/leras/optimizers.py
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@ -73,7 +73,7 @@ def initialize_optimizers(nn):
e = tf.device('/CPU:0') if vars_on_cpu else None
if e: e.__enter__()
with tf.variable_scope(self.name):
accumulators = [ tf.get_variable ( f'acc_{i+self.accumulator_counter}', v.shape, initializer=tf.initializers.constant(0.0), trainable=False)
accumulators = [ tf.get_variable ( f'acc_{i+self.accumulator_counter}', v.shape, dtype=v.dtype, initializer=tf.initializers.constant(0.0), trainable=False)
for (i, v ) in enumerate(trainable_weights) ]
self.accumulators_dict.update ( { v.name : acc for v,acc in zip(trainable_weights,accumulators) } )
@ -81,13 +81,13 @@ def initialize_optimizers(nn):
self.accumulator_counter += len(trainable_weights)
if self.lr_dropout != 1.0:
lr_rnds = [ nn.tf_random_binomial( v.shape, p=self.lr_dropout) for v in trainable_weights ]
lr_rnds = [ nn.tf_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 = []
lr = self.lr
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) ]
@ -96,8 +96,14 @@ def initialize_optimizers(nn):
g = self.tf_clip_norm(g, self.clipnorm, norm)
a = self.accumulators_dict[v.name]
new_a = self.rho * a + (1. - self.rho) * tf.square(g)
v_diff = - lr * g / (tf.sqrt(new_a) + self.epsilon)
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