global refactoring and fixes,

removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG.

fanseg model file in facelib/ is renamed

core/leras/optimizers/RMSprop.py

@@ -0,0 +1,69 @@
+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