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/OptimizerBase.py

@@ -0,0 +1,42 @@
+import copy
+from core.leras import nn
+tf = nn.tf
+
+class OptimizerBase(nn.Saveable):
+    def __init__(self, name=None):
+        super().__init__(name=name)
+
+    def tf_clip_norm(self, g, c, n):
+        """Clip the gradient `g` if the L2 norm `n` exceeds `c`.
+        # Arguments
+            g: Tensor, the gradient tensor
+            c: float >= 0. Gradients will be clipped
+                when their L2 norm exceeds this value.
+            n: Tensor, actual norm of `g`.
+        # Returns
+            Tensor, the gradient clipped if required.
+        """
+        if c <= 0:  # if clipnorm == 0 no need to add ops to the graph
+            return g
+
+        condition = n >= c
+        then_expression = tf.scalar_mul(c / n, g)
+        else_expression = g
+
+        # saving the shape to avoid converting sparse tensor to dense
+        if isinstance(then_expression, tf.Tensor):
+            g_shape = copy.copy(then_expression.get_shape())
+        elif isinstance(then_expression, tf.IndexedSlices):
+            g_shape = copy.copy(then_expression.dense_shape)
+        if condition.dtype != tf.bool:
+            condition = tf.cast(condition, 'bool')
+        g = tf.cond(condition,
+                    lambda: then_expression,
+                    lambda: else_expression)
+        if isinstance(then_expression, tf.Tensor):
+            g.set_shape(g_shape)
+        elif isinstance(then_expression, tf.IndexedSlices):
+            g._dense_shape = g_shape
+
+        return g
+nn.OptimizerBase = OptimizerBase