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/layers/BatchNorm2D.py

@@ -0,0 +1,42 @@
+from core.leras import nn
+tf = nn.tf
+
+class BatchNorm2D(nn.LayerBase):
+    """
+    currently not for training
+    """
+    def __init__(self, dim, eps=1e-05, momentum=0.1, dtype=None, **kwargs):
+        self.dim = dim
+        self.eps = eps
+        self.momentum = momentum
+        if dtype is None:
+            dtype = nn.floatx
+        self.dtype = dtype
+        super().__init__(**kwargs)
+
+    def build_weights(self):
+        self.weight       = tf.get_variable("weight",   (self.dim,), dtype=self.dtype, initializer=tf.initializers.ones() )
+        self.bias         = tf.get_variable("bias",     (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros() )
+        self.running_mean = tf.get_variable("running_mean", (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros(), trainable=False )
+        self.running_var  = tf.get_variable("running_var",  (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros(), trainable=False )
+
+    def get_weights(self):
+        return [self.weight, self.bias, self.running_mean, self.running_var]
+
+    def forward(self, x):
+        if nn.data_format == "NHWC":
+            shape = (1,1,1,self.dim)
+        else:
+            shape = (1,self.dim,1,1)
+
+        weight       = tf.reshape ( self.weight      , shape )
+        bias         = tf.reshape ( self.bias        , shape )
+        running_mean = tf.reshape ( self.running_mean, shape )
+        running_var  = tf.reshape ( self.running_var , shape )
+
+        x = (x - running_mean) / tf.sqrt( running_var + self.eps )
+        x *= weight
+        x += bias
+        return x
+
+nn.BatchNorm2D = BatchNorm2D