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/FRNorm2D.py

@@ -0,0 +1,38 @@
+from core.leras import nn
+tf = nn.tf
+
+class FRNorm2D(nn.LayerBase):
+    """
+    Tensorflow implementation of
+    Filter Response Normalization Layer: Eliminating Batch Dependence in theTraining of Deep Neural Networks
+    https://arxiv.org/pdf/1911.09737.pdf
+    """
+    def __init__(self, in_ch, dtype=None, **kwargs):
+        self.in_ch = in_ch
+
+        if dtype is None:
+            dtype = nn.floatx
+        self.dtype = dtype
+
+        super().__init__(**kwargs)
+
+    def build_weights(self):
+        self.weight      = tf.get_variable("weight", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.ones() )
+        self.bias        = tf.get_variable("bias",   (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros() )
+        self.eps         = tf.get_variable("eps",    (1,), dtype=self.dtype, initializer=tf.initializers.constant(1e-6) )
+
+    def get_weights(self):
+        return [self.weight, self.bias, self.eps]
+
+    def forward(self, x):
+        if nn.data_format == "NHWC":
+            shape = (1,1,1,self.in_ch)
+        else:
+            shape = (1,self.in_ch,1,1)
+        weight       = tf.reshape ( self.weight, shape )
+        bias         = tf.reshape ( self.bias  , shape )
+        nu2 = tf.reduce_mean(tf.square(x), axis=nn.conv2d_spatial_axes, keepdims=True)
+        x = x * ( 1.0/tf.sqrt(nu2 + tf.abs(self.eps) ) )
+
+        return x*weight + bias
+nn.FRNorm2D = FRNorm2D