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/InstanceNorm2D.py

@@ -0,0 +1,40 @@
+from core.leras import nn
+tf = nn.tf
+
+class InstanceNorm2D(nn.LayerBase):
+    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):
+        kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
+        self.weight       = tf.get_variable("weight",   (self.in_ch,), dtype=self.dtype, initializer=kernel_initializer )
+        self.bias         = tf.get_variable("bias",     (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros() )
+
+    def get_weights(self):
+        return [self.weight, self.bias]
+
+    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 )
+
+        x_mean = tf.reduce_mean(x, axis=nn.conv2d_spatial_axes, keepdims=True )
+        x_std  = tf.math.reduce_std(x, axis=nn.conv2d_spatial_axes, keepdims=True ) + 1e-5
+
+        x = (x - x_mean) / x_std
+        x *= weight
+        x += bias
+
+        return x
+
+nn.InstanceNorm2D = InstanceNorm2D