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/AdaIN.py

@@ -0,0 +1,56 @@
+from core.leras import nn
+tf = nn.tf
+
+class AdaIN(nn.LayerBase):
+    """
+    """
+    def __init__(self, in_ch, mlp_ch, kernel_initializer=None, dtype=None, **kwargs):
+        self.in_ch = in_ch
+        self.mlp_ch = mlp_ch
+        self.kernel_initializer = kernel_initializer
+
+        if dtype is None:
+            dtype = nn.floatx
+        self.dtype = dtype
+
+        super().__init__(**kwargs)
+
+    def build_weights(self):
+        kernel_initializer = self.kernel_initializer
+        if kernel_initializer is None:
+            kernel_initializer = tf.initializers.he_normal()
+
+        self.weight1   = tf.get_variable("weight1", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
+        self.bias1     = tf.get_variable("bias1",   (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
+        self.weight2   = tf.get_variable("weight2", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
+        self.bias2     = tf.get_variable("bias2",   (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
+
+    def get_weights(self):
+        return [self.weight1, self.bias1, self.weight2, self.bias2]
+
+    def forward(self, inputs):
+        x, mlp = inputs
+
+        gamma = tf.matmul(mlp, self.weight1)
+        gamma = tf.add(gamma, tf.reshape(self.bias1, (1,self.in_ch) ) )
+
+        beta = tf.matmul(mlp, self.weight2)
+        beta = tf.add(beta, tf.reshape(self.bias2, (1,self.in_ch) ) )
+
+
+        if nn.data_format == "NHWC":
+            shape = (-1,1,1,self.in_ch)
+        else:
+            shape = (-1,self.in_ch,1,1)
+
+        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 *= tf.reshape(gamma, shape)
+
+        x += tf.reshape(beta, shape)
+
+        return x
+
+nn.AdaIN = AdaIN