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/Dense.py

@@ -0,0 +1,76 @@
+import numpy as np
+from core.leras import nn
+tf = nn.tf
+
+class Dense(nn.LayerBase):
+    def __init__(self, in_ch, out_ch, use_bias=True, use_wscale=False, maxout_ch=0, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
+        """
+        use_wscale          enables weight scale (equalized learning rate)
+                            if kernel_initializer is None, it will be forced to random_normal
+
+        maxout_ch     https://link.springer.com/article/10.1186/s40537-019-0233-0
+                            typical 2-4 if you want to enable DenseMaxout behaviour
+        """
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+        self.use_bias = use_bias
+        self.use_wscale = use_wscale
+        self.maxout_ch = maxout_ch
+        self.kernel_initializer = kernel_initializer
+        self.bias_initializer = bias_initializer
+        self.trainable = trainable
+        if dtype is None:
+            dtype = nn.floatx
+
+        self.dtype = dtype
+        super().__init__(**kwargs)
+
+    def build_weights(self):
+        if self.maxout_ch > 1:
+            weight_shape = (self.in_ch,self.out_ch*self.maxout_ch)
+        else:
+            weight_shape = (self.in_ch,self.out_ch)
+
+        kernel_initializer = self.kernel_initializer
+
+        if self.use_wscale:
+            gain = 1.0
+            fan_in = np.prod( weight_shape[:-1] )
+            he_std = gain / np.sqrt(fan_in) # He init
+            self.wscale = tf.constant(he_std, dtype=self.dtype )
+            if kernel_initializer is None:
+                kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
+
+        if kernel_initializer is None:
+            kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
+
+        self.weight = tf.get_variable("weight", weight_shape, dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
+
+        if self.use_bias:
+            bias_initializer = self.bias_initializer
+            if bias_initializer is None:
+                bias_initializer = tf.initializers.zeros(dtype=self.dtype)
+            self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable )
+
+    def get_weights(self):
+        weights = [self.weight]
+        if self.use_bias:
+            weights += [self.bias]
+        return weights
+
+    def forward(self, x):
+        weight = self.weight
+        if self.use_wscale:
+            weight = weight * self.wscale
+
+        x = tf.matmul(x, weight)
+
+        if self.maxout_ch > 1:
+            x = tf.reshape (x, (-1, self.out_ch, self.maxout_ch) )
+            x = tf.reduce_max(x, axis=-1)
+
+        if self.use_bias:
+            x = tf.add(x, tf.reshape(self.bias, (1,self.out_ch) ) )
+
+        return x
+nn.Dense = Dense