diff --git a/__dev/test.py b/__dev/test.py
index 1389358..68a725c 100644
--- a/__dev/test.py
+++ b/__dev/test.py
@@ -250,25 +250,25 @@ def umeyama(src, dst, estimate_scale):
 
     return T
     
-mean_face_x = np.array([
-0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
-0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
-0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
-0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
-0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
-0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
-0.553364, 0.490127, 0.42689 ])
-
-mean_face_y = np.array([
-0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
-0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
-0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
-0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
-0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
-0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
-0.784792, 0.824182, 0.831803, 0.824182 ])
-
-landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
+#mean_face_x = np.array([
+#0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
+#0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
+#0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
+#0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
+#0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
+#0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
+#0.553364, 0.490127, 0.42689 ])
+#
+#mean_face_y = np.array([
+#0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
+#0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
+#0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
+#0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
+#0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
+#0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
+#0.784792, 0.824182, 0.831803, 0.824182 ])
+#
+#landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
 
 def get_transform_mat (image_landmarks, output_size, scale=1.0):
     if not isinstance(image_landmarks, np.ndarray):
@@ -306,11 +306,7 @@ def get_transform_mat (image_landmarks, output_size, scale=1.0):
 import mathlib
 def main():
 
-    def f ( *args, asd=True, **kwargs ):
-        import code
-        code.interact(local=dict(globals(), **locals()))
-    
-    f( 1, asd=True, bg=0)
+
     
     from nnlib import nnlib
     exec( nnlib.import_all( device_config=nnlib.device.Config() ), locals(), globals() )
@@ -1357,6 +1353,64 @@ O[i0, i1, i2, i3: (1 + 1 - 1)/1, (64 + 1 - 1)/1, (64 + 2 - 1)/2, (1 + 1 - 1)/1]
 
 
 if __name__ == "__main__":
+        
+    #import os
+    #os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
+    #os.environ["PLAIDML_DEVICE_IDS"] = "opencl_nvidia_geforce_gtx_1060_6gb.0"
+    #import keras
+    #import numpy as np
+    #import cv2
+    #import time
+    #K = keras.backend
+    #
+    #
+    #
+    #PNet_Input = keras.layers.Input ( (None, None,3) )
+    #x = PNet_Input
+    #x = keras.layers.Conv2D (10, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
+    #x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU1" )(x)
+    #x = keras.layers.MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
+    #x = keras.layers.Conv2D (16, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
+    #x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU2" )(x)
+    #x = keras.layers.Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
+    #x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU3" )(x)
+    #prob = keras.layers.Conv2D (2, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv41")(x)
+    #x = keras.layers.Conv2D (4, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv42")(x)
+    #
+    #pnet = K.function ([PNet_Input], [x,prob] )
+    #
+    #img = np.random.uniform ( size=(1920,1920,3) )
+    #minsize=80
+    #factor=0.95
+    #factor_count=0
+    #h=img.shape[0]
+    #w=img.shape[1]
+    #
+    #minl=np.amin([h, w])
+    #m=12.0/minsize
+    #minl=minl*m
+    ## create scale pyramid
+    #scales=[]
+    #while minl>=12:
+    #    scales += [m*np.power(factor, factor_count)]
+    #    minl = minl*factor
+    #    factor_count += 1
+    #    # first stage
+    #    for scale in scales:
+    #        hs=int(np.ceil(h*scale))
+    #        ws=int(np.ceil(w*scale))
+    #        im_data = cv2.resize(img, (ws, hs), interpolation=cv2.INTER_LINEAR)
+    #        im_data = (im_data-127.5)*0.0078125
+    #        img_x = np.expand_dims(im_data, 0)
+    #        img_x = np.transpose(img_x, (0,2,1,3))
+    #        t = time.time()
+    #        out = pnet([img_x])
+    #        t = time.time() - t
+    #        print (img_x.shape, t)
+    #    
+    #import code
+    #code.interact(local=dict(globals(), **locals()))
+    
     #os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
     #os.environ["PLAIDML_DEVICE_IDS"] = "opencl_nvidia_geforce_gtx_1060_6gb.0"
     #import keras