Merge commit 'a6438ca494' into merge-from-upstream/2021-06-02

# Conflicts:
#	core/imagelib/warp.py
#	core/leras/nn.py
#	main.py
#	mainscripts/Trainer.py
#	models/Model_SAEHD/Model.py
#	requirements-cuda.txt

README.md

@@ -26,9 +26,9 @@ https://arxiv.org/abs/2005.05535</a>
 
 <p align="center">
 
-![](doc/logo_cuda.png)
 ![](doc/logo_tensorflow.png)
-![](doc/logo_python.png)
+![](doc/logo_cuda.png)
+![](doc/logo_directx.png)
 
 </p>
 
@@ -201,7 +201,7 @@ Unfortunately, there is no "make everything ok" button in DeepFaceLab. You shoul
 </td></tr>
 
 <tr><td align="right">
-<a href="https://tinyurl.com/y8lntghz">Windows (magnet link)</a>
+<a href="https://tinyurl.com/87vwbtr4">Windows (magnet link)</a>
 </td><td align="center">Last release. Use torrent client to download.</td></tr>
 
 <tr><td align="right">
@@ -340,10 +340,6 @@ QQ 951138799
 bitcoin:bc1qkhh7h0gwwhxgg6h6gpllfgstkd645fefrd5s6z
 </td></tr>
 
-<tr><td align="right">
-Alipay 捐款
-</td><td align="center"> <img src="doc/Alipay_donation.jpg" align="center"> </td></tr>
-
 <tr><td colspan=2 align="center">
 
 ### Collect facesets

XSegEditor/QIconDB.py

@@ -17,6 +17,7 @@ class QIconDB():
         QIconDB.poly_type_exclude = QIcon ( str(icon_path / 'poly_type_exclude.png') )
         QIconDB.left  = QIcon ( str(icon_path / 'left.png') )
         QIconDB.right = QIcon ( str(icon_path / 'right.png') )
+        QIconDB.trashcan = QIcon ( str(icon_path / 'trashcan.png') )
         QIconDB.pt_edit_mode = QIcon ( str(icon_path / 'pt_edit_mode.png') )
         QIconDB.view_lock_center = QIcon ( str(icon_path / 'view_lock_center.png') )
         QIconDB.view_baked = QIcon ( str(icon_path / 'view_baked.png') )

XSegEditor/QStringDB.py

@@ -85,6 +85,11 @@ class QStringDB():
                                             'zh' : '保存并转到下一张图片\n按住SHIFT : 加快\n按住CTRL : 跳过未标记的\n',
                                            }[lang]  
         
+        QStringDB.btn_delete_image_tip = {  'en' : 'Move to _trash and Next image\n',
+                                            'ru' : 'Переместить в _trash и следующее изображение\n',
+                                            'zh' : '移至_trash，转到下一张图片 ',
+                                           }[lang]  
+                                           
         QStringDB.loading_tip = {'en' : 'Loading',
                                  'ru' : 'Загрузка',
                                  'zh' : '正在载入',

XSegEditor/XSegEditor.py

@@ -1164,6 +1164,7 @@ class MainWindow(QXMainWindow):
         super().__init__()
 
         self.input_dirpath = input_dirpath
+        self.trash_dirpath = input_dirpath.parent / (input_dirpath.name + '_trash')
         self.cfg_root_path = cfg_root_path
 
         self.cfg_path = cfg_root_path / 'MainWindow_cfg.dat'
@@ -1341,7 +1342,18 @@ class MainWindow(QXMainWindow):
 
         self.update_cached_images()
         self.update_preview_bar()
-
+        
+    def trash_current_image(self):
+        self.process_next_image()
+        
+        img_path = self.image_paths_done.pop(-1)
+        img_path = Path(img_path)
+        self.trash_dirpath.mkdir(parents=True, exist_ok=True)
+        img_path.rename( self.trash_dirpath / img_path.name )
+        
+        self.update_cached_images()
+        self.update_preview_bar()
+        
     def initialize_ui(self):
 
         self.canvas = QCanvas()
@@ -1356,20 +1368,36 @@ class MainWindow(QXMainWindow):
         btn_next_image = QXIconButton(QIconDB.right, QStringDB.btn_next_image_tip, shortcut='D', click_func=self.process_next_image)
         btn_next_image.setIconSize(QUIConfig.preview_bar_icon_q_size)
 
-
+        btn_delete_image = QXIconButton(QIconDB.trashcan, QStringDB.btn_delete_image_tip, shortcut='X', click_func=self.trash_current_image)
+        btn_delete_image.setIconSize(QUIConfig.preview_bar_icon_q_size)
+    
+        pad_image = QWidget()
+        pad_image.setFixedSize(QUIConfig.preview_bar_icon_q_size)
+        
         preview_image_bar_frame_l = QHBoxLayout()
         preview_image_bar_frame_l.setContentsMargins(0,0,0,0)
+        preview_image_bar_frame_l.addWidget ( pad_image, alignment=Qt.AlignCenter)
         preview_image_bar_frame_l.addWidget ( btn_prev_image, alignment=Qt.AlignCenter)
         preview_image_bar_frame_l.addWidget ( image_bar)
         preview_image_bar_frame_l.addWidget ( btn_next_image, alignment=Qt.AlignCenter)
+        #preview_image_bar_frame_l.addWidget ( btn_delete_image, alignment=Qt.AlignCenter)
 
         preview_image_bar_frame = QFrame()
         preview_image_bar_frame.setSizePolicy ( QSizePolicy.Fixed, QSizePolicy.Fixed )
         preview_image_bar_frame.setLayout(preview_image_bar_frame_l)
 
-        preview_image_bar_l = QHBoxLayout()
+        preview_image_bar_frame2_l = QHBoxLayout()
-        preview_image_bar_l.addWidget (preview_image_bar_frame)
+        preview_image_bar_frame2_l.setContentsMargins(0,0,0,0)
+        preview_image_bar_frame2_l.addWidget ( btn_delete_image, alignment=Qt.AlignCenter)
 
+        preview_image_bar_frame2 = QFrame()
+        preview_image_bar_frame2.setSizePolicy ( QSizePolicy.Fixed, QSizePolicy.Fixed )
+        preview_image_bar_frame2.setLayout(preview_image_bar_frame2_l)
+        
+        preview_image_bar_l = QHBoxLayout()
+        preview_image_bar_l.addWidget (preview_image_bar_frame, alignment=Qt.AlignCenter)
+        preview_image_bar_l.addWidget (preview_image_bar_frame2)
+        
         preview_image_bar = QFrame()
         preview_image_bar.setFrameShape(QFrame.StyledPanel)
         preview_image_bar.setSizePolicy ( QSizePolicy.Expanding, QSizePolicy.Fixed )

core/imagelib/SegIEPolys.py

@@ -77,6 +77,8 @@ class SegIEPoly():
         self.pts = np.array(pts)
         self.n_max = self.n = len(pts)
         
+    def mult_points(self, val):
+        self.pts *= val       
     
         
 
@@ -136,7 +138,11 @@ class SegIEPolys():
 
     def dump(self):
         return {'polys' : [ poly.dump() for poly in self.polys ] }
-
+    
+    def mult_points(self, val):
+        for poly in self.polys:
+            poly.mult_points(val)
+        
     @staticmethod
     def load(data=None):
         ie_polys = SegIEPolys()

core/imagelib/__init__.py

@@ -14,14 +14,19 @@ from .reduce_colors import reduce_colors
 
 from .color_transfer import color_transfer, color_transfer_mix, color_transfer_sot, color_transfer_mkl, color_transfer_idt, color_hist_match, reinhard_color_transfer, linear_color_transfer, color_augmentation 
 
-from .common import normalize_channels, cut_odd_image, overlay_alpha_image
+from .common import random_crop, normalize_channels, cut_odd_image, overlay_alpha_image
 
 from .SegIEPolys import *
 
 from .blursharpen import LinearMotionBlur, blursharpen
 
 from .filters import apply_random_rgb_levels, \
+                     apply_random_overlay_triangle, \
                      apply_random_hsv_shift, \
+                     apply_random_sharpen, \
                      apply_random_motion_blur, \
                      apply_random_gaussian_blur, \
-                     apply_random_bilinear_resize
+                     apply_random_nearest_resize, \
+                     apply_random_bilinear_resize, \
+                     apply_random_jpeg_compress, \
+                     apply_random_relight

core/imagelib/common.py

@@ -1,5 +1,16 @@
 import numpy as np
 
+def random_crop(img, w, h):
+    height, width = img.shape[:2]
+    
+    h_rnd = height - h
+    w_rnd = width - w
+    
+    y = np.random.randint(0, h_rnd) if h_rnd > 0 else 0
+    x = np.random.randint(0, w_rnd) if w_rnd > 0 else 0
+    
+    return img[y:y+height, x:x+width]
+                        
 def normalize_channels(img, target_channels):
     img_shape_len = len(img.shape)
     if img_shape_len == 2:

core/imagelib/filters.py

@@ -1,47 +1,65 @@
 import numpy as np
-from .blursharpen import LinearMotionBlur
+from .blursharpen import LinearMotionBlur, blursharpen
 import cv2
 
 def apply_random_rgb_levels(img, mask=None, rnd_state=None):
     if rnd_state is None:
         rnd_state = np.random
     np_rnd = rnd_state.rand
-    
+
     inBlack  = np.array([np_rnd()*0.25    , np_rnd()*0.25    , np_rnd()*0.25], dtype=np.float32)
     inWhite  = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
     inGamma  = np.array([0.5+np_rnd(), 0.5+np_rnd(), 0.5+np_rnd()], dtype=np.float32)
-    
+
     outBlack  = np.array([np_rnd()*0.25    , np_rnd()*0.25    , np_rnd()*0.25], dtype=np.float32)
     outWhite  = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
 
     result = np.clip( (img - inBlack) / (inWhite - inBlack), 0, 1 )
     result = ( result ** (1/inGamma) ) *  (outWhite - outBlack) + outBlack
     result = np.clip(result, 0, 1)
-    
+
     if mask is not None:
         result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
 def apply_random_hsv_shift(img, mask=None, rnd_state=None):
     if rnd_state is None:
         rnd_state = np.random
-    
+
     h, s, v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
     h = ( h + rnd_state.randint(360) ) % 360
     s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
-    v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )                    
+    v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
-    
+
     result = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
     if mask is not None:
         result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
+def apply_random_sharpen( img, chance, kernel_max_size, mask=None, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
+
+    sharp_rnd_kernel = rnd_state.randint(kernel_max_size)+1
+
+    result = img
+    if rnd_state.randint(100) < np.clip(chance, 0, 100):
+        if rnd_state.randint(2) == 0:
+            result = blursharpen(result, 1, sharp_rnd_kernel, rnd_state.randint(10) )
+        else:
+            result = blursharpen(result, 2, sharp_rnd_kernel, rnd_state.randint(50) )
+                
+        if mask is not None:
+            result = img*(1-mask) + result*mask
+
+    return result
+
 def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
-    
+
     mblur_rnd_kernel = rnd_state.randint(mb_max_size)+1
     mblur_rnd_deg    = rnd_state.randint(360)
 
@@ -50,38 +68,178 @@ def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=Non
         result = LinearMotionBlur (result, mblur_rnd_kernel, mblur_rnd_deg )
         if mask is not None:
             result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
 def apply_random_gaussian_blur( img, chance, kernel_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
-        
+
     result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         gblur_rnd_kernel = rnd_state.randint(kernel_max_size)*2+1
         result = cv2.GaussianBlur(result, (gblur_rnd_kernel,)*2 , 0)
         if mask is not None:
             result = img*(1-mask) + result*mask
-            
+
     return result
-    
+
-    
+def apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=None, rnd_state=None ):
-def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
 
     result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         h,w,c = result.shape
-        
+
         trg = rnd_state.rand()
-        rw = w - int( trg * int(w*(max_size_per/100.0)) )                        
+        rw = w - int( trg * int(w*(max_size_per/100.0)) )
-        rh = h - int( trg * int(h*(max_size_per/100.0)) )   
+        rh = h - int( trg * int(h*(max_size_per/100.0)) )
-             
+
-        result = cv2.resize (result, (rw,rh), interpolation=cv2.INTER_LINEAR )
+        result = cv2.resize (result, (rw,rh), interpolation=interpolation )
-        result = cv2.resize (result, (w,h), interpolation=cv2.INTER_LINEAR )
+        result = cv2.resize (result, (w,h), interpolation=interpolation )
         if mask is not None:
             result = img*(1-mask) + result*mask
-            
+
+    return result
+
+def apply_random_nearest_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
+    return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_NEAREST, mask=mask, rnd_state=rnd_state )
+
+def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
+    return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=mask, rnd_state=rnd_state )
+
+def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
+
+    result = img
+    if rnd_state.randint(100) < np.clip(chance, 0, 100):
+        h,w,c = result.shape
+
+        quality = rnd_state.randint(10,101)
+
+        ret, result = cv2.imencode('.jpg', np.clip(img*255, 0,255).astype(np.uint8), [int(cv2.IMWRITE_JPEG_QUALITY), quality] )
+        if ret == True:
+            result = cv2.imdecode(result, flags=cv2.IMREAD_UNCHANGED)
+            result = result.astype(np.float32) / 255.0
+            if mask is not None:
+                result = img*(1-mask) + result*mask
+
+    return result
+    
+def apply_random_overlay_triangle( img, max_alpha, mask=None, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
+
+    h,w,c = img.shape
+    pt1 = [rnd_state.randint(w), rnd_state.randint(h) ]
+    pt2 = [rnd_state.randint(w), rnd_state.randint(h) ]
+    pt3 = [rnd_state.randint(w), rnd_state.randint(h) ]
+    
+    alpha = rnd_state.uniform()*max_alpha
+    
+    tri_mask = cv2.fillPoly( np.zeros_like(img), [ np.array([pt1,pt2,pt3], np.int32) ], (alpha,)*c )
+    
+    if rnd_state.randint(2) == 0:
+        result = np.clip(img+tri_mask, 0, 1)
+    else:
+        result = np.clip(img-tri_mask, 0, 1)
+    
+    if mask is not None:
+        result = img*(1-mask) + result*mask
+
+    return result
+    
+def _min_resize(x, m):
+    if x.shape[0] < x.shape[1]:
+        s0 = m
+        s1 = int(float(m) / float(x.shape[0]) * float(x.shape[1]))
+    else:
+        s0 = int(float(m) / float(x.shape[1]) * float(x.shape[0]))
+        s1 = m
+    new_max = min(s1, s0)
+    raw_max = min(x.shape[0], x.shape[1])
+    return cv2.resize(x, (s1, s0), interpolation=cv2.INTER_LANCZOS4)
+    
+def _d_resize(x, d, fac=1.0):
+    new_min = min(int(d[1] * fac), int(d[0] * fac))
+    raw_min = min(x.shape[0], x.shape[1])
+    if new_min < raw_min:
+        interpolation = cv2.INTER_AREA
+    else:
+        interpolation = cv2.INTER_LANCZOS4
+    y = cv2.resize(x, (int(d[1] * fac), int(d[0] * fac)), interpolation=interpolation)
+    return y
+    
+def _get_image_gradient(dist):
+    cols = cv2.filter2D(dist, cv2.CV_32F, np.array([[-1, 0, +1], [-2, 0, +2], [-1, 0, +1]]))
+    rows = cv2.filter2D(dist, cv2.CV_32F, np.array([[-1, -2, -1], [0, 0, 0], [+1, +2, +1]]))
+    return cols, rows
+
+def _generate_lighting_effects(content):
+    h512 = content
+    h256 = cv2.pyrDown(h512)
+    h128 = cv2.pyrDown(h256)
+    h64 = cv2.pyrDown(h128)
+    h32 = cv2.pyrDown(h64)
+    h16 = cv2.pyrDown(h32)
+    c512, r512 = _get_image_gradient(h512)
+    c256, r256 = _get_image_gradient(h256)
+    c128, r128 = _get_image_gradient(h128)
+    c64, r64 = _get_image_gradient(h64)
+    c32, r32 = _get_image_gradient(h32)
+    c16, r16 = _get_image_gradient(h16)
+    c = c16
+    c = _d_resize(cv2.pyrUp(c), c32.shape) * 4.0 + c32
+    c = _d_resize(cv2.pyrUp(c), c64.shape) * 4.0 + c64
+    c = _d_resize(cv2.pyrUp(c), c128.shape) * 4.0 + c128
+    c = _d_resize(cv2.pyrUp(c), c256.shape) * 4.0 + c256
+    c = _d_resize(cv2.pyrUp(c), c512.shape) * 4.0 + c512
+    r = r16
+    r = _d_resize(cv2.pyrUp(r), r32.shape) * 4.0 + r32
+    r = _d_resize(cv2.pyrUp(r), r64.shape) * 4.0 + r64
+    r = _d_resize(cv2.pyrUp(r), r128.shape) * 4.0 + r128
+    r = _d_resize(cv2.pyrUp(r), r256.shape) * 4.0 + r256
+    r = _d_resize(cv2.pyrUp(r), r512.shape) * 4.0 + r512
+    coarse_effect_cols = c
+    coarse_effect_rows = r
+    EPS = 1e-10
+
+    max_effect = np.max((coarse_effect_cols**2 + coarse_effect_rows**2)**0.5, axis=0, keepdims=True, ).max(1, keepdims=True)
+    coarse_effect_cols = (coarse_effect_cols + EPS) / (max_effect + EPS)
+    coarse_effect_rows = (coarse_effect_rows + EPS) / (max_effect + EPS)
+
+    return np.stack([ np.zeros_like(coarse_effect_rows), coarse_effect_rows, coarse_effect_cols], axis=-1)
+    
+def apply_random_relight(img, mask=None, rnd_state=None):
+    if rnd_state is None:
+        rnd_state = np.random
+        
+    def_img = img
+        
+    if rnd_state.randint(2) == 0:
+        light_pos_y = 1.0 if rnd_state.randint(2) == 0 else -1.0
+        light_pos_x = rnd_state.uniform()*2-1.0
+    else:
+        light_pos_y = rnd_state.uniform()*2-1.0
+        light_pos_x = 1.0 if rnd_state.randint(2) == 0 else -1.0
+                    
+    light_source_height = 0.3*rnd_state.uniform()*0.7
+    light_intensity = 1.0+rnd_state.uniform()
+    ambient_intensity = 0.5
+    
+    light_source_location = np.array([[[light_source_height, light_pos_y, light_pos_x ]]], dtype=np.float32)
+    light_source_direction = light_source_location / np.sqrt(np.sum(np.square(light_source_location)))
+
+    lighting_effect = _generate_lighting_effects(img)
+    lighting_effect = np.sum(lighting_effect * light_source_direction, axis=-1).clip(0, 1)
+    lighting_effect = np.mean(lighting_effect, axis=-1, keepdims=True)
+
+    result = def_img * (ambient_intensity + lighting_effect * light_intensity) #light_source_color
+    result = np.clip(result, 0, 1)
+    
+    if mask is not None:
+        result = def_img*(1-mask) + result*mask
+    
     return result

core/imagelib/sd/__init__.py

@@ -1,2 +1,2 @@
-from .draw import *
+from .draw import circle_faded, random_circle_faded, bezier, random_bezier_split_faded, random_faded
 from .calc import *

core/imagelib/sd/draw.py

@@ -1,23 +1,36 @@
 """
 Signed distance drawing functions using numpy.
 """
+import math
 
 import numpy as np
 from numpy import linalg as npla
 
-def circle_faded( hw, center, fade_dists ):
+
+def vector2_dot(a,b):
+    return a[...,0]*b[...,0]+a[...,1]*b[...,1]
+    
+def vector2_dot2(a):
+    return a[...,0]*a[...,0]+a[...,1]*a[...,1]
+    
+def vector2_cross(a,b):
+    return a[...,0]*b[...,1]-a[...,1]*b[...,0]
+
+
+def circle_faded( wh, center, fade_dists ):
     """
     returns drawn circle in [h,w,1] output range [0..1.0] float32
     
-    hw         = [h,w]                      resolution
+    wh         = [w,h]                      resolution
-    center     = [y,x]                      center of circle
+    center     = [x,y]                      center of circle
     fade_dists = [fade_start, fade_end]     fade values
     """
-    h,w = hw      
+    w,h = wh      
     
     pts = np.empty( (h,w,2), dtype=np.float32 )
-    pts[...,1] = np.arange(h)[None,:]
     pts[...,0] = np.arange(w)[:,None]
+    pts[...,1] = np.arange(h)[None,:]
+    
     pts = pts.reshape ( (h*w, -1) )
 
     pts_dists = np.abs ( npla.norm(pts-center, axis=-1) )
@@ -30,15 +43,158 @@ def circle_faded( hw, center, fade_dists ):
     pts_dists = np.clip( 1-pts_dists, 0, 1)
     
     return pts_dists.reshape ( (h,w,1) ).astype(np.float32)
+
+
+def bezier( wh, A, B, C ):
+    """
+    returns drawn bezier in [h,w,1] output range float32, 
+    every pixel contains signed distance to bezier line
+
+        wh      [w,h]       resolution
+        A,B,C   points [x,y]
+    """
     
-def random_circle_faded ( hw, rnd_state=None ):
+    width,height = wh
+    
+    A = np.float32(A)
+    B = np.float32(B)
+    C = np.float32(C)
+    
+
+    pos = np.empty( (height,width,2), dtype=np.float32 )
+    pos[...,0] = np.arange(width)[:,None]
+    pos[...,1] = np.arange(height)[None,:]
+    
+
+    a = B-A
+    b = A - 2.0*B + C
+    c = a * 2.0
+    d = A - pos
+
+    b_dot = vector2_dot(b,b)
+    if b_dot == 0.0:
+        return np.zeros( (height,width), dtype=np.float32 )
+    
+    kk = 1.0 / b_dot
+
+    kx = kk * vector2_dot(a,b)
+    ky = kk * (2.0*vector2_dot(a,a)+vector2_dot(d,b))/3.0;
+    kz = kk * vector2_dot(d,a);
+
+    res = 0.0;
+    sgn = 0.0;
+
+    p = ky - kx*kx;
+    
+    p3 = p*p*p;
+    q = kx*(2.0*kx*kx - 3.0*ky) + kz;
+    h = q*q + 4.0*p3;
+    
+    hp_sel = h >= 0.0
+    
+    hp_p = h[hp_sel]
+    hp_p = np.sqrt(hp_p)
+    
+    hp_x = ( np.stack( (hp_p,-hp_p), -1) -q[hp_sel,None] ) / 2.0
+    hp_uv = np.sign(hp_x) * np.power( np.abs(hp_x), [1.0/3.0, 1.0/3.0] )
+    hp_t = np.clip( hp_uv[...,0] + hp_uv[...,1] - kx, 0.0, 1.0 )
+    
+    hp_t = hp_t[...,None]
+    hp_q = d[hp_sel]+(c+b*hp_t)*hp_t
+    hp_res = vector2_dot2(hp_q)
+    hp_sgn = vector2_cross(c+2.0*b*hp_t,hp_q)
+    
+    hl_sel = h < 0.0
+    
+    hl_q = q[hl_sel]
+    hl_p = p[hl_sel]
+    hl_z = np.sqrt(-hl_p)
+    hl_v = np.arccos( hl_q / (hl_p*hl_z*2.0)) / 3.0
+    
+    hl_m = np.cos(hl_v)
+    hl_n = np.sin(hl_v)*1.732050808;
+    
+    hl_t = np.clip( np.stack( (hl_m+hl_m,-hl_n-hl_m,hl_n-hl_m), -1)*hl_z[...,None]-kx, 0.0, 1.0 );
+    
+    hl_d = d[hl_sel]
+    
+    hl_qx = hl_d+(c+b*hl_t[...,0:1])*hl_t[...,0:1]
+    
+    hl_dx = vector2_dot2(hl_qx)
+    hl_sx = vector2_cross(c+2.0*b*hl_t[...,0:1], hl_qx)
+    
+    hl_qy = hl_d+(c+b*hl_t[...,1:2])*hl_t[...,1:2]
+    hl_dy = vector2_dot2(hl_qy)
+    hl_sy = vector2_cross(c+2.0*b*hl_t[...,1:2],hl_qy);
+    
+    hl_dx_l_dy = hl_dx<hl_dy
+    hl_dx_ge_dy = hl_dx>=hl_dy
+    
+    hl_res = np.empty_like(hl_dx)
+    hl_res[hl_dx_l_dy] = hl_dx[hl_dx_l_dy]
+    hl_res[hl_dx_ge_dy] = hl_dy[hl_dx_ge_dy]
+    
+    hl_sgn = np.empty_like(hl_sx)
+    hl_sgn[hl_dx_l_dy] = hl_sx[hl_dx_l_dy]
+    hl_sgn[hl_dx_ge_dy] = hl_sy[hl_dx_ge_dy]
+    
+    res = np.empty( (height, width), np.float32 )
+    res[hp_sel] = hp_res
+    res[hl_sel] = hl_res
+    
+    sgn = np.empty( (height, width), np.float32 )
+    sgn[hp_sel] = hp_sgn
+    sgn[hl_sel] = hl_sgn
+    
+    sgn = np.sign(sgn)
+    res = np.sqrt(res)*sgn
+    
+    return res[...,None]
+        
+def random_faded(wh):
+    """
+    apply one of them:
+     random_circle_faded
+     random_bezier_split_faded
+    """
+    rnd = np.random.randint(2)
+    if rnd == 0:
+        return random_circle_faded(wh)
+    elif rnd == 1:
+        return random_bezier_split_faded(wh)
+    
+def random_circle_faded ( wh, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
         
-    h,w = hw
+    w,h = wh
-    hw_max = max(h,w)
+    wh_max = max(w,h)
-    fade_start = rnd_state.randint(hw_max)
+    fade_start = rnd_state.randint(wh_max)
-    fade_end = fade_start + rnd_state.randint(hw_max- fade_start)
+    fade_end = fade_start + rnd_state.randint(wh_max- fade_start)
     
-    return circle_faded (hw, [ rnd_state.randint(h), rnd_state.randint(w) ], 
+    return circle_faded (wh, [ rnd_state.randint(h), rnd_state.randint(w) ], 
-                             [fade_start, fade_end] ) 
+                             [fade_start, fade_end] ) 
+                             
+def random_bezier_split_faded( wh ):
+    width, height = wh
+    
+    degA = np.random.randint(360)
+    degB = np.random.randint(360)
+    degC = np.random.randint(360)
+    
+    deg_2_rad = math.pi / 180.0
+    
+    center = np.float32([width / 2.0, height / 2.0])
+    
+    radius = max(width, height)
+    
+    A = center + radius*np.float32([ math.sin( degA * deg_2_rad), math.cos( degA * deg_2_rad) ] ) 
+    B = center + np.random.randint(radius)*np.float32([ math.sin( degB * deg_2_rad), math.cos( degB * deg_2_rad) ] ) 
+    C = center + radius*np.float32([ math.sin( degC * deg_2_rad), math.cos( degC * deg_2_rad) ] ) 
+    
+    x = bezier( (width,height), A, B, C )
+    
+    x = x / (1+np.random.randint(radius)) + 0.5
+    
+    x = np.clip(x, 0, 1)
+    return x

core/imagelib/warp.py

@@ -2,7 +2,7 @@ import numpy as np
 import cv2
 from core import randomex
 
-def gen_warp_params (w, flip, rotation_range=[-2,2], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None  ):
+def gen_warp_params (w, flip=False, rotation_range=[-2,2], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None  ):
     if rnd_state is None:
         rnd_state = np.random
 

core/leras/device.py

@@ -1,12 +1,19 @@
 import sys
 import ctypes
 import os
+import multiprocessing
+import json
+import time
+from pathlib import Path
+from core.interact import interact as io
+
 
 class Device(object):
-    def __init__(self, index, name, total_mem, free_mem, cc=0):
+    def __init__(self, index, tf_dev_type, name, total_mem, free_mem):
         self.index = index
+        self.tf_dev_type = tf_dev_type
         self.name = name
-        self.cc = cc
+        
         self.total_mem = total_mem
         self.total_mem_gb = total_mem / 1024**3
         self.free_mem = free_mem
@@ -82,12 +89,135 @@ class Devices(object):
                 result.append (device)
         return Devices(result)
 
+    @staticmethod
+    def _get_tf_devices_proc(q : multiprocessing.Queue):
+        
+        if sys.platform[0:3] == 'win':
+            compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache_ALL')
+            os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
+            if not compute_cache_path.exists():
+                io.log_info("Caching GPU kernels...")
+                compute_cache_path.mkdir(parents=True, exist_ok=True)
+                
+        import tensorflow
+        
+        tf_version = tensorflow.version.VERSION
+        #if tf_version is None:
+        #    tf_version = tensorflow.version.GIT_VERSION
+        if tf_version[0] == 'v':
+            tf_version = tf_version[1:]
+        if tf_version[0] == '2':
+            tf = tensorflow.compat.v1
+        else:
+            tf = tensorflow
+        
+        import logging
+        # Disable tensorflow warnings
+        tf_logger = logging.getLogger('tensorflow')
+        tf_logger.setLevel(logging.ERROR)
+
+        from tensorflow.python.client import device_lib
+
+        devices = []
+        
+        physical_devices = device_lib.list_local_devices()
+        physical_devices_f = {}
+        for dev in physical_devices:
+            dev_type = dev.device_type
+            dev_tf_name = dev.name
+            dev_tf_name = dev_tf_name[ dev_tf_name.index(dev_type) : ]
+            
+            dev_idx = int(dev_tf_name.split(':')[-1])
+            
+            if dev_type in ['GPU','DML']:
+                dev_name = dev_tf_name
+                
+                dev_desc = dev.physical_device_desc
+                if len(dev_desc) != 0:
+                    if dev_desc[0] == '{':
+                        dev_desc_json = json.loads(dev_desc)
+                        dev_desc_json_name = dev_desc_json.get('name',None)
+                        if dev_desc_json_name is not None:
+                            dev_name = dev_desc_json_name
+                    else:
+                        for param, value in ( v.split(':') for v in dev_desc.split(',') ):
+                            param = param.strip()
+                            value = value.strip()
+                            if param == 'name':
+                                dev_name = value
+                                break
+                
+                physical_devices_f[dev_idx] = (dev_type, dev_name, dev.memory_limit)
+                        
+        q.put(physical_devices_f)
+        time.sleep(0.1)
+        
+        
     @staticmethod
     def initialize_main_env():
-        os.environ['NN_DEVICES_INITIALIZED'] = '1'
+        if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 0:
-        os.environ['NN_DEVICES_COUNT'] = '0'
+            return
+            
+        if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
+            os.environ.pop('CUDA_VISIBLE_DEVICES')
         
         os.environ['CUDA_​CACHE_​MAXSIZE'] = '2147483647'
+        os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tf log errors only
+        
+        q = multiprocessing.Queue()
+        p = multiprocessing.Process(target=Devices._get_tf_devices_proc, args=(q,), daemon=True)
+        p.start()
+        p.join()
+        
+        visible_devices = q.get()
+
+        os.environ['NN_DEVICES_INITIALIZED'] = '1'
+        os.environ['NN_DEVICES_COUNT'] = str(len(visible_devices))
+        
+        for i in visible_devices:
+            dev_type, name, total_mem = visible_devices[i]
+
+            os.environ[f'NN_DEVICE_{i}_TF_DEV_TYPE'] = dev_type
+            os.environ[f'NN_DEVICE_{i}_NAME'] = name
+            os.environ[f'NN_DEVICE_{i}_TOTAL_MEM'] = str(total_mem)
+            os.environ[f'NN_DEVICE_{i}_FREE_MEM'] = str(total_mem)
+            
+        
+
+    @staticmethod
+    def getDevices():
+        if Devices.all_devices is None:
+            if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 1:
+                raise Exception("nn devices are not initialized. Run initialize_main_env() in main process.")
+            devices = []
+            for i in range ( int(os.environ['NN_DEVICES_COUNT']) ):
+                devices.append ( Device(index=i,
+                                        tf_dev_type=os.environ[f'NN_DEVICE_{i}_TF_DEV_TYPE'],
+                                        name=os.environ[f'NN_DEVICE_{i}_NAME'],
+                                        total_mem=int(os.environ[f'NN_DEVICE_{i}_TOTAL_MEM']),
+                                        free_mem=int(os.environ[f'NN_DEVICE_{i}_FREE_MEM']), )
+                                )
+            Devices.all_devices = Devices(devices)
+
+        return Devices.all_devices
+
+"""
+
+        
+        # {'name'      : name.split(b'\0', 1)[0].decode(),
+        #     'total_mem' : totalMem.value
+        # }
+
+        
+        
+        
+        
+        return
+
+        
+        
+        
         min_cc = int(os.environ.get("TF_MIN_REQ_CAP", 35))
         libnames = ('libcuda.so', 'libcuda.dylib', 'nvcuda.dll')
         for libname in libnames:
@@ -139,70 +269,4 @@ class Devices(object):
             os.environ[f'NN_DEVICE_{i}_TOTAL_MEM'] = str(device['total_mem'])
             os.environ[f'NN_DEVICE_{i}_FREE_MEM'] = str(device['free_mem'])
             os.environ[f'NN_DEVICE_{i}_CC'] = str(device['cc'])
-
-    @staticmethod
-    def getDevices():
-        if Devices.all_devices is None:
-            if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 1:
-                raise Exception("nn devices are not initialized. Run initialize_main_env() in main process.")
-            devices = []
-            for i in range ( int(os.environ['NN_DEVICES_COUNT']) ):
-                devices.append ( Device(index=i,
-                                        name=os.environ[f'NN_DEVICE_{i}_NAME'],
-                                        total_mem=int(os.environ[f'NN_DEVICE_{i}_TOTAL_MEM']),
-                                        free_mem=int(os.environ[f'NN_DEVICE_{i}_FREE_MEM']),
-                                        cc=int(os.environ[f'NN_DEVICE_{i}_CC']) ))
-            Devices.all_devices = Devices(devices)
-
-        return Devices.all_devices
-
-"""
-if Devices.all_devices is None:
-            min_cc = int(os.environ.get("TF_MIN_REQ_CAP", 35))
-
-            libnames = ('libcuda.so', 'libcuda.dylib', 'nvcuda.dll')
-            for libname in libnames:
-                try:
-                    cuda = ctypes.CDLL(libname)
-                except:
-                    continue
-                else:
-                    break
-            else:
-                return Devices([])
-
-            nGpus = ctypes.c_int()
-            name = b' ' * 200
-            cc_major = ctypes.c_int()
-            cc_minor = ctypes.c_int()
-            freeMem = ctypes.c_size_t()
-            totalMem = ctypes.c_size_t()
-
-            result = ctypes.c_int()
-            device = ctypes.c_int()
-            context = ctypes.c_void_p()
-            error_str = ctypes.c_char_p()
-
-            devices = []
-
-            if cuda.cuInit(0) == 0 and \
-               cuda.cuDeviceGetCount(ctypes.byref(nGpus)) == 0:
-                for i in range(nGpus.value):
-                    if cuda.cuDeviceGet(ctypes.byref(device), i) != 0 or \
-                       cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name), device) != 0 or \
-                       cuda.cuDeviceComputeCapability(ctypes.byref(cc_major), ctypes.byref(cc_minor), device) != 0:
-                        continue
-
-                    if cuda.cuCtxCreate_v2(ctypes.byref(context), 0, device) == 0:
-                        if cuda.cuMemGetInfo_v2(ctypes.byref(freeMem), ctypes.byref(totalMem)) == 0:
-                            cc = cc_major.value * 10 + cc_minor.value
-                            if cc >= min_cc:
-                                devices.append ( Device(index=i,
-                                                        name=name.split(b'\0', 1)[0].decode(),
-                                                        total_mem=totalMem.value,
-                                                        free_mem=freeMem.value,
-                                                        cc=cc) )
-                        cuda.cuCtxDetach(context)
-            Devices.all_devices = Devices(devices)
-        return Devices.all_devices
 """

core/leras/models/XSeg.py

@@ -28,11 +28,12 @@ class XSeg(nn.ModelBase):
                 x = self.frn(x)
                 x = self.tlu(x)
                 return x
+                
+        self.base_ch = base_ch
 
         self.conv01 = ConvBlock(in_ch, base_ch)
         self.conv02 = ConvBlock(base_ch, base_ch)
-        self.bp0 = nn.BlurPool (filt_size=3)
+        self.bp0 = nn.BlurPool (filt_size=4)
-
 
         self.conv11 = ConvBlock(base_ch, base_ch*2)
         self.conv12 = ConvBlock(base_ch*2, base_ch*2)
@@ -40,19 +41,30 @@ class XSeg(nn.ModelBase):
 
         self.conv21 = ConvBlock(base_ch*2, base_ch*4)
         self.conv22 = ConvBlock(base_ch*4, base_ch*4)
-        self.conv23 = ConvBlock(base_ch*4, base_ch*4)
+        self.bp2 = nn.BlurPool (filt_size=2)
-        self.bp2 = nn.BlurPool (filt_size=3)
-
 
         self.conv31 = ConvBlock(base_ch*4, base_ch*8)
         self.conv32 = ConvBlock(base_ch*8, base_ch*8)
         self.conv33 = ConvBlock(base_ch*8, base_ch*8)
-        self.bp3 = nn.BlurPool (filt_size=3)
+        self.bp3 = nn.BlurPool (filt_size=2)
 
         self.conv41 = ConvBlock(base_ch*8, base_ch*8)
         self.conv42 = ConvBlock(base_ch*8, base_ch*8)
         self.conv43 = ConvBlock(base_ch*8, base_ch*8)
-        self.bp4 = nn.BlurPool (filt_size=3)
+        self.bp4 = nn.BlurPool (filt_size=2)
+        
+        self.conv51 = ConvBlock(base_ch*8, base_ch*8)
+        self.conv52 = ConvBlock(base_ch*8, base_ch*8)
+        self.conv53 = ConvBlock(base_ch*8, base_ch*8)
+        self.bp5 = nn.BlurPool (filt_size=2)
+        
+        self.dense1 = nn.Dense ( 4*4* base_ch*8, 512)
+        self.dense2 = nn.Dense ( 512, 4*4* base_ch*8)
+                
+        self.up5 = UpConvBlock (base_ch*8, base_ch*4)
+        self.uconv53 = ConvBlock(base_ch*12, base_ch*8)
+        self.uconv52 = ConvBlock(base_ch*8, base_ch*8)
+        self.uconv51 = ConvBlock(base_ch*8, base_ch*8)
         
         self.up4 = UpConvBlock (base_ch*8, base_ch*4)
         self.uconv43 = ConvBlock(base_ch*12, base_ch*8)
@@ -65,8 +77,7 @@ class XSeg(nn.ModelBase):
         self.uconv31 = ConvBlock(base_ch*8, base_ch*8)
 
         self.up2 = UpConvBlock (base_ch*8, base_ch*4)
-        self.uconv23 = ConvBlock(base_ch*8, base_ch*4)
+        self.uconv22 = ConvBlock(base_ch*8, base_ch*4)
-        self.uconv22 = ConvBlock(base_ch*4, base_ch*4)
         self.uconv21 = ConvBlock(base_ch*4, base_ch*4)
 
         self.up1 = UpConvBlock (base_ch*4, base_ch*2)
@@ -78,8 +89,7 @@ class XSeg(nn.ModelBase):
         self.uconv01 = ConvBlock(base_ch, base_ch)
         self.out_conv = nn.Conv2D (base_ch, out_ch, kernel_size=3, padding='SAME')
         
-        self.conv_center = ConvBlock(base_ch*8, base_ch*8)
+        
-
     def forward(self, inp):
         x = inp
 
@@ -92,8 +102,7 @@ class XSeg(nn.ModelBase):
         x = self.bp1(x)
 
         x = self.conv21(x)
-        x = self.conv22(x)
+        x = x2 = self.conv22(x)
-        x = x2 = self.conv23(x)
         x = self.bp2(x)
 
         x = self.conv31(x)
@@ -106,8 +115,21 @@ class XSeg(nn.ModelBase):
         x = x4 = self.conv43(x)
         x = self.bp4(x)
 
-        x = self.conv_center(x)
+        x = self.conv51(x)
-
+        x = self.conv52(x)
+        x = x5 = self.conv53(x)
+        x = self.bp5(x)
+        
+        x = nn.flatten(x)
+        x = self.dense1(x)
+        x = self.dense2(x)
+        x = nn.reshape_4D (x, 4, 4, self.base_ch*8 )
+                          
+        x = self.up5(x)
+        x = self.uconv53(tf.concat([x,x5],axis=nn.conv2d_ch_axis))
+        x = self.uconv52(x)
+        x = self.uconv51(x)
+        
         x = self.up4(x)
         x = self.uconv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
         x = self.uconv42(x)
@@ -119,8 +141,7 @@ class XSeg(nn.ModelBase):
         x = self.uconv31(x)
 
         x = self.up2(x)
-        x = self.uconv23(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
+        x = self.uconv22(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
-        x = self.uconv22(x)
         x = self.uconv21(x)
 
         x = self.up1(x)

core/leras/nn.py

@@ -33,8 +33,8 @@ class nn():
     tf = None
     tf_sess = None
     tf_sess_config = None
-    tf_default_device = None
+    tf_default_device_name = None
-
+    
     data_format = None
     conv2d_ch_axis = None
     conv2d_spatial_axes = None
@@ -51,9 +51,6 @@ class nn():
 
             # Manipulate environment variables before import tensorflow
 
-            if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
-                os.environ.pop('CUDA_VISIBLE_DEVICES')
-
             first_run = False
             if len(device_config.devices) != 0:
                 if sys.platform[0:3] == 'win':
@@ -68,22 +65,19 @@ class nn():
                     compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache' + devices_str)
                     if not compute_cache_path.exists():
                         first_run = True
+                        compute_cache_path.mkdir(parents=True, exist_ok=True)
                     os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
-
+            
-            os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'
-            os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tf log errors only
-
             if first_run:
                 io.log_info("Caching GPU kernels...")
 
             import tensorflow
 
-            tf_version = getattr(tensorflow,'VERSION', None)
+            tf_version = tensorflow.version.VERSION
-            if tf_version is None:
+            #if tf_version is None:
-                tf_version = tensorflow.version.GIT_VERSION
+            #    tf_version = tensorflow.version.GIT_VERSION
-                if tf_version[0] == 'v':
+            if tf_version[0] == 'v':
-                    tf_version = tf_version[1:]
+                tf_version = tf_version[1:]
-
             if tf_version[0] == '2':
                 tf = tensorflow.compat.v1
             else:
@@ -108,13 +102,14 @@ class nn():
 
             # Configure tensorflow session-config
             if len(device_config.devices) == 0:
-                nn.tf_default_device = "/CPU:0"
                 config = tf.ConfigProto(device_count={'GPU': 0})
+                nn.tf_default_device_name = '/CPU:0'
             else:
-                nn.tf_default_device = "/GPU:0"
+                nn.tf_default_device_name = f'/{device_config.devices[0].tf_dev_type}:0'
+                
                 config = tf.ConfigProto(allow_soft_placement=True)
                 config.gpu_options.visible_device_list = ','.join([str(device.index) for device in device_config.devices])
-
+                
             config.gpu_options.force_gpu_compatible = True
             config.gpu_options.allow_growth = True
             nn.tf_sess_config = config
@@ -202,14 +197,6 @@ class nn():
             nn.tf_sess.close()
             nn.tf_sess = None
 
-    @staticmethod
-    def get_current_device():
-        # Undocumented access to last tf.device(...)
-        objs = nn.tf.get_default_graph()._device_function_stack.peek_objs()
-        if len(objs) != 0:
-            return objs[0].display_name
-        return nn.tf_default_device
-
     @staticmethod
     def ask_choose_device_idxs(choose_only_one=False, allow_cpu=True, suggest_best_multi_gpu=False, suggest_all_gpu=False):
         devices = Devices.getDevices()

core/leras/ops/__init__.py

@@ -204,7 +204,7 @@ def random_binomial(shape, p=0.0, dtype=None, seed=None):
         seed = np.random.randint(10e6)
     return array_ops.where(
         random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
-        array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
+             array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
 nn.random_binomial = random_binomial
 
 def gaussian_blur(input, radius=2.0):
@@ -346,7 +346,9 @@ def depth_to_space(x, size):
         x = tf.reshape(x, (-1, oh, ow, oc, ))
         return x
     else:
-        return tf.depth_to_space(x, size, data_format=nn.data_format)
+        cfg = nn.getCurrentDeviceConfig()
+        if not cfg.cpu_only:
+            return tf.depth_to_space(x, size, data_format=nn.data_format)
         b,c,h,w = x.shape.as_list()
         oh, ow = h * size, w * size
         oc = c // (size * size)
@@ -357,11 +359,6 @@ def depth_to_space(x, size):
         return x
 nn.depth_to_space = depth_to_space
 
-def pixel_norm(x, power = 1.0):
-    return x * power * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=nn.conv2d_spatial_axes, keepdims=True) + 1e-06)
-nn.pixel_norm = pixel_norm
-
-
 def rgb_to_lab(srgb):
     srgb_pixels = tf.reshape(srgb, [-1, 3])
     linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
@@ -404,6 +401,11 @@ def total_variation_mse(images):
     return tot_var
 nn.total_variation_mse = total_variation_mse
 
+
+def pixel_norm(x, axes):
+    return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=axes, keepdims=True) + 1e-06)
+nn.pixel_norm = pixel_norm
+        
 """
 def tf_suppress_lower_mean(t, eps=0.00001):
     if t.shape.ndims != 1:

core/mathlib/__init__.py

@@ -1,7 +1,12 @@
-import numpy as np
 import math
+
+import cv2
+import numpy as np
+import numpy.linalg as npla
+
 from .umeyama import umeyama
 
+
 def get_power_of_two(x):
     i = 0
     while (1 << i) < x:
@@ -23,3 +28,70 @@ def rotationMatrixToEulerAngles(R) :
 
 def polygon_area(x,y):
     return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))
+
+def rotate_point(origin, point, deg):
+    """
+    Rotate a point counterclockwise by a given angle around a given origin.
+
+    The angle should be given in radians.
+    """
+    ox, oy = origin
+    px, py = point
+
+    rad = deg * math.pi / 180.0
+    qx = ox + math.cos(rad) * (px - ox) - math.sin(rad) * (py - oy)
+    qy = oy + math.sin(rad) * (px - ox) + math.cos(rad) * (py - oy)
+    return np.float32([qx, qy])
+    
+def transform_points(points, mat, invert=False):
+    if invert:
+        mat = cv2.invertAffineTransform (mat)
+    points = np.expand_dims(points, axis=1)
+    points = cv2.transform(points, mat, points.shape)
+    points = np.squeeze(points)
+    return points
+
+    
+def transform_mat(mat, res, tx, ty, rotation, scale):
+    """
+    transform mat in local space of res
+    scale -> translate -> rotate
+    
+        tx,ty       float
+        rotation    int degrees
+        scale       float
+    """
+    
+    
+    lt, rt, lb, ct = transform_points (  np.float32([(0,0),(res,0),(0,res),(res / 2, res/2) ]),mat, True)
+    
+    hor_v = (rt-lt).astype(np.float32)
+    hor_size = npla.norm(hor_v)
+    hor_v /= hor_size
+    
+    ver_v = (lb-lt).astype(np.float32)
+    ver_size = npla.norm(ver_v)
+    ver_v /= ver_size
+    
+    bt_diag_vec = (rt-ct).astype(np.float32)
+    half_diag_len = npla.norm(bt_diag_vec)
+    bt_diag_vec /= half_diag_len
+    
+    tb_diag_vec = np.float32( [ -bt_diag_vec[1], bt_diag_vec[0] ]  )
+
+    rt = ct + bt_diag_vec*half_diag_len*scale 
+    lb = ct - bt_diag_vec*half_diag_len*scale
+    lt = ct - tb_diag_vec*half_diag_len*scale
+    
+    rt[0] += tx*hor_size
+    lb[0] += tx*hor_size
+    lt[0] += tx*hor_size
+    rt[1] += ty*ver_size
+    lb[1] += ty*ver_size
+    lt[1] += ty*ver_size
+    
+    rt = rotate_point(ct, rt, rotation)
+    lb = rotate_point(ct, lb, rotation)
+    lt = rotate_point(ct, lt, rotation)
+    
+    return cv2.getAffineTransform( np.float32([lt, rt, lb]), np.float32([ [0,0], [res,0], [0,res] ]) )

facelib/FaceEnhancer.py

@@ -161,11 +161,11 @@ class FaceEnhancer(object):
         if not model_path.exists():
             raise Exception("Unable to load FaceEnhancer.npy")
 
-        with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
+        with tf.device ('/CPU:0' if place_model_on_cpu else nn.tf_default_device_name):
             self.model = FaceEnhancer()
             self.model.load_weights (model_path)
         
-        with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
+        with tf.device ('/CPU:0' if run_on_cpu else nn.tf_default_device_name):
             self.model.build_for_run ([ (tf.float32, nn.get4Dshape (192,192,3) ),
                                         (tf.float32, (None,1,) ),
                                         (tf.float32, (None,1,) ),

facelib/XSegNet.py

@@ -39,7 +39,7 @@ class XSegNet(object):
             self.target_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,1) )
 
         # Initializing model classes
-        with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
+        with tf.device ('/CPU:0' if place_model_on_cpu else nn.tf_default_device_name):
             self.model = nn.XSeg(3, 32, 1, name=name)
             self.model_weights = self.model.get_weights()
             if training:
@@ -53,7 +53,7 @@ class XSegNet(object):
         self.model_filename_list += [ [self.model, f'{model_name}.npy'] ]
 
         if not training:
-            with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
+            with tf.device ('/CPU:0' if run_on_cpu else nn.tf_default_device_name):
                 _, pred = self.model(self.input_t)
 
             def net_run(input_np):

main.py

@@ -127,6 +127,7 @@ if __name__ == "__main__":
                   'silent_start'             : arguments.silent_start,
                   'execute_programs'         : [ [int(x[0]), x[1] ] for x in arguments.execute_program ],
                   'debug'                    : arguments.debug,
+                  'dump_ckpt'                : arguments.dump_ckpt,
                   'flask_preview'            : arguments.flask_preview,
                   }
         from mainscripts import Trainer
@@ -145,6 +146,7 @@ if __name__ == "__main__":
     p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
     p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
     p.add_argument('--silent-start', action="store_true", dest="silent_start", default=False, help="Silent start. Automatically chooses Best GPU and last used model.")
+    p.add_argument('--dump-ckpt', action="store_true", dest="dump_ckpt", default=False, help="Dump the model to ckpt format.")
     p.add_argument('--flask-preview', action="store_true", dest="flask_preview", default=False,
                    help="Launches a flask server to view the previews in a web browser")
 
@@ -254,7 +256,17 @@ if __name__ == "__main__":
     p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
 
     p.set_defaults(func=process_faceset_enhancer)
+    
+    
+    p = facesettool_parser.add_parser ("resize", help="Resize DFL faceset.")
+    p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
 
+    def process_faceset_resizer(arguments):
+        osex.set_process_lowest_prio()
+        from mainscripts import FacesetResizer
+        FacesetResizer.process_folder ( Path(arguments.input_dir) )
+    p.set_defaults(func=process_faceset_resizer)
+    
     def process_dev_test(arguments):
         osex.set_process_lowest_prio()
         from mainscripts import dev_misc

mainscripts/Extractor.py

@@ -97,9 +97,6 @@ class ExtractSubprocessor(Subprocessor):
 
             h, w, c = image.shape
 
-            dflimg = DFLIMG.load (filepath)
-            extract_from_dflimg = (h == w and (dflimg is not None and dflimg.has_data()) )
-
             if 'rects' in self.type or self.type == 'all':
                 data = ExtractSubprocessor.Cli.rects_stage (data=data,
                                                             image=image,
@@ -110,7 +107,6 @@ class ExtractSubprocessor(Subprocessor):
             if 'landmarks' in self.type or self.type == 'all':
                 data = ExtractSubprocessor.Cli.landmarks_stage (data=data,
                                                                 image=image,
-                                                                extract_from_dflimg=extract_from_dflimg,
                                                                 landmarks_extractor=self.landmarks_extractor,
                                                                 rects_extractor=self.rects_extractor,
                                                                 )
@@ -121,7 +117,6 @@ class ExtractSubprocessor(Subprocessor):
                                                            face_type=self.face_type,
                                                            image_size=self.image_size,
                                                            jpeg_quality=self.jpeg_quality,
-                                                           extract_from_dflimg=extract_from_dflimg,
                                                            output_debug_path=self.output_debug_path,
                                                            final_output_path=self.final_output_path,
                                                            )
@@ -161,7 +156,6 @@ class ExtractSubprocessor(Subprocessor):
         @staticmethod
         def landmarks_stage(data,
                             image,
-                            extract_from_dflimg,
                             landmarks_extractor,
                             rects_extractor,
                             ):
@@ -176,7 +170,7 @@ class ExtractSubprocessor(Subprocessor):
             elif data.rects_rotation == 270:
                 rotated_image = image.swapaxes( 0,1 )[::-1,:,:]
 
-            data.landmarks = landmarks_extractor.extract (rotated_image, data.rects, rects_extractor if (not extract_from_dflimg and data.landmarks_accurate) else None, is_bgr=True)
+            data.landmarks = landmarks_extractor.extract (rotated_image, data.rects, rects_extractor if (data.landmarks_accurate) else None, is_bgr=True)
             if data.rects_rotation != 0:
                 for i, (rect, lmrks) in enumerate(zip(data.rects, data.landmarks)):
                     new_rect, new_lmrks = rect, lmrks
@@ -207,7 +201,6 @@ class ExtractSubprocessor(Subprocessor):
                         face_type,
                         image_size,
                         jpeg_quality,
-                        extract_from_dflimg = False,
                         output_debug_path=None,
                         final_output_path=None,
                         ):
@@ -219,72 +212,53 @@ class ExtractSubprocessor(Subprocessor):
             if output_debug_path is not None:
                 debug_image = image.copy()
 
-            if extract_from_dflimg and len(rects) != 1:
+            face_idx = 0
-                #if re-extracting from dflimg and more than 1 or zero faces detected - dont process and just copy it
+            for rect, image_landmarks in zip( rects, landmarks ):
-                print("extract_from_dflimg and len(rects) != 1", filepath )
+                if image_landmarks is None:
-                output_filepath = final_output_path / filepath.name
+                    continue
-                if filepath != str(output_file):
-                    shutil.copy ( str(filepath), str(output_filepath) )
-                data.final_output_files.append (output_filepath)
-            else:
-                face_idx = 0
-                for rect, image_landmarks in zip( rects, landmarks ):
 
-                    if extract_from_dflimg and face_idx > 1:
+                rect = np.array(rect)
-                        #cannot extract more than 1 face from dflimg
-                        break
 
-                    if image_landmarks is None:
+                if face_type == FaceType.MARK_ONLY:
+                    image_to_face_mat = None
+                    face_image = image
+                    face_image_landmarks = image_landmarks
+                else:
+                    image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, image_size, face_type)
+
+                    face_image = cv2.warpAffine(image, image_to_face_mat, (image_size, image_size), cv2.INTER_LANCZOS4)
+                    face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
+
+                    landmarks_bbox = LandmarksProcessor.transform_points ( [ (0,0), (0,image_size-1), (image_size-1, image_size-1), (image_size-1,0) ], image_to_face_mat, True)
+
+                    rect_area      = mathlib.polygon_area(np.array(rect[[0,2,2,0]]).astype(np.float32), np.array(rect[[1,1,3,3]]).astype(np.float32))
+                    landmarks_area = mathlib.polygon_area(landmarks_bbox[:,0].astype(np.float32), landmarks_bbox[:,1].astype(np.float32) )
+
+                    if not data.manual and face_type <= FaceType.FULL_NO_ALIGN and landmarks_area > 4*rect_area: #get rid of faces which umeyama-landmark-area > 4*detector-rect-area
                         continue
 
-                    rect = np.array(rect)
+                    if output_debug_path is not None:
+                        LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, face_type, image_size, transparent_mask=True)
 
-                    if face_type == FaceType.MARK_ONLY:
+                output_path = final_output_path
-                        image_to_face_mat = None
+                if data.force_output_path is not None:
-                        face_image = image
+                    output_path = data.force_output_path
-                        face_image_landmarks = image_landmarks
-                    else:
-                        image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, image_size, face_type)
 
-                        face_image = cv2.warpAffine(image, image_to_face_mat, (image_size, image_size), cv2.INTER_LANCZOS4)
+                output_filepath = output_path / f"{filepath.stem}_{face_idx}.jpg"
-                        face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
+                cv2_imwrite(output_filepath, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality ] )
 
-                        landmarks_bbox = LandmarksProcessor.transform_points ( [ (0,0), (0,image_size-1), (image_size-1, image_size-1), (image_size-1,0) ], image_to_face_mat, True)
+                dflimg = DFLJPG.load(output_filepath)
+                dflimg.set_face_type(FaceType.toString(face_type))
+                dflimg.set_landmarks(face_image_landmarks.tolist())
+                dflimg.set_source_filename(filepath.name)
+                dflimg.set_source_rect(rect)
+                dflimg.set_source_landmarks(image_landmarks.tolist())
+                dflimg.set_image_to_face_mat(image_to_face_mat)
+                dflimg.save()
 
-                        rect_area      = mathlib.polygon_area(np.array(rect[[0,2,2,0]]).astype(np.float32), np.array(rect[[1,1,3,3]]).astype(np.float32))
+                data.final_output_files.append (output_filepath)
-                        landmarks_area = mathlib.polygon_area(landmarks_bbox[:,0].astype(np.float32), landmarks_bbox[:,1].astype(np.float32) )
+                face_idx += 1
-
+            data.faces_detected = face_idx
-                        if not data.manual and face_type <= FaceType.FULL_NO_ALIGN and landmarks_area > 4*rect_area: #get rid of faces which umeyama-landmark-area > 4*detector-rect-area
-                            continue
-
-                        if output_debug_path is not None:
-                            LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, face_type, image_size, transparent_mask=True)
-
-                    output_path = final_output_path
-                    if data.force_output_path is not None:
-                        output_path = data.force_output_path
-
-                    if extract_from_dflimg and filepath.suffix == '.jpg':
-                        #if extracting from dflimg and jpg copy it in order not to lose quality
-                        output_filepath = output_path / filepath.name
-                        if filepath != output_filepath:
-                            shutil.copy ( str(filepath), str(output_filepath) )
-                    else:
-                        output_filepath = output_path / f"{filepath.stem}_{face_idx}.jpg"
-                        cv2_imwrite(output_filepath, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality ] )
-
-                    dflimg = DFLJPG.load(output_filepath)
-                    dflimg.set_face_type(FaceType.toString(face_type))
-                    dflimg.set_landmarks(face_image_landmarks.tolist())
-                    dflimg.set_source_filename(filepath.name)
-                    dflimg.set_source_rect(rect)
-                    dflimg.set_source_landmarks(image_landmarks.tolist())
-                    dflimg.set_image_to_face_mat(image_to_face_mat)
-                    dflimg.save()
-
-                    data.final_output_files.append (output_filepath)
-                    face_idx += 1
-                data.faces_detected = face_idx
 
             if output_debug_path is not None:
                 cv2_imwrite( output_debug_path / (filepath.stem+'.jpg'), debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )

mainscripts/FacesetResizer.py

@@ -0,0 +1,209 @@
+import multiprocessing
+import shutil
+
+import cv2
+from core import pathex
+from core.cv2ex import *
+from core.interact import interact as io
+from core.joblib import Subprocessor
+from DFLIMG import *
+from facelib import FaceType, LandmarksProcessor
+
+
+class FacesetResizerSubprocessor(Subprocessor):
+
+    #override
+    def __init__(self, image_paths, output_dirpath, image_size, face_type=None):
+        self.image_paths = image_paths
+        self.output_dirpath = output_dirpath
+        self.image_size = image_size
+        self.face_type = face_type
+        self.result = []
+
+        super().__init__('FacesetResizer', FacesetResizerSubprocessor.Cli, 600)
+
+    #override
+    def on_clients_initialized(self):
+        io.progress_bar (None, len (self.image_paths))
+
+    #override
+    def on_clients_finalized(self):
+        io.progress_bar_close()
+
+    #override
+    def process_info_generator(self):
+        base_dict = {'output_dirpath':self.output_dirpath, 'image_size':self.image_size, 'face_type':self.face_type}
+
+        for device_idx in range( min(8, multiprocessing.cpu_count()) ):
+            client_dict = base_dict.copy()
+            device_name = f'CPU #{device_idx}'
+            client_dict['device_name'] = device_name
+            yield device_name, {}, client_dict
+
+    #override
+    def get_data(self, host_dict):
+        if len (self.image_paths) > 0:
+            return self.image_paths.pop(0)
+
+    #override
+    def on_data_return (self, host_dict, data):
+        self.image_paths.insert(0, data)
+
+    #override
+    def on_result (self, host_dict, data, result):
+        io.progress_bar_inc(1)
+        if result[0] == 1:
+            self.result +=[ (result[1], result[2]) ]
+
+    #override
+    def get_result(self):
+        return self.result
+
+    class Cli(Subprocessor.Cli):
+
+        #override
+        def on_initialize(self, client_dict):
+            self.output_dirpath = client_dict['output_dirpath']
+            self.image_size = client_dict['image_size']
+            self.face_type = client_dict['face_type']
+            self.log_info (f"Running on { client_dict['device_name'] }")
+
+        #override
+        def process_data(self, filepath):
+            try:
+                dflimg = DFLIMG.load (filepath)
+                if dflimg is None or not dflimg.has_data():
+                    self.log_err (f"{filepath.name} is not a dfl image file")
+                else:
+                    img = cv2_imread(filepath)
+                    h,w = img.shape[:2]
+                    if h != w:
+                        raise Exception(f'w != h in {filepath}')
+                    
+                    image_size = self.image_size
+                    face_type = self.face_type
+                    output_filepath = self.output_dirpath / filepath.name
+                    
+                    if face_type is not None:
+                        lmrks = dflimg.get_landmarks()
+                        mat = LandmarksProcessor.get_transform_mat(lmrks, image_size, face_type)
+                        
+                        img = cv2.warpAffine(img, mat, (image_size, image_size), flags=cv2.INTER_LANCZOS4 )
+                        img = np.clip(img, 0, 255).astype(np.uint8)
+                        
+                        cv2_imwrite ( str(output_filepath), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
+
+                        dfl_dict = dflimg.get_dict()
+                        dflimg = DFLIMG.load (output_filepath)
+                        dflimg.set_dict(dfl_dict)
+                        
+                        xseg_mask = dflimg.get_xseg_mask()
+                        if xseg_mask is not None:
+                            xseg_res = 256
+                            
+                            xseg_lmrks = lmrks.copy()
+                            xseg_lmrks *= (xseg_res / w)
+                            xseg_mat = LandmarksProcessor.get_transform_mat(xseg_lmrks, xseg_res, face_type)
+                            
+                            xseg_mask = cv2.warpAffine(xseg_mask, xseg_mat, (xseg_res, xseg_res), flags=cv2.INTER_LANCZOS4 )
+                            xseg_mask[xseg_mask < 0.5] = 0
+                            xseg_mask[xseg_mask >= 0.5] = 1
+
+                            dflimg.set_xseg_mask(xseg_mask)
+                        
+                        seg_ie_polys = dflimg.get_seg_ie_polys()
+                        
+                        for poly in seg_ie_polys.get_polys():
+                            poly_pts = poly.get_pts()
+                            poly_pts = LandmarksProcessor.transform_points(poly_pts, mat)
+                            poly.set_points(poly_pts)
+                            
+                        dflimg.set_seg_ie_polys(seg_ie_polys)
+                        
+                        lmrks = LandmarksProcessor.transform_points(lmrks, mat)
+                        dflimg.set_landmarks(lmrks)
+    
+                        image_to_face_mat = dflimg.get_image_to_face_mat()
+                        if image_to_face_mat is not None:
+                            image_to_face_mat = LandmarksProcessor.get_transform_mat ( dflimg.get_source_landmarks(), image_size, face_type )
+                            dflimg.set_image_to_face_mat(image_to_face_mat)
+                        dflimg.set_face_type( FaceType.toString(face_type) )
+                        dflimg.save()
+                        
+                    else:
+                        dfl_dict = dflimg.get_dict()
+                         
+                        scale = w / image_size
+                        
+                        img = cv2.resize(img, (image_size, image_size), interpolation=cv2.INTER_LANCZOS4)                    
+                        
+                        cv2_imwrite ( str(output_filepath), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
+
+                        dflimg = DFLIMG.load (output_filepath)
+                        dflimg.set_dict(dfl_dict)
+                        
+                        lmrks = dflimg.get_landmarks()                    
+                        lmrks /= scale
+                        dflimg.set_landmarks(lmrks)
+                        
+                        seg_ie_polys = dflimg.get_seg_ie_polys()
+                        seg_ie_polys.mult_points( 1.0 / scale)
+                        dflimg.set_seg_ie_polys(seg_ie_polys)
+                        
+                        image_to_face_mat = dflimg.get_image_to_face_mat()
+    
+                        if image_to_face_mat is not None:
+                            face_type = FaceType.fromString ( dflimg.get_face_type() )
+                            image_to_face_mat = LandmarksProcessor.get_transform_mat ( dflimg.get_source_landmarks(), image_size, face_type )
+                            dflimg.set_image_to_face_mat(image_to_face_mat)
+                        dflimg.save()
+
+                    return (1, filepath, output_filepath)
+            except:
+                self.log_err (f"Exception occured while processing file {filepath}. Error: {traceback.format_exc()}")
+
+            return (0, filepath, None)
+
+def process_folder ( dirpath):
+    
+    image_size = io.input_int(f"New image size", 512, valid_range=[256,2048])
+    
+    face_type = io.input_str ("Change face type", 'same', ['h','mf','f','wf','head','same']).lower()
+    if face_type == 'same':
+        face_type = None
+    else:
+        face_type = {'h'  : FaceType.HALF,
+                     'mf' : FaceType.MID_FULL,
+                     'f'  : FaceType.FULL,
+                     'wf' : FaceType.WHOLE_FACE,
+                     'head' : FaceType.HEAD}[face_type]
+                     
+
+    output_dirpath = dirpath.parent / (dirpath.name + '_resized')
+    output_dirpath.mkdir (exist_ok=True, parents=True)
+
+    dirpath_parts = '/'.join( dirpath.parts[-2:])
+    output_dirpath_parts = '/'.join( output_dirpath.parts[-2:] )
+    io.log_info (f"Resizing faceset in {dirpath_parts}")
+    io.log_info ( f"Processing to {output_dirpath_parts}")
+
+    output_images_paths = pathex.get_image_paths(output_dirpath)
+    if len(output_images_paths) > 0:
+        for filename in output_images_paths:
+            Path(filename).unlink()
+
+    image_paths = [Path(x) for x in pathex.get_image_paths( dirpath )]
+    result = FacesetResizerSubprocessor ( image_paths, output_dirpath, image_size, face_type).run()
+
+    is_merge = io.input_bool (f"\r\nMerge {output_dirpath_parts} to {dirpath_parts} ?", True)
+    if is_merge:
+        io.log_info (f"Copying processed files to {dirpath_parts}")
+
+        for (filepath, output_filepath) in result:
+            try:
+                shutil.copy (output_filepath, filepath)
+            except:
+                pass
+
+        io.log_info (f"Removing {output_dirpath_parts}")
+        shutil.rmtree(output_dirpath)

mainscripts/Trainer.py

@@ -1,4 +1,4 @@
-import os
+import os
 import sys
 import traceback
 import queue
@@ -15,23 +15,23 @@ import cv2
 import models
 from core.interact import interact as io
 
-
+def trainerThread (s2c, c2s, e,
-def trainerThread(s2c, c2s, e,
+                    socketio=None,
-                  socketio=None,
+                    model_class_name = None,
-                  model_class_name=None,
+                    saved_models_path = None,
-                  saved_models_path=None,
+                    training_data_src_path = None,
-                  training_data_src_path=None,
+                    training_data_dst_path = None,
-                  training_data_dst_path=None,
+                    pretraining_data_path = None,
-                  pretraining_data_path=None,
+                    pretrained_model_path = None,
-                  pretrained_model_path=None,
+                    no_preview=False,
-                  no_preview=False,
+                    force_model_name=None,
-                  force_model_name=None,
+                    force_gpu_idxs=None,
-                  force_gpu_idxs=None,
+                    cpu_only=None,
-                  cpu_only=None,
+                    silent_start=False,
-                  silent_start=False,
+                    execute_programs = None,
-                  execute_programs=None,
+                    debug=False,
-                  debug=False,
+                    dump_ckpt=False,
-                  **kwargs):
+                    **kwargs):
     while True:
         try:
             start_time = time.time()
@@ -46,22 +46,29 @@ def trainerThread(s2c, c2s, e,
 
             if not saved_models_path.exists():
                 saved_models_path.mkdir(exist_ok=True, parents=True)
-
+            
+            if dump_ckpt:
+                cpu_only=True
+                
             model = models.import_model(model_class_name)(
-                is_training=True,
+                        is_training=not dump_ckpt,
-                saved_models_path=saved_models_path,
+                        saved_models_path=saved_models_path,
-                training_data_src_path=training_data_src_path,
+                        training_data_src_path=training_data_src_path,
-                training_data_dst_path=training_data_dst_path,
+                        training_data_dst_path=training_data_dst_path,
-                pretraining_data_path=pretraining_data_path,
+                        pretraining_data_path=pretraining_data_path,
-                pretrained_model_path=pretrained_model_path,
+                        pretrained_model_path=pretrained_model_path,
-                no_preview=no_preview,
+                        no_preview=no_preview,
-                force_model_name=force_model_name,
+                        force_model_name=force_model_name,
-                force_gpu_idxs=force_gpu_idxs,
+                        force_gpu_idxs=force_gpu_idxs,
-                cpu_only=cpu_only,
+                        cpu_only=cpu_only,
-                silent_start=silent_start,
+                        silent_start=silent_start,
-                debug=debug,
+                        debug=debug)
-            )
 
+            if dump_ckpt:
+                e.set()
+                model.dump_ckpt()
+                break
+                
             is_reached_goal = model.is_reached_iter_goal()
 
             shared_state = {'after_save': False}

mainscripts/XSegUtil.py

@@ -10,8 +10,8 @@ from core.cv2ex import *
 from core.interact import interact as io
 from core.leras import nn
 from DFLIMG import *
-from facelib import XSegNet
+from facelib import XSegNet, LandmarksProcessor, FaceType
-
+import pickle
 
 def apply_xseg(input_path, model_path):
     if not input_path.exists():
@@ -20,17 +20,42 @@ def apply_xseg(input_path, model_path):
     if not model_path.exists():
         raise ValueError(f'{model_path} not found. Please ensure it exists.')
         
+    face_type = None
+    
+    model_dat = model_path / 'XSeg_data.dat'
+    if model_dat.exists():
+        dat = pickle.loads( model_dat.read_bytes() )
+        dat_options = dat.get('options', None)
+        if dat_options is not None:
+            face_type = dat_options.get('face_type', None)
+        
+        
+        
+    if face_type is None:
+        face_type = io.input_str ("XSeg model face type", 'same', ['h','mf','f','wf','head','same'], help_message="Specify face type of trained XSeg model. For example if XSeg model trained as WF, but faceset is HEAD, specify WF to apply xseg only on WF part of HEAD. Default is 'same'").lower()
+        if face_type == 'same':
+            face_type = None
+    
+    if face_type is not None:
+        face_type = {'h'  : FaceType.HALF,
+                     'mf' : FaceType.MID_FULL,
+                     'f'  : FaceType.FULL,
+                     'wf' : FaceType.WHOLE_FACE,
+                     'head' : FaceType.HEAD}[face_type]
+                     
     io.log_info(f'Applying trained XSeg model to {input_path.name}/ folder.')
 
     device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True)
     nn.initialize(device_config)
         
+    
+    
     xseg = XSegNet(name='XSeg', 
                     load_weights=True,
                     weights_file_root=model_path,
                     data_format=nn.data_format,
                     raise_on_no_model_files=True)
-    res = xseg.get_resolution()
+    xseg_res = xseg.get_resolution()
               
     images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
     
@@ -42,15 +67,36 @@ def apply_xseg(input_path, model_path):
         
         img = cv2_imread(filepath).astype(np.float32) / 255.0
         h,w,c = img.shape
-        if w != res:
-            img = cv2.resize( img, (res,res), interpolation=cv2.INTER_CUBIC )        
-            if len(img.shape) == 2:
-                img = img[...,None]            
-            
-        mask = xseg.extract(img)
-        mask[mask < 0.5]=0
-        mask[mask >= 0.5]=1
         
+        img_face_type = FaceType.fromString( dflimg.get_face_type() )
+        if face_type is not None and img_face_type != face_type:
+            lmrks = dflimg.get_source_landmarks()
+            
+            fmat = LandmarksProcessor.get_transform_mat(lmrks, w, face_type)
+            imat = LandmarksProcessor.get_transform_mat(lmrks, w, img_face_type)
+            
+            g_p = LandmarksProcessor.transform_points (np.float32([(0,0),(w,0),(0,w) ]), fmat, True)
+            g_p2 = LandmarksProcessor.transform_points (g_p, imat)
+            
+            mat = cv2.getAffineTransform( g_p2, np.float32([(0,0),(w,0),(0,w) ]) )
+            
+            img = cv2.warpAffine(img, mat, (w, w), cv2.INTER_LANCZOS4)
+            img = cv2.resize(img, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4)
+        else:
+            if w != xseg_res:
+                img = cv2.resize( img, (xseg_res,xseg_res), interpolation=cv2.INTER_LANCZOS4 )    
+                    
+        if len(img.shape) == 2:
+            img = img[...,None]            
+    
+        mask = xseg.extract(img)
+        
+        if face_type is not None and img_face_type != face_type:
+            mask = cv2.resize(mask, (w, w), interpolation=cv2.INTER_LANCZOS4)
+            mask = cv2.warpAffine( mask, mat, (w,w), np.zeros( (h,w,c), dtype=np.float), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4)
+            mask = cv2.resize(mask, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4)
+        mask[mask < 0.5]=0
+        mask[mask >= 0.5]=1    
         dflimg.set_xseg_mask(mask)
         dflimg.save()
 
@@ -67,7 +113,8 @@ def fetch_xseg(input_path):
     
     images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
     
-    files_copied = 0
+    
+    files_copied = []
     for filepath in io.progress_bar_generator(images_paths, "Processing"):
         dflimg = DFLIMG.load(filepath)
         if dflimg is None or not dflimg.has_data():
@@ -77,10 +124,16 @@ def fetch_xseg(input_path):
         ie_polys = dflimg.get_seg_ie_polys()
 
         if ie_polys.has_polys():
-            files_copied += 1
+            files_copied.append(filepath)
             shutil.copy ( str(filepath), str(output_path / filepath.name) )
     
-    io.log_info(f'Files copied: {files_copied}')
+    io.log_info(f'Files copied: {len(files_copied)}')
+    
+    is_delete = io.input_bool (f"\r\nDelete original files?", True)
+    if is_delete:
+        for filepath in files_copied:
+            Path(filepath).unlink()
+            
     
 def remove_xseg(input_path):
     if not input_path.exists():

merger/MergeMasked.py

@@ -142,7 +142,9 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
 
     elif 'raw' in cfg.mode:
         if cfg.mode == 'raw-rgb':
-            out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )
+            out_img_face = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
+            out_img_face_mask = cv2.warpAffine( np.ones_like(prd_face_bgr), face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
+            out_img = img_bgr*(1-out_img_face_mask) + out_img_face*out_img_face_mask
             out_merging_mask_a = img_face_mask_a
         elif cfg.mode == 'raw-predict':
             out_img = prd_face_bgr

models/ModelBase.py

@@ -185,7 +185,9 @@ class ModelBase(object):
         self.write_preview_history = self.options.get('write_preview_history', False)
         self.target_iter = self.options.get('target_iter',0)
         self.random_flip = self.options.get('random_flip',True)
-
+        self.random_src_flip = self.options.get('random_src_flip', False)
+        self.random_dst_flip = self.options.get('random_dst_flip', True)
+        
         self.on_initialize()
         self.options['batch_size'] = self.batch_size
 
@@ -297,6 +299,14 @@ class ModelBase(object):
     def ask_random_flip(self):
         default_random_flip = self.load_or_def_option('random_flip', True)
         self.options['random_flip'] = io.input_bool("Flip faces randomly", default_random_flip, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
+    
+    def ask_random_src_flip(self):
+        default_random_src_flip = self.load_or_def_option('random_src_flip', False)
+        self.options['random_src_flip'] = io.input_bool("Flip SRC faces randomly", default_random_src_flip, help_message="Random horizontal flip SRC faceset. Covers more angles, but the face may look less naturally.")
+
+    def ask_random_dst_flip(self):
+        default_random_dst_flip = self.load_or_def_option('random_dst_flip', True)
+        self.options['random_dst_flip'] = io.input_bool("Flip DST faces randomly", default_random_dst_flip, help_message="Random horizontal flip DST faceset. Makes generalization of src->dst better, if src random flip is not enabled.")
 
     def ask_batch_size(self, suggest_batch_size=None, range=None):
         default_batch_size = self.load_or_def_option('batch_size', suggest_batch_size or self.batch_size)

models/Model_AMP/Model.py

@@ -0,0 +1,810 @@
+import multiprocessing
+import operator
+from functools import partial
+
+import numpy as np
+
+from core import mathlib
+from core.interact import interact as io
+from core.leras import nn
+from facelib import FaceType
+from models import ModelBase
+from samplelib import *
+from core.cv2ex import *
+
+class AMPModel(ModelBase):
+
+    #override
+    def on_initialize_options(self):
+        device_config = nn.getCurrentDeviceConfig()
+
+        lowest_vram = 2
+        if len(device_config.devices) != 0:
+            lowest_vram = device_config.devices.get_worst_device().total_mem_gb
+
+        if lowest_vram >= 4:
+            suggest_batch_size = 8
+        else:
+            suggest_batch_size = 4
+
+        yn_str = {True:'y',False:'n'}
+        min_res = 64
+        max_res = 640
+
+        default_resolution         = self.options['resolution']         = self.load_or_def_option('resolution', 224)
+        default_face_type          = self.options['face_type']          = self.load_or_def_option('face_type', 'wf')
+        default_models_opt_on_gpu  = self.options['models_opt_on_gpu']  = self.load_or_def_option('models_opt_on_gpu', True)
+
+        default_ae_dims            = self.options['ae_dims']            = self.load_or_def_option('ae_dims', 256)
+        default_e_dims             = self.options['e_dims']             = self.load_or_def_option('e_dims', 64)
+        default_d_dims             = self.options['d_dims']             = self.options.get('d_dims', None)
+        default_d_mask_dims        = self.options['d_mask_dims']        = self.options.get('d_mask_dims', None)
+        default_morph_factor       = self.options['morph_factor']       = self.options.get('morph_factor', 0.33)
+        default_masked_training    = self.options['masked_training']    = self.load_or_def_option('masked_training', True)
+        default_eyes_mouth_prio    = self.options['eyes_mouth_prio']    = self.load_or_def_option('eyes_mouth_prio', True)
+        default_uniform_yaw        = self.options['uniform_yaw']        = self.load_or_def_option('uniform_yaw', False)
+
+        lr_dropout = self.load_or_def_option('lr_dropout', 'n')
+        lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
+        default_lr_dropout         = self.options['lr_dropout'] = lr_dropout
+
+        default_random_warp        = self.options['random_warp']        = self.load_or_def_option('random_warp', True)
+        default_ct_mode            = self.options['ct_mode']            = self.load_or_def_option('ct_mode', 'none')
+        default_clipgrad           = self.options['clipgrad']           = self.load_or_def_option('clipgrad', False)
+        default_pretrain           = self.options['pretrain']      = self.load_or_def_option('pretrain', False)
+
+
+        ask_override = self.ask_override()
+        if self.is_first_run() or ask_override:
+            self.ask_autobackup_hour()
+            self.ask_write_preview_history()
+            self.ask_target_iter()
+            self.ask_random_src_flip()
+            self.ask_random_dst_flip()
+            self.ask_batch_size(suggest_batch_size)
+
+        if self.is_first_run():
+            resolution = io.input_int("Resolution", default_resolution, add_info="64-640", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 32 .")
+            resolution = np.clip ( (resolution // 32) * 32, min_res, max_res)
+            self.options['resolution'] = resolution
+            self.options['face_type'] = io.input_str ("Face type", default_face_type, ['wf','head'], help_message="whole face / head").lower()
+
+
+        default_d_dims             = self.options['d_dims']             = self.load_or_def_option('d_dims', 64)
+
+        default_d_mask_dims        = default_d_dims // 3
+        default_d_mask_dims        += default_d_mask_dims % 2
+        default_d_mask_dims        = self.options['d_mask_dims']        = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
+
+        if self.is_first_run():
+            self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
+
+            e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
+            self.options['e_dims'] = e_dims + e_dims % 2
+
+            d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
+            self.options['d_dims'] = d_dims + d_dims % 2
+
+            d_mask_dims = np.clip ( io.input_int("Decoder mask dimensions", default_d_mask_dims, add_info="16-256", help_message="Typical mask dimensions = decoder dimensions / 3. If you manually cut out obstacles from the dst mask, you can increase this parameter to achieve better quality." ), 16, 256 )
+            self.options['d_mask_dims'] = d_mask_dims + d_mask_dims % 2
+            
+            morph_factor = np.clip ( io.input_number ("Morph factor.", default_morph_factor, add_info="0.1 .. 0.5", help_message="The smaller the value, the more src-like facial expressions will appear. The larger the value, the less space there is to train a large dst faceset in the neural network. Typical fine value is 0.33"), 0.1, 0.5 )
+            self.options['morph_factor'] = morph_factor
+
+
+        if self.is_first_run() or ask_override:
+            if self.options['face_type'] == 'wf' or self.options['face_type'] == 'head':
+                self.options['masked_training']  = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' or 'head' type. Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly.")
+
+            self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
+            self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
+
+        default_gan_power          = self.options['gan_power']          = self.load_or_def_option('gan_power', 0.0)
+        default_gan_patch_size     = self.options['gan_patch_size']     = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
+        default_gan_dims           = self.options['gan_dims']           = self.load_or_def_option('gan_dims', 16)
+
+        if self.is_first_run() or ask_override:
+            self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
+
+            self.options['lr_dropout']  = io.input_str (f"Use learning rate dropout", default_lr_dropout, ['n','y','cpu'], help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations. Enabled it before `disable random warp` and before GAN. \nn - disabled.\ny - enabled\ncpu - enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.")
+
+            self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness and reduce subpixel shake for less amount of iterations.")
+
+            self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 )
+
+            if self.options['gan_power'] != 0.0:
+                gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
+                self.options['gan_patch_size'] = gan_patch_size
+
+                gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
+                self.options['gan_dims'] = gan_dims
+
+            self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
+            self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
+            
+            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly. Forces random_warp=N, random_flips=Y, gan_power=0.0, lr_dropout=N, uniform_yaw=Y")
+        
+        self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
+        self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
+
+    #override
+    def on_initialize(self):
+        device_config = nn.getCurrentDeviceConfig()
+        devices = device_config.devices
+        self.model_data_format = "NCHW"
+        nn.initialize(data_format=self.model_data_format)
+        tf = nn.tf
+
+        self.resolution = resolution = self.options['resolution']
+
+        lowest_dense_res = self.lowest_dense_res = resolution // 32
+
+        class Downscale(nn.ModelBase):
+            def __init__(self, in_ch, out_ch, kernel_size=5, *kwargs ):
+                self.in_ch = in_ch
+                self.out_ch = out_ch
+                self.kernel_size = kernel_size
+                super().__init__(*kwargs)
+
+            def on_build(self, *args, **kwargs ):
+                self.conv1 = nn.Conv2D( self.in_ch, self.out_ch, kernel_size=self.kernel_size, strides=2, padding='SAME')
+
+            def forward(self, x):
+                x = self.conv1(x)
+                x = tf.nn.leaky_relu(x, 0.1)
+                return x
+
+            def get_out_ch(self):
+                return self.out_ch
+
+        class Upscale(nn.ModelBase):
+            def on_build(self, in_ch, out_ch, kernel_size=3 ):
+                self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
+
+            def forward(self, x):
+                x = self.conv1(x)
+                x = tf.nn.leaky_relu(x, 0.1)
+                x = nn.depth_to_space(x, 2)
+                return x
+
+        class ResidualBlock(nn.ModelBase):
+            def on_build(self, ch, kernel_size=3 ):
+                self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
+                self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
+
+            def forward(self, inp):
+                x = self.conv1(inp)
+                x = tf.nn.leaky_relu(x, 0.2)
+                x = self.conv2(x)
+                x = tf.nn.leaky_relu(inp+x, 0.2)
+                return x
+
+        class Encoder(nn.ModelBase):
+            def on_build(self, in_ch, e_ch, ae_ch):
+                self.down1 = Downscale(in_ch, e_ch, kernel_size=5)
+                self.res1 = ResidualBlock(e_ch)
+                self.down2 = Downscale(e_ch, e_ch*2, kernel_size=5)
+                self.down3 = Downscale(e_ch*2, e_ch*4, kernel_size=5)
+                self.down4 = Downscale(e_ch*4, e_ch*8, kernel_size=5)
+                self.down5 = Downscale(e_ch*8, e_ch*8, kernel_size=5)
+                self.res5 = ResidualBlock(e_ch*8)
+                self.dense1 = nn.Dense( lowest_dense_res*lowest_dense_res*e_ch*8, ae_ch )
+
+            def forward(self, inp):
+                x = inp
+                x = self.down1(x)
+                x = self.res1(x)
+                x = self.down2(x)
+                x = self.down3(x)
+                x = self.down4(x)
+                x = self.down5(x)
+                x = self.res5(x)
+                x = nn.flatten(x)
+                x = nn.pixel_norm(x, axes=-1)
+                x = self.dense1(x)
+                return x
+
+
+        class Inter(nn.ModelBase):
+            def __init__(self, ae_ch, ae_out_ch, **kwargs):
+                self.ae_ch, self.ae_out_ch = ae_ch, ae_out_ch
+                super().__init__(**kwargs)
+
+            def on_build(self):
+                ae_ch, ae_out_ch = self.ae_ch, self.ae_out_ch
+                self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
+
+            def forward(self, inp):
+                x = inp
+                x = self.dense2(x)
+                x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
+                return x
+
+            def get_out_ch(self):
+                return self.ae_out_ch
+
+        class Decoder(nn.ModelBase):
+            def on_build(self, in_ch, d_ch, d_mask_ch ):
+                self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
+                self.upscale1 = Upscale(d_ch*8, d_ch*8, kernel_size=3)
+                self.upscale2 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
+                self.upscale3 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
+
+                self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
+                self.res1 = ResidualBlock(d_ch*8, kernel_size=3)
+                self.res2 = ResidualBlock(d_ch*4, kernel_size=3)
+                self.res3 = ResidualBlock(d_ch*2, kernel_size=3)
+
+                self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
+                self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*8, kernel_size=3)
+                self.upscalem2 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
+                self.upscalem3 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
+                self.upscalem4 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
+                self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME')
+
+                self.out_conv  = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
+                self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
+                self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
+                self.out_conv3 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
+
+            def forward(self, inp):
+                z = inp
+
+                x = self.upscale0(z)
+                x = self.res0(x)
+                x = self.upscale1(x)
+                x = self.res1(x)
+                x = self.upscale2(x)
+                x = self.res2(x)
+                x = self.upscale3(x)
+                x = self.res3(x)
+
+                x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
+                                                                 self.out_conv1(x),
+                                                                 self.out_conv2(x),
+                                                                 self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
+
+                m = self.upscalem0(z)
+                m = self.upscalem1(m)
+                m = self.upscalem2(m)
+                m = self.upscalem3(m)
+                m = self.upscalem4(m)
+                m = tf.nn.sigmoid(self.out_convm(m))
+                return x, m
+
+        self.face_type = {'wf' : FaceType.WHOLE_FACE,
+                          'head' : FaceType.HEAD}[ self.options['face_type'] ]
+
+        if 'eyes_prio' in self.options:
+            self.options.pop('eyes_prio')
+
+        eyes_mouth_prio = self.options['eyes_mouth_prio']
+
+        ae_dims = self.ae_dims = self.options['ae_dims']
+        e_dims = self.options['e_dims']
+        d_dims = self.options['d_dims']
+        d_mask_dims = self.options['d_mask_dims']
+        morph_factor = self.options['morph_factor']
+        
+        pretrain = self.pretrain = self.options['pretrain']
+        if self.pretrain_just_disabled:
+            self.set_iter(0)
+            
+        self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
+        random_warp = False if self.pretrain else self.options['random_warp']
+        random_src_flip = self.random_src_flip if not self.pretrain else True
+        random_dst_flip = self.random_dst_flip if not self.pretrain else True
+        
+        if self.pretrain:
+            self.options_show_override['gan_power'] = 0.0
+            self.options_show_override['random_warp'] = False
+            self.options_show_override['lr_dropout'] = 'n'
+            self.options_show_override['uniform_yaw'] = True
+            
+        masked_training = self.options['masked_training']
+        ct_mode = self.options['ct_mode']
+        if ct_mode == 'none':
+            ct_mode = None
+
+        models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
+        models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
+        optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
+
+        input_ch=3
+        bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
+        mask_shape = nn.get4Dshape(resolution,resolution,1)
+        self.model_filename_list = []
+
+        with tf.device ('/CPU:0'):
+            #Place holders on CPU
+            self.warped_src = tf.placeholder (nn.floatx, bgr_shape, name='warped_src')
+            self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')
+
+            self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
+            self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')
+
+            self.target_srcm    = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
+            self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
+            self.target_dstm    = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
+            self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
+
+            self.morph_value_t = tf.placeholder (nn.floatx, (1,), name='morph_value_t')
+
+        # Initializing model classes
+
+        with tf.device (models_opt_device):
+            self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, ae_ch=ae_dims,  name='encoder')
+            self.inter_src  = Inter(ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter_src')
+            self.inter_dst  = Inter(ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter_dst')
+            self.decoder = Decoder(in_ch=ae_dims, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder')
+
+            self.model_filename_list += [   [self.encoder,  'encoder.npy'],
+                                            [self.inter_src, 'inter_src.npy'],
+                                            [self.inter_dst , 'inter_dst.npy'],
+                                            [self.decoder , 'decoder.npy'] ]
+
+            if self.is_training:
+                if gan_power != 0:
+                    self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="GAN")
+                    self.model_filename_list += [ [self.GAN, 'GAN.npy'] ]
+
+                # Initialize optimizers
+                lr=5e-5
+                lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
+                
+                clipnorm = 1.0 if self.options['clipgrad'] else 0.0
+
+                self.all_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
+                if pretrain:
+                    self.trainable_weights = self.encoder.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
+                else:
+                    self.trainable_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
+
+                self.src_dst_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
+                self.src_dst_opt.initialize_variables (self.all_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
+                self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
+
+                if gan_power != 0:
+                    self.GAN_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
+                    self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
+                    self.model_filename_list += [ (self.GAN_opt, 'GAN_opt.npy') ]
+
+        if self.is_training:
+            # Adjust batch size for multiple GPU
+            gpu_count = max(1, len(devices) )
+            bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
+            self.set_batch_size( gpu_count*bs_per_gpu)
+
+            # Compute losses per GPU
+            gpu_pred_src_src_list = []
+            gpu_pred_dst_dst_list = []
+            gpu_pred_src_dst_list = []
+            gpu_pred_src_srcm_list = []
+            gpu_pred_dst_dstm_list = []
+            gpu_pred_src_dstm_list = []
+
+            gpu_src_losses = []
+            gpu_dst_losses = []
+            gpu_G_loss_gvs = []
+            gpu_GAN_loss_gvs = []
+            gpu_D_code_loss_gvs = []
+            gpu_D_src_dst_loss_gvs = []
+
+            for gpu_id in range(gpu_count):
+                with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
+                    with tf.device(f'/CPU:0'):
+                        # slice on CPU, otherwise all batch data will be transfered to GPU first
+                        batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
+                        gpu_warped_src      = self.warped_src [batch_slice,:,:,:]
+                        gpu_warped_dst      = self.warped_dst [batch_slice,:,:,:]
+                        gpu_target_src      = self.target_src [batch_slice,:,:,:]
+                        gpu_target_dst      = self.target_dst [batch_slice,:,:,:]
+                        gpu_target_srcm     = self.target_srcm[batch_slice,:,:,:]
+                        gpu_target_srcm_em  = self.target_srcm_em[batch_slice,:,:,:]
+                        gpu_target_dstm     = self.target_dstm[batch_slice,:,:,:]
+                        gpu_target_dstm_em  = self.target_dstm_em[batch_slice,:,:,:]
+
+                    # process model tensors
+                    gpu_src_code = self.encoder (gpu_warped_src)
+                    gpu_dst_code = self.encoder (gpu_warped_dst)
+                    
+                    if pretrain:
+                        gpu_src_inter_src_code = self.inter_src (gpu_src_code)
+                        gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
+                        gpu_src_code = gpu_src_inter_src_code * nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
+                        gpu_dst_code = gpu_src_dst_code = gpu_dst_inter_dst_code * nn.random_binomial( [bs_per_gpu, gpu_dst_inter_dst_code.shape.as_list()[1], 1,1] , p=0.25)
+                    else:
+                        gpu_src_inter_src_code = self.inter_src (gpu_src_code)
+                        gpu_src_inter_dst_code = self.inter_dst (gpu_src_code)
+                        gpu_dst_inter_src_code = self.inter_src (gpu_dst_code)
+                        gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
+
+                        inter_rnd_binomial = nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
+                        gpu_src_code = gpu_src_inter_src_code * inter_rnd_binomial + gpu_src_inter_dst_code * (1-inter_rnd_binomial)
+                        gpu_dst_code = gpu_dst_inter_dst_code
+
+                        ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
+                        gpu_src_dst_code =  tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0],   [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
+                                                        tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
+
+                    gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
+                    gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
+                    gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
+
+                    gpu_pred_src_src_list.append(gpu_pred_src_src)
+                    gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
+                    gpu_pred_src_dst_list.append(gpu_pred_src_dst)
+
+                    gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
+                    gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
+                    gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
+
+                    gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm,  max(1, resolution // 32) )
+                    gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
+
+                    gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm,  max(1, resolution // 32) )
+                    gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
+
+                    gpu_target_dst_anti_masked = gpu_target_dst*(1.0-gpu_target_dstm_blur)
+                    gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
+                    gpu_target_src_masked_opt  = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
+                    gpu_target_dst_masked_opt  = gpu_target_dst*gpu_target_dstm_blur if masked_training else gpu_target_dst
+
+                    gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
+                    gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
+                    gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
+                    gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*(1.0-gpu_target_dstm_blur)
+                    
+                    if resolution < 256:
+                        gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
+                    else:
+                        gpu_dst_loss = tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
+                        gpu_dst_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
+                    gpu_dst_loss += tf.reduce_mean ( 10*tf.square(  gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
+                    if eyes_mouth_prio:
+                        gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
+                    gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
+                    gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
+                    gpu_dst_losses += [gpu_dst_loss]
+
+                    if not pretrain:
+                        if resolution < 256:
+                            gpu_src_loss =  tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                        else:
+                            gpu_src_loss =  tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                            gpu_src_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
+                        gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
+
+                        if eyes_mouth_prio:
+                            gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3])
+
+                        gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
+                    else:
+                        gpu_src_loss = gpu_dst_loss
+                    
+                    gpu_src_losses += [gpu_src_loss]
+                    
+                    if pretrain:
+                        gpu_G_loss = gpu_dst_loss
+                    else:     
+                        gpu_G_loss = gpu_src_loss + gpu_dst_loss
+
+                    def DLossOnes(logits):
+                        return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits), logits=logits), axis=[1,2,3])
+
+                    def DLossZeros(logits):
+                        return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits), logits=logits), axis=[1,2,3])
+
+                    if gan_power != 0:
+                        gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_masked_opt)
+                        gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_masked_opt)
+                        gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked_opt)
+                        gpu_target_dst_d, gpu_target_dst_d2 = self.GAN(gpu_target_dst_masked_opt)
+
+                        gpu_D_src_dst_loss = (DLossOnes (gpu_target_src_d)   + DLossOnes (gpu_target_src_d2) + \
+                                              DLossZeros(gpu_pred_src_src_d) + DLossZeros(gpu_pred_src_src_d2) + \
+                                              DLossOnes (gpu_target_dst_d)   + DLossOnes (gpu_target_dst_d2) + \
+                                              DLossZeros(gpu_pred_dst_dst_d) + DLossZeros(gpu_pred_dst_dst_d2)
+                                             ) * ( 1.0 / 8)
+
+                        gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.GAN.get_weights() ) ]
+
+                        gpu_G_loss += (DLossOnes(gpu_pred_src_src_d) + DLossOnes(gpu_pred_src_src_d2) + \
+                                       DLossOnes(gpu_pred_dst_dst_d) + DLossOnes(gpu_pred_dst_dst_d2)
+                                      ) * gan_power
+
+                        if masked_training:
+                            # Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
+                            gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
+                            gpu_G_loss += 0.02*tf.reduce_mean(tf.square(gpu_pred_src_src_anti_masked-gpu_target_src_anti_masked),axis=[1,2,3] )
+
+                    gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.trainable_weights ) ]
+
+
+            # Average losses and gradients, and create optimizer update ops
+            with tf.device(f'/CPU:0'):
+                pred_src_src  = nn.concat(gpu_pred_src_src_list, 0)
+                pred_dst_dst  = nn.concat(gpu_pred_dst_dst_list, 0)
+                pred_src_dst  = nn.concat(gpu_pred_src_dst_list, 0)
+                pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
+                pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
+                pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
+
+            with tf.device (models_opt_device):
+                src_loss = tf.concat(gpu_src_losses, 0)
+                dst_loss = tf.concat(gpu_dst_losses, 0)
+                src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
+
+                if gan_power != 0:
+                    src_D_src_dst_loss_gv_op = self.GAN_opt.get_update_op (nn.average_gv_list(gpu_D_src_dst_loss_gvs) )
+                    #GAN_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list(gpu_GAN_loss_gvs) )
+
+
+            # Initializing training and view functions
+            def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em,  \
+                              warped_dst, target_dst, target_dstm, target_dstm_em, ):
+                s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
+                                            feed_dict={self.warped_src :warped_src,
+                                                       self.target_src :target_src,
+                                                       self.target_srcm:target_srcm,
+                                                       self.target_srcm_em:target_srcm_em,
+                                                       self.warped_dst :warped_dst,
+                                                       self.target_dst :target_dst,
+                                                       self.target_dstm:target_dstm,
+                                                       self.target_dstm_em:target_dstm_em,
+                                                       })
+                return s, d
+            self.src_dst_train = src_dst_train
+
+            if gan_power != 0:
+                def D_src_dst_train(warped_src, target_src, target_srcm, target_srcm_em,  \
+                                    warped_dst, target_dst, target_dstm, target_dstm_em, ):
+                    nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
+                                                                           self.target_src :target_src,
+                                                                           self.target_srcm:target_srcm,
+                                                                           self.target_srcm_em:target_srcm_em,
+                                                                           self.warped_dst :warped_dst,
+                                                                           self.target_dst :target_dst,
+                                                                           self.target_dstm:target_dstm,
+                                                                           self.target_dstm_em:target_dstm_em})
+                self.D_src_dst_train = D_src_dst_train
+
+
+            def AE_view(warped_src, warped_dst, morph_value):
+                return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
+                                            feed_dict={self.warped_src:warped_src, self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
+
+            self.AE_view = AE_view
+        else:
+            #Initializing merge function
+            with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
+                gpu_dst_code = self.encoder (self.warped_dst)
+                gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
+                gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
+
+                ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
+                gpu_src_dst_code =  tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0],   [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
+                                                 tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
+
+                gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_dst_inter_src_code)
+                _, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
+
+            def AE_merge(warped_dst, morph_value):
+                return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
+
+            self.AE_merge = AE_merge
+
+        # Loading/initializing all models/optimizers weights
+        for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
+            if self.pretrain_just_disabled:
+                do_init = False
+                if model == self.inter_src or model == self.inter_dst:
+                    do_init = True
+            else:
+                do_init = self.is_first_run()
+                if self.is_training and gan_power != 0 and model == self.GAN:
+                    if self.gan_model_changed:
+                        do_init = True
+                        
+            if not do_init:
+                do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
+            if do_init:
+                model.init_weights()
+
+
+        ###############
+
+        # initializing sample generators
+        if self.is_training:
+            training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
+            training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
+
+            random_ct_samples_path=training_data_dst_path if ct_mode is not None and not self.pretrain else None
+
+
+            cpu_count = min(multiprocessing.cpu_count(), 8)
+            src_generators_count = cpu_count // 2
+            dst_generators_count = cpu_count // 2
+            if ct_mode is not None:
+                src_generators_count = int(src_generators_count * 1.5)
+
+            self.set_training_data_generators ([
+                    SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
+                        sample_process_options=SampleProcessor.Options(random_flip=random_src_flip),
+                        output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                              ],
+                        uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
+                        generators_count=src_generators_count ),
+
+                    SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
+                        sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
+                        output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                                'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                                'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                                {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                                              ],
+                        uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
+                        generators_count=dst_generators_count )
+                             ])
+
+            self.last_src_samples_loss = []
+            self.last_dst_samples_loss = []
+            if self.pretrain_just_disabled:
+                self.update_sample_for_preview(force_new=True)
+    
+
+    def dump_ckpt(self):
+        tf = nn.tf
+        with tf.device (nn.tf_default_device_name):
+            warped_dst = tf.placeholder (nn.floatx, (None, self.resolution, self.resolution, 3), name='in_face')
+            warped_dst = tf.transpose(warped_dst, (0,3,1,2))
+            morph_value = tf.placeholder (nn.floatx, (1,), name='morph_value')
+
+            gpu_dst_code = self.encoder (warped_dst)
+            gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
+            gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
+
+            ae_dims_slice = tf.cast(self.ae_dims*morph_value[0], tf.int32)
+            gpu_src_dst_code =  tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0],   [-1, ae_dims_slice , self.lowest_dense_res, self.lowest_dense_res]),
+                                            tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,self.ae_dims-ae_dims_slice, self.lowest_dense_res,self.lowest_dense_res]) ), 1 )
+
+            gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
+            _, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
+
+            gpu_pred_src_dst = tf.transpose(gpu_pred_src_dst, (0,2,3,1))
+            gpu_pred_dst_dstm = tf.transpose(gpu_pred_dst_dstm, (0,2,3,1))
+            gpu_pred_src_dstm = tf.transpose(gpu_pred_src_dstm, (0,2,3,1))
+
+        tf.identity(gpu_pred_dst_dstm, name='out_face_mask')
+        tf.identity(gpu_pred_src_dst, name='out_celeb_face')
+        tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')
+        
+        output_graph_def = tf.graph_util.convert_variables_to_constants(
+            nn.tf_sess, 
+            tf.get_default_graph().as_graph_def(), 
+            ['out_face_mask','out_celeb_face','out_celeb_face_mask']
+        ) 
+        
+        pb_filepath = self.get_strpath_storage_for_file('.pb')
+        with tf.gfile.GFile(pb_filepath, "wb") as f:
+            f.write(output_graph_def.SerializeToString())
+
+    #override
+    def get_model_filename_list(self):
+        return self.model_filename_list
+
+    #override
+    def onSave(self):
+        for model, filename in io.progress_bar_generator(self.get_model_filename_list(), "Saving", leave=False):
+            model.save_weights ( self.get_strpath_storage_for_file(filename) )
+
+    #override
+    def should_save_preview_history(self):
+        return (not io.is_colab() and self.iter % ( 10*(max(1,self.resolution // 64)) ) == 0) or \
+               (io.is_colab() and self.iter % 100 == 0)
+
+    #override
+    def onTrainOneIter(self):
+        bs = self.get_batch_size()
+
+        ( (warped_src, target_src, target_srcm, target_srcm_em), \
+          (warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
+
+        src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
+
+        for i in range(bs):
+            self.last_src_samples_loss.append (  (target_src[i], target_srcm[i], target_srcm_em[i], src_loss[i] )  )
+            self.last_dst_samples_loss.append (  (target_dst[i], target_dstm[i], target_dstm_em[i], dst_loss[i] )  )
+
+        if len(self.last_src_samples_loss) >= bs*16:
+            src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(3), reverse=True)
+            dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(3), reverse=True)
+
+            target_src        = np.stack( [ x[0] for x in src_samples_loss[:bs] ] )
+            target_srcm       = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
+            target_srcm_em = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
+
+            target_dst        = np.stack( [ x[0] for x in dst_samples_loss[:bs] ] )
+            target_dstm       = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
+            target_dstm_em = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
+
+            src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
+            self.last_src_samples_loss = []
+            self.last_dst_samples_loss = []
+
+        if self.gan_power != 0:
+            self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
+
+        return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
+
+    #override
+    def onGetPreview(self, samples):
+        ( (warped_src, target_src, target_srcm, target_srcm_em),
+          (warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
+
+        S, D, SS, DD, DDM_000, _, _ = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst, 0.0)  ) ]
+
+        _, _, DDM_025, SD_025, SDM_025 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.25) ]
+        _, _, DDM_050, SD_050, SDM_050 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.50) ]
+        _, _, DDM_065, SD_065, SDM_065 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.65) ]
+        _, _, DDM_075, SD_075, SDM_075 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.75) ]
+        _, _, DDM_100, SD_100, SDM_100 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 1.00) ]
+
+        (DDM_000,
+         DDM_025, SDM_025,
+         DDM_050, SDM_050,
+         DDM_065, SDM_065,
+         DDM_075, SDM_075,
+         DDM_100, SDM_100) = [ np.repeat (x, (3,), -1) for x in (DDM_000,
+                                                                 DDM_025, SDM_025,
+                                                                 DDM_050, SDM_050,
+                                                                 DDM_065, SDM_065,
+                                                                 DDM_075, SDM_075,
+                                                                 DDM_100, SDM_100) ]
+
+        target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
+
+        n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
+
+        result = []
+
+        i = np.random.randint(n_samples)
+
+        st =  [ np.concatenate ((S[i],  D[i],  DD[i]*DDM_000[i]), axis=1) ]
+        st += [ np.concatenate ((SS[i], DD[i], SD_075[i] ), axis=1) ]
+
+        result += [ ('AMP morph 0.75', np.concatenate (st, axis=0 )), ]
+
+        st =  [ np.concatenate ((DD[i], SD_025[i],  SD_050[i]), axis=1) ]
+        st += [ np.concatenate ((SD_065[i], SD_075[i], SD_100[i]), axis=1) ]
+        result += [ ('AMP morph list', np.concatenate (st, axis=0 )), ]
+
+
+        st =  [ np.concatenate ((DD[i], SD_025[i]*DDM_025[i]*SDM_025[i],  SD_050[i]*DDM_050[i]*SDM_050[i]), axis=1) ]
+        st += [ np.concatenate ((SD_065[i]*DDM_065[i]*SDM_065[i], SD_075[i]*DDM_075[i]*SDM_075[i], SD_100[i]*DDM_100[i]*SDM_100[i]), axis=1) ]
+        result += [ ('AMP morph list masked', np.concatenate (st, axis=0 )), ]
+
+        return result
+
+    def predictor_func (self, face, morph_value):
+        face = nn.to_data_format(face[None,...], self.model_data_format, "NHWC")
+    
+        bgr, mask_dst_dstm, mask_src_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face, morph_value) ]
+
+        return bgr[0], mask_src_dstm[0][...,0], mask_dst_dstm[0][...,0]
+
+    #override
+    def get_MergerConfig(self):
+        morph_factor = np.clip ( io.input_number ("Morph factor", 0.75, add_info="0.0 .. 1.0"), 0.0, 1.0 )
+
+        def predictor_morph(face):
+            return self.predictor_func(face, morph_factor)
+            
+
+        import merger        
+        return predictor_morph, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
+
+Model = AMPModel

models/Model_AMP/__init__.py

@@ -0,0 +1 @@
+from .Model import Model

models/Model_Quick96/Model.py

@@ -31,7 +31,7 @@ class QModel(ModelBase):
         masked_training = True
 
         models_opt_on_gpu = len(devices) >= 1 and all([dev.total_mem_gb >= 4 for dev in devices])
-        models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
+        models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
         optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
 
         input_ch = 3
@@ -96,7 +96,7 @@ class QModel(ModelBase):
             gpu_src_dst_loss_gvs = []
             
             for gpu_id in range(gpu_count):
-                with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
+                with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
                     batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
                     with tf.device(f'/CPU:0'):
                         # slice on CPU, otherwise all batch data will be transfered to GPU first
@@ -190,7 +190,7 @@ class QModel(ModelBase):
             self.AE_view = AE_view
         else:
             # Initializing merge function
-            with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
+            with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
                 gpu_dst_code     = self.inter(self.encoder(self.warped_dst))
                 gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
                 _, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)

models/Model_SAEHD/Model.py

@@ -70,7 +70,8 @@ class SAEHDModel(ModelBase):
             self.ask_autobackup_hour()
             self.ask_write_preview_history()
             self.ask_target_iter()
-            self.ask_random_flip()
+            self.ask_random_src_flip()
+            self.ask_random_dst_flip()
             self.ask_batch_size(suggest_batch_size)
 
         if self.is_first_run():
@@ -191,7 +192,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             self.options['random_color'] = io.input_bool ("Random color", default_random_color, help_message="Samples are randomly rotated around the L axis in LAB colorspace, helps generalize training")
             self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
 
-            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
+            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly. Forces random_warp=N, random_flips=Y, gan_power=0.0, lr_dropout=N, styles=0.0, uniform_yaw=Y")
 
         if self.options['pretrain'] and self.get_pretraining_data_path() is None:
             raise Exception("pretraining_data_path is not defined")
@@ -225,6 +226,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             archi_type, archi_opts = archi_split
         elif len(archi_split) == 1:
             archi_type, archi_opts = archi_split[0], None
+            
+        self.archi_type = archi_type
 
         ae_dims = self.options['ae_dims']
         e_dims = self.options['e_dims']
@@ -238,7 +241,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
         self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
         random_warp = False if self.pretrain else self.options['random_warp']
-
+        random_src_flip = self.random_src_flip if not self.pretrain else True
+        random_dst_flip = self.random_dst_flip if not self.pretrain else True
+        
         if self.pretrain:
             self.options_show_override['gan_power'] = 0.0
             self.options_show_override['random_warp'] = False
@@ -251,28 +256,29 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
         ct_mode = self.options['ct_mode']
         if ct_mode == 'none':
             ct_mode = None
-
+        
+        
         models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
-        models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
+        models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
         optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
 
         input_ch=3
-        bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
+        bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
         mask_shape = nn.get4Dshape(resolution,resolution,1)
         self.model_filename_list = []
 
         with tf.device ('/CPU:0'):
             #Place holders on CPU
-            self.warped_src = tf.placeholder (nn.floatx, bgr_shape)
+            self.warped_src = tf.placeholder (nn.floatx, bgr_shape, name='warped_src')
-            self.warped_dst = tf.placeholder (nn.floatx, bgr_shape)
+            self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')
 
-            self.target_src = tf.placeholder (nn.floatx, bgr_shape)
+            self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
-            self.target_dst = tf.placeholder (nn.floatx, bgr_shape)
+            self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')
 
-            self.target_srcm    = tf.placeholder (nn.floatx, mask_shape)
+            self.target_srcm    = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
-            self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape)
+            self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
-            self.target_dstm    = tf.placeholder (nn.floatx, mask_shape)
+            self.target_dstm    = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
-            self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape)
+            self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
 
         # Initializing model classes
         model_archi = nn.DeepFakeArchi(resolution, opts=archi_opts)
@@ -361,7 +367,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
             self.set_batch_size( gpu_count*bs_per_gpu)
 
-
             # Compute losses per GPU
             gpu_pred_src_src_list = []
             gpu_pred_dst_dst_list = []
@@ -375,9 +380,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             gpu_G_loss_gvs = []
             gpu_D_code_loss_gvs = []
             gpu_D_src_dst_loss_gvs = []
+            
             for gpu_id in range(gpu_count):
-                with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
+                with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
-
                     with tf.device(f'/CPU:0'):
                         # slice on CPU, otherwise all batch data will be transfered to GPU first
                         batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
@@ -679,7 +684,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             self.AE_view = AE_view
         else:
             # Initializing merge function
-            with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
+            with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
                 if 'df' in archi_type:
                     gpu_dst_code     = self.inter(self.encoder(self.warped_dst))
                     gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
@@ -722,7 +727,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             if do_init:
                 model.init_weights()
-
+        
+        
+        ###############
+       
         # initializing sample generators
         if self.is_training:
             training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
@@ -744,7 +752,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             self.set_training_data_generators ([
                     SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=random_src_flip),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
@@ -754,7 +762,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                         generators_count=src_generators_count ),
 
                     SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : channel_type, 'ct_mode': fs_aug,                                                                 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : channel_type, 'ct_mode': fs_aug,                                                                 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
@@ -769,7 +777,44 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             if self.pretrain_just_disabled:
                 self.update_sample_for_preview(force_new=True)
+    
+    def dump_ckpt(self):
+        tf = nn.tf
+        
+        
+        with tf.device ('/CPU:0'):
+            warped_dst = tf.placeholder (nn.floatx, (None, self.resolution, self.resolution, 3), name='in_face')
+            warped_dst = tf.transpose(warped_dst, (0,3,1,2))
+            
+            
+            if 'df' in self.archi_type:
+                gpu_dst_code     = self.inter(self.encoder(warped_dst))
+                gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
+                _, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
 
+            elif 'liae' in self.archi_type:
+                gpu_dst_code = self.encoder (warped_dst)
+                gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
+                gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
+                gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
+                gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
+
+                gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
+                _, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
+                
+            gpu_pred_src_dst = tf.transpose(gpu_pred_src_dst, (0,2,3,1))
+            gpu_pred_dst_dstm = tf.transpose(gpu_pred_dst_dstm, (0,2,3,1))
+            gpu_pred_src_dstm = tf.transpose(gpu_pred_src_dstm, (0,2,3,1))
+
+            
+        saver = tf.train.Saver()
+        tf.identity(gpu_pred_dst_dstm, name='out_face_mask')
+        tf.identity(gpu_pred_src_dst, name='out_celeb_face')
+        tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')       
+        
+        saver.save(nn.tf_sess, self.get_strpath_storage_for_file('.ckpt') )
+
+        
     #override
     def get_model_filename_list(self):
         return self.model_filename_list

models/Model_XSeg/Model.py

@@ -52,7 +52,7 @@ class XSegModel(ModelBase):
                           'head' : FaceType.HEAD}[ self.options['face_type'] ]
 
         place_model_on_cpu = len(devices) == 0
-        models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
+        models_opt_device = '/CPU:0' if place_model_on_cpu else nn.tf_default_device_name
 
         bgr_shape = nn.get4Dshape(resolution,resolution,3)
         mask_shape = nn.get4Dshape(resolution,resolution,1)
@@ -83,7 +83,7 @@ class XSegModel(ModelBase):
             for gpu_id in range(gpu_count):
 
 
-                with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
+                with tf.device(f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
                     with tf.device(f'/CPU:0'):
                         # slice on CPU, otherwise all batch data will be transfered to GPU first
                         batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
@@ -95,6 +95,7 @@ class XSegModel(ModelBase):
                     gpu_pred_list.append(gpu_pred_t)
 
                     gpu_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=gpu_target_t, logits=gpu_pred_logits_t), axis=[1,2,3])
+                      
                     gpu_losses += [gpu_loss]
 
                     gpu_loss_gvs += [ nn.gradients ( gpu_loss, self.model.get_weights() ) ]

requirements-cuda.txt

@@ -1,12 +1,12 @@
 tqdm
 numpy==1.19.3
-h5py==2.9.0
+h5py==2.10.0
 opencv-python==4.1.0.25
 ffmpeg-python==0.1.17
 scikit-image==0.14.2
 scipy==1.4.1
 colorama
-tensorflow-gpu==2.3.1
+tensorflow-gpu==2.4.0
 pyqt5
 Flask==1.1.1
 flask-socketio==4.2.1

samplelib/PackedFaceset.py

@@ -84,17 +84,18 @@ class PackedFaceset():
             of.write ( struct.pack("Q", offset) )
         of.seek(0,2)
         of.close()
+        
+        if io.input_bool(f"Delete original files?", True):
+            for filename in io.progress_bar_generator(image_paths, "Deleting files"):
+                Path(filename).unlink()
 
-        for filename in io.progress_bar_generator(image_paths, "Deleting files"):
+            if as_person_faceset:
-            Path(filename).unlink()
+                for dir_name in io.progress_bar_generator(dir_names, "Deleting dirs"):
-
+                    dir_path = samples_path / dir_name
-        if as_person_faceset:
+                    try:
-            for dir_name in io.progress_bar_generator(dir_names, "Deleting dirs"):
+                        shutil.rmtree(dir_path)
-                dir_path = samples_path / dir_name
+                    except:
-                try:
+                        io.log_info (f"unable to remove: {dir_path} ")
-                    shutil.rmtree(dir_path)
-                except:
-                    io.log_info (f"unable to remove: {dir_path} ")
 
     @staticmethod
     def unpack(samples_path):

samplelib/SampleGeneratorFaceXSeg.py

@@ -6,7 +6,7 @@ from enum import IntEnum
 
 import cv2
 import numpy as np
-
+from pathlib import Path
 from core import imagelib, mplib, pathex
 from core.imagelib import sd
 from core.cv2ex import *
@@ -31,7 +31,7 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
             if len(seg_sample_idxs) == 0:
                 raise Exception(f"No segmented faces found.")
             else:
-                io.log_info(f"Using {len(seg_sample_idxs)} xseg labeled samples.")                
+                io.log_info(f"Using {len(seg_sample_idxs)} xseg labeled samples.")
         else:
             io.log_info(f"Using {len(seg_sample_idxs)} segmented samples.")
 
@@ -40,11 +40,11 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
         else:
             self.generators_count = max(1, generators_count)
 
+        args = (samples, seg_sample_idxs, resolution, face_type, data_format)
         if self.debug:
-            self.generators = [ThisThreadGenerator ( self.batch_func, (samples, seg_sample_idxs, resolution, face_type, data_format) )]
+            self.generators = [ThisThreadGenerator ( self.batch_func, args )]
         else:
-            self.generators = [SubprocessGenerator ( self.batch_func, (samples, seg_sample_idxs, resolution, face_type, data_format), start_now=False ) \
+            self.generators = [SubprocessGenerator ( self.batch_func, args, start_now=False ) for i in range(self.generators_count) ]
-                               for i in range(self.generators_count) ]
 
             SubprocessGenerator.start_in_parallel( self.generators )
 
@@ -77,17 +77,19 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
         ty_range=[-0.05, 0.05]
 
         random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
+        sharpen_chance, sharpen_kernel_max_size = 25, 5
         motion_blur_chance, motion_blur_mb_max_size = 25, 5
         gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
+        random_jpeg_compress_chance = 25
 
         def gen_img_mask(sample):
             img = sample.load_bgr()
             h,w,c = img.shape
-            
+
             if sample.seg_ie_polys.has_polys():
                 mask = np.zeros ((h,w,1), dtype=np.float32)
                 sample.seg_ie_polys.overlay_mask(mask)
-            elif sample.has_xseg_mask():                
+            elif sample.has_xseg_mask():
                 mask = sample.get_xseg_mask()
                 mask[mask < 0.5] = 0.0
                 mask[mask >= 0.5] = 1.0
@@ -121,7 +123,6 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
                     img, mask = gen_img_mask(sample)
 
                     if np.random.randint(2) == 0:
-
                         if len(bg_shuffle_idxs) == 0:
                             bg_shuffle_idxs = seg_sample_idxs.copy()
                             np.random.shuffle(bg_shuffle_idxs)
@@ -130,29 +131,57 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
                         bg_img, bg_mask = gen_img_mask(bg_sample)
 
                         bg_wp   = imagelib.gen_warp_params(resolution, True, rotation_range=[-180,180], scale_range=[-0.10, 0.10], tx_range=[-0.10, 0.10], ty_range=[-0.10, 0.10] )
-                        bg_img  = imagelib.warp_by_params (bg_wp, bg_img,  can_warp=False, can_transform=True, can_flip=True, border_replicate=False)
+                        bg_img  = imagelib.warp_by_params (bg_wp, bg_img,  can_warp=False, can_transform=True, can_flip=True, border_replicate=True)
                         bg_mask = imagelib.warp_by_params (bg_wp, bg_mask, can_warp=False, can_transform=True, can_flip=True, border_replicate=False)
+                        bg_img = bg_img*(1-bg_mask)
+                        if np.random.randint(2) == 0:
+                            bg_img = imagelib.apply_random_hsv_shift(bg_img)
+                        else:
+                            bg_img = imagelib.apply_random_rgb_levels(bg_img)
 
-                        c_mask = (1-bg_mask) * (1-mask)
+                        c_mask = 1.0 - (1-bg_mask) * (1-mask)
-                        img = img*(1-c_mask) + bg_img * c_mask
+                        rnd = 0.15 + np.random.uniform()*0.85
+                        img = img*(c_mask) + img*(1-c_mask)*rnd + bg_img*(1-c_mask)*(1-rnd)
 
                     warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
-                    img   = imagelib.warp_by_params (warp_params, img,  can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
+                    img   = imagelib.warp_by_params (warp_params, img,  can_warp=True, can_transform=True, can_flip=True, border_replicate=True)
                     mask  = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
 
                     img = np.clip(img.astype(np.float32), 0, 1)
                     mask[mask < 0.5] = 0.0
                     mask[mask >= 0.5] = 1.0
                     mask = np.clip(mask, 0, 1)
+                    
+                    if np.random.randint(2) == 0:
+                        # random face flare
+                        krn = np.random.randint( resolution//4, resolution )
+                        krn = krn - krn % 2 + 1
+                        img = img + cv2.GaussianBlur(img*mask, (krn,krn), 0)
+
+                    if np.random.randint(2) == 0:
+                        # random bg flare
+                        krn = np.random.randint( resolution//4, resolution )
+                        krn = krn - krn % 2 + 1
+                        img = img + cv2.GaussianBlur(img*(1-mask), (krn,krn), 0)
 
                     if np.random.randint(2) == 0:
                         img = imagelib.apply_random_hsv_shift(img, mask=sd.random_circle_faded ([resolution,resolution]))
                     else:
                         img = imagelib.apply_random_rgb_levels(img, mask=sd.random_circle_faded ([resolution,resolution]))
+                        
+                    if np.random.randint(2) == 0:
+                        img = imagelib.apply_random_sharpen( img, sharpen_chance, sharpen_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
+                    else:
+                        img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
+                        img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
+                        
+                    if np.random.randint(2) == 0:
+                        img = imagelib.apply_random_nearest_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
+                    else:
+                        img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
+                    img = np.clip(img, 0, 1)
 
-                    img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
+                    img = imagelib.apply_random_jpeg_compress( img, random_jpeg_compress_chance, mask=sd.random_circle_faded ([resolution,resolution]))
-                    img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
-                    img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
 
                     if data_format == "NCHW":
                         img = np.transpose(img, (2,0,1) )
@@ -221,4 +250,48 @@ class SegmentedSampleFilterSubprocessor(Subprocessor):
             if self.count_xseg_mask:
                 return idx, self.samples[idx].has_xseg_mask()
             else:
-                return idx, self.samples[idx].seg_ie_polys.get_pts_count() != 0
+                return idx, self.samples[idx].seg_ie_polys.get_pts_count() != 0
+
+"""
+  bg_path = None
+        for path in paths:
+            bg_path = Path(path) / 'backgrounds'
+            if bg_path.exists():
+
+                break
+        if bg_path is None:
+            io.log_info(f'Random backgrounds will not be used. Place no face jpg images to aligned\backgrounds folder. ')
+            bg_pathes = None
+        else:
+            bg_pathes = pathex.get_image_paths(bg_path, image_extensions=['.jpg'], return_Path_class=True)
+            io.log_info(f'Using {len(bg_pathes)} random backgrounds from {bg_path}')
+
+if bg_pathes is not None:
+            bg_path = bg_pathes[ np.random.randint(len(bg_pathes)) ]
+
+            bg_img = cv2_imread(bg_path)
+            if bg_img is not None:
+                bg_img = bg_img.astype(np.float32) / 255.0
+                bg_img = imagelib.normalize_channels(bg_img, 3)
+
+                bg_img = imagelib.random_crop(bg_img, resolution, resolution)
+                bg_img = cv2.resize(bg_img, (resolution, resolution), interpolation=cv2.INTER_LINEAR)
+
+            if np.random.randint(2) == 0:
+                bg_img = imagelib.apply_random_hsv_shift(bg_img)
+            else:
+                bg_img = imagelib.apply_random_rgb_levels(bg_img)
+
+            bg_wp   = imagelib.gen_warp_params(resolution, True, rotation_range=[-180,180], scale_range=[0,0], tx_range=[0,0], ty_range=[0,0])
+            bg_img  = imagelib.warp_by_params (bg_wp, bg_img,  can_warp=False, can_transform=True, can_flip=True, border_replicate=True)
+
+            bg = img*(1-mask)
+            fg = img*mask
+
+            c_mask = sd.random_circle_faded ([resolution,resolution])
+            bg = ( bg_img*c_mask + bg*(1-c_mask) )*(1-mask)
+
+            img = fg+bg
+
+        else:
+"""