superb improved fanseg

converters/ConverterMasked.py

@@ -171,13 +171,13 @@ class ConverterMasked(Converter):
 
             if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6: 
                 prd_face_bgr_256 = cv2.resize (prd_face_bgr, (256,256) )
-                prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( prd_face_bgr_256[np.newaxis,...] ) [0]
+                prd_face_bgr_256_mask = self.fan_seg.extract( prd_face_bgr_256 )
                 FAN_prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
 
             if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6: 
                 face_256_mat     = LandmarksProcessor.get_transform_mat (img_face_landmarks, 256, face_type=FaceType.FULL)
                 dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4 )
-                dst_face_256_mask = self.fan_seg.extract_from_bgr( dst_face_256_bgr[np.newaxis,...] ) [0]
+                dst_face_256_mask = self.fan_seg.extract( dst_face_256_bgr )
                 FAN_dst_face_mask_a_0 = cv2.resize (dst_face_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
 
             if self.mask_mode == 3:   #FAN-prd

facelib/FANSegmentator.py

@@ -1,15 +1,27 @@
-import numpy as np
 import os
-import cv2
+import pickle
+from functools import partial
 from pathlib import Path
-from nnlib import nnlib
+
+import cv2
+import numpy as np
+
 from interact import interact as io
+from nnlib import nnlib
+
+"""
+FANSegmentator is designed to segment faces aligned by 2DFAN-4 landmarks extractor.
+
+using https://github.com/ternaus/TernausNet
+TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
+"""
 
 class FANSegmentator(object):
+    VERSION = 1
     def __init__ (self, resolution, face_type_str, load_weights=True, weights_file_root=None, training=False):
         exec( nnlib.import_all(), locals(), globals() )
 
-        self.model = FANSegmentator.BuildModel(resolution, ngf=32)
+        self.model = FANSegmentator.BuildModel(resolution, ngf=64)
 
         if weights_file_root:
             weights_file_root = Path(weights_file_root)
@@ -21,12 +33,18 @@ class FANSegmentator(object):
         if load_weights:
             self.model.load_weights (str(self.weights_path))
         else:
-            if training:
+            if training:                
-                conv_weights_list = []
+                try:
-                for layer in self.model.layers:
+                    with open( Path(__file__).parent / 'vgg11_enc_weights.npy', 'rb' ) as f:
-                    if type(layer) == keras.layers.Conv2D:
+                        d = pickle.loads (f.read())
-                        conv_weights_list += [layer.weights[0]]  # Conv2D kernel_weights
+
-                CAInitializerMP(conv_weights_list)
+                    for i in [0,3,6,8,11,13,16,18]:
+                        s = 'features.%d' % i
+                        
+                        self.model.get_layer (s).set_weights ( d[s] )
+                except:
+                    io.log_err("Unable to load VGG11 pretrained weights from vgg11_enc_weights.npy")
+                    
         if training:
             self.model.compile(loss='mse', optimizer=Adam(tf_cpu_mode=2))
 
@@ -42,66 +60,75 @@ class FANSegmentator(object):
     def train_on_batch(self, inp, outp):
         return self.model.train_on_batch(inp, outp)
 
-    def extract_from_bgr (self, input_image):
+    def extract (self, input_image, is_input_tanh=False):
-        return np.clip ( (self.model.predict(input_image) + 1) / 2.0, 0, 1.0 )
+        input_shape_len = len(input_image.shape)
-
+        if input_shape_len == 3:
+            input_image = input_image[np.newaxis,...]
+        
+        result = np.clip ( self.model.predict( [input_image] ), 0, 1.0 )
+        
+        if input_shape_len == 3:
+            result = result[0]
+            
+        return result
+     
     @staticmethod
-    def BuildModel ( resolution, ngf=64):
+    def BuildModel ( resolution, ngf=64, norm='', act='lrelu'):
         exec( nnlib.import_all(), locals(), globals() )
         inp = Input ( (resolution,resolution,3) )
         x = inp
-        x = FANSegmentator.EncFlow(ngf=ngf)(x)
+        x = FANSegmentator.Flow(ngf=ngf, norm=norm, act=act)(x)
-        x = FANSegmentator.DecFlow(ngf=ngf)(x)
         model = Model(inp,x)
         return model
 
     @staticmethod
-    def EncFlow(ngf=64, num_downs=4):
+    def Flow(ngf=64, num_downs=4, norm='', act='lrelu'):
         exec( nnlib.import_all(), locals(), globals() )
 
-        use_bias = True
-        def XNormalization(x):
-            return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
-
-        def downscale (dim):
-            def func(x):
-                return LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=5, strides=2, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x)))
-            return func
-
         def func(input):
             x = input
 
-            result = []
+            x0 = x = Conv2D(ngf, kernel_size=3, strides=1, padding='same', activation='relu', name='features.0')(x)
-            for i in range(num_downs):
+            x = MaxPooling2D()(x)
-               x = downscale ( min(ngf*(2**i), ngf*8) )(x)
+            
-               result += [x]
+            x1 = x = Conv2D(ngf*2, kernel_size=3, strides=1, padding='same', activation='relu', name='features.3')(x)
-
+            x = MaxPooling2D()(x)
-            return result
+            
+            x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.6')(x)
+            x2 = x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.8')(x)
+            x = MaxPooling2D()(x)
+            
+            x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.11')(x)
+            x3 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.13')(x)
+            x = MaxPooling2D()(x)
+            
+            x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.16')(x)
+            x4 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.18')(x)
+            x = MaxPooling2D()(x)
+            
+            x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same')(x)
+            
+            x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu') (x)  
+            x = Concatenate(axis=3)([ x, x4])            
+            x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu') (x)         
+               
+            x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu') (x)            
+            x = Concatenate(axis=3)([ x, x3])            
+            x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu') (x)
+            
+            x = Conv2DTranspose (ngf*2, 3, strides=2, padding='same', activation='relu') (x)            
+            x = Concatenate(axis=3)([ x, x2])            
+            x = Conv2D (ngf*4, 3, strides=1, padding='same', activation='relu') (x)     
+                   
+            x = Conv2DTranspose (ngf, 3, strides=2, padding='same', activation='relu') (x)            
+            x = Concatenate(axis=3)([ x, x1])            
+            x = Conv2D (ngf*2, 3, strides=1, padding='same', activation='relu') (x)     
+                   
+            x = Conv2DTranspose (ngf // 2, 3, strides=2, padding='same', activation='relu') (x)            
+            x = Concatenate(axis=3)([ x, x0])            
+            x = Conv2D (ngf, 3, strides=1, padding='same', activation='relu') (x)
+            
+            return Conv2D(1, 3, strides=1, padding='same', activation='sigmoid')(x)
+            
+            
         return func
-
-    @staticmethod
-    def DecFlow(output_nc=1, ngf=64, activation='tanh'):
-        exec (nnlib.import_all(), locals(), globals())
-
-        use_bias = True
-        def XNormalization(x):
-            return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
-
-        def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
-            return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint )
-
-        def upscale (dim):
-            def func(x):
-                return SubpixelUpscaler()( LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x))))
-            return func
-
-        def func(input):
-            input_len = len(input)
-            x = input[input_len-1]
-            for i in range(input_len-1, -1, -1):
-                x = upscale( min(ngf* (2**i) *4, ngf*8 *4 ) )(x)
-                if i != 0:
-                    x = Concatenate(axis=3)([ input[i-1] , x])
-
-            return Conv2D(output_nc, 3, 1, 'same', activation=activation)(x)
-        return func

models/Model_FANSegmentator/Model.py

@@ -37,13 +37,13 @@ class Model(ModelBase):
             
             self.set_training_data_generators ([    
                     SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, 
-                            sample_process_options=SampleProcessor.Options(random_flip=True, motion_blur = [25, 1], normalize_tanh = True ), 
+                            sample_process_options=SampleProcessor.Options(random_flip=True, motion_blur = [25, 1] ), 
-                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution],
+                            output_sample_types=[ [f.WARPED_TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution],
-                                                  [f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
+                                                  [f.WARPED_TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
                                                 ]),
                                                 
                     SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, 
-                            sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ), 
+                            sample_process_options=SampleProcessor.Options(random_flip=True ), 
                             output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution]
                                                 ])
                                                ])
@@ -65,11 +65,9 @@ class Model(ModelBase):
         test_A   = sample[0][0][0:4] #first 4 samples
         test_B   = sample[1][0][0:4] #first 4 samples
         
-        mAA = self.fan_seg.extract_from_bgr([test_A])
+        mAA = self.fan_seg.extract(test_A)
-        mBB = self.fan_seg.extract_from_bgr([test_B])
+        mBB = self.fan_seg.extract(test_B)
-        
+
-        test_A, test_B, = [ np.clip( (x + 1.0)/2.0, 0.0, 1.0)  for x in [test_A, test_B] ]
-        
         mAA = np.repeat ( mAA, (3,), -1)
         mBB = np.repeat ( mBB, (3,), -1)
         
@@ -89,6 +87,6 @@ class Model(ModelBase):
                 test_B[i,:,:,0:3]*mBB[i],
                 ), axis=1) )
                 
-        return [ ('FANSegmentator', np.concatenate ( st, axis=0 ) ),
+        return [ ('training data', np.concatenate ( st, axis=0 ) ),
-                 ('never seen', np.concatenate ( st2, axis=0 ) ),
+                 ('evaluating data', np.concatenate ( st2, axis=0 ) ),
                  ]

nnlib/nnlib.py

@@ -52,7 +52,7 @@ Input = KL.Input
 
 Dense = KL.Dense
 Conv2D = nnlib.Conv2D
-Conv2DTranspose = KL.Conv2DTranspose
+Conv2DTranspose = nnlib.Conv2DTranspose
 SeparableConv2D = KL.SeparableConv2D
 MaxPooling2D = KL.MaxPooling2D
 UpSampling2D = KL.UpSampling2D
@@ -695,6 +695,26 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                     x = ReflectionPadding2D( self.pad ) (x)
                 return self.func(x)
         nnlib.Conv2D = Conv2D
+        
+        class Conv2DTranspose():
+            def __init__ (self, *args, **kwargs):
+                self.reflect_pad = False
+                padding = kwargs.get('padding','')
+                if padding == 'zero':
+                    kwargs['padding'] = 'same'
+                if padding == 'reflect':
+                    kernel_size = kwargs['kernel_size']
+                    if (kernel_size % 2) == 1:
+                        self.pad = (kernel_size // 2,)*2
+                        kwargs['padding'] = 'valid'
+                        self.reflect_pad = True
+                self.func = keras.layers.Conv2DTranspose (*args, **kwargs)
+
+            def __call__(self,x):
+                if self.reflect_pad:
+                    x = ReflectionPadding2D( self.pad ) (x)
+                return self.func(x)
+        nnlib.Conv2DTranspose = Conv2DTranspose
 
     @staticmethod
     def import_keras_contrib(device_config):