Merge branch 'feature/gan-v2' into feature/gan-updates

core/leras/models/PatchDiscriminator.py

@@ -111,7 +111,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
                 for i in range(layers_count-1):
                     st = 1 + (1 if val & (1 << i) !=0 else 0 )
                     layers.append ( [3, st ])
-                    sum_st += st                
+                    sum_st += st
 
                 rf = self.calc_receptive_field_size(layers)
 
@@ -131,7 +131,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
         return s[q][2]
 
     def on_build(self, patch_size, in_ch, base_ch = 16):
-    
+
         class ResidualBlock(nn.ModelBase):
             def on_build(self, ch, kernel_size=3 ):
                 self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
@@ -152,7 +152,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
         self.upres1 = []
         self.upres2 = []
         layers = self.find_archi(patch_size)
-        
+
         level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
 
         self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
@@ -162,12 +162,12 @@ class UNetPatchDiscriminator(nn.ModelBase):
 
             self.res1.append ( ResidualBlock(level_chs[i]) )
             self.res2.append ( ResidualBlock(level_chs[i]) )
-            
+
             self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
 
             self.upres1.insert (0, ResidualBlock(level_chs[i-1]*2) )
             self.upres2.insert (0, ResidualBlock(level_chs[i-1]*2) )
-            
+
         self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
 
         self.center_out  =  nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
@@ -183,7 +183,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
             x = tf.nn.leaky_relu( conv(x), 0.2 )
             x = res1(x)
             x = res2(x)
-            
+
         center_out, x = self.center_out(x), tf.nn.leaky_relu( self.center_conv(x), 0.2 )
 
         for i, (upconv, enc, upres1, upres2 ) in enumerate(zip(self.upconvs, encs, self.upres1, self.upres2)):
@@ -195,3 +195,117 @@ class UNetPatchDiscriminator(nn.ModelBase):
         return center_out, self.out_conv(x)
 
 nn.UNetPatchDiscriminator = UNetPatchDiscriminator
+
+class UNetPatchDiscriminatorV2(nn.ModelBase):
+    """
+    Inspired by https://arxiv.org/abs/2002.12655 "A U-Net Based Discriminator for Generative Adversarial Networks"
+    """
+    def calc_receptive_field_size(self, layers):
+        """
+        result the same as https://fomoro.com/research/article/receptive-field-calculatorindex.html
+        """
+        rf = 0
+        ts = 1
+        for i, (k, s) in enumerate(layers):
+            if i == 0:
+                rf = k
+            else:
+                rf += (k-1)*ts
+            ts *= s
+        return rf
+
+    def find_archi(self, target_patch_size, max_layers=6):
+        """
+        Find the best configuration of layers using only 3x3 convs for target patch size
+        """
+        s = {}
+        for layers_count in range(1,max_layers+1):
+            val = 1 << (layers_count-1)
+            while True:
+                val -= 1
+
+                layers = []
+                sum_st = 0
+                for i in range(layers_count-1):
+                    st = 1 + (1 if val & (1 << i) !=0 else 0 )
+                    layers.append ( [3, st ])
+                    sum_st += st
+                layers.append ( [3, 2])
+                sum_st += 2
+
+                rf = self.calc_receptive_field_size(layers)
+
+                s_rf = s.get(rf, None)
+                if s_rf is None:
+                    s[rf] = (layers_count, sum_st, layers)
+                else:
+                    if layers_count < s_rf[0] or \
+                            ( layers_count == s_rf[0] and sum_st > s_rf[1] ):
+                        s[rf] = (layers_count, sum_st, layers)
+
+                if val == 0:
+                    break
+
+        x = sorted(list(s.keys()))
+        q=x[np.abs(np.array(x)-target_patch_size).argmin()]
+        return s[q][2]
+
+    def on_build(self, patch_size, in_ch):
+        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
+
+        prev_ch = in_ch
+        self.convs = []
+        self.res = []
+        self.upconvs = []
+        self.upres = []
+        layers = self.find_archi(patch_size)
+        base_ch = 16
+
+        level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
+
+        self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
+
+        for i, (kernel_size, strides) in enumerate(layers):
+            self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME') )
+
+            self.res.append ( ResidualBlock(level_chs[i]) )
+
+            self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
+
+            self.upres.insert (0, ResidualBlock(level_chs[i-1]*2) )
+
+        self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
+
+        self.center_out  =  nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
+        self.center_conv =  nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID')
+
+
+    def forward(self, x):
+        x = tf.nn.leaky_relu( self.in_conv(x), 0.1 )
+
+        encs = []
+        for conv, res in zip(self.convs, self.res):
+            encs.insert(0, x)
+            x = tf.nn.leaky_relu( conv(x), 0.1 )
+            x = res(x)
+
+        center_out, x = self.center_out(x), self.center_conv(x)
+
+        for i, (upconv, enc, upres) in enumerate(zip(self.upconvs, encs, self.upres)):
+            x = tf.nn.leaky_relu( upconv(x), 0.1 )
+            x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
+            x = upres(x)
+
+        return center_out, self.out_conv(x)
+
+nn.UNetPatchDiscriminatorV2 = UNetPatchDiscriminatorV2

models/Model_SAEHD/Model.py

@@ -138,6 +138,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             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_version        = self.options['gan_version']        = self.load_or_def_option('gan_version', 2)
         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)
@@ -153,9 +154,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             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 )
+            self.options['gan_version'] = np.clip (io.input_int("GAN version", default_gan_version, add_info="2 or 3", help_message="Choose GAN version (v2: 7/16/2020, v3: 1/3/2021):"), 2, 3)
 
-            if self.options['gan_power'] != 0.0:
+            if self.options['gan_version'] == 2:
+                self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical value is 0.1"), 0.0, 10.0 )
+            else:
+                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 and self.options['gan_version'] == 3:
                 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
 
@@ -304,8 +310,12 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             if self.is_training:
                 if gan_power != 0:
-                    self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
+                    if self.options['gan_version'] == 2:
-                    self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
+                        self.D_src = nn.UNetPatchDiscriminatorV2(patch_size=resolution//16, in_ch=input_ch, name="D_src")
+                        self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
+                    else:
+                        self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
+                        self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
 
                 # Initialize optimizers
                 lr=5e-5
@@ -330,9 +340,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
 
                 if gan_power != 0:
-                    self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
+                    if self.options['gan_version'] == 2:
-                    self.D_src_dst_opt.initialize_variables ( self.D_src.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.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
-                    self.model_filename_list += [ (self.D_src_dst_opt, 'GAN_opt.npy') ]
+                        self.D_src_dst_opt.initialize_variables ( self.D_src.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.D_src_dst_opt, 'D_src_v2_opt.npy') ]
+                    else:
+                        self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
+                        self.D_src_dst_opt.initialize_variables ( self.D_src.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.D_src_dst_opt, 'GAN_opt.npy') ]
 
         if self.is_training:
             # Adjust batch size for multiple GPU