fix for plaidml

models/Model_SAEHD/Model.py

@@ -483,7 +483,8 @@ class SAEv2Model(ModelBase):
             dst_loss =  K.mean( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_opt, pred_dst_dst_masked_opt) )
             dst_loss += K.mean( 10*K.square( target_dst_masked_opt - pred_dst_dst_masked_opt ) )
 
-            opt_D_loss = []
+            G_loss = src_loss+dst_loss
+            
             if self.true_face_training:                
                 def DLoss(labels,logits):
                     return K.mean(K.binary_crossentropy(labels,logits))
@@ -493,7 +494,7 @@ class SAEv2Model(ModelBase):
                 src_code_d_zeros = K.zeros_like(src_code_d)
                 dst_code_d = self.dis( self.model.dst_code )
                 dst_code_d_ones = K.ones_like(dst_code_d)
-                opt_D_loss = [ 0.01*DLoss(src_code_d_ones, src_code_d) ]
+                G_loss += 0.01*DLoss(src_code_d_ones, src_code_d)
                 
                 loss_D = (DLoss(dst_code_d_ones , dst_code_d) + \
                           DLoss(src_code_d_zeros, src_code_d) ) * 0.5
@@ -502,7 +503,7 @@ class SAEv2Model(ModelBase):
                 
             self.src_dst_train = K.function ([self.model.warped_src, self.model.warped_dst, self.model.target_src, self.model.target_srcm, self.model.target_dst, self.model.target_dstm],
                                              [src_loss,dst_loss], 
-                                             self.src_dst_opt.get_updates( [src_loss+dst_loss]+opt_D_loss, self.model.src_dst_trainable_weights) 
+                                             self.src_dst_opt.get_updates( G_loss, self.model.src_dst_trainable_weights) 
                                              )
 
             if self.options['learn_mask']:

nnlib/device.py

@@ -232,6 +232,7 @@ class device:
 
 plaidML_build = os.environ.get("DFL_PLAIDML_BUILD", "0") == "1"
 plaidML_devices = None
+plaidML_devices_count = 0
 cuda_devices = None
 
 if plaidML_build:
@@ -253,8 +254,8 @@ if plaidML_build:
             ctx.shutdown()
         except:
             pass
-    
-    if len(plaidML_devices) != 0:
+    plaidML_devices_count = len(plaidML_devices)
+    if plaidML_devices_count != 0:
         device.backend = "plaidML"
 else:      
     if cuda_devices is None: