      def Decoder_original(self):       
          decoder_shape = self.IMAGE_SHAPE[0]//8        
          inpt = Input(shape=(decoder_shape, decoder_shape, 512))
          
          x = self.upscale(512, kernel_initializer=RandomNormal(0, 0.02))(inpt)
          x = self.upscale(256, kernel_initializer=RandomNormal(0, 0.02))(x)
          x = self.upscale(self.IMAGE_SHAPE[0], kernel_initializer=RandomNormal(0, 0.02))(x)
          
          x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
          
          return KerasModel(inpt, x)
 
</s> remove         return block    
 </s> add         return block   

    def conv_sep2(self, filters, kernel_size=5, strides=2, use_instance_norm=True, **kwargs):
        def block(x):
            x = SeparableConv2D(filters, kernel_size=kernel_size, strides=strides, kernel_initializer=conv_init, padding='same', **kwargs)(x)
            x = Activation("relu")(x)
            return x    
        return block  </s> remove         x = self.conv(1024)(x)
 </s> add         x = self.conv_sep2(1024)(x) </s> remove         x = self.conv(256)(x)
 </s> add         x = self.conv_sep2(256)(x) </s> remove         x = Dense(dense_shape * dense_shape * 768)(x)
        x = Reshape((dense_shape, dense_shape, 768))(x)
 </s> add         x = Dense(dense_shape * dense_shape * 512)(x)
        x = Reshape((dense_shape, dense_shape, 512))(x) </s> remove ENCODER = EncoderType.SHAOANLU 
 </s> add ENCODER = EncoderType.ORIGINAL 
        x = self.upscale(384, kernel_initializer=RandomNormal(0, 0.02))(inpt)
        x = self.upscale(256-32, kernel_initializer=RandomNormal(0, 0.02))(x)
      def Decoder_original(self):       
          decoder_shape = self.IMAGE_SHAPE[0]//8        
          inpt = Input(shape=(decoder_shape, decoder_shape, 512))
          
        x = self.upscale(384, kernel_initializer=RandomNormal(0, 0.02))(inpt)
        x = self.upscale(256-32, kernel_initializer=RandomNormal(0, 0.02))(x)
        x = self.upscale(384, kernel_initializer=RandomNormal(0, 0.02))(inpt)
        x = self.upscale(256-32, kernel_initializer=RandomNormal(0, 0.02))(x)
          x = self.upscale(self.IMAGE_SHAPE[0], kernel_initializer=RandomNormal(0, 0.02))(x)
          
          x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
          
          return KerasModel(inpt, x)
keep keep keep keep replace replace keep keep keep keep keep
 Fixed memory usage at 6GB cards. (#423)

- Switched default encoder to ORIGINAL
- Fixed memory consumption. Tested with geforce gtx 9800 ti with 6Gb; batch_size 8 no OOM or memory warnings now. 
plugins/Model_OriginalHighRes/Model.py