          length = len(data)
          msg = ("Number of images is lower than batch-size (Note that too few "
                 "images may lead to bad training). # images: {}, "
                 "batch-size: {}".format(length, self.batchsize))
          assert length >= self.batchsize, msg
  
      @staticmethod
      def minibatch(side, is_display, load_process):
          """ A generator function that yields epoch, batchsize of warped_img
              and batchsize of target_img from the load queue """
 
</s> remove         assert height == width and height % 2 == 0
 </s> add         try:
            assert height == width and height % 2 == 0
        except AssertionError as err:
            msg = ("Training images should be square with an even number of pixels across each "
                   "side. An image was found with width: {}, height: {}."
                   "\nMost likely this is a frame rather than a face within your training set. "
                   "\nMake sure that the only images within your training set are faces generated "
                   "from the Extract process.".format(width, height))
            raise FaceswapError(msg) from err
        try:
            assert length >= self.batchsize, msg
        except AssertionError as err:
            msg += ("\nYou should increase the number of images in your training set or lower "
                    "your batch-size.")
            raise FaceswapError(msg) from err
          length = len(data)
          msg = ("Number of images is lower than batch-size (Note that too few "
                 "images may lead to bad training). # images: {}, "
                 "batch-size: {}".format(length, self.batchsize))
        try:
            assert length >= self.batchsize, msg
        except AssertionError as err:
            msg += ("\nYou should increase the number of images in your training set or lower "
                    "your batch-size.")
            raise FaceswapError(msg) from err
  
      @staticmethod
      def minibatch(side, is_display, load_process):
          """ A generator function that yields epoch, batchsize of warped_img
              and batchsize of target_img from the load queue """
keep keep keep keep replace keep keep keep keep keep
 Deprecation Warnings, Error Handling and Bugfix

Deprecation Warning: Rotation in Extract
Deprecation Warning: Multiple models within a single folder
Error Handling: Useful message for training size assertion error
Error Handling: Useful message for fewer images than batch size
Bugfix: BoundingBox object sometimes not available inside spawned process 
lib/training_data.py