                     "\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
  
          range_ = np.linspace(height // 2 - coverage // 2,
                               height // 2 + coverage // 2,
                               5, dtype='float32')
          mapx = np.broadcast_to(range_, (5, 5)).copy()
          mapy = mapx.T
          # mapx, mapy = np.float32(np.meshgrid(range_,range_)) # instead of broadcast
  
          pad = int(1.25 * self.input_size)
 
</s> remove         slices = slice(size // 2 - coverage // 2, size // 2 + coverage // 2)
 </s> add         slices = slice(size // 2 - coverage, size // 2 + coverage) </s> remove         coverage = self.get_coverage(image)
 </s> add         coverage = self.get_coverage(image) // 2 </s> remove         coverage = self.get_coverage(image)
 </s> add         coverage = self.get_coverage(image) // 2 </s> remove         return pad(x, [[0,0],
                          [padding_top, padding_bot],
                          [padding_left, padding_right],
                          [0,0] ],
                          'REFLECT')
 </s> add         return pad(x,
                   [[0, 0],
                    [padding_top, padding_bot],
                    [padding_left, padding_right],
                    [0, 0]],
                   'REFLECT') </s> remove         if (in_width % self.stride == 0):
 </s> add         if (in_width % self.stride) == 0:
        range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32')
                     "\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
  
        range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32')
        range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32')
        range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32')
          mapx = np.broadcast_to(range_, (5, 5)).copy()
          mapy = mapx.T
          # mapx, mapy = np.float32(np.meshgrid(range_,range_)) # instead of broadcast
  
          pad = int(1.25 * self.input_size)
keep keep keep keep replace replace replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
lib/training_data.py