          augmentation, otherwise ``False``
  
          * **warp_to_landmarks** (`bool`) - ``True`` if the random warp method should warp to \
          similar landmarks from the other side, ``False`` if the standard random warp method \
          should be used. If ``True`` then the additional key ``landmarks`` must be provided.
  
 
</s> remove         * **landmarks** (`numpy.ndarray`, `optional`). Required if :attr:`warp_to_landmarks` is \
        ``True``. The 68 point face landmarks from an alignments file.
 </s> add         * **landmarks** (`numpy.ndarray`, `optional`). Required if using a :attr:`mask_type` is \
        not ``None`` or :attr:`warp_to_landmarks` is ``True``. The 68 point face landmarks from \
        an alignments file. </s> remove             the format (`batchsize`, `height`, `width`, `1`).
 </s> add             the format (`batchsize`, `height`, `width`, `1`). **NB:** This item will only exist \
            in the ``dict`` if the :attr:`mask_type` is not ``None`` </s> remove             the format (`batchsize`, `height`, `width`, `1`).
 </s> add             the format (`batchsize`, `height`, `width`, `1`). **NB:** This item will only exist \
            in the ``dict`` if a batch of 4 channel images has been passed in :attr:`batch` </s> remove         config item ``warp_to_landmarks`` is ``True``. If the landmarks for an image cannot be
        found, then an error is raised. """
 </s> add         config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
        the landmarks for an image cannot be found, then an error is raised. """ </s> remove         alignments:             dict containing paths to alignments files for keys 'a' and 'b'
        preview_scaling:        How much to scale the preview out by
        training_size:          Size of the training images
        coverage_ratio:         Ratio of face to be cropped out for training
        replicate_input_mask:   Replicate input masks with additional model dedicated layers
        no_logs:                Disable tensorboard logging
        snapshot_interval:      Interval for saving model snapshots
        warp_to_landmarks:      Use random_warp_landmarks instead of random_warp
        augment_color:          Perform random shifting of L*a*b* colors
        no_flip:                Don't perform a random flip on the image
        pingpong:               Train each side seperately per save iteration rather than together
 </s> add         alignments:         dict containing paths to alignments files for keys 'a' and 'b'
        preview_scaling:    How much to scale the preview out by
        training_size:      Size of the training images
        coverage_ratio:     Ratio of face to be cropped out for training
        mask_type:          Type of mask to use. See lib.model.masks for valid mask names.
                            Set to None for not used
        no_logs:            Disable tensorboard logging
        snapshot_interval:  Interval for saving model snapshots
        warp_to_landmarks:  Use random_warp_landmarks instead of random_warp
        augment_color:      Perform random shifting of L*a*b* colors
        no_flip:            Don't perform a random flip on the image
        pingpong:           Train each side seperately per save iteration rather than together
        * **mask_type** (`str`) - The mask type to be used (as defined in \
        :mod:`lib.model.masks`). If not ``None`` then the additional key ``landmarks`` must be \
        provided.

          augmentation, otherwise ``False``
  
        * **mask_type** (`str`) - The mask type to be used (as defined in \
        :mod:`lib.model.masks`). If not ``None`` then the additional key ``landmarks`` must be \
        provided.

          * **warp_to_landmarks** (`bool`) - ``True`` if the random warp method should warp to \
          similar landmarks from the other side, ``False`` if the standard random warp method \
          should be used. If ``True`` then the additional key ``landmarks`` must be provided.
  
keep add keep keep keep keep
 Serialize masks to alignments file

- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version 
lib/training_data.py