          configfile = self.args.configfile if hasattr(self.args, "configfile") else None
          augment_color = not self.args.no_augment_color
          model = PluginLoader.get_model(self.trainer_name)(
              model_dir,
              self.args.gpus,
              configfile=configfile,
              snapshot_interval=self.args.snapshot_interval,
              no_logs=self.args.no_logs,
              warp_to_landmarks=self.args.warp_to_landmarks,
              augment_color=augment_color,
 
</s> remove         dummy = np.zeros((20, 20, 3)).astype("uint8")
 </s> add         dummy = np.zeros((20, 20, 3), dtype="uint8") </s> remove         if swap:
            model = self.predictors["a"]
        else:
            model = self.predictors["b"]
 </s> add         side = "a" if swap else "b"
        model = self.predictors[side] </s> remove 
        if self.config.get("dssim_loss", False):
            if not predict and side.lower() == "a":
                logger.verbose("Using DSSIM Loss")
            loss_func = DSSIMObjective()
        else:
            loss_func = losses.mean_absolute_error
            if not predict and side.lower() == "a":
                logger.verbose("Using Mean Absolute Error Loss")
 </s> add         loss_dict = {'mae':                     losses.mean_absolute_error,
                     'mse':                     losses.mean_squared_error,
                     'logcosh':                 losses.logcosh,
                     'smooth_l1':               generalized_loss,
                     'l_inf_norm':              l_inf_norm,
                     'ssim':                    DSSIMObjective(),
                     'gmsd':                    gmsd_loss,
                     'pixel_gradient_diff':     gradient_loss}
        img_loss_config = self.config.get("loss_function", "mae")
        mask_loss_config = "mse" </s> remove             closest = (np.mean(np.square(src_points - dst_points),
                               axis=(1, 2))).argsort()[:10]
 </s> add             closest = (np.mean(np.square(src_points - dst_points), axis=(1, 2))).argsort()[:10]
            gpus=self.args.gpus,
          configfile = self.args.configfile if hasattr(self.args, "configfile") else None
          augment_color = not self.args.no_augment_color
          model = PluginLoader.get_model(self.trainer_name)(
              model_dir,
            gpus=self.args.gpus,
              configfile=configfile,
              snapshot_interval=self.args.snapshot_interval,
              no_logs=self.args.no_logs,
              warp_to_landmarks=self.args.warp_to_landmarks,
              augment_color=augment_color,
keep keep keep keep 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 
scripts/train.py