- Bugfix: Training - Disable loss multipliers if penalized loss not selected

- Training: Half mouth/eye multiplier defaults
- GUI: Remove analysis callback from convert tab

lib/gui/control_helper.py

@@ -293,7 +293,7 @@ class ControlPanelOption():
             var.trace("w",
                       lambda name, index, mode, cmd=self._command: self._modified_callback(cmd))
 
-        if track_modified and self._command in ("train", "convert") and self.title == "Model Dir":
+        if track_modified and self._command == "train" and self.title == "Model Dir":
             var.trace("w", lambda name, index, mode, v=var: self._model_callback(v))
 
         return var

lib/training_data.py

@@ -283,19 +283,19 @@ class TrainingDataGenerator():  # pylint:disable=too-few-public-methods
             item = self._masks[key]
             if item is None and key != "masks":
                 continue
-            if item is None and key == "masks":
-                logger.trace("Creating dummy masks. side: %s", side)
-                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
-                continue
 
             # Expand out partials for eye and mouth masks on first epoch
             if item is not None and key in ("eyes", "mouths"):
                 self._expand_partials(side, item, filenames)
 
-            logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
-            masks = np.array([self._get_mask(item[side][filename], size)
-                              for filename in filenames], dtype=batch.dtype)
-            masks = self._resize_masks(size, masks)
+            if item is None and key == "masks":
+                logger.trace("Creating dummy masks. side: %s", side)
+                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
+            else:
+                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
+                masks = np.array([self._get_mask(item[side][filename], size)
+                                  for filename in filenames], dtype=batch.dtype)
+                masks = self._resize_masks(size, masks)
 
             logger.trace("masks: (key: %s, shape: %s)", key, masks.shape)
             batch = np.concatenate((batch, masks), axis=-1)

plugins/train/_config.py

@@ -271,7 +271,7 @@ class Config(FaceswapConfig):
             group="loss",
             min_max=(1, 40),
             rounding=1,
-            default=6,
+            default=3,
             fixed=False,
             info="The amount of priority to give to the eyes.\n\nThe value given here is as a "
                  "multiplier of the main loss score. For example:"
@@ -286,7 +286,7 @@ class Config(FaceswapConfig):
             group="loss",
             min_max=(1, 40),
             rounding=1,
-            default=4,
+            default=2,
             fixed=False,
             info="The amount of priority to give to the mouth.\n\nThe value given here is as a "
                  "multiplier of the main loss score. For Example:"

plugins/train/model/_base.py

@@ -1044,9 +1044,17 @@ class _Loss():
         list:
             A list of channel indices that contain the mask for the corresponding config item
         """
+        eye_multiplier = self._config["eye_multiplier"]
+        mouth_multiplier = self._config["mouth_multiplier"]
+        if not self._config["penalized_mask_loss"] and (eye_multiplier > 1 or
+                                                        mouth_multiplier > 1):
+            logger.warning("You have selected eye/mouth loss multipliers greate than 1x, but "
+                           "Penalized Mask Loss is disabled. Disabling all multipliers.")
+            eye_multiplier = 1
+            mouth_multiplier = 1
         uses_masks = (self._config["penalized_mask_loss"],
-                      self._config["eye_multiplier"] > 1,
-                      self._config["mouth_multiplier"] > 1)
+                      eye_multiplier > 1,
+                      mouth_multiplier > 1)
         mask_channels = [-1 for _ in range(len(uses_masks))]
         current_channel = 3
         for idx, mask_required in enumerate(uses_masks):

plugins/train/trainer/_base.py

@@ -107,9 +107,10 @@ class TrainerBase():
             are required for training, `masks_eye` if eye masks are required and `masks_mouth` if
             mouth masks are required. """
         retval = dict()
+        penalized_loss = self._model.config["penalized_mask_loss"]
 
         if not any([self._model.config["learn_mask"],
-                    self._model.config["penalized_mask_loss"],
+                    penalized_loss,
                     self._model.config["eye_multiplier"] > 1,
                     self._model.config["mouth_multiplier"] > 1,
                     self._model.command_line_arguments.warp_to_landmarks]):
@@ -121,14 +122,14 @@ class TrainerBase():
             logger.debug("Adding landmarks to training opts dict")
             retval["landmarks"] = alignments.landmarks
 
-        if self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]:
+        if self._model.config["learn_mask"] or penalized_loss:
             logger.debug("Adding masks to training opts dict")
             retval["masks"] = alignments.masks
 
-        if self._model.config["eye_multiplier"] > 1:
+        if penalized_loss and self._model.config["eye_multiplier"] > 1:
             retval["masks_eye"] = alignments.masks_eye
 
-        if self._model.config["mouth_multiplier"] > 1:
+        if penalized_loss and self._model.config["mouth_multiplier"] > 1:
             retval["masks_mouth"] = alignments.masks_mouth
 
         logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()})