changes

face_swapper/src/models/loss.py

@@ -106,32 +106,56 @@ class IdentityLoss(nn.Module):
 		return identity_loss, weighted_identity_loss
 
 
-class PoseLoss(nn.Module):
-	def __init__(self, motion_extractor : MotionExtractorModule) -> None:
+class MotionLoss(nn.Module):
+	def __init__(self, motion_extractor : MotionExtractorModule):
 		super().__init__()
 		self.motion_extractor = motion_extractor
+		self.pose_loss = PoseLoss()
+		self.expression_loss = ExpressionLoss()
+
+	def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+		target_pose_features, target_expression = self.get_motion_features(target_tensor)
+		output_pose_features, output_expression = self.get_motion_features(output_tensor)
+		pose_loss, weighted_pose_loss = self.pose_loss(target_pose_features, output_pose_features)
+		expression_loss, weighted_expression_loss = self.expression_loss(target_expression, output_expression)
+		return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss
+
+	def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], Tensor]:
+		input_tensor = (input_tensor + 1) * 0.5
+		pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor)
+		rotation = torch.cat([ pitch, yaw, roll ], dim = 1)
+		pose_features = translation, scale, rotation, motion_points
+		return pose_features, expression
+
+
+class ExpressionLoss(nn.Module):
+	def __init__(self) -> None:
+		super().__init__()
+
+	def forward(self, target_expression : Tensor, output_expression : Tensor, ) -> Tuple[Tensor, Tensor]:
+		expression_weight = CONFIG.getfloat('training.losses', 'expression_weight')
+		expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean()
+		weighted_expression_loss = expression_loss * expression_weight
+		return expression_loss, weighted_expression_loss
+
+
+class PoseLoss(nn.Module):
+	def __init__(self) -> None:
+		super().__init__()
 		self.mse_loss = nn.MSELoss()
 
-	def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]:
+	def forward(self, target_pose_features : Tensor, output_pose_features : Tensor, ) -> Tuple[Tensor, Tensor]:
 		pose_weight = CONFIG.getfloat('training.losses', 'pose_weight')
-		output_motion_features = self.get_motion_features(output_tensor)
-		target_motion_features = self.get_motion_features(target_tensor)
 		temp_tensors = []
 
-		for target_motion_feature, output_motion_feature in zip(target_motion_features, output_motion_features):
-			temp_tensor = self.mse_loss(target_motion_feature, output_motion_feature)
+		for target_pose_feature, output_pose_feature in zip(target_pose_features, output_pose_features):
+			temp_tensor = self.mse_loss(target_pose_feature, output_pose_feature)
 			temp_tensors.append(temp_tensor)
 
 		pose_loss = torch.stack(temp_tensors).mean()
 		weighted_pose_loss = pose_loss * pose_weight
 		return pose_loss, weighted_pose_loss
 
-	def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]:
-		input_tensor = (input_tensor + 1) * 0.5
-		pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(input_tensor)
-		rotation = torch.cat([ pitch, yaw, roll ], dim = 1)
-		return translation, scale, rotation
-
 
 class GazeLoss(nn.Module):
 	def __init__(self, gazer : GazerModule) -> None: