changes

face_swapper/README.md

@@ -72,6 +72,7 @@ reconstruction_weight = 20
 identity_weight = 20
 pose_weight = 0
 gaze_weight = 0
+expression_weight = 0
 ```
 
 ```

face_swapper/config.ini

@@ -34,6 +34,7 @@ reconstruction_weight =
 identity_weight =
 pose_weight =
 gaze_weight =
+expression_weight =
 
 [training.trainer]
 learning_rate =

face_swapper/src/dataset.py

@@ -36,9 +36,9 @@ class DynamicDataset(Dataset[Tensor]):
 	def compose_transforms(self) -> transforms:
 		return transforms.Compose(
 		[
+			AugmentTransform(),
 			transforms.ToPILImage(),
 			transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC),
-			AugmentTransform(),
 			transforms.ToTensor(),
 			WarpTransform(self.warp_template),
 			transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
@@ -74,7 +74,7 @@ class AugmentTransform:
 
 	def __call__(self, input_tensor : Tensor) -> Tensor:
 		temp_tensor = input_tensor.numpy().transpose(1, 2, 0)
-		return self.transforms(temp_tensor).get('image')
+		return self.transforms(image = temp_tensor).get('image')
 
 	@staticmethod
 	def compose_transforms() -> albumentations.Compose:

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:

face_swapper/src/training.py

@@ -17,7 +17,7 @@ from .dataset import DynamicDataset
 from .helper import calc_embedding
 from .models.discriminator import Discriminator
 from .models.generator import Generator
-from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss
+from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MotionLoss, ReconstructionLoss
 from .types import Batch, BatchMode, Embedding, OptimizerConfig, WarpTemplate
 
 warnings.filterwarnings('ignore', category = UserWarning, module = 'torch')
@@ -44,7 +44,7 @@ class FaceSwapperTrainer(lightning.LightningModule):
 		self.attribute_loss = AttributeLoss()
 		self.reconstruction_loss = ReconstructionLoss(self.embedder)
 		self.identity_loss = IdentityLoss(self.embedder)
-		self.pose_loss = PoseLoss(self.motion_extractor)
+		self.motion_loss = MotionLoss(self.motion_extractor)
 		self.gaze_loss = GazeLoss(self.gazer)
 		self.automatic_optimization = False
 
@@ -95,9 +95,9 @@ class FaceSwapperTrainer(lightning.LightningModule):
 		attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes)
 		reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor)
 		identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor)
-		pose_loss, weighted_pose_loss = self.pose_loss(target_tensor, generator_output_tensor)
+		pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor)
 		gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor)
-		generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss
+		generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss
 
 		generator_optimizer.zero_grad()
 		self.manual_backward(generator_loss)