changes

face_swapper/src/models/loss.py

@@ -70,32 +70,29 @@ def forward(self, target_attributes : Attributes, output_attributes : Attributes
 
 
 class ReconstructionLoss(nn.Module):
-	def __init__(self) -> None:
+	def __init__(self, embedder : nn.Module) -> None:
 		super().__init__()
+		self.embedder = embedder
 		self.mse_loss = nn.MSELoss()
 
 	def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]:
 		reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight')
-		temp_tensors = []
-
-		for __source_tensor__, __target_tensor__ in zip(source_tensor, target_tensor):
-			temp_tensor = self.mse_loss(__source_tensor__, __target_tensor__) * torch.equal(__source_tensor__, __target_tensor__)
-			temp_tensors.append(temp_tensor)
-
-		reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5
+		source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0))
+		target_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0))
+		same_person = torch.cosine_similarity(source_embedding, target_embedding) > 0.8
+		reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3))
+		reconstruction_loss = (reconstruction_loss * same_person).mean() * 0.5
 		data_range = float(torch.max(output_tensor) - torch.min(output_tensor))
 		similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean()
-
 		reconstruction_loss = (reconstruction_loss + similarity) * 0.5
 		weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight
 		return reconstruction_loss, weighted_reconstruction_loss
 
 
 class IdentityLoss(nn.Module):
-	def __init__(self) -> None:
+	def __init__(self, embedder : nn.Module) -> None:
 		super().__init__()
-		embedder_path = CONFIG.get('training.model', 'embedder_path')
-		self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call]
+		self.embedder = embedder
 
 	def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]:
 		identity_weight = CONFIG.getfloat('training.losses', 'identity_weight')

face_swapper/src/training.py

@@ -28,16 +28,16 @@ def __init__(self) -> None:
 		super().__init__()
 		embedder_path = CONFIG.get('training.model', 'embedder_path')
 
+		self.embedder = torch.jit.load(embedder_path, map_location='cpu')  # type:ignore[no-untyped-call]
 		self.generator = Generator()
 		self.discriminator = Discriminator()
 		self.discriminator_loss = DiscriminatorLoss()
 		self.adversarial_loss = AdversarialLoss()
 		self.attribute_loss = AttributeLoss()
-		self.reconstruction_loss = ReconstructionLoss()
-		self.identity_loss = IdentityLoss()
+		self.reconstruction_loss = ReconstructionLoss(self.embedder)
+		self.identity_loss = IdentityLoss(self.embedder)
 		self.pose_loss = PoseLoss()
 		self.gaze_loss = GazeLoss()
-		self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call]
 		self.automatic_optimization = False
 
 	def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: