Migrate most to self.config and self.context

face_swapper/src/models/loss.py

@@ -53,31 +53,37 @@ class AdversarialLoss(nn.Module):
 
 
 class AttributeLoss(nn.Module):
-	def __init__(self) -> None:
+	def __init__(self, config_parser : ConfigParser) -> None:
 		super().__init__()
+		self.config =\
+		{
+			'batch_size': config_parser.getint('training.loader', 'batch_size'),
+			'attribute_weight': config_parser.getfloat('training.losses', 'attribute_weight')
+		}
 
 	def forward(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]:
-		batch_size = CONFIG.getint('training.loader', 'batch_size')
-		attribute_weight = CONFIG.getfloat('training.losses', 'attribute_weight')
 		temp_tensors = []
 
 		for target_attribute, output_attribute in zip(target_attributes, output_attributes):
-			temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(batch_size, -1), dim = 1).mean()
+			temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(self.config.get('batch_size'), -1), dim = 1).mean()
 			temp_tensors.append(temp_tensor)
 
 		attribute_loss = torch.stack(temp_tensors).mean() * 0.5
-		weighted_attribute_loss = attribute_loss * attribute_weight
+		weighted_attribute_loss = attribute_loss * self.config.get('attribute_weight')
 		return attribute_loss, weighted_attribute_loss
 
 
 class ReconstructionLoss(nn.Module):
-	def __init__(self, embedder : EmbedderModule) -> None:
+	def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None:
 		super().__init__()
+		self.config =\
+		{
+			'reconstruction_weight': config_parser.getfloat('training.losses', 'reconstruction_weight')
+		}
 		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')
 		source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0))
 		target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0))
 		has_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8
@@ -88,27 +94,35 @@ class ReconstructionLoss(nn.Module):
 		data_range = float(torch.max(output_tensor) - torch.min(output_tensor))
 		visual_loss = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean()
 		reconstruction_loss = (reconstruction_loss + visual_loss) * 0.5
-		weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight
+		weighted_reconstruction_loss = reconstruction_loss * self.config.get('reconstruction_weight')
 		return reconstruction_loss, weighted_reconstruction_loss
 
 
 class IdentityLoss(nn.Module):
-	def __init__(self, embedder : EmbedderModule) -> None:
+	def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None:
 		super().__init__()
+		self.config =\
+		{
+			'identity_weight': config_parser.getfloat('training.losses', 'identity_weight')
+		}
 		self.embedder = embedder
 
 	def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]:
-		identity_weight = CONFIG.getfloat('training.losses', 'identity_weight')
 		output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10))
 		source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10))
 		identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean()
-		weighted_identity_loss = identity_loss * identity_weight
+		weighted_identity_loss = identity_loss * self.config.get('identity_weight')
 		return identity_loss, weighted_identity_loss
 
 
 class MotionLoss(nn.Module):
-	def __init__(self, motion_extractor : MotionExtractorModule):
+	def __init__(self, config_parser : ConfigParser, motion_extractor : MotionExtractorModule):
 		super().__init__()
+		self.config =\
+		{
+			'pose_weight': config_parser.getfloat('training.losses', 'pose_weight'),
+			'expression_weight': config_parser.getfloat('training.losses', 'expression_weight')
+		}
 		self.motion_extractor = motion_extractor
 		self.mse_loss = nn.MSELoss()
 
@@ -120,7 +134,6 @@ class MotionLoss(nn.Module):
 		return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss
 
 	def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Tensor, Tensor]:
-		pose_weight = CONFIG.getfloat('training.losses', 'pose_weight')
 		temp_tensors = []
 
 		for target_pose, output_pose in zip(target_poses, output_poses):
@@ -128,13 +141,12 @@ class MotionLoss(nn.Module):
 			temp_tensors.append(temp_tensor)
 
 		pose_loss = torch.stack(temp_tensors).mean()
-		weighted_pose_loss = pose_loss * pose_weight
+		weighted_pose_loss = pose_loss * self.config.get('pose_weight')
 		return pose_loss, weighted_pose_loss
 
 	def calc_expression_loss(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
+		weighted_expression_loss = expression_loss * self.config.get('expression_weight')
 		return expression_loss, weighted_expression_loss
 
 	def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]:
@@ -148,13 +160,17 @@ class MotionLoss(nn.Module):
 
 
 class GazeLoss(nn.Module):
-	def __init__(self, gazer : GazerModule) -> None:
+	def __init__(self, config_parser : ConfigParser, gazer : GazerModule) -> None:
 		super().__init__()
+		self.config =\
+		{
+			'gaze_weight': config_parser.getfloat('training.losses', 'gaze_weight'),
+			'output_size': config_parser.getint('training.model.generator', 'output_size')
+		}
 		self.gazer = gazer
 		self.l1_loss = nn.L1Loss()
 
 	def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]:
-		gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight')
 		output_pitch, output_yaw = self.detect_gaze(output_tensor)
 		target_pitch, target_yaw = self.detect_gaze(target_tensor)
 
@@ -162,12 +178,11 @@ class GazeLoss(nn.Module):
 		yaw_loss = self.l1_loss(output_yaw, target_yaw)
 
 		gaze_loss = (pitch_loss + yaw_loss) * 0.5
-		weighted_gaze_loss = gaze_loss * gaze_weight
+		weighted_gaze_loss = gaze_loss * self.config.get('gaze_weight')
 		return gaze_loss, weighted_gaze_loss
 
 	def detect_gaze(self, input_tensor : Tensor) -> Gaze:
-		output_size = CONFIG.getint('training.model.generator', 'output_size')
-		crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * output_size).int()
+		crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * self.config.get('output_size')).int()
 		crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]]
 		crop_tensor = (crop_tensor + 1) * 0.5
 		crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor)