src/baselines/dnn.py [336:411]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if args.sigmoid: self.output = nn.Sigmoid() else: if self.num_labels > 1: self.output = nn.Softmax(dim=1) else: self.output = None # self.net = nn.Sequential(*layers) self.loss_type = args.loss_type # weight for the loss function if hasattr(config, "pos_weight"): self.pos_weight = config.pos_weight elif hasattr(args, "pos_weight"): self.pos_weight = args.pos_weight else: self.pos_weight = None if hasattr(config, "weight"): self.weight = config.weight elif hasattr(args, "weight"): self.weight = args.weight else: self.weight = None if self.output_mode in ["regression"]: self.loss_fct = MSELoss() elif self.output_mode in ["multi_label", "multi-label"]: if self.loss_type == "bce": if self.pos_weight: # [1, 1, 1, ,1, 1...] length: self.num_labels assert self.pos_weight.ndim == 1 and self.pos_weight.shape[0] == self.num_labels self.loss_fct = BCEWithLogitsLoss(pos_weight=self.pos_weight) else: self.loss_fct = BCEWithLogitsLoss(reduction=config.loss_reduction if hasattr(config, "loss_reduction") else "sum") elif self.loss_type == "asl": self.loss_fct = AsymmetricLossOptimized(gamma_neg=args.asl_gamma_neg if hasattr(args, "asl_gamma_neg") else 4, gamma_pos=args.asl_gamma_pos if hasattr(args, "asl_gamma_pos") else 1, clip=args.clip if hasattr(args, "clip") else 0.05, eps=args.eps if hasattr(args, "eps") else 1e-8, disable_torch_grad_focal_loss=args.disable_torch_grad_focal_loss if hasattr(args, "disable_torch_grad_focal_loss") else False) elif self.loss_type == "focal_loss": self.loss_fct = FocalLoss(alpha=args.focal_loss_alpha if hasattr(args, "focal_loss_alpha") else 1, gamma=args.focal_loss_gamma if hasattr(args, "focal_loss_gamma") else 0.25, normalization=False, reduce=args.focal_loss_reduce if hasattr(args, "focal_loss_reduce") else False) elif self.loss_type == "multilabel_cce": self.loss_fct = MultiLabel_CCE(normalization=False) elif self.output_mode in ["binary_class", "binary-class"]: if self.loss_type == "bce": if self.pos_weight: # [0.9] if isinstance(self.pos_weight, int): self.pos_weight = torch.tensor([self.pos_weight], dtype=torch.long).to(args.device) elif isinstance(self.pos_weight, float): self.pos_weight = torch.tensor([self.pos_weight], dtype=torch.float32).to(args.device) assert self.pos_weight.ndim == 1 and self.pos_weight.shape[0] == 1 self.loss_fct = BCEWithLogitsLoss(pos_weight=self.pos_weight) else: self.loss_fct = BCEWithLogitsLoss() elif self.loss_type == "focal_loss": self.loss_fct = FocalLoss(alpha=args.focal_loss_alpha if hasattr(args, "focal_loss_alpha") else 1, gamma=args.focal_loss_gamma if hasattr(args, "focal_loss_gamma") else 0.25, normalization=False, reduce=args.focal_loss_reduce if hasattr(args, "focal_loss_reduce") else False) elif self.output_mode in ["multi_class", "multi-class"]: if self.weight: # [1, 1, 1, ,1, 1...] length: self.num_labels assert self.weight.ndim == 1 and self.weight.shape[0] == self.num_labels self.loss_fct = CrossEntropyLoss(weight=self.weight) else: self.loss_fct = CrossEntropyLoss() else: raise Exception("Not support output mode: %s." % self.output_mode) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - src/deep_baselines/virtifier.py [119:193]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if args.sigmoid: self.output = nn.Sigmoid() else: if self.num_labels > 1: self.output = nn.Softmax(dim=1) else: self.output = None self.loss_type = args.loss_type # positive weight if hasattr(config, "pos_weight"): self.pos_weight = config.pos_weight elif hasattr(args, "pos_weight"): self.pos_weight = args.pos_weight else: self.pos_weight = None if hasattr(config, "weight"): self.weight = config.weight elif hasattr(args, "weight"): self.weight = args.weight else: self.weight = None if self.output_mode in ["regression"]: self.loss_fct = MSELoss() elif self.output_mode in ["multi_label", "multi-label"]: if self.loss_type == "bce": if self.pos_weight: # [1, 1, 1, ,1, 1...] length: self.num_labels assert self.pos_weight.ndim == 1 and self.pos_weight.shape[0] == self.num_labels self.loss_fct = BCEWithLogitsLoss(pos_weight=self.pos_weight) else: self.loss_fct = BCEWithLogitsLoss(reduction=config.loss_reduction if hasattr(config, "loss_reduction") else "sum") elif self.loss_type == "asl": self.loss_fct = AsymmetricLossOptimized(gamma_neg=args.asl_gamma_neg if hasattr(args, "asl_gamma_neg") else 4, gamma_pos=args.asl_gamma_pos if hasattr(args, "asl_gamma_pos") else 1, clip=args.clip if hasattr(args, "clip") else 0.05, eps=args.eps if hasattr(args, "eps") else 1e-8, disable_torch_grad_focal_loss=args.disable_torch_grad_focal_loss if hasattr(args, "disable_torch_grad_focal_loss") else False) elif self.loss_type == "focal_loss": self.loss_fct = FocalLoss(alpha=args.focal_loss_alpha if hasattr(args, "focal_loss_alpha") else 1, gamma=args.focal_loss_gamma if hasattr(args, "focal_loss_gamma") else 0.25, normalization=False, reduce=args.focal_loss_reduce if hasattr(args, "focal_loss_reduce") else False) elif self.loss_type == "multilabel_cce": self.loss_fct = MultiLabel_CCE(normalization=False) elif self.output_mode in ["binary_class", "binary-class"]: if self.loss_type == "bce": if self.pos_weight: # [0.9] if isinstance(self.pos_weight, int): self.pos_weight = torch.tensor([self.pos_weight], dtype=torch.long).to(args.device) elif isinstance(self.pos_weight, float): self.pos_weight = torch.tensor([self.pos_weight], dtype=torch.float32).to(args.device) assert self.pos_weight.ndim == 1 and self.pos_weight.shape[0] == 1 self.loss_fct = BCEWithLogitsLoss(pos_weight=self.pos_weight) else: self.loss_fct = BCEWithLogitsLoss() elif self.loss_type == "focal_loss": self.loss_fct = FocalLoss(alpha=args.focal_loss_alpha if hasattr(args, "focal_loss_alpha") else 1, gamma=args.focal_loss_gamma if hasattr(args, "focal_loss_gamma") else 0.25, normalization=False, reduce=args.focal_loss_reduce if hasattr(args, "focal_loss_reduce") else False) elif self.output_mode in ["multi_class", "multi-class"]: if self.weight: # [1, 1, 1, ,1, 1...] length: self.num_labels assert self.weight.ndim == 1 and self.weight.shape[0] == self.num_labels self.loss_fct = CrossEntropyLoss(weight=self.weight) else: self.loss_fct = CrossEntropyLoss() else: raise Exception("Not support output mode: %s." % self.output_mode) - 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