add no other

@@ -39,6 +39,7 @@ solver:
   name: 'VITSolver'
   name: 'VITSolver'
   args:
   args:
     epoch: 100
     epoch: 100
+    no_other: false
 
 
   optimizer:
   optimizer:
     name: 'Adam'
     name: 'Adam'

@@ -29,6 +29,7 @@ class VITSolver(object):
         self.optimizer = build_optimizer(cfg)(self.model.parameters(), **cfg['solver']['optimizer']['args'])
         self.optimizer = build_optimizer(cfg)(self.model.parameters(), **cfg['solver']['optimizer']['args'])
 
 
         self.hyper_params = cfg['solver']['args']
         self.hyper_params = cfg['solver']['args']
+        self.no_other = self.hyper_params['no_other'] 
         try:
         try:
             self.epoch = self.hyper_params['epoch']
             self.epoch = self.hyper_params['epoch']
         except Exception:
         except Exception:
@@ -36,9 +37,8 @@ class VITSolver(object):
 
 
         self.logger, self.log_dir = get_logger_and_log_dir(**cfg['solver']['logger'])
         self.logger, self.log_dir = get_logger_and_log_dir(**cfg['solver']['logger'])
 
 
-    @staticmethod
-    def evaluate(y_pred, y_true, thresholds=0.5, no_other=False):
-        if no_other:
+    def evaluate(self, y_pred, y_true, thresholds=0.5):
+        if self.no_other:
             return (y_pred.argmax(1) == y_true.argmax(1)).type(torch.float).sum().item()
             return (y_pred.argmax(1) == y_true.argmax(1)).type(torch.float).sum().item()
         else:
         else:
             y_pred_idx = torch.argmax(y_pred, dim=1) + 1
             y_pred_idx = torch.argmax(y_pred, dim=1) + 1
@@ -59,7 +59,10 @@ class VITSolver(object):
         for batch, (X, y) in enumerate(self.train_loader):
         for batch, (X, y) in enumerate(self.train_loader):
             X, y = X.to(self.device), y.to(self.device)
             X, y = X.to(self.device), y.to(self.device)
             
             
+            if self.no_other:
                 pred = torch.nn.Softmax(dim=1)(self.model(X))
                 pred = torch.nn.Softmax(dim=1)(self.model(X))
+            else:
+                pred = torch.nn.Sigmoid(self.model(X))
 
 
             correct += self.evaluate(pred, y)
             correct += self.evaluate(pred, y)
 
 
@@ -88,7 +91,10 @@ class VITSolver(object):
         for X, y in self.val_loader:
         for X, y in self.val_loader:
             X, y = X.to(self.device), y.to(self.device)
             X, y = X.to(self.device), y.to(self.device)
 
 
+            if self.no_other:
                 pred = torch.nn.Softmax(dim=1)(self.model(X))
                 pred = torch.nn.Softmax(dim=1)(self.model(X))
+            else:
+                pred = torch.nn.Sigmoid(self.model(X))
 
 
             correct += self.evaluate(pred, y)
             correct += self.evaluate(pred, y)