add eval

@@ -41,6 +41,7 @@ solver:
     epoch: 100
     no_other: false
     base_on: null
+    model_path: null
 
   optimizer:
     name: 'Adam'

@@ -7,6 +7,7 @@ from solver.builder import build_solver
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument('--config', default='./config/mlp.yaml', type=str, help='config file')
+    parser.add_argument('-e', '--eval', action="store_true")
     args = parser.parse_args()
 
     cfg = yaml.load(open(args.config, 'r').read(), Loader=yaml.FullLoader)
@@ -14,6 +15,10 @@ def main():
     # print(torch.cuda.is_available())
 
     solver = build_solver(cfg)
+
+    if args.eval:
+        solver.evaluate()
+    else:
         solver.run()
 
 

@@ -8,6 +8,7 @@ from loss import build_loss
 from model import build_model
 from optimizer import build_lr_scheduler, build_optimizer
 from utils import SOLVER_REGISTRY, get_logger_and_log_dir
+from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
 
 
 @SOLVER_REGISTRY.register()
@@ -32,6 +33,7 @@ class VITSolver(object):
         self.hyper_params = cfg['solver']['args']
         self.no_other = self.hyper_params['no_other'] 
         self.base_on = self.hyper_params['base_on']
+        self.model_path = self.hyper_params['model_path']
         try:
             self.epoch = self.hyper_params['epoch']
         except Exception:
@@ -39,7 +41,7 @@ class VITSolver(object):
 
         self.logger, self.log_dir = get_logger_and_log_dir(**cfg['solver']['logger'])
 
-    def evaluate(self, y_pred, y_true, thresholds=0.5):
+    def accuracy(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()
         else:
@@ -80,9 +82,9 @@ class VITSolver(object):
             self.optimizer.step()
 
             if self.no_other:
-                correct += self.evaluate(pred, y)
+                correct += self.accuracy(pred, y)
             else:
-                correct += self.evaluate(torch.nn.Sigmoid()(pred), y)
+                correct += self.accuracy(torch.nn.Sigmoid()(pred), y)
 
         correct /= self.train_dataset_size
         train_loss /= self.train_loader_size
@@ -107,9 +109,9 @@ class VITSolver(object):
             val_loss += loss.item()
 
             if self.no_other:
-                correct += self.evaluate(pred, y)
+                correct += self.accuracy(pred, y)
             else:
-                correct += self.evaluate(torch.nn.Sigmoid()(pred), y)
+                correct += self.accuracy(torch.nn.Sigmoid()(pred), y)
 
         correct /= self.val_dataset_size
         val_loss /= self.val_loader_size
@@ -140,3 +142,44 @@ class VITSolver(object):
             lr_scheduler.step()
 
         self.logger.info('==> End Training')
+
+    def evaluate(self):
+        if isinstance(self.model_path, str) and os.path.exists(self.model_path):
+            self.model.load_state_dict(torch.load(self.model_path))
+            self.logger.info(f'==> Load Model from {self.model_path}')
+        else:
+            return
+
+        self.model.eval()
+
+        label_true_list = []
+        label_pred_list = []
+        for X, y in self.val_loader:
+            X, y_true = X.to(self.device), y.to(self.device)
+
+            if self.no_other:
+                pred = torch.nn.Softmax(dim=1)(self.model(X))
+            else:
+                # pred = torch.nn.Sigmoid()(self.model(X))
+                pred = self.model(X)
+            
+            y_pred = torch.nn.Sigmoid()(pred)
+
+            y_pred_idx = torch.argmax(y_pred, dim=1) + 1
+            y_pred_is_other = (torch.amax(y_pred, dim=1) > 0.5).int()
+            y_pred_rebuild = torch.multiply(y_pred_idx, y_pred_is_other)
+
+            y_true_idx = torch.argmax(y_true, dim=1) + 1
+            y_true_is_other = torch.sum(y_true, dim=1)
+            y_true_rebuild = torch.multiply(y_true_idx, y_true_is_other)
+
+            label_true_list.extend(y_true_rebuild.cpu().numpy().tolist())
+            label_pred_list.extend(y_pred_rebuild.cpu().numpy().tolist())
+        
+
+        acc = accuracy_score(label_true_list, label_pred_list)
+        cm = confusion_matrix(label_true_list, label_pred_list)
+        report = classification_report(label_true_list, label_pred_list)
+        print(acc)
+        print(cm)
+        print(report)