remove some unnecessary code

szy-9916 · Jul 22, 2021 · 0fc63df · 0fc63df
1 parent 9c36a7f
commit 0fc63df
Show file tree

Hide file tree

Showing 7 changed files with 24 additions and 25 deletions.
diff --git a/code/test_urpc.py b/code/test_urpc.py
@@ -44,19 +44,18 @@ def Inference(FLAGS):
 
 
 if __name__ == '__main__':
-    for exp_id in ["GTV_Uncertain_Aware_Deep_Supervised_V2_90_labeled"]:
-        print(exp_id)
-        model = os.listdir(
-            "/media/xdluo/ssd/Projects/UADS/data_ratio_model/{}".format(exp_id))[0]
-        parser = argparse.ArgumentParser()
-        parser.add_argument('--root_path', type=str,
-                            default='../data/WestChina', help='Name of Experiment')
-        parser.add_argument('--exp', type=str,
-                            default=exp_id, help='experiment_name')
-        parser.add_argument('--model', type=str,
-                            default=model, help='model_name')
-        FLAGS = parser.parse_args()
-
-        metric = Inference(FLAGS)
-        print(metric)
-        print((metric[0] + metric[1]) / 2)
+
+    model = os.listdir(
+        "/media/xdluo/ssd/Projects/UADS/data_ratio_model/URPC")
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--root_path', type=str,
+                        default='../data/WestChina', help='Name of Experiment')
+    parser.add_argument('--exp', type=str,
+                        default="URPC", help='experiment_name')
+    parser.add_argument('--model', type=str,
+                        default=model, help='model_name')
+    FLAGS = parser.parse_args()
+
+    metric = Inference(FLAGS)
+    print(metric)
+
diff --git a/code/train_adversarial_network_3D.py b/code/train_adversarial_network_3D.py
@@ -140,7 +140,7 @@ def worker_init_fn(worker_id):
             outputs_soft = torch.softmax(outputs, dim=1)
 
             loss_ce = ce_loss(outputs[:args.labeled_bs],
-                              label_batch[:args.labeled_bs][:])
+                              label_batch[:args.labeled_bs])
             loss_dice = dice_loss(
                 outputs_soft[:args.labeled_bs], label_batch[:args.labeled_bs].unsqueeze(1))
             supervised_loss = 0.5 * (loss_dice + loss_ce)

diff --git a/code/train_cross_pseudo_supervision_3D.py b/code/train_cross_pseudo_supervision_3D.py
@@ -265,7 +265,7 @@ def worker_init_fn(worker_id):
 
                 writer.add_scalar('info/model2_val_dice_score',
                                   avg_metric2[0, 0], iter_num)
-                writer.add_scalar('info/model1_val_hd95',
+                writer.add_scalar('info/model2_val_hd95',
                                   avg_metric2[0, 1], iter_num)
                 logging.info(
                     'iteration %d : model2_dice_score : %f model2_hd95 : %f' % (

diff --git a/code/train_entropy_minimization_3D.py b/code/train_entropy_minimization_3D.py
@@ -131,7 +131,7 @@ def worker_init_fn(worker_id):
             outputs_soft = torch.softmax(outputs, dim=1)
 
             loss_ce = ce_loss(outputs[:args.labeled_bs],
-                              label_batch[:args.labeled_bs][:])
+                              label_batch[:args.labeled_bs])
             loss_dice = dice_loss(
                 outputs_soft[:args.labeled_bs], label_batch[:args.labeled_bs].unsqueeze(1))
             supervised_loss = 0.5 * (loss_dice + loss_ce)

diff --git a/code/train_fully_supervised_3D.py b/code/train_fully_supervised_3D.py
@@ -97,7 +97,7 @@ def worker_init_fn(worker_id):
             outputs = model(volume_batch)
             outputs_soft = torch.softmax(outputs, dim=1)
 
-            loss_ce = ce_loss(outputs, label_batch[:])
+            loss_ce = ce_loss(outputs, label_batch)
             loss_dice = dice_loss(outputs_soft, label_batch.unsqueeze(1))
             loss = 0.5 * (loss_dice + loss_ce)
             optimizer.zero_grad()

diff --git a/code/train_uncertainty_aware_mean_teacher_3D.py b/code/train_uncertainty_aware_mean_teacher_3D.py
@@ -165,7 +165,7 @@ def worker_init_fn(worker_id):
                 torch.sum(preds*torch.log(preds + 1e-6), dim=1, keepdim=True)
 
             loss_ce = ce_loss(outputs[:args.labeled_bs],
-                              label_batch[:args.labeled_bs][:])
+                              label_batch[:args.labeled_bs])
             loss_dice = dice_loss(
                 outputs_soft[:args.labeled_bs], label_batch[:args.labeled_bs].unsqueeze(1))
             supervised_loss = 0.5 * (loss_dice + loss_ce)

diff --git a/code/train_uncertainty_rectified_pyramid_consistency_3D.py b/code/train_uncertainty_rectified_pyramid_consistency_3D.py
@@ -137,13 +137,13 @@ def worker_init_fn(worker_id):
             outputs_aux4_soft = torch.softmax(outputs_aux4, dim=1)
 
             loss_ce_aux1 = ce_loss(outputs_aux1[:args.labeled_bs],
-                                   label_batch[:args.labeled_bs][:])
+                                   label_batch[:args.labeled_bs])
             loss_ce_aux2 = ce_loss(outputs_aux2[:args.labeled_bs],
-                                   label_batch[:args.labeled_bs][:])
+                                   label_batch[:args.labeled_bs])
             loss_ce_aux3 = ce_loss(outputs_aux3[:args.labeled_bs],
-                                   label_batch[:args.labeled_bs][:])
+                                   label_batch[:args.labeled_bs])
             loss_ce_aux4 = ce_loss(outputs_aux4[:args.labeled_bs],
-                                   label_batch[:args.labeled_bs][:])
+                                   label_batch[:args.labeled_bs])
 
             loss_dice_aux1 = dice_loss(
                 outputs_aux1_soft[:args.labeled_bs], label_batch[:args.labeled_bs].unsqueeze(1))