monai v1.2 api

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monailabel/monaivista/lib/model/vista_point_2pt5/trainer_2pt5d.py

@@ -768,7 +768,7 @@ def val_epoch(model, loader, epoch, acc_func, args, iterative=False, post_label=
                 not_nans_total += not_nans
 
             acc, not_nans = acc_sum_total / not_nans_total, not_nans_total
-            f_name = batch_data["image_meta_dict"]["filename_or_obj"]
+            f_name = batch_data["image"].meta["filename_or_obj"]
             print(f"Rank: {args.rank}, Case: {f_name}, Acc: {acc:.4f}, N_prompts: {int(not_nans)} ")
 
             acc = torch.tensor(acc).cuda(args.rank)

training/trainer_2pt5d.py

@@ -466,7 +466,7 @@ def val_epoch(model, loader, epoch, acc_func, args, iterative=False, post_label=
                 not_nans_total += not_nans
 
             acc, not_nans = acc_sum_total / not_nans_total, not_nans_total
-            f_name = batch_data["image_meta_dict"]["filename_or_obj"]
+            f_name = batch_data["image"].meta["filename_or_obj"]
             print(f"Rank: {args.rank}, Case: {f_name}, Acc: {acc:.4f}, N_prompts: {int(not_nans)} ")
 
             acc = torch.tensor(acc).cuda(args.rank)

training/utils/data_utils.py

@@ -101,7 +101,7 @@ def get_loader(args):
 
     train_transform = transforms.Compose(
         [
-            LoadImaged(keys=["image", "label"]),
+            LoadImaged(keys=["image", "label"], image_only=True),
             EnsureChannelFirstd(keys=["image", "label"]),
             Orientationd(keys=["image", "label"], axcodes="RAS"),
             Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),
@@ -114,7 +114,7 @@ def get_loader(args):
 
     val_transform = transforms.Compose(
         [
-            LoadImaged(keys=["image", "label"]),
+            LoadImaged(keys=["image", "label"], image_only=True),
             EnsureChannelFirstd(keys=["image", "label"]),
             Orientationd(keys=["image", "label"], axcodes="RAS"),
             Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 1.5), mode=("bilinear", "nearest")),