nerge

nnunetv2/imageio/base_reader_writer.py

@@ -21,11 +21,11 @@
 class BaseReaderWriter(ABC):
     @staticmethod
     def _check_all_same(input_list):
-        # compare all entries to the first
-        for i in input_list[1:]:
-            if i != input_list[0]:
-                return False
-        return True
+        if len(input_list) == 1:
+            return True
+        else:
+            # compare all entries to the first
+            return np.allclose(input_list[0], input_list[1:])
 
     @staticmethod
     def _check_all_same_array(input_list):

nnunetv2/training/dataloading/data_loader_2d.py

@@ -90,6 +90,7 @@ def generate_train_batch(self):
         if self.transforms is not None:
             with torch.no_grad():
                 with threadpool_limits(limits=1, user_api=None):
+
                     data_all = torch.from_numpy(data_all).float()
                     seg_all = torch.from_numpy(seg_all).to(torch.int16)
                     images = []
@@ -99,7 +100,10 @@ def generate_train_batch(self):
                         images.append(tmp['image'])
                         segs.append(tmp['segmentation'])
                     data_all = torch.stack(images)
-                    seg_all = [torch.stack([s[i] for s in segs]) for i in range(len(segs[0]))]
+                    if isinstance(segs[0], list):
+                        seg_all = [torch.stack([s[i] for s in segs]) for i in range(len(segs[0]))]
+                    else:
+                        seg_all = torch.stack(segs)
                     del segs, images
 
             return {'data': data_all, 'target': seg_all, 'keys': selected_keys}

nnunetv2/training/dataloading/data_loader_3d.py

@@ -62,7 +62,10 @@ def generate_train_batch(self):
                         images.append(tmp['image'])
                         segs.append(tmp['segmentation'])
                     data_all = torch.stack(images)
-                    seg_all = [torch.stack([s[i] for s in segs]) for i in range(len(segs[0]))]
+                    if isinstance(segs[0], list):
+                        seg_all = [torch.stack([s[i] for s in segs]) for i in range(len(segs[0]))]
+                    else:
+                        seg_all = torch.stack(segs)
                     del segs, images
 
             return {'data': data_all, 'target': seg_all, 'keys': selected_keys}

nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs.py

@@ -55,6 +55,20 @@ def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dic
         self.num_epochs = 250
 
 
+class nnUNetTrainer_500epochs(nnUNetTrainer):
+    def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,
+                 device: torch.device = torch.device('cuda')):
+        super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device)
+        self.num_epochs = 500
+
+
+class nnUNetTrainer_750epochs(nnUNetTrainer):
+    def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,
+                 device: torch.device = torch.device('cuda')):
+        super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device)
+        self.num_epochs = 750
+
+
 class nnUNetTrainer_2000epochs(nnUNetTrainer):
     def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,
                  device: torch.device = torch.device('cuda')):