Merge branch 'dev'

Author: taigw

docs/history.txt

@@ -2,4 +2,6 @@
 2025.12.23 Add code of PS-Seg
 
 To do:
-(2025.8.25) data augmentation in Crop4vox2vec should be implemented in external transforms
+(2025.8.25) data augmentation in Crop4vox2vec should be implemented in external transforms
+2026.4.28 In windows, the multiple process has some problem --> use a new pytorch version
+dependency should be added in requirement: imops, monai, ml_collections, einops, timm

pymic/net_run/agent_abstract.py

@@ -25,6 +25,12 @@ def seed_torch(seed=1):
     torch.backends.cudnn.benchmark = False
     torch.backends.cudnn.deterministic = True
 
+def worker_init_fn(worker_id):
+    # workder_seed = self.random_seed+worker_id 
+    workder_seed = torch.initial_seed() % 2 ** 32
+    np.random.seed(workder_seed)
+    random.seed(workder_seed)  
+
 class NetRunAgent(object):
     """
     The abstract class for medical image segmentation.
@@ -273,12 +279,7 @@ def create_dataset(self):
                     self.valid_set = self.get_stage_dataset_from_config('valid')
                 else:
                     logging.warning("Dataset for validation is not created, as valid_dir is not provided.")
-            if(self.deterministic):
-                def worker_init_fn(worker_id):
-                    # workder_seed = self.random_seed+worker_id 
-                    workder_seed = torch.initial_seed() % 2 ** 32
-                    np.random.seed(workder_seed)
-                    random.seed(workder_seed)                    
+            if(self.deterministic):                  
                 worker_init = worker_init_fn
             else:
                 worker_init = None

pymic/net_run/agent_seg.py

@@ -481,7 +481,7 @@ def test_time_dropout(m):
 
         # load network parameters and set the network as evaluation mode
         print("ckpt name", ckpt_name)
-        checkpoint = torch.load(ckpt_name, map_location = device)
+        checkpoint = torch.load(ckpt_name, map_location = device, weights_only = False)
         self.net.load_state_dict(checkpoint['model_state_dict'])
 
         if(self.inferer is None):