random seed init

examples/ScanNet/data.py

@@ -10,7 +10,7 @@ val_reps=1 # Number of test views, 1 or more
 batch_size=32
 elastic_deformation=False
 
-import torch, numpy as np, glob, math, torch.utils.data, scipy.ndimage, multiprocessing as mp
+import torch, numpy as np, glob, math, torch.utils.data, scipy.ndimage, multiprocessing as mp, time
 
 dimension=3
 full_scale=4096 #Input field size
@@ -82,8 +82,14 @@ def trainMerge(tbl):
     labels=torch.cat(labels,0)
     return {'x': [locs,feats], 'y': labels.long(), 'id': tbl}
 train_data_loader = torch.utils.data.DataLoader(
-    list(range(len(train))),batch_size=batch_size, collate_fn=trainMerge, num_workers=20, shuffle=True)
-
+    list(range(len(train))),
+    batch_size=batch_size,
+    collate_fn=trainMerge,
+    num_workers=20, 
+    shuffle=True,
+    drop_last=True,
+    worker_init_fn=lambda x: np.random.seed(x+int(time.time()))
+)
 
 valOffsets=[0]
 valLabels=[]
@@ -125,4 +131,10 @@ def valMerge(tbl):
     point_ids=torch.cat(point_ids,0)
     return {'x': [locs,feats], 'y': labels.long(), 'id': tbl, 'point_ids': point_ids}
 val_data_loader = torch.utils.data.DataLoader(
-    list(range(len(val))),batch_size=batch_size, collate_fn=valMerge, num_workers=20,shuffle=True)
+    list(range(len(val))),
+    batch_size=batch_size,
+    collate_fn=valMerge,
+    num_workers=20,
+    shuffle=True,
+    worker_init_fn=lambda x: np.random.seed(x+int(time.time()))
+)