Add InfiniteDataLoader class

utils/datasets.py

@@ -63,15 +63,51 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
     batch_size = min(batch_size, len(dataset))
     nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
     train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
-    dataloader = torch.utils.data.DataLoader(dataset,
-                                             batch_size=batch_size,
-                                             num_workers=nw,
-                                             sampler=train_sampler,
-                                             pin_memory=True,
-                                             collate_fn=LoadImagesAndLabels.collate_fn)
+    dataloader = InfiniteDataLoader (dataset,
+                                    batch_size=batch_size,
+                                    num_workers=nw,
+                                    sampler=train_sampler,
+                                    pin_memory=True,
+                                    collate_fn=LoadImagesAndLabels.collate_fn)
     return dataloader, dataset
 
 
+class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
+    '''
+    Dataloader that reuses workers.
+
+    Uses same syntax as vanilla DataLoader.
+    '''
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        for i in range(len(self)):
+            yield next(self.iterator)
+
+
+class _RepeatSampler(object):
+    ''' 
+    Sampler that repeats forever. 
+
+    Args:
+        sampler (Sampler)
+    '''
+
+    def __init__(self, sampler):
+        self.sampler = sampler
+
+    def __iter__(self):
+        while True:
+            yield from iter(self.sampler)
+
+
 class LoadImages:  # for inference
     def __init__(self, path, img_size=640):
         p = str(Path(path))  # os-agnostic