Update datasets.py

utils/datasets.py

@@ -62,26 +62,25 @@ 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 = InfiniteDataLoader (dataset,
+    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
+    dataloader = InfiniteDataLoader(dataset,
                                     batch_size=batch_size,
                                     num_workers=nw,
-                                    sampler=train_sampler,
+                                    sampler=sampler,
                                     pin_memory=True,
                                     collate_fn=LoadImagesAndLabels.collate_fn)
     return dataloader, dataset
 
 
 class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
-    '''
-    Dataloader that reuses workers.
+    """ 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))
+        object.__setattr__(self, 'batch_sampler', self._RepeatSampler(self.batch_sampler))
         self.iterator = super().__iter__()
 
     def __len__(self):
@@ -91,22 +90,20 @@ class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
         for i in range(len(self)):
             yield next(self.iterator)
 
+    class _RepeatSampler(object):
+        """ Sampler that repeats forever.
 
-class _RepeatSampler(object):
-    ''' 
-    Sampler that repeats forever. 
+        Args:
+            sampler (Sampler)
+        """
 
-    Args:
-        sampler (Sampler)
-    '''
+        def __init__(self, sampler):
+            self.sampler = sampler
 
-    def __init__(self, sampler):
-        self.sampler = sampler
+        def __iter__(self):
+            while True:
+                yield from iter(self.sampler)
 
-    def __iter__(self):
-        while True:
-            yield from iter(self.sampler)
-            
 
 class LoadImages:  # for inference
     def __init__(self, path, img_size=640):
@@ -684,14 +681,10 @@ def load_mosaic(self, index):
     # Concat/clip labels
     if len(labels4):
         labels4 = np.concatenate(labels4, 0)
-        # np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:])  # use with center crop
-        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])  # use with random_affine
-
-        # Replicate
-        # img4, labels4 = replicate(img4, labels4)
+        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])  # use with random_perspective
+        # img4, labels4 = replicate(img4, labels4)  # replicate
 
     # Augment
-    # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]  # center crop (WARNING, requires box pruning)
     img4, labels4 = random_perspective(img4, labels4,
                                        degrees=self.hyp['degrees'],
                                        translate=self.hyp['translate'],