Use `torch.repeat_interleave()` to generate repeated indices faster (#1058)

* update: use numpy to generate repeated indices faster

* update: use torch.repeat_interleave() instead of np.repeat()

* refactor: remove unused import, numpy

* refactor: torch.range to torch.arange

* update: tensor to list before appending the extra samples

* update: concatenate the paddings with torch.cat

timm/data/distributed_sampler.py

@@ -103,15 +103,16 @@ class RepeatAugSampler(Sampler):
         g = torch.Generator()
         g.manual_seed(self.epoch)
         if self.shuffle:
-            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+            indices = torch.randperm(len(self.dataset), generator=g)
         else:
-            indices = list(range(len(self.dataset)))
+            indices = torch.arange(start=0, end=len(self.dataset))
 
         # produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....]
-        indices = [x for x in indices for _ in range(self.num_repeats)]
+        indices = torch.repeat_interleave(indices, repeats=self.num_repeats, dim=0)
         # add extra samples to make it evenly divisible
         padding_size = self.total_size - len(indices)
-        indices += indices[:padding_size]
+        if padding_size > 0:
+            indices = torch.cat([indices, indices[:padding_size]], dim=0)
         assert len(indices) == self.total_size
 
         # subsample per rank