Fix another low use path where only numpy arrays are supported

Author: rwightman

timm/data/loader.py

@@ -33,6 +33,7 @@ def fast_collate(batch):
     if isinstance(batch[0][0], tuple):
         # This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position
         # such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position
+        is_np = isinstance(batch[0][0], np.ndarray)
         inner_tuple_size = len(batch[0][0])
         flattened_batch_size = batch_size * inner_tuple_size
         targets = torch.zeros(flattened_batch_size, dtype=torch.int64)
@@ -41,7 +42,10 @@ def fast_collate(batch):
             assert len(batch[i][0]) == inner_tuple_size  # all input tensor tuples must be same length
             for j in range(inner_tuple_size):
                 targets[i + j * batch_size] = batch[i][1]
-                tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j])
+                if is_np:
+                    tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j])
+                else:
+                    tensor[i + j * batch_size] += batch[i][0][j]
         return tensor, targets
     elif isinstance(batch[0][0], np.ndarray):
         targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)