bug fix for #157 (#158)

* Separate condition list/tuple case into separated cases

* Add test for tuple of tensor list and list of tensor dict

* Update test_models.py

pytorch_lightning/models/trainer.py

@@ -921,12 +921,19 @@ If you want each process to load the full dataset, ignore this warning.
         if isinstance(batch, torch.Tensor):
             return batch.cuda(gpu_id)
 
-        # when list/tuple
-        elif isinstance(batch, list) or isinstance(batch, tuple):
+        # when list
+        elif isinstance(batch, list):
             for i, x in enumerate(batch):
                 batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
             return batch
 
+        # when tuple
+        elif isinstance(batch, tuple):
+            batch = list(batch)
+            for i, x in enumerate(batch):
+                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
+            return tuple(batch)
+
         # when dict
         elif isinstance(batch, dict):
             for k, v in batch.items():

tests/test_models.py

@@ -92,6 +92,18 @@ def test_single_gpu_batch_parse():
     assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
     assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
 
+    # tuple of tensor list and list of tensor dict
+    batch = ([torch.rand(2, 3) for _ in range(2)],
+             [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
+
+    assert batch[1][0]['a'].device.index == 0
+    assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
+
+    assert batch[1][0]['b'].device.index == 0
+    assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
+
 
 def test_early_stopping_cpu_model():
     """