[Fix] Fix `BaseDataPreprocessor.cast_data` cound not handle string data (#602)

* [Fix] Fix  cound not handle string data

* Minor refine

* Refine type hint

Refine type hint

* fix as comment

* Minor refine

* Update mmengine/model/base_model/data_preprocessor.py

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

mmengine/model/base_model/data_preprocessor.py

@@ -11,7 +11,8 @@ from mmengine.structures import BaseDataElement
 from mmengine.utils import is_list_of
 from ..utils import stack_batch
 
-CastData = Union[tuple, dict, BaseDataElement, torch.Tensor, list]
+CastData = Union[tuple, dict, BaseDataElement, torch.Tensor, list, bytes, str,
+                 None]
 
 
 @MODELS.register_module()
@@ -48,17 +49,20 @@ class BaseDataPreprocessor(nn.Module):
         """
         if isinstance(data, Mapping):
             return {key: self.cast_data(data[key]) for key in data}
+        elif isinstance(data, (str, bytes)) or data is None:
+            return data
         elif isinstance(data, tuple) and hasattr(data, '_fields'):
             # namedtuple
-            return type(data)(*(self.cast_data(sample)for sample in data))  # type: ignore  # noqa: E501  # yapf:disable
+            return type(data)(*(self.cast_data(sample) for sample in data))  # type: ignore  # noqa: E501  # yapf:disable
         elif isinstance(data, Sequence):
-            return [self.cast_data(sample) for sample in data]
-        elif isinstance(data, torch.Tensor):
-            return data.to(self.device, non_blocking=self._non_blocking)
-        elif isinstance(data, BaseDataElement):
+            return type(data)(self.cast_data(sample) for sample in data)  # type: ignore  # noqa: E501  # yapf:disable
+        elif isinstance(data, (torch.Tensor, BaseDataElement)):
             return data.to(self.device, non_blocking=self._non_blocking)
         else:
-            return data
+            raise TypeError(
+                '`BaseDataPreprocessor.cast_data`: batch data must contain '
+                'tensors, numpy arrays, numbers, dicts or lists, but '
+                f'found {type(data)}')
 
     def forward(self, data: dict, training: bool = False) -> Union[dict, list]:
         """Preprocesses the data into the model input format.

tests/test_model/test_base_model/test_data_preprocessor.py

@@ -28,8 +28,8 @@ class TestBaseDataPreprocessor(TestCase):
         label1 = torch.randn(1)
         label2 = torch.randn(1)
 
+        # Test with dict of batch inputs and batch data samples
         data = dict(inputs=[input1, input2], data_sample=[label1, label2])
-
         output = base_data_preprocessor(data)
         batch_inputs, batch_labels = output['inputs'], output['data_sample']
         self.assertTrue(torch.is_floating_point(batch_inputs[0]))
@@ -41,40 +41,54 @@ class TestBaseDataPreprocessor(TestCase):
         assert_allclose(label2, batch_labels[1])
 
         # Test with tuple of batch inputs and batch data samples
-        data = dict(
-            inputs=torch.stack([input1, input2]), data_sample=[label1, label2])
-        output = base_data_preprocessor(data)['inputs']
+        data = (torch.stack([input1, input2]), (label1, label2))
+        batch_inputs, batch_labels = base_data_preprocessor(data)
+        self.assertTrue(torch.is_floating_point(batch_inputs))
+        self.assertEqual(batch_inputs[0].shape, (1, 3, 5))
+        self.assertEqual(batch_inputs[1].shape, (1, 3, 5))
         self.assertTrue(torch.is_floating_point(batch_inputs[0]))
 
         # Test cuda forward
         if torch.cuda.is_available():
             # Test with list of data samples.
+            data = dict(inputs=[input1, input2], data_sample=[label1, label2])
             base_data_preprocessor = base_data_preprocessor.cuda()
             output = base_data_preprocessor(data)
             batch_inputs, batch_labels = output['inputs'], output[
                 'data_sample']
-            self.assertTrue(torch.is_floating_point(batch_inputs))
-            self.assertEqual(batch_inputs.device.type, 'cuda')
+            self.assertTrue(torch.is_floating_point(batch_inputs[0]))
+            self.assertEqual(batch_inputs[0].device.type, 'cuda')
 
+            # Fallback to test with cpu.
             base_data_preprocessor = base_data_preprocessor.cpu()
             output = base_data_preprocessor(data)
             batch_inputs, batch_labels = output['inputs'], output[
                 'data_sample']
-            self.assertTrue(torch.is_floating_point(batch_inputs))
-            self.assertEqual(batch_inputs.device.type, 'cpu')
+            self.assertTrue(torch.is_floating_point(batch_inputs[0]))
+            self.assertEqual(batch_inputs[0].device.type, 'cpu')
 
+            # Test `base_data_preprocessor` can be moved to cuda again.
             base_data_preprocessor = base_data_preprocessor.to('cuda:0')
             output = base_data_preprocessor(data)
             batch_inputs, batch_labels = output['inputs'], output[
                 'data_sample']
-            self.assertTrue(torch.is_floating_point(batch_inputs))
-            self.assertEqual(batch_inputs.device.type, 'cuda')
+            self.assertTrue(torch.is_floating_point(batch_inputs[0]))
+            self.assertEqual(batch_inputs[0].device.type, 'cuda')
 
             # device of `base_data_preprocessor` is cuda, output should be
             # cuda tensor.
-            self.assertEqual(batch_inputs.device.type, 'cuda')
+            self.assertEqual(batch_inputs[0].device.type, 'cuda')
             self.assertEqual(batch_labels[0].device.type, 'cuda')
 
+        # Test forward with string value
+        data = dict(string='abc')
+        base_data_preprocessor(data)
+
+        with self.assertRaisesRegex(TypeError,
+                                    '`BaseDataPreprocessor.cast_data`:'):
+            data = dict(string=object())
+            base_data_preprocessor(data)
+
 
 class TestImgDataPreprocessor(TestBaseDataPreprocessor):