[cherry pick] add warning for dataloader incompatable upgrade (#33514)

* add warning log for DataLoader output format imcompatible upgrade. test=develop

* add unittest. test=develop

* fix ci converage. test=develop

* fix ci coverage. test=develop

Author: heavengate

python/paddle/fluid/dataloader/fetcher.py

@@ -12,18 +12,51 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+from ..log_helper import get_logger
+
+from collections.abc import Sequence
+
 
 class _DatasetFetcher(object):
     def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
         self.dataset = dataset
         self.auto_collate_batch = auto_collate_batch
         self.collate_fn = collate_fn
         self.drop_last = drop_last
+        self._is_warning_logged = False
 
     def fetch(self, batch_indices):
         raise NotImplementedError("'fetch' not implement for class {}".format(
             self.__class__.__name__))
 
+    def _log_warning(self):
+        warn_str = "Detect dataset only contains single fileds, return format " \
+                   "changed since Paddle 2.1. In Paddle <= 2.0, DataLoader add " \
+                   "a list surround output data(e.g. return [data]), and in " \
+                   "Paddle >= 2.1, DataLoader return the single filed directly " \
+                   "(e.g. return data). For example, in following code: \n\n"
+        warn_str += \
+                "import numpy as np\n" \
+                "from paddle.io import DataLoader, Dataset\n\n" \
+                "class RandomDataset(Dataset):\n" \
+                "    def __getitem__(self, idx):\n" \
+                "        data = np.random.random((2, 3)).astype('float32')\n\n" \
+                "        return data\n\n" \
+                "    def __len__(self):\n" \
+                "        return 10\n\n" \
+                "dataset = RandomDataset()\n" \
+                "loader = DataLoader(dataset, batch_size=1)\n" \
+                "data = next(loader())\n\n"
+
+        warn_str += "In Paddle <= 2.0, data is in format '[Tensor(shape=(1, 2, 3), " \
+                    "dtype=float32)]', and in Paddle >= 2.1, data is in format" \
+                    " 'Tensor(shape=(1, 2, 3), dtype=float32)'\n"
+
+        logger = get_logger(
+            "DataLoader", logging.INFO, fmt='%(levelname)s: %(message)s')
+        logger.warning(warn_str)
+
 
 class _IterableDatasetFetcher(_DatasetFetcher):
     def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
@@ -40,9 +73,14 @@ def fetch(self, batch_indices):
                     data.append(next(self.dataset_iter))
                 except StopIteration:
                     break
+
             if len(data) == 0 or (self.drop_last and
                                   len(data) < len(batch_indices)):
                 raise StopIteration
+            if not isinstance(data[0],
+                              Sequence) and not self._is_warning_logged:
+                self._log_warning()
+                self._is_warning_logged = True
         else:
             data = next(self.dataset_iter)
 
@@ -59,6 +97,11 @@ def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
     def fetch(self, batch_indices):
         if self.auto_collate_batch:
             data = [self.dataset[idx] for idx in batch_indices]
+
+            if not isinstance(data[0],
+                              Sequence) and not self._is_warning_logged:
+                self._log_warning()
+                self._is_warning_logged = True
         else:
             data = self.dataset[batch_indices]
 

python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py

@@ -330,6 +330,59 @@ def test_main(self):
             self.run_main(num_workers)
 
 
+class SingleFieldDataset(Dataset):
+    def __init__(self, sample_num):
+        self.sample_num = sample_num
+
+    def __len__(self):
+        return self.sample_num
+
+    def __getitem__(self, idx):
+        return np.random.random((2, 3)).astype('float32')
+
+
+class TestSingleFieldDataset(unittest.TestCase):
+    def init_dataset(self):
+        self.sample_num = 16
+        self.dataset = SingleFieldDataset(self.sample_num)
+
+    def run_main(self, num_workers):
+        paddle.static.default_startup_program().random_seed = 1
+        paddle.static.default_main_program().random_seed = 1
+        place = paddle.CPUPlace()
+        with fluid.dygraph.guard(place):
+            self.init_dataset()
+            dataloader = DataLoader(
+                self.dataset,
+                places=place,
+                num_workers=num_workers,
+                batch_size=2,
+                drop_last=True)
+
+            for i, data in enumerate(dataloader()):
+                assert isinstance(data, paddle.Tensor)
+                assert data.shape == [2, 2, 3]
+
+    def test_main(self):
+        for num_workers in [0, 2]:
+            self.run_main(num_workers)
+
+
+class SingleFieldIterableDataset(IterableDataset):
+    def __init__(self, sample_num):
+        self.sample_num = sample_num
+
+    def __iter__(self):
+        for _ in range(self.sample_num):
+            yield np.random.random((2, 3)).astype('float32')
+
+
+class TestSingleFieldIterableDataset(TestSingleFieldDataset):
+    def init_dataset(self):
+        self.sample_num = 16
+        self.dataset = SingleFieldIterableDataset(self.sample_num)
+
+
 class TestDataLoaderGenerateStates(unittest.TestCase):
     def setUp(self):
         self.inputs = [(0, 1), (0, 2), (1, 3)]