delete DataWrapper

mindspore/nn/wrap/__init__.py

@@ -17,7 +17,7 @@ Wrap cells for networks.
 
 Use the Wrapper to combine the loss or build the training steps.
 """
-from .cell_wrapper import TrainOneStepCell, WithLossCell, WithGradCell, WithEvalCell, DataWrapper, \
+from .cell_wrapper import TrainOneStepCell, WithLossCell, WithGradCell, WithEvalCell, \
      ParameterUpdate, GetNextSingleOp, VirtualDatasetCellTriple
 from .loss_scale import TrainOneStepWithLossScaleCell, DynamicLossScaleUpdateCell, FixedLossScaleUpdateCell
 from .grad_reducer import DistributedGradReducer
@@ -27,7 +27,6 @@ __all__ = [
     "WithLossCell",
     "WithGradCell",
     "WithEvalCell",
-    "DataWrapper",
     "GetNextSingleOp",
     "TrainOneStepWithLossScaleCell",
     "DistributedGradReducer",

mindspore/nn/wrap/cell_wrapper.py

@@ -205,45 +205,6 @@ class TrainOneStepCell(Cell):
         return F.depend(loss, self.optimizer(grads))
 
 
-class DataWrapper(Cell):
-    """
-    Network training package class for dataset.
-
-    DataWrapper wraps the input network with a dataset which automatically fetches data with 'GetNext'
-    function from the dataset channel 'queue_name' and does forward computation in the construct function.
-
-    Args:
-        network (Cell): The training network for dataset.
-        dataset_types (list): The type of dataset. The list contains the types of the inputs.
-        dataset_shapes (list): The shapes of dataset. The list contains multiple sublists that describe
-            the shape of the inputs.
-        queue_name (str): The identification of dataset channel which specifies the dataset channel to supply
-            data for the network.
-
-    Outputs:
-        Tensor, network output whose shape depends on the network.
-
-    Examples:
-        >>> # call create_dataset function to create a regular dataset, refer to mindspore.dataset
-        >>> train_dataset = create_dataset()
-        >>> dataset_helper = mindspore.DatasetHelper(train_dataset)
-        >>> net = Net()
-        >>> net = DataWrapper(net, *(dataset_helper.types_shapes()), train_dataset.queue_name)
-    """
-
-    def __init__(self, network, dataset_types, dataset_shapes, queue_name):
-        super(DataWrapper, self).__init__(auto_prefix=False, flags=network.get_flags())
-        # Also copy the flag in `network` construct
-        flags = getattr(network.__class__.construct, "_mindspore_flags", {})
-        self.add_flags(**flags)
-        self.get_next = P.GetNext(dataset_types, dataset_shapes, len(dataset_types), queue_name)
-        self.network = network
-
-    def construct(self):
-        outputs = self.get_next()
-        return self.network(*outputs)
-
-
 class GetNextSingleOp(Cell):
     """
     Cell to run for getting the next operation.

mindspore/ops/operations/nn_ops.py

@@ -2538,7 +2538,7 @@ class GetNext(PrimitiveWithInfer):
     Note:
         The GetNext operation needs to be associated with network and it also depends on the init_dataset interface,
         it can't be used directly as a single operation.
-        For details, please refer to `nn.DataWrapper` source code.
+        For details, please refer to `connect_network_with_dataset` source code.
 
     Args:
         types (list[:class:`mindspore.dtype`]): The type of the outputs.