fix get daataset size error

mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc

@@ -212,12 +212,12 @@ Status DeviceQueueOp::SendDataToGPU() {
         RETURN_IF_NOT_OK(RetryPushGPUData(data_size, curr_row, handle));
         total_batch++;
       }
-      if (!TaskManager::FindMe()->Interrupted())
+      if (!TaskManager::FindMe()->Interrupted() && !GpuBufferMgr::GetInstance().IsClosed())
         RETURN_IF_NOT_OK(GetNextInput(&current_buffer));
       else
         is_break_loop = true;
     }
-    if (!TaskManager::FindMe()->Interrupted())
+    if (!TaskManager::FindMe()->Interrupted() && !GpuBufferMgr::GetInstance().IsClosed())
       RETURN_IF_NOT_OK(GetNextInput(&current_buffer));
     else
       is_break_loop = true;

mindspore/dataset/engine/datasets.py

@@ -2401,7 +2401,7 @@ class TransferDataset(DatasetOp):
         # need to keep iterator alive so the executionTree is not destroyed
         if self._noop_mode():
             return
-        self.iterator = TupleIterator(self, num_epochs=-1)
+        self.iterator = TupleIterator(self, num_epochs=num_epochs)
 
     def stop_send(self):
         self.iterator.depipeline.StopSend()

mindspore/train/dataset_helper.py

@@ -24,13 +24,18 @@ from ..nn.wrap import GetNextSingleOp
 from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full
 
 
-def _send_data(dataset):
+def _send_data(dataset, epoch_num):
     """Engine dataset to write data to tdt queue."""
     if not hasattr(dataset, '__has_sent__'):
         exec_dataset = dataset.__TRANSFER_DATASET__
-        exec_dataset.send()
+        exec_dataset.send(epoch_num)
         dataset.__has_sent__ = True
 
+def _send_data_no_flag(dataset, epoch_num):
+    """Engine dataset to write data to tdt queue directly."""
+    exec_dataset = dataset.__TRANSFER_DATASET__
+    exec_dataset.send(epoch_num)
+
 
 class DatasetHelper:
     """
@@ -54,7 +59,7 @@ class DatasetHelper:
         >>>     outputs = network(*inputs)
     """
 
-    def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1):
+    def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1):
         check_bool(dataset_sink_mode)
         check_int(sink_size)
         if sink_size < -1 or sink_size == 0:
@@ -74,7 +79,7 @@ class DatasetHelper:
                         iterclass = _DatasetIterMS
                 elif context.get_context("device_target") == "CPU":
                     raise RuntimeError("Currently dataset sink mode is not supported when the device target is CPU.")
-            self.iter = iterclass(dataset, sink_size)
+            self.iter = iterclass(dataset, sink_size, epoch_num)
         else:
             iterclass = _DatasetIterNormal
             self.iter = iterclass(dataset)
@@ -98,7 +103,7 @@ class DatasetHelper:
 
 class _DatasetIter:
     """Base iter for dataset helper"""
-    def __init__(self, dataset, sink_size):
+    def __init__(self, dataset, sink_size, epoch_num):
         self.dataset = dataset
         self.sink_size = sink_size
         self.sink_count = 1
@@ -110,9 +115,9 @@ class _DatasetIter:
             dataset.__ME_INITED__ = dataset.__TRANSFER_DATASET__.queue_name
 
             if not hasattr(dataset, '__no_send__'):
-                _send_data(dataset)
+                _send_data(dataset, epoch_num)
         else:
-            _send_data(dataset)
+            _send_data_no_flag(dataset, epoch_num)
 
         self.stop_send = dataset.__TRANSFER_DATASET__.stop_send
         self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
@@ -156,8 +161,8 @@ class _DatasetIter:
 
 class _DatasetIterGE(_DatasetIter):
     """Iter for GE."""
-    def __init__(self, dataset, sink_size):
-        super().__init__(dataset, sink_size)
+    def __init__(self, dataset, sink_size, epoch_num):
+        super().__init__(dataset, sink_size, epoch_num)
         self.sink_count = self.get_sink_count(dataset)
         batch_expand_num = 1
         if _need_to_full():
@@ -172,8 +177,8 @@ class _DatasetIterGE(_DatasetIter):
 
 class _DatasetIterMSLoopSink(_DatasetIter):
     """Iter for context (device_target=Ascend)"""
-    def __init__(self, dataset, sink_size):
-        super().__init__(dataset, sink_size)
+    def __init__(self, dataset, sink_size, epoch_num):
+        super().__init__(dataset, sink_size, epoch_num)
         self.sink_count = self.get_sink_count(dataset)
         ms_role = os.getenv("MS_ROLE")
         if ms_role in ("MS_PSERVER", "MS_SCHED"):
@@ -193,8 +198,8 @@ class _DatasetIterMSLoopSink(_DatasetIter):
 
 class _DatasetIterMS(_DatasetIter):
     """Iter for MS(enable_loop_sink=False)."""
-    def __init__(self, dataset, sink_size):
-        super().__init__(dataset, sink_size)
+    def __init__(self, dataset, sink_size, epoch_num):
+        super().__init__(dataset, sink_size, epoch_num)
         if sink_size > 0:
             self.sink_count = sink_size
         else:
@@ -206,8 +211,8 @@ class _DatasetIterMS(_DatasetIter):
 
 class _DatasetIterPSLite(_DatasetIter):
     """Iter for context (device_target=GPU) on MS_PSERVER or MS_SCHED"""
-    def __init__(self, dataset, sink_size):
-        super().__init__(dataset, sink_size)
+    def __init__(self, dataset, sink_size, epoch_num):
+        super().__init__(dataset, sink_size, epoch_num)
         self.sink_count = 1
         self.sink_size = 1
         self.op = None

mindspore/train/model.py

@@ -227,7 +227,7 @@ class Model:
             scaling_sens /= self._device_number
         return scaling_sens
 
-    def _exec_preprocess(self, network, is_train, phase, dataset, dataset_sink_mode, sink_size=-1):
+    def _exec_preprocess(self, network, is_train, phase, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1):
         """Initializes dataset."""
         need_wrap = False
         if dataset_sink_mode:
@@ -239,7 +239,7 @@ class Model:
             if not is_train:
                 dataset.__loop_size__ = 1
 
-        dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size)
+        dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
 
         # remove later to deal with loop sink
         if need_wrap:
@@ -399,12 +399,18 @@ class Model:
             cb_params (_InternalCallbackParam): Callback parameters. Default: None.
             sink_size (int): Control the amount of data each sink. Default: -1.
         """
+        if sink_size == -1:
+            epoch_num = epoch
+        else:
+            epoch_num = epoch * sink_size // train_dataset.get_dataset_size()
+
         dataset_helper, train_network = self._exec_preprocess(self._train_network,
                                                               is_train=True,
                                                               phase='train',
                                                               dataset=train_dataset,
                                                               dataset_sink_mode=True,
-                                                              sink_size=sink_size)
+                                                              sink_size=sink_size,
+                                                              epoch_num=epoch_num)
         self._train_network = train_network
         cb_params.train_network = self._train_network
         cb_params.cur_step_num = 0
@@ -621,6 +627,8 @@ class Model:
             list_callback.step_end(run_context)
             self._update_metrics(outputs)
 
+        valid_dataset.reset()
+
         metrics = self._get_metrics()
         cb_params.metrics = metrics
         list_callback.end(run_context)

tests/dataset_mock.py

@@ -58,7 +58,7 @@ class MindData:
     def create_tuple_iterator(self):
         return self.__iter__()
 
-    def send(self):
+    def send(self, num_epochs=-1):
         pass
 
     def stop_send(self):

tests/st/networks/models/resnet50/src_thor/dataset_helper.py

@@ -15,11 +15,16 @@
 """Dataset help for minddata dataset"""
 from mindspore._checkparam import check_bool
 from mindspore.parallel._utils import _get_device_num, _get_parallel_mode
-from mindspore.train.dataset_helper import _send_data
 from mindspore.train._utils import _exec_datagraph, _get_types_and_shapes, \
     _to_full_shapes
 from mindspore.train.parallel_utils import ParallelMode
 
+def _send_data(dataset):
+    """Engine dataset to write data to tdt queue."""
+    if not hasattr(dataset, '__has_sent__'):
+        exec_dataset = dataset.__TRANSFER_DATASET__
+        exec_dataset.send()
+        dataset.__has_sent__ = True
 
 class DatasetHelper:
     """