fix the examples to pass the doctest

mindspore/ops/operations/debug_ops.py

@@ -62,6 +62,10 @@ class ScalarSummary(PrimitiveWithInfer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>>
+        >>>
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()
@@ -105,6 +109,11 @@ class ImageSummary(PrimitiveWithInfer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>>
+        >>>
         >>> class Net(nn.Cell):
         ...     def __init__(self):
         ...         super(Net, self).__init__()
@@ -147,6 +156,10 @@ class TensorSummary(PrimitiveWithInfer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>>
+        >>>
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()
@@ -190,6 +203,10 @@ class HistogramSummary(PrimitiveWithInfer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>>
+        >>>
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()

mindspore/train/callback/_summary_collector.py

@@ -146,28 +146,32 @@ class SummaryCollector(Callback):
         RuntimeError: If an error occurs during data collection.
 
     Examples:
-        >>> # Simple usage:
+        >>> import mindspore.nn as nn
+        >>> from mindspore import context
+        >>> from mindspore.train.callback import SummaryCollector
         >>> from mindspore.train import Model
-        >>> summary_collector = SummaryCollector(summary_dir='./summary_dir')
-        >>> dataset = get_dataset('/path/to/MNIST')
-        >>> network = LeNet5()
-        >>> model = Model(network)
-        >>> model.train(epoch=1, dataset=dataset, callbacks=summary_collector)
+        >>> from mindspore.nn.metrics import Accuracy
         >>>
-        >>> # Do not collect metric and collect the first layer parameter, others are collected by default
-        >>> specified={'collect_metric': False, 'histogram_regular': '^conv1.*'}
-        >>> summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_specified_data=specified)
-        >>> model.train(epoch=1, dataset=dataset, callbacks=summary_collector)
-        >>>
-        >>> # Only collect metric, custom lineage data and record data that collected by the summary operator,
-        >>> # others are not collected
-        >>> specified = {'collect_metric': True}
-        >>> summary_collector = SummaryCollector('./summary_dir',
-        >>>                                      collect_specified_data=specified,
-        >>>                                      keep_default_action=False,
-        >>>                                      custom_lineage_data={'version': 'resnet50_v1'}
-        >>>                                      )
-        >>> model.train(epoch=1, dataset=dataset, callbacks=summary_collector)
+        >>> if __name__ == '__main__':
+        ...     # If the device_target is GPU, set the device_target to "GPU"
+        ...     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+        ...     mnist_dataset_dir = '/path/to/mnist_dataset_directory'
+        ...     # The detail of create_dataset method shown in model_zoo.official.cv.lenet.src.dataset.py
+        ...     ds_train = create_dataset(mnist_dataset_dir, 32)
+        ...     # The detail of LeNet5 shown in model_zoo.official.cv.lenet.src.lenet.py
+        ...     network = LeNet5(10)
+        ...     net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+        ...     net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
+        ...     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2")
+        ...
+        ...     # Simple usage:
+        ...     summary_collector = SummaryCollector(summary_dir='./summary_dir')
+        ...     model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=False)
+        ...
+        ...     # Do not collect metric and collect the first layer parameter, others are collected by default
+        ...     specified={'collect_metric': False, 'histogram_regular': '^conv1.*'}
+        ...     summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_specified_data=specified)
+        ...     model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=False)
     """
 
     _DEFAULT_SPECIFIED_DATA = {

mindspore/train/summary/summary_record.py

@@ -129,16 +129,17 @@ class SummaryRecord:
         TypeError: If the parameter type is incorrect.
 
     Examples:
-        >>> # use in with statement to auto close
         >>> from mindspore.train.summary import SummaryRecord
-        >>> with SummaryRecord(log_dir="./summary_dir") as summary_record:
-        ...     pass
-        >>>
-        >>> # use in try .. finally .. to ensure closing
-        >>> try:
-        ...     summary_record = SummaryRecord(log_dir="./summary_dir")
-        ... finally:
-        ...     summary_record.close()
+        >>> if __name__ == '__main__':
+        ...     # use in with statement to auto close
+        ...     with SummaryRecord(log_dir="./summary_dir") as summary_record:
+        ...         pass
+        ...
+        ...     # use in try .. finally .. to ensure closing
+        ...     try:
+        ...         summary_record = SummaryRecord(log_dir="./summary_dir")
+        ...     finally:
+        ...         summary_record.close()
     """
 
     def __init__(self, log_dir, file_prefix="events", file_suffix="_MS",
@@ -212,8 +213,10 @@ class SummaryRecord:
             ValueError: When the mode is not recognized.
 
         Examples:
-            >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
-            ...     summary_record.set_mode('eval')
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
+            ...         summary_record.set_mode('eval')
         """
         mode_spec = 'train', 'eval'
         if mode not in mode_spec:
@@ -249,8 +252,11 @@ class SummaryRecord:
             TypeError: If the parameter type is error.
 
         Examples:
-            >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
-            ...     summary_record.add_value('scalar', 'loss', Tensor(0.1))
+            >>> from mindspore import Tensor
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
+            ...         summary_record.add_value('scalar', 'loss', Tensor(0.1))
         """
         if plugin in ('tensor', 'scalar', 'image', 'histogram'):
             if not name or not isinstance(name, str):
@@ -297,8 +303,10 @@ class SummaryRecord:
             RuntimeError: If the disk space is insufficient.
 
         Examples:
-            >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
-            ...     summary_record.record(step=2)
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
+            ...         summary_record.record(step=2)
             ...
             True
         """
@@ -364,8 +372,10 @@ class SummaryRecord:
             str, the full path of log file.
 
         Examples:
-            >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
-            ...     log_dir = summary_record.log_dir
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
+            ...         log_dir = summary_record.log_dir
         """
         return self.full_file_name
 
@@ -376,8 +386,10 @@ class SummaryRecord:
         Call it to make sure that all pending events have been written to disk.
 
         Examples:
-            >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
-            ...     summary_record.flush()
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
+            ...         summary_record.flush()
         """
         if self._closed:
             logger.error("The record writer is closed and can not flush.")
@@ -389,10 +401,12 @@ class SummaryRecord:
         Flush all events and close summary records. Please use the statement to autoclose.
 
         Examples:
-            >>> try:
-            ...     summary_record = SummaryRecord(log_dir="./summary_dir")
-            ... finally:
-            ...     summary_record.close()
+            >>> from mindspore.train.summary import SummaryRecord
+            >>> if __name__ == '__main__':
+            ...     try:
+            ...         summary_record = SummaryRecord(log_dir="./summary_dir")
+            ...     finally:
+            ...         summary_record.close()
         """
         if not self._closed and self._event_writer:
             # event writer flush and close