fix api bugs

mindspore/common/tensor.py

@@ -293,7 +293,7 @@ class Tensor(Tensor_):
 
     @property
     def dtype(self):
-        """Returns the dtype of the tensor (:class:`mindspore.dtype`)."""
+        """Return the dtype of the tensor (:class:`mindspore.dtype`)."""
         return self._dtype
 
     @property
@@ -303,7 +303,7 @@ class Tensor(Tensor_):
 
     @property
     def ndim(self):
-        """Returns the number of tensor dimensions."""
+        """Return the number of tensor dimensions."""
         return len(self._shape)
 
     @property
@@ -313,22 +313,22 @@ class Tensor(Tensor_):
 
     @property
     def itemsize(self):
-        """Returns the length of one tensor element in bytes."""
+        """Return the length of one tensor element in bytes."""
         return self._itemsize
 
     @property
     def strides(self):
-        """Returns the tuple of bytes to step in each dimension when traversing a tensor."""
+        """Return the tuple of bytes to step in each dimension when traversing a tensor."""
         return self._strides
 
     @property
     def nbytes(self):
-        """Returns the total number of bytes taken by the tensor."""
+        """Return the total number of bytes taken by the tensor."""
         return self._nbytes
 
     @property
     def T(self):
-        """Returns the transposed tensor."""
+        """Return the transposed tensor."""
         return self.transpose()
 
     @property
@@ -439,7 +439,7 @@ class Tensor(Tensor_):
 
     def mean(self, axis=(), keep_dims=False):
         """
-        Reduces a dimension of a tensor by averaging all elements in the dimension.
+        Reduce a dimension of a tensor by averaging all elements in the dimension.
 
         Args:
             axis (Union[None, int, tuple(int), list(int)]): Dimensions of reduction,
@@ -456,7 +456,7 @@ class Tensor(Tensor_):
 
     def transpose(self, *axes):
         r"""
-        Returns a view of the tensor with axes transposed.
+        Return a view of the tensor with axes transposed.
 
         For a 1-D tensor this has no effect, as a transposed vector is simply the
         same vector. For a 2-D tensor, this is a standard matrix transpose. For a
@@ -480,7 +480,7 @@ class Tensor(Tensor_):
 
     def reshape(self, *shape):
         """
-        Gives a new shape to a tensor without changing its data.
+        Give a new shape to a tensor without changing its data.
 
         Args:
             shape(Union[int, tuple(int), list(int)]): The new shape should be compatible
@@ -497,7 +497,7 @@ class Tensor(Tensor_):
 
     def ravel(self):
         """
-        Returns a contiguous flattened tensor.
+        Return a contiguous flattened tensor.
 
         Returns:
             Tensor, a 1-D tensor, containing the same elements of the input.
@@ -508,7 +508,7 @@ class Tensor(Tensor_):
 
     def flatten(self, order='C'):
         r"""
-        Returns a copy of the tensor collapsed into one dimension.
+        Return a copy of the tensor collapsed into one dimension.
 
         Args:
             order (str, optional): Can choose between 'C' and 'F'. 'C' means to
@@ -531,7 +531,7 @@ class Tensor(Tensor_):
 
     def swapaxes(self, axis1, axis2):
         """
-        Interchanges two axes of a tensor.
+        Interchange two axes of a tensor.
 
         Args:
             axis1 (int): First axis.
@@ -561,7 +561,7 @@ class Tensor(Tensor_):
 
     def squeeze(self, axis=None):
         """
-        Removes single-dimensional entries from the shape of a tensor.
+        Remove single-dimensional entries from the shape of a tensor.
 
         Args:
             axis (Union[None, int, list(int), tuple(int)], optional): Default is None.
@@ -577,7 +577,7 @@ class Tensor(Tensor_):
 
     def astype(self, dtype, copy=True):
         """
-        Returns a copy of the tensor, casted to a specified type.
+        Return a copy of the tensor, casted to a specified type.
 
         Args:
             dtype (Union[:class:`mindspore.dtype`, str]): Designated tensor dtype, can be in format

mindspore/communication/management.py

@@ -175,7 +175,7 @@ def get_local_rank_size(group=GlobalComm.WORLD_COMM_GROUP):
 
 def get_world_rank_from_group_rank(group, group_rank_id):
     """
-    Gets the rank ID in the world communication group corresponding to
+    Get the rank ID in the world communication group corresponding to
     the rank ID in the specified user communication group.
 
     Note:

mindspore/compression/quant/qat.py

@@ -43,7 +43,7 @@ def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQ
                         symmetric=(False, False),
                         narrow_range=(False, False)):
     r"""
-    Configs the observer type of weights and data flow with quant params.
+    Config the observer type of weights and data flow with quant params.
 
     Args:
         quant_observer (Union[Observer, list, tuple]): The observer type to do quantization. The first element represent

mindspore/context.py

@@ -86,12 +86,12 @@ class _ThreadLocalInfo(threading.local):
 
     @property
     def reserve_class_name_in_scope(self):
-        """Gets whether to save the network class name in the scope."""
+        """Get whether to save the network class name in the scope."""
         return self._reserve_class_name_in_scope
 
     @reserve_class_name_in_scope.setter
     def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
-        """Sets whether to save the network class name in the scope."""
+        """Set whether to save the network class name in the scope."""
         if not isinstance(reserve_class_name_in_scope, bool):
             raise ValueError(
                 "Set reserve_class_name_in_scope value must be bool!")
@@ -295,12 +295,12 @@ class _Context:
 
     @property
     def reserve_class_name_in_scope(self):
-        """Gets whether to save the network class name in the scope."""
+        """Get whether to save the network class name in the scope."""
         return self._thread_local_info.reserve_class_name_in_scope
 
     @reserve_class_name_in_scope.setter
     def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
-        """Sets whether to save the network class name in the scope."""
+        """Set whether to save the network class name in the scope."""
         self._thread_local_info.reserve_class_name_in_scope = reserve_class_name_in_scope
 
     @property
@@ -444,7 +444,7 @@ def set_auto_parallel_context(**kwargs):
 
 def get_auto_parallel_context(attr_key):
     """
-    Gets auto parallel context attribute value according to the key.
+    Get auto parallel context attribute value according to the key.
 
     Args:
         attr_key (str): The key of the attribute.
@@ -512,7 +512,7 @@ def _check_target_specific_cfgs(device, arg_key):
                  enable_sparse=bool, max_call_depth=int, env_config_path=str)
 def set_context(**kwargs):
     """
-    Sets context for running environment.
+    Set context for running environment.
 
     Context should be configured before running your program. If there is no configuration,
     the "Ascend" device target will be used by default. GRAPH_MODE or
@@ -679,7 +679,7 @@ def set_context(**kwargs):
 
 def get_context(attr_key):
     """
-    Gets context attribute value according to the input key.
+    Get context attribute value according to the input key.
 
     Args:
         attr_key (str): The key of the attribute.

mindspore/dataset/engine/datasets.py

@@ -4812,7 +4812,7 @@ class _NumpySlicesDataset:
 
 class NumpySlicesDataset(GeneratorDataset):
     """
-    Create a dataset with given data slices, mainly for loading Python data into dataset.
+    Creates a dataset with given data slices, mainly for loading Python data into dataset.
 
     This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
     below shows what input arguments are allowed and their expected behavior.
@@ -4911,7 +4911,7 @@ class _PaddedDataset:
 
 class PaddedDataset(GeneratorDataset):
     """
-    Create a dataset with filler data provided by user. Mainly used to add to the original data set
+    Creates a dataset with filler data provided by user. Mainly used to add to the original data set
     and assign it to the corresponding shard.
 
     Args:

mindspore/dataset/vision/c_transforms.py

@@ -162,7 +162,7 @@ class BoundingBoxAugment(ImageTensorOperation):
 
 class CenterCrop(ImageTensorOperation):
     """
-    Crops the input image at the center to the given size.
+    Crop the input image at the center to the given size.
 
     Args:
         size (Union[int, sequence]): The output size of the cropped image.
@@ -417,7 +417,7 @@ class NormalizePad(ImageTensorOperation):
 
 class Pad(ImageTensorOperation):
     """
-    Pads the image according to padding parameters.
+    Pad the image according to padding parameters.
 
     Args:
         padding (Union[int, sequence]): The number of pixels to pad the image.

mindspore/dataset/vision/py_transforms_util.py

@@ -827,7 +827,7 @@ def grayscale(img, num_output_channels):
 
 def pad(img, padding, fill_value, padding_mode):
     """
-    Pads the image according to padding parameters.
+    Pad the image according to padding parameters.
 
     Args:
         img (PIL image): Image to be padded.

mindspore/mindrecord/tools/cifar100_to_mr.py

@@ -70,7 +70,7 @@ class Cifar100ToMR:
 
     def run(self, fields=None):
         """
-        Executes transformation from cifar100 to MindRecord.
+        Execute transformation from cifar100 to MindRecord.
 
         Args:
             fields (list[str]): A list of index field, e.g.["fine_label", "coarse_label"].

mindspore/mindrecord/tools/cifar10_to_mr.py

@@ -70,7 +70,7 @@ class Cifar10ToMR:
 
     def run(self, fields=None):
         """
-        Executes transformation from cifar10 to MindRecord.
+        Execute transformation from cifar10 to MindRecord.
 
         Args:
             fields (list[str], optional): A list of index fields, e.g.["label"] (default=None).

mindspore/mindrecord/tools/csv_to_mr.py

@@ -119,7 +119,7 @@ class CsvToMR:
 
     def run(self):
         """
-        Executes transformation from csv to MindRecord.
+        Execute transformation from csv to MindRecord.
 
         Returns:
             MSRStatus, whether csv is successfully transformed to MindRecord.

mindspore/mindrecord/tools/imagenet_to_mr.py

@@ -120,7 +120,7 @@ class ImageNetToMR:
 
     def run(self):
         """
-        Executes transformation from imagenet to MindRecord.
+        Execute transformation from imagenet to MindRecord.
 
         Returns:
             MSRStatus, whether imagenet is successfully transformed to MindRecord.

mindspore/mindrecord/tools/mnist_to_mr.py

@@ -123,7 +123,7 @@ class MnistToMR:
 
     def _transform_train(self):
         """
-        Executes transformation from Mnist train part to MindRecord.
+        Execute transformation from Mnist train part to MindRecord.
 
         Returns:
             MSRStatus, whether successfully written into MindRecord.
@@ -171,7 +171,7 @@ class MnistToMR:
 
     def _transform_test(self):
         """
-        Executes transformation from Mnist test part to MindRecord.
+        Execute transformation from Mnist test part to MindRecord.
 
         Returns:
             MSRStatus, whether Mnist is successfully transformed to MindRecord.
@@ -220,7 +220,7 @@ class MnistToMR:
 
     def run(self):
         """
-        Executes transformation from Mnist to MindRecord.
+        Execute transformation from Mnist to MindRecord.
 
         Returns:
             MSRStatus, whether successfully written into MindRecord.

mindspore/nn/dynamic_lr.py

@@ -75,7 +75,7 @@ def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_e
 
 def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
     r"""
-    Calculate learning rate base on exponential decay function.
+    Calculates learning rate base on exponential decay function.
 
     For the i-th step, the formula of computing decayed_learning_rate[i] is:
 
@@ -118,7 +118,7 @@ def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
 
 def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
     r"""
-    Calculate learning rate base on natural exponential decay function.
+    Calculates learning rate base on natural exponential decay function.
 
     For the i-th step, the formula of computing decayed_learning_rate[i] is:
 
@@ -162,7 +162,7 @@ def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
 
 def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
     r"""
-    Calculate learning rate base on inverse-time decay function.
+    Calculates learning rate base on inverse-time decay function.
 
     For the i-th step, the formula of computing decayed_learning_rate[i] is:
 
@@ -205,7 +205,7 @@ def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, deca
 
 def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
     r"""
-    Calculate learning rate base on cosine decay function.
+    Calculates learning rate base on cosine decay function.
 
     For the i-th step, the formula of computing decayed_learning_rate[i] is:
 
@@ -257,7 +257,7 @@ def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
 def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
                         update_decay_epoch=False):
     r"""
-    Calculate learning rate base on polynomial decay function.
+    Calculates learning rate base on polynomial decay function.
 
     For the i-th step, the formula of computing decayed_learning_rate[i] is:
 
@@ -332,7 +332,7 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
 
 def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
     r"""
-    Get learning rate warming up.
+    Gets learning rate warming up.
 
     For the i-th step, the formula of computing warmup_learning_rate[i] is:
 

mindspore/nn/layer/basic.py

@@ -38,7 +38,7 @@ __all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad',
 
 class L1Regularizer(Cell):
     r"""
-    Apply l1 regularization to weights
+    Applies l1 regularization to weights.
 
     l1 regularization makes weights sparsity
 
@@ -730,7 +730,7 @@ class ResizeBilinear(Cell):
 
 class Unfold(Cell):
     r"""
-    Extract patches from images.
+    Extracts patches from images.
     The input tensor must be a 4-D tensor and the data format is NCHW.
 
     Args:

mindspore/nn/layer/image.py

@@ -479,7 +479,7 @@ def _get_bbox(rank, shape, central_fraction):
 
 class CentralCrop(Cell):
     """
-    Crop the centeral region of the images with the central_fraction.
+    Crops the centeral region of the images with the central_fraction.
 
     Args:
         central_fraction (float): Fraction of size to crop. It must be float and in range (0.0, 1.0].

mindspore/nn/layer/quant.py

@@ -1148,7 +1148,7 @@ class DenseQuant(Cell):
 
 class _QuantActivation(Cell):
     r"""
-    Base class for quantization aware training activation function. Add fake quantized operation
+    Base class for quantization aware training activation function. Adds fake quantized operation
     after activation operation.
     """
 
@@ -1232,7 +1232,7 @@ class ActQuant(_QuantActivation):
 
 class TensorAddQuant(Cell):
     r"""
-    Add fake quantized operation after TensorAdd operation.
+    Adds fake quantized operation after TensorAdd operation.
 
     This part is a more detailed overview of TensorAdd operation. For more detials about Quantilization,
     please refer to :class:`mindspore.nn.FakeQuantWithMinMaxObserver`.
@@ -1288,7 +1288,7 @@ class TensorAddQuant(Cell):
 
 class MulQuant(Cell):
     r"""
-    Add fake quantized operation after `Mul` operation.
+    Adds fake quantized operation after `Mul` operation.
 
     This part is a more detailed overview of `Mul` operation. For more detials about Quantilization,
     please refer to :class:`mindspore.nn.FakeQuantWithMinMaxObserver`.

mindspore/nn/metrics/auc.py

@@ -18,7 +18,7 @@ import numpy as np
 
 def auc(x, y, reorder=False):
     """
-    Compute the Area Under the Curve (AUC) using the trapezoidal rule. This is a general function, given points on a
+    Computes the Area Under the Curve (AUC) using the trapezoidal rule. This is a general function, given points on a
     curve. For computing the area under the ROC-curve.
 
     Args:

mindspore/nn/metrics/bleu_score.py

@@ -21,7 +21,7 @@ from .metric import Metric
 
 class BleuScore(Metric):
     """
-    Calculate BLEU score of machine translated text with one or more references.
+    Calculates BLEU score of machine translated text with one or more references.
 
     Args:
         n_gram (int): The n_gram value ranged from 1 to 4. Default: 4

mindspore/nn/metrics/hausdorff_distance.py

@@ -66,7 +66,7 @@ class _ROISpatialData(metaclass=ABCMeta):
 
 class HausdorffDistance(Metric):
     r"""
-    Calculate the Hausdorff distance. Hausdorff distance is the maximum and minimum distance between two point sets.
+    Calculates the Hausdorff distance. Hausdorff distance is the maximum and minimum distance between two point sets.
     Given two feature sets A and B, the Hausdorff distance between two point sets A and B is defined as follows:
 
     .. math::

mindspore/nn/metrics/roc.py

@@ -20,7 +20,7 @@ from .metric import Metric
 
 class ROC(Metric):
     """
-    Calculate the ROC curve. It is suitable for solving binary classification and multi classification problems.
+    Calculates the ROC curve. It is suitable for solving binary classification and multi classification problems.
     In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
 
     Args:

mindspore/numpy/array_ops.py

@@ -1671,7 +1671,7 @@ def flipud(m):
 
 def fliplr(m):
     """
-    Flip the entries in each row in the left/right direction.
+    Flips the entries in each row in the left/right direction.
     Columns are preserved, but appear in a different order than before.
 
     Note:

mindspore/ops/operations/inner_ops.py

@@ -126,7 +126,7 @@ class Randperm(PrimitiveWithInfer):
 
 class NoRepeatNGram(PrimitiveWithInfer):
     """
-    Update log_probs with repeat n-grams.
+    Updates log_probs with repeat n-grams.
 
     During beam search, if consecutive `ngram_size` words exist in the generated word sequence,
     the consecutive `ngram_size` words will be avoided during subsequent prediction.

mindspore/train/callback/_callback.py

@@ -227,9 +227,9 @@ class InternalCallbackParam(dict):
 
 class RunContext:
     """
-    Provides information about the model.
+    Provide information about the model.
 
-    Provides information about original request to model function.
+    Provide information about original request to model function.
     Callback objects can stop the loop by calling request_stop() of run_context.
 
     Args:
@@ -252,7 +252,7 @@ class RunContext:
 
     def request_stop(self):
         """
-        Sets stop requirement during training.
+        Set stop requirement during training.
 
         Callbacks can use this function to request stop of iterations.
         model.train() checks whether this is called or not.
@@ -261,7 +261,7 @@ class RunContext:
 
     def get_stop_requested(self):
         """
-        Returns whether a stop is requested or not.
+        Return whether a stop is requested or not.
 
         Returns:
             bool, if true, model.train() stops iterations.

mindspore/train/summary/summary_record.py

@@ -202,7 +202,7 @@ class SummaryRecord:
 
     def set_mode(self, mode):
         """
-        Sets the training phase. Different training phases affect data recording.
+        Set the training phase. Different training phases affect data recording.
 
         Args:
             mode (str): The mode to be set, which should be 'train' or 'eval'. When the mode is 'eval',