Fix minddata python doc

mindspore/dataset/__init__.py

@@ -15,7 +15,7 @@
 This module provides APIs to load and process various common datasets such as MNIST,
 CIFAR-10, CIFAR-100, VOC, COCO, ImageNet, CelebA, CLUE, etc. It also supports datasets
 in standard format, including MindRecord, TFRecord, Manifest, etc. Users can also define
-their owndatasets with this module.
+their own datasets with this module.
 
 Besides, this module provides APIs to sample data while loading.
 

mindspore/dataset/core/config.py

@@ -25,8 +25,9 @@ from mindspore import log as logger
 
 __all__ = ['set_seed', 'get_seed', 'set_prefetch_size', 'get_prefetch_size', 'set_num_parallel_workers',
            'get_num_parallel_workers', 'set_numa_enable', 'get_numa_enable', 'set_monitor_sampling_interval',
-           'get_monitor_sampling_interval', 'load', 'get_callback_timeout', 'set_auto_num_workers',
+           'get_monitor_sampling_interval', 'set_callback_timeout', 'get_callback_timeout',
-           'get_auto_num_workers', '_init_device_info', 'set_enable_shared_mem', 'get_enable_shared_mem']
+           'set_auto_num_workers', 'get_auto_num_workers', 'set_enable_shared_mem', 'get_enable_shared_mem',
+           'set_sending_batches', 'load', '_init_device_info']
 
 INT32_MAX = 2147483647
 UINT32_MAX = 4294967295

mindspore/dataset/engine/__init__.py

@@ -22,6 +22,7 @@ high performance and parse data precisely. It also provides the following
 operations for users to preprocess data: shuffle, batch, repeat, map, and zip.
 """
 
+from ..callback import DSCallback, WaitedDSCallback
 from ..core import config
 from .cache_client import DatasetCache
 from .datasets import *
@@ -35,4 +36,5 @@ __all__ = ["CelebADataset", "Cifar100Dataset", "Cifar10Dataset", "CLUEDataset",
            "NumpySlicesDataset", "PaddedDataset", "TextFileDataset", "TFRecordDataset", "VOCDataset",
            "DistributedSampler", "PKSampler", "RandomSampler", "SequentialSampler", "SubsetRandomSampler",
            "WeightedRandomSampler", "SubsetSampler",
-           "config", "DatasetCache", "Schema", "zip"]
+           "DatasetCache", "DSCallback", "Schema", "WaitedDSCallback", "compare", "deserialize",
+           "serialize", "show", "zip"]

mindspore/dataset/engine/datasets.py

@@ -655,7 +655,7 @@ class Dataset:
                 option could be beneficial if the Python operation is computational heavy (default=False).
             cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
                 (default=None, which means no cache is used).
-            callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None).
+            callbacks (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None).
 
 
         Returns:
@@ -2562,7 +2562,7 @@ class MapDataset(Dataset):
             option could be beneficial if the Python operation is computational heavy (default=False).
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
             (default=None, which means no cache is used).
-        callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None)
+        callbacks (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None)
         max_rowsize(int, optional): Maximum size of row in MB that is used for shared memory allocation to copy
             data between processes.  This is only used if python_multiprocessing is set to True (default 16 MB).
 

mindspore/dataset/transforms/c_transforms.py

@@ -85,7 +85,7 @@ class Fill(TensorOperation):
     The output tensor will have the same shape and type as the input tensor.
 
     Args:
-        fill_value (Union[str, bytes, int, float, bool])) : scalar value
+        fill_value (Union[str, bytes, int, float, bool]) : scalar value
             to fill the tensor with.
 
     Examples:
@@ -432,7 +432,7 @@ class RandomApply(TensorOperation):
 
     Args:
         transforms (list): List of transformations to be applied.
-        prob (float, optional): The probability to apply the transformation list (default=0.5)
+        prob (float, optional): The probability to apply the transformation list (default=0.5).
 
     Examples:
         >>> rand_apply = c_transforms.RandomApply([c_vision.RandomCrop(512)])

mindspore/dataset/transforms/py_transforms.py

@@ -140,13 +140,13 @@ class Compose:
     @staticmethod
     def reduce(operations):
         """
-        Wraps adjacent Python operations in a Compose to allow mixing of Python and C++ operations
+        Wraps adjacent Python operations in a Compose to allow mixing of Python and C++ operations.
 
         Args:
-            operations (list): list of tensor operations
+            operations (list): list of tensor operations.
 
         Returns:
-            list, the reduced list of operations
+            list, the reduced list of operations.
         """
         if len(operations) == 1:
             if str(operations).find("c_transform") >= 0 or isinstance(operations[0], TensorOperation):

mindspore/dataset/vision/c_transforms.py

@@ -231,7 +231,7 @@ class CutMixBatch(ImageTensorOperation):
 
     Args:
         image_batch_format (Image Batch Format): The method of padding. Can be any of
-            [ImageBatchFormat.NHWC, ImageBatchFormat.NCHW]
+            [ImageBatchFormat.NHWC, ImageBatchFormat.NCHW].
         alpha (float, optional): hyperparameter of beta distribution (default = 1.0).
         prob (float, optional): The probability by which CutMix is applied to each image (default = 1.0).
 
@@ -591,7 +591,7 @@ class RandomAffine(ImageTensorOperation):
         TypeError: If degrees is not a number or a list or a tuple.
             If degrees is a list or tuple, its length is not 2.
         TypeError: If translate is specified but is not list or a tuple of length 2 or 4.
-        TypeError: If scale is not a list or tuple of length 2.''
+        TypeError: If scale is not a list or tuple of length 2.
         TypeError: If shear is not a list or tuple of length 2 or 4.
         TypeError: If fill_value is not a single integer or a 3-tuple.
 

mindspore/dataset/vision/py_transforms_util.py

@@ -580,7 +580,10 @@ def to_type(img, output_type):
     if not is_numpy(img):
         raise TypeError("img should be NumPy image. Got {}.".format(type(img)))
 
-    return img.astype(output_type)
+    try:
+        return img.astype(output_type)
+    except:
+        raise RuntimeError("output_type: " + str(output_type) + " is not a valid datatype.")
 
 
 def rotate(img, angle, resample, expand, center, fill_value):