code format opt

mindspore/dataset/engine/datasets.py

@@ -205,12 +205,12 @@ class Dataset:
     @check_sync_wait
     def sync_wait(self, condition_name, num_batch=1, callback=None):
         '''
-        Add a blocking condition to the input Dataset
+        Add a blocking condition to the input Dataset.
 
         Args:
-            num_batch (int): the number of batches without blocking at the start of each epoch
-            condition_name (str): The condition name that is used to toggle sending next row
-            callback (function): The callback funciton that will be invoked when sync_update is called
+            num_batch (int): the number of batches without blocking at the start of each epoch.
+            condition_name (str): The condition name that is used to toggle sending next row.
+            callback (function): The callback funciton that will be invoked when sync_update is called.
 
         Raises:
             RuntimeError: If condition name already exists.
@@ -920,13 +920,13 @@ class Dataset:
 
     def sync_update(self, condition_name, num_batch=None, data=None):
         """
-        Release a blocking condition and triger callback with given data
+        Release a blocking condition and triger callback with given data.
 
         Args:
-            condition_name (str): The condition name that is used to toggle sending next row
-            num_batch (int or None): The number of batches(rows) that are released
-            When num_batch is None, it will default to the number specified by the sync_wait operator
-            data (dict or None): The data passed to the callback
+            condition_name (str): The condition name that is used to toggle sending next row.
+            num_batch (int or None): The number of batches(rows) that are released.
+                When num_batch is None, it will default to the number specified by the sync_wait operator.
+            data (dict or None): The data passed to the callback.
         """
         notifiers_dict = self.get_sync_notifiers()
         if condition_name not in notifiers_dict:
@@ -948,7 +948,7 @@ class Dataset:
 
     def get_repeat_count(self):
         """
-        Get the replication times in RepeatDataset else 1
+        Get the replication times in RepeatDataset else 1.
 
         Return:
             Number, the count of repeat.
@@ -969,7 +969,7 @@ class Dataset:
         raise NotImplementedError("Dataset {} has not supported api get_class_indexing yet.".format(type(self)))
 
     def reset(self):
-        """Reset the dataset for next epoch"""
+        """Reset the dataset for next epoch."""
 
 
 class SourceDataset(Dataset):
@@ -1085,9 +1085,9 @@ class BatchDataset(DatasetOp):
         Utility function to find the case where repeat is used before batch.
 
         Args:
-             dataset (Dataset): dataset to be checked
+             dataset (Dataset): dataset to be checked.
         Return:
-            True or False
+            True or False.
         """
         if isinstance(dataset, RepeatDataset):
             return True
@@ -1102,8 +1102,8 @@ class BatchDataset(DatasetOp):
         Utility function to notify batch size to sync_wait.
 
         Args:
-             dataset (Dataset): dataset to be checked
-             batchsize (int): batch size to notify
+             dataset (Dataset): dataset to be checked.
+             batchsize (int): batch size to notify.
         """
         if isinstance(dataset, SyncWaitDataset):
             dataset.update_sync_batch_size(batch_size)
@@ -1136,11 +1136,11 @@ class BatchInfo(CBatchInfo):
 
 class BlockReleasePair:
     """
-    The blocking condition class used by SyncWaitDataset
+    The blocking condition class used by SyncWaitDataset.
 
     Args:
-        init_release_rows (int): Number of lines to allow through the pipeline
-        callback (function): The callback funciton that will be called when release is called
+        init_release_rows (int): Number of lines to allow through the pipeline.
+        callback (function): The callback funciton that will be called when release is called.
     """
     def __init__(self, init_release_rows, callback=None):
         self.row_count = -init_release_rows
@@ -1183,13 +1183,13 @@ class BlockReleasePair:
 
 class SyncWaitDataset(DatasetOp):
     """
-    The result of adding a blocking condition to the input Dataset
+    The result of adding a blocking condition to the input Dataset.
 
     Args:
-        input_dataset (Dataset): Input dataset to apply flow control
-        num_batch (int): the number of batches without blocking at the start of each epoch
-        condition_name (str): The condition name that is used to toggle sending next row
-        callback (function): The callback funciton that will be invoked when sync_update is called
+        input_dataset (Dataset): Input dataset to apply flow control.
+        num_batch (int): the number of batches without blocking at the start of each epoch.
+        condition_name (str): The condition name that is used to toggle sending next row.
+        callback (function): The callback funciton that will be invoked when sync_update is called.
 
     Raises:
         RuntimeError: If condition name already exists.
@@ -1226,9 +1226,9 @@ class SyncWaitDataset(DatasetOp):
         Utility function to find the case where sync_wait is used before batch.
 
         Args:
-             dataset (Dataset): dataset to be checked
+             dataset (Dataset): dataset to be checked.
         Return:
-            True or False
+            True or False.
         """
         if isinstance(dataset, BatchDataset):
             return True
@@ -1289,7 +1289,7 @@ def _pyfunc_worker_exec(index, *args):
 # PythonCallable wrapper for multiprocess pyfunc
 class _PythonCallable:
     """
-    Internal python function wrapper for multiprocessing pyfunc
+    Internal python function wrapper for multiprocessing pyfunc.
     """
     def __init__(self, py_callable, idx, pool=None):
         # Original python callable from user.
@@ -1467,7 +1467,7 @@ class FilterDataset(DatasetOp):
     def get_dataset_size(self):
         """
         Get the number of batches in an epoch.
-        the size cannot be determined before we run the pipeline
+        the size cannot be determined before we run the pipeline.
         Return:
             0
         """
@@ -1759,7 +1759,7 @@ class StorageDataset(SourceDataset):
         columns_list (list[str], optional): List of columns to be read (default=None, read all columns).
         num_parallel_workers (int, optional): Number of parallel working threads (default=None).
         deterministic_output (bool, optional): Whether the result of this dataset can be reproduced
-                or not (default=True). If True, performance might be affected.
+            or not (default=True). If True, performance might be affected.
         prefetch_size (int, optional): Prefetch number of records ahead of the user's request (default=None).
 
     Raises:
@@ -1889,11 +1889,11 @@ def _select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id):
     Create sampler based on user input.
 
     Args:
-        num_samples (int): Number of samples
-        input_sampler (Iterable / Sampler): Sampler from user
-        shuffle (bool): Shuffle
-        num_shards (int): Number of shard for sharding
-        shard_id (int): Shard ID
+        num_samples (int): Number of samples.
+        input_sampler (Iterable / Sampler): Sampler from user.
+        shuffle (bool): Shuffle.
+        num_shards (int): Number of shard for sharding.
+        shard_id (int): Shard ID.
     """
     if shuffle is None:
         if input_sampler is not None:
@@ -2265,7 +2265,7 @@ class MindDataset(SourceDataset):
 
 def _iter_fn(dataset, num_samples):
     """
-    Generator function wrapper for iterable dataset
+    Generator function wrapper for iterable dataset.
     """
     if num_samples is not None:
         ds_iter = iter(dataset)
@@ -2284,7 +2284,7 @@ def _iter_fn(dataset, num_samples):
 
 def _generator_fn(generator, num_samples):
     """
-    Generator function wrapper for generator function dataset
+    Generator function wrapper for generator function dataset.
     """
     if num_samples is not None:
         gen_iter = generator()
@@ -2302,7 +2302,7 @@ def _generator_fn(generator, num_samples):
 
 def _py_sampler_fn(sampler, num_samples, dataset):
     """
-    Generator function wrapper for mappable dataset with python sampler
+    Generator function wrapper for mappable dataset with python sampler.
     """
     if num_samples is not None:
         sampler_iter = iter(sampler)
@@ -2323,7 +2323,7 @@ def _py_sampler_fn(sampler, num_samples, dataset):
 
 def _cpp_sampler_fn(sampler, dataset):
     """
-    Generator function wrapper for mappable dataset with cpp sampler
+    Generator function wrapper for mappable dataset with cpp sampler.
     """
     indices = sampler.get_indices()
     for i in indices:
@@ -2334,7 +2334,7 @@ def _cpp_sampler_fn(sampler, dataset):
 
 def _cpp_sampler_fn_mp(sampler, dataset, num_worker):
     """
-    Multiprocessing generator function wrapper for mappable dataset with cpp sampler
+    Multiprocessing generator function wrapper for mappable dataset with cpp sampler.
     """
     indices = sampler.get_indices()
     return _sampler_fn_mp(indices, dataset, num_worker)
@@ -2342,7 +2342,7 @@ def _cpp_sampler_fn_mp(sampler, dataset, num_worker):
 
 def _py_sampler_fn_mp(sampler, num_samples, dataset, num_worker):
     """
-    Multiprocessing generator function wrapper for mappable dataset with python sampler
+    Multiprocessing generator function wrapper for mappable dataset with python sampler.
     """
     indices = _fetch_py_sampler_indices(sampler, num_samples)
     return _sampler_fn_mp(indices, dataset, num_worker)
@@ -2350,7 +2350,7 @@ def _py_sampler_fn_mp(sampler, num_samples, dataset, num_worker):
 
 def _fetch_py_sampler_indices(sampler, num_samples):
     """
-    Indices fetcher for python sampler
+    Indices fetcher for python sampler.
     """
     if num_samples is not None:
         sampler_iter = iter(sampler)
@@ -2367,7 +2367,7 @@ def _fetch_py_sampler_indices(sampler, num_samples):
 
 def _fill_worker_indices(workers, indices, idx):
     """
-    Worker index queue filler, fill worker index queue in round robin order
+    Worker index queue filler, fill worker index queue in round robin order.
     """
     num_worker = len(workers)
     while idx < len(indices):
@@ -2381,7 +2381,7 @@ def _fill_worker_indices(workers, indices, idx):
 
 def _sampler_fn_mp(indices, dataset, num_worker):
     """
-    Multiprocessing generator function wrapper master process
+    Multiprocessing generator function wrapper master process.
     """
     workers = []
     # Event for end of epoch
@@ -2423,7 +2423,7 @@ def _sampler_fn_mp(indices, dataset, num_worker):
 
 def _generator_worker_loop(dataset, idx_queue, result_queue, eoe):
     """
-    Multiprocessing generator worker process loop
+    Multiprocessing generator worker process loop.
     """
     while True:
         # Fetch index, block
@@ -2448,7 +2448,7 @@ def _generator_worker_loop(dataset, idx_queue, result_queue, eoe):
 
 class _GeneratorWorker(multiprocessing.Process):
     """
-    Worker process for multiprocess Generator
+    Worker process for multiprocess Generator.
     """
     def __init__(self, dataset, eoe):
         self.idx_queue = multiprocessing.Queue(16)
@@ -2932,7 +2932,7 @@ class ManifestDataset(SourceDataset):
 
     def get_class_indexing(self):
         """
-        Get the class index
+        Get the class index.
 
         Return:
             Dict, A str-to-int mapping from label name to index.
@@ -3500,7 +3500,7 @@ class VOCDataset(SourceDataset):
 
 class CelebADataset(SourceDataset):
     """
-    A source dataset for reading and parsing CelebA dataset.Only support list_attr_celeba.txt currently
+    A source dataset for reading and parsing CelebA dataset.Only support list_attr_celeba.txt currently.
 
     Note:
         The generated dataset has two columns ['image', 'attr'].