更改了引用格式 (#47963)

* fix some docs bugs; test=document_fix

* Update batch_sampler.py

* Update dataset.py

* Update dataset.py

* Update sampler.py

* for codestyle; test=document_fix

* fix copy-from issue; test=document_fix
Co-authored-by: NLigoml <39876205+Ligoml@users.noreply.github.com>
Co-authored-by: Ligoml <limengliu@tiaozhan.com>

python/paddle/fluid/dataloader/batch_sampler.py

@@ -37,20 +37,20 @@ class BatchSampler(Sampler):
 
 
     Args:
-        dataset(Dataset): this could be a :code:`paddle.io.Dataset`
-                implement or other python object which implemented
+        dataset(Dataset, optional): this should be an instance of a subclass of :ref:`api_paddle_io_Dataset` or
+                :ref:`api_paddle_io_IterableDataset` or other python object which implemented
                 :code:`__len__` for BatchSampler to get indices as the
-                range of :attr:`dataset` length. Default None.
-        sampler (Sampler): this could be a :code:`paddle.io.Dataset`
-                instance which implemented :code:`__iter__` to yield
+                range of :attr:`dataset` length. Default None, disabled.
+        sampler (Sampler, optional): this should be a :ref:`api_paddle_io_Sample`
+                instance which implemented :code:`__iter__` to generate
                 sample indices. :attr:`sampler` and :attr:`dataset`
                 can not be set in the same time.  If :attr:`sampler`
-                is set, :attr:`shuffle` should not be set. Default None.
-        shuffle(bool): whether to shuffle indices order before genrating
-                batch indices. Default False.
-        batch_size(int): sample indice number in a mini-batch indices.
-        drop_last(bool): whether drop the last incomplete batch dataset size
-            is not divisible by the batch size. Default False
+                is set, :attr:`dataset` should not be set. Default None, disabled.
+        shuffle(bool, optional): whether to shuffle indices order before generating
+                batch indices. Default False, don't shuffle indices before generating batch indices.
+        batch_size(int, optional): sample indice number in a mini-batch indices. default 1, each mini-batch includes 1 sample.
+        drop_last(bool, optional): whether drop the last incomplete (less than 1 mini-batch) batch dataset. Default False, keep it.
+    see :ref:`api_paddle_io_DataLoader`
 
     Returns:
         BatchSampler: an iterable object for indices iterating
@@ -92,7 +92,6 @@ class BatchSampler(Sampler):
                 print(batch_indices)
 
 
-    see `paddle.io.DataLoader`
 
     """
 
@@ -183,22 +182,24 @@ class DistributedBatchSampler(BatchSampler):
         Dataset is assumed to be of constant size.
 
     Args:
-        dataset(paddle.io.Dataset): this could be a `paddle.io.Dataset` implement
+        dataset(Dataset): this could be an instance of subclass of :ref:`api_paddle_io_Dataset`
                      or other python object which implemented
-                     `__len__` for BatchSampler to get sample
-                     number of data source.
-        batch_size(int): sample indice number in a mini-batch indices.
+                     `__len__` for BatchSampler to get indices of samples.
+        batch_size(int): sample size of each mini-batch.
         num_replicas(int, optional): porcess number in distributed training.
             If :attr:`num_replicas` is None, :attr:`num_replicas` will be
-            retrieved from :code:`paddle.distributed.ParallenEnv`.
+            retrieved from :ref:`api_paddle_distributed_ParallelEnv` .
             Default None.
         rank(int, optional): the rank of the current process among :attr:`num_replicas`
             processes. If :attr:`rank` is None, :attr:`rank` is retrieved from
-            :code:`paddle.distributed.ParallenEnv`. Default None.
-        shuffle(bool): whther to shuffle indices order before genrating
+            :ref:`api_paddle_distributed_ParallelEnv`. Default None.
+        shuffle(bool, optional): whther to shuffle indices order before genrating
             batch indices. Default False.
-        drop_last(bool): whether drop the last incomplete batch dataset size
-            is not divisible by the batch size. Default False
+        drop_last(bool, optional): whether drop the last incomplete(less than a mini-batch) batch dataset size.
+            Default False.
+
+    Returns:
+        DistributedBatchSampler, return an iterable object for indices iterating.
 
     Examples:
         .. code-block:: python

python/paddle/fluid/dataloader/dataset.py

@@ -89,19 +89,20 @@ class IterableDataset(Dataset):
     An abstract class to encapsulate methods and behaviors of iterable datasets.
 
     All datasets in iterable-style (can only get sample one by one sequentially, like
-    a Python iterator) should be a subclass of `paddle.io.IterableDataset`. All subclasses should
+    a Python iterator) should be a subclass of :ref:`api_paddle_io_IterableDataset` . All subclasses should
     implement following methods:
 
-    :code:`__iter__`: yield sample sequentially. This method is required by reading dataset sample in :code:`paddle.io.DataLoader`.
+    :code:`__iter__`: yield sample sequentially. This method is required by reading dataset sample in :ref:`api_paddle_io_DataLoader` .
 
     .. note::
         do not implement :code:`__getitem__` and :code:`__len__` in IterableDataset, should not be called either.
 
-    see :code:`paddle.io.DataLoader`.
+    see :ref:`api_paddle_io_DataLoader` .
 
     Examples:
 
         .. code-block:: python
+            :name: code-example1
 
             import numpy as np
             from paddle.io import IterableDataset
@@ -128,9 +129,10 @@ class IterableDataset(Dataset):
     among workers as follows. In both the methods, worker information that can be getted in
     a worker process by `paddle.io.get_worker_info` will be needed.
 
-    Example 1: splitting data copy in each worker in :code:`__iter__`
+    splitting data copy in each worker in :code:`__iter__`
 
         .. code-block:: python
+            :name: code-example2
 
             import math
             import paddle
@@ -169,9 +171,10 @@ class IterableDataset(Dataset):
                 print(data)
                 # outputs: [2, 5, 3, 6, 4, 7]
 
-    Example 2: splitting data copy in each worker by :code:`worker_init_fn`
+    splitting data copy in each worker by :code:`worker_init_fn`
 
         .. code-block:: python
+            :name: code-example3
 
             import math
             import paddle
@@ -370,16 +373,16 @@ class ComposeDataset(Dataset):
 
 class ChainDataset(IterableDataset):
     """
-    A Dataset which chains multiple iterable-tyle datasets.
+    A Dataset which chains multiple iterable-style datasets.
 
     This dataset is used for assembling multiple datasets which should
-    be :code:`paddle.io.IterableDataset`.
+    be :ref:`api_paddle_io_IterableDataset`.
 
     Args:
-        datasets(list of Dataset): List of datasets to be chainned.
+        datasets(list of IterableDatasets): List of datasets to be chainned.
 
     Returns:
-        Dataset: A Dataset which chains fields of multiple datasets.
+        paddle.io.IterableDataset: A Dataset which chains fields of multiple datasets.
 
     Examples:
 

python/paddle/fluid/dataloader/sampler.py

@@ -151,16 +151,16 @@ class RandomSampler(Sampler):
 
     Args:
         data_source(Dataset): dataset to sample, this could be an
-                instance of :code:`paddle.io.Dataset` other Python
-                object which implemented :code:`__len__`.
-        replacement(bool): If False, sample the whole dataset, If False,
-                set :attr:`num_samples` for how many sample to draw. Default False.
-        num_samples(int): set sample number to draw if :attr:`replacement`
-                is True. Default None.
-        generator(Generator): specify a generator to sample the data source. Default None
+                instance of :ref:`api_paddle_io_Dataset` or :ref:`api_paddle_io_IterableDataset` or other Python
+                object which implemented :code:`__len__` to get indices as the range of :code:`dataset` length. Default None.
+        replacement(bool, optional): If False, sample the whole dataset, If True,
+                set :attr:`num_samples` for how many samples to draw. Default False.
+        num_samples(int, optional): set sample number to draw if :attr:`replacement`
+                is True, then it will take samples according to the number you set. Default None, disabled.
+        generator(Generator, optional): specify a generator to sample the :code:`data_source`. Default None, disabled.
 
     Returns:
-        Sampler: a Sampler yield sample index randomly
+        RandomSampler: a Sampler yield sample index randomly.
 
     Examples:
 
@@ -185,7 +185,6 @@ class RandomSampler(Sampler):
             for index in sampler:
                 print(index)
 
-    see `paddle.io.Sampler`
     """
 
     def __init__(