[Doc]: refactor docs for basedataset (open-mmlab#318)

docs/zh_cn/tutorials/basedataset.md

@@ -112,7 +112,7 @@ data
 ```python
 import os.path as osp
 
-from mmengine.data import BaseDataset
+from mmengine.dataset import BaseDataset
 
 
 class ToyDataset(BaseDataset):
@@ -125,10 +125,10 @@ class ToyDataset(BaseDataset):
     # }
     def parse_data_info(self, raw_data_info):
         data_info = raw_data_info
-        img_prefix = self.data_prefix.get('img', None)
+        img_prefix = self.data_prefix.get('img_path', None)
         if img_prefix is not None:
             data_info['img_path'] = osp.join(
-                img_prefix, data_info['img_path')
+                img_prefix, data_info['img_path'])
         return data_info
 
 ```
@@ -146,7 +146,7 @@ pipeline = [
 
 toy_dataset = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline)
 ```
@@ -188,7 +188,7 @@ len(toy_dataset)
 在上面的例子中，标注文件的每个原始数据只包含一个训练/测试样本（通常是图像领域）。如果每个原始数据包含若干个训练/测试样本（通常是视频领域），则只需保证 `parse_data_info()` 的返回值为 `list[dict]` 即可：
 
 ```python
-from mmengine.data import BaseDataset
+from mmengine.dataset import BaseDataset
 
 
 class ToyVideoDataset(BaseDataset):
@@ -238,7 +238,7 @@ pipeline = [
 
 toy_dataset = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline,
     # 在这里传入 lazy_init 变量
@@ -279,7 +279,7 @@ pipeline = [
 
 toy_dataset = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline,
     # 在这里传入 serialize_data 变量
@@ -297,7 +297,7 @@ toy_dataset = ToyDataset(
 MMEngine 提供了 `ConcatDataset` 包装来拼接多个数据集，使用方法如下：
 
 ```python
-from mmengine.data import ConcatDataset
+from mmengine.dataset import ConcatDataset
 
 pipeline = [
     dict(type='xxx', ...),
@@ -307,13 +307,13 @@ pipeline = [
 
 toy_dataset_1 = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline)
 
 toy_dataset_2 = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='val/'),
+    data_prefix=dict(img_path='val/'),
     ann_file='annotations/val.json',
     pipeline=pipeline)
 
@@ -328,7 +328,7 @@ toy_dataset_12 = ConcatDataset(datasets=[toy_dataset_1, toy_dataset_2])
 MMEngine 提供了 `RepeatDataset` 包装来重复采样某个数据集若干次，使用方法如下：
 
 ```python
-from mmengine.data import RepeatDataset
+from mmengine.dataset import RepeatDataset
 
 pipeline = [
     dict(type='xxx', ...),
@@ -338,7 +338,7 @@ pipeline = [
 
 toy_dataset = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline)
 
@@ -357,16 +357,16 @@ MMEngine 提供了 `ClassBalancedDataset` 包装，来基于数据集中类别
 `ClassBalancedDataset` 包装假设了被包装的数据集类支持 `get_cat_ids(idx)` 方法，`get_cat_ids(idx)` 方法返回一个列表，该列表包含了 `idx` 指定的 `data_info` 包含的样本类别，使用方法如下：
 
 ```python
-from mmengine.data import BaseDataset, ClassBalancedDataset
+from mmengine.dataset import BaseDataset, ClassBalancedDataset
 
 class ToyDataset(BaseDataset):
 
     def parse_data_info(self, raw_data_info):
         data_info = raw_data_info
-        img_prefix = self.data_prefix.get('img', None)
+        img_prefix = self.data_prefix.get('img_path', None)
         if img_prefix is not None:
             data_info['img_path'] = osp.join(
-                img_prefix, data_info['img_path')
+                img_prefix, data_info['img_path'])
         return data_info
 
     # 必须支持的方法，需要返回样本的类别
@@ -382,7 +382,7 @@ pipeline = [
 
 toy_dataset = ToyDataset(
     data_root='data/',
-    data_prefix=dict(img='train/'),
+    data_prefix=dict(img_path='train/'),
     ann_file='annotations/train.json',
     pipeline=pipeline)
 
@@ -404,7 +404,7 @@ from mmengine.registry import DATASETS
 @DATASETS.register_module()
 class ExampleDatasetWrapper:
 
-    def __init__(self, dataset, lazy_init = False, ...):
+    def __init__(self, dataset, lazy_init=False, ...):
         # 构建原数据集（self.dataset）
         if isinstance(dataset, dict):
             self.dataset = DATASETS.build(dataset)