[Doc]: refactor docs for basedataset (#318)
Tao Gong
GitHub
parent
44538e56c5
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45f5859b50
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@ -112,7 +112,7 @@ data
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```python
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import os.path as osp
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from mmengine.data import BaseDataset
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from mmengine.dataset import BaseDataset
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class ToyDataset(BaseDataset):
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@ -125,10 +125,10 @@ class ToyDataset(BaseDataset):
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# }
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def parse_data_info(self, raw_data_info):
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data_info = raw_data_info
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img_prefix = self.data_prefix.get('img', None)
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img_prefix = self.data_prefix.get('img_path', None)
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if img_prefix is not None:
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data_info['img_path'] = osp.join(
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img_prefix, data_info['img_path')
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img_prefix, data_info['img_path'])
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return data_info
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```
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@ -146,7 +146,7 @@ pipeline = [
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toy_dataset = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline)
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```
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@ -188,7 +188,7 @@ len(toy_dataset)
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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：
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```python
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from mmengine.data import BaseDataset
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from mmengine.dataset import BaseDataset
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class ToyVideoDataset(BaseDataset):
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@ -238,7 +238,7 @@ pipeline = [
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toy_dataset = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline,
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# 在这里传入 lazy_init 变量
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@ -279,7 +279,7 @@ pipeline = [
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toy_dataset = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline,
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# 在这里传入 serialize_data 变量
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@ -297,7 +297,7 @@ toy_dataset = ToyDataset(
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MMEngine 提供了 `ConcatDataset` 包装来拼接多个数据集，使用方法如下：
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```python
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from mmengine.data import ConcatDataset
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from mmengine.dataset import ConcatDataset
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pipeline = [
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dict(type='xxx', ...),
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@ -307,13 +307,13 @@ pipeline = [
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toy_dataset_1 = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline)
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toy_dataset_2 = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='val/'),
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data_prefix=dict(img_path='val/'),
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ann_file='annotations/val.json',
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pipeline=pipeline)
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@ -328,7 +328,7 @@ toy_dataset_12 = ConcatDataset(datasets=[toy_dataset_1, toy_dataset_2])
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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：
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```python
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from mmengine.data import RepeatDataset
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from mmengine.dataset import RepeatDataset
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pipeline = [
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dict(type='xxx', ...),
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@ -338,7 +338,7 @@ pipeline = [
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toy_dataset = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline)
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@ -357,16 +357,16 @@ MMEngine 提供了 `ClassBalancedDataset` 包装，来基于数据集中类别
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`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：
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```python
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from mmengine.data import BaseDataset, ClassBalancedDataset
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from mmengine.dataset import BaseDataset, ClassBalancedDataset
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class ToyDataset(BaseDataset):
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def parse_data_info(self, raw_data_info):
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data_info = raw_data_info
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img_prefix = self.data_prefix.get('img', None)
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img_prefix = self.data_prefix.get('img_path', None)
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if img_prefix is not None:
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data_info['img_path'] = osp.join(
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img_prefix, data_info['img_path')
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img_prefix, data_info['img_path'])
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return data_info
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# 必须支持的方法，需要返回样本的类别
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@ -382,7 +382,7 @@ pipeline = [
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toy_dataset = ToyDataset(
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data_root='data/',
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data_prefix=dict(img='train/'),
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data_prefix=dict(img_path='train/'),
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ann_file='annotations/train.json',
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pipeline=pipeline)
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@ -404,7 +404,7 @@ from mmengine.registry import DATASETS
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@DATASETS.register_module()
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class ExampleDatasetWrapper:
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def __init__(self, dataset, lazy_init = False, ...):
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def __init__(self, dataset, lazy_init=False, ...):
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# 构建原数据集（self.dataset）
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if isinstance(dataset, dict):
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self.dataset = DATASETS.build(dataset)
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