Dataset classes in MMSegmentation have two functions: (1) load data information after [data preparation](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/user_guides/2_dataset_prepare.md)
and (2) send data into [dataset transform pipeline](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/datasets/basesegdataset.py#L141) to do [data augmentation](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/zh_cn/advanced_guides/transforms.md).
There are 2 kinds of loaded information: (1) meta information which is original dataset information such as categories (classes) of dataset and their corresponding palette information, (2) data information which includes
the path of dataset images and labels.
The tutorial includes some main interfaces in MMSegmentation 1.x dataset class: methods of loading data information and modifying dataset classes in base dataset class, and the relationship between dataset and the data transform pipeline.
## Main Interfaces
Take Cityscapes as an example, if you want to run the example, please download and [preprocess](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/user_guides/2_dataset_prepare.md#cityscapes)
Cityscapes dataset in `data` directory, before running the demo code:
Get dataset meta information: the type of MMSegmentation meta information is also `dict`, which includes `'classes'` field for dataset classes and `'palette'` field for corresponding colors in visualization, and has `'label_map'` field and `'reduce_zero_label'` filed.
```python
print(dataset.metainfo)
{'classes': ('road',
'sidewalk',
'building',
'wall',
'fence',
'pole',
'traffic light',
'traffic sign',
'vegetation',
'terrain',
'sky',
'person',
'rider',
'car',
'truck',
'bus',
'train',
'motorcycle',
'bicycle'),
'palette': [[128, 64, 128],
[244, 35, 232],
[70, 70, 70],
[102, 102, 156],
[190, 153, 153],
[153, 153, 153],
[250, 170, 30],
[220, 220, 0],
[107, 142, 35],
[152, 251, 152],
[70, 130, 180],
[220, 20, 60],
[255, 0, 0],
[0, 0, 142],
[0, 0, 70],
[0, 60, 100],
[0, 80, 100],
[0, 0, 230],
[119, 11, 32]],
'label_map': None,
'reduce_zero_label': False}
```
The return value of dataset `__getitem__` method is the output of data samples after data augmentation, whose type is also `dict`. It has two fields: `'inputs'` corresponding to images after data augmentation,
and `'data_samples'` corresponding to `SegDataSample`\](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/zh_cn/advanced_guides/structures.md) which is new data structures in MMSegmentation 1.x,
and `gt_sem_seg` of `SegDataSample` has labels after data augmentation operations.
As mentioned above, dataset classes have the same functions, we implemented [`BaseSegDataset`](https://mmsegmentation.readthedocs.io/en/dev-1.x/api.html?highlight=BaseSegDataset#mmseg.datasets.BaseSegDataset) to reues the common functions.
It inherits [`BaseDataset` of MMEngine](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/basedataset.md) and follows unified initialization process of OpenMMLab. It supports the highly effective interior storing format, some functions like
dataset concatenation and repeatedly sampling. In MMSegmentation `BaseSegDataset`, the **method of loading data information** (`load_data_list`) is redefined and adds new `get_label_map` method to **modify dataset classes information**.
### Loading Dataset Information
The loaded data information includes the path of images samples and annotations samples, the detailed implementation could be found in
[`load_data_list`](https://github.com/open-mmlab/mmsegmentation/blob/163277bfe0fa8fefb63ee5137917fafada1b301c/mmseg/datasets/basesegdataset.py#L231) of `BaseSegDataset` in MMSegmentation.
There are two main methods to acquire the path of images and labels:
1. Load file paths according to the dirictory and suffix of input images and annotations
If the dataset directory structure is organized as below, the [`load_data_list`](https://github.com/open-mmlab/mmsegmentation/blob/163277bfe0fa8fefb63ee5137917fafada1b301c/mmseg/datasets/basesegdataset.py#L231) can parse dataset directory Structure:
```
├── data
│ ├── my_dataset
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ │ ├── xxx{img_suffix}
│ │ │ │ ├── yyy{img_suffix}
│ │ │ ├── val
│ │ │ │ ├── zzz{img_suffix}
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ │ ├── xxx{seg_map_suffix}
│ │ │ │ ├── yyy{seg_map_suffix}
│ │ │ ├── val
│ │ │ │ ├── zzz{seg_map_suffix}
```
Here is an example pf ADE20K, and below the directory structure of the dataset:
Here `'classes'` defines class names of Cityscapes dataset annotations, if users only concern some classes about vehicles and **ignore other classes**,
the meta information of dataset could be modified by defined input argument `metainfo` when instantiating Cityscapes dataset:
# pixels whose label index are 255 would be ignored when calculating loss
'label_map': {0: 255,
1: 255,
2: 255,
3: 255,
4: 255,
5: 255,
6: 255,
7: 255,
8: 255,
9: 255,
10: 255,
11: 255,
12: 255,
13: 0,
14: 1,
15: 2,
16: 3,
17: 4,
18: 5},
'reduce_zero_label': False}
```
Meta information is different from default setting of Cityscapes dataset. Moreover, `label_map` field is also defined, which is used for modifying label index of each pixel on segmentation mask.
The segmentation label would re-map class information by `label_map`, [here](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/datasets/basesegdataset.py#L151) is detailed implementation:
```python
gt_semantic_seg_copy = gt_semantic_seg.copy()
for old_id, new_id in results['label_map'].items():
To ignore label 0 (such as ADE20K dataset), we can use `reduce_zero_label` (default to `False`) argument of BaseSegDataset and its subclasses.
When `reduce_zero_label` is `True`, label 0 in segmentation annotations would be set as 255 (models of MMSegmentation would ignore label 255 in calculating loss) and indices of other labels will minus 1:
```python
gt_semantic_seg[gt_semantic_seg == 0] = 255
gt_semantic_seg = gt_semantic_seg - 1
gt_semantic_seg[gt_semantic_seg == 254] = 255
```
## Dataset and Data Transform Pipeline
If the argument `pipeline` is defined, the return value of `__getitem__` method is after data argument.
If dataset input argument does not define pipeline, it is the same as return value of `get_data_info` method.
