code_test/docs/tutorials/data_pipeline.md

# Tutorial 3: Customize Data Pipelines

## Design of Data pipelines

Following typical conventions, we use `Dataset` and `DataLoader` for data loading
with multiple workers. `Dataset` returns a dict of data items corresponding
the arguments of models' forward method.
Since the data in object detection may not be the same size (image size, gt bbox size, etc.),
we introduce a new `DataContainer` type in MMCV to help collect and distribute
data of different size.
See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details.

The data preparation pipeline and the dataset is decomposed. Usually a dataset
defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.
A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.

We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
![pipeline figure](../../resources/data_pipeline.png)

The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.

Here is a pipeline example for Faster R-CNN.

```python
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
```

For each operation, we list the related dict fields that are added/updated/removed.

### Data loading

`LoadImageFromFile`

- add: img, img_shape, ori_shape

`LoadAnnotations`

- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields

`LoadProposals`

- add: proposals

### Pre-processing

`Resize`

- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
- update: img, img_shape, *bbox_fields, *mask_fields, *seg_fields

`RandomFlip`

- add: flip
- update: img, *bbox_fields, *mask_fields, *seg_fields

`Pad`

- add: pad_fixed_size, pad_size_divisor
- update: img, pad_shape, *mask_fields, *seg_fields

`RandomCrop`

- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields

`Normalize`

- add: img_norm_cfg
- update: img

`SegRescale`

- update: gt_semantic_seg

`PhotoMetricDistortion`

- update: img

`Expand`

- update: img, gt_bboxes

`MinIoURandomCrop`

- update: img, gt_bboxes, gt_labels

`Corrupt`

- update: img

### Formatting

`ToTensor`

- update: specified by `keys`.

`ImageToTensor`

- update: specified by `keys`.

`Transpose`

- update: specified by `keys`.

`ToDataContainer`

- update: specified by `fields`.

`DefaultFormatBundle`

- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg

`Collect`

- add: img_meta (the keys of img_meta is specified by `meta_keys`)
- remove: all other keys except for those specified by `keys`

### Test time augmentation

`MultiScaleFlipAug`

## Extend and use custom pipelines

1. Write a new pipeline in a file, e.g., in `my_pipeline.py`. It takes a dict as input and returns a dict.

    ```python
    import random
    from mmdet.datasets import PIPELINES


    @PIPELINES.register_module()
    class MyTransform:
        """Add your transform

        Args:
            p (float): Probability of shifts. Default 0.5.
        """

        def __init__(self, p=0.5):
            self.p = p

        def __call__(self, results):
            if random.random() > self.p:
                results['dummy'] = True
            return results
    ```

2. Import and use the pipeline in your config file.
   Make sure the import is relative to where your train script is located.

    ```python
    custom_imports = dict(imports=['path.to.my_pipeline'], allow_failed_imports=False)

    img_norm_cfg = dict(
        mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
    train_pipeline = [
        dict(type='LoadImageFromFile'),
        dict(type='LoadAnnotations', with_bbox=True),
        dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
        dict(type='RandomFlip', flip_ratio=0.5),
        dict(type='Normalize', **img_norm_cfg),
        dict(type='Pad', size_divisor=32),
        dict(type='MyTransform', p=0.2),
        dict(type='DefaultFormatBundle'),
        dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
    ]
    ```

3. Visualize the output of your augmentation pipeline

   To visualize the output of your agmentation pipeline, `tools/misc/browse_dataset.py`
   can help the user to browse a detection dataset (both images and bounding box annotations)
   visually, or save the image to a designated directory. More detials can refer to
   [useful_tools](../useful_tools.md)