# Tutorial 3: Custom 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 action recognition & localization may not be the same size (image size, gt bbox size, etc.),
The `DataContainer` in MMCV is used to help collect and distribute data of different sizes.
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 operation.

We present a typical 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](/docs/imgs/data_pipeline.png)

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

Here is a pipeline example for TSN.
```python
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
    dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),
    dict(type='FrameSelector', io_backend='disk'),
    dict(type='Resize', scale=(-1, 256)),
    dict(
        type='MultiScaleCrop',
        input_size=224,
        scales=(1, 0.875, 0.75, 0.66),
        random_crop=False,
        max_wh_scale_gap=1),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=3,
        test_mode=True),
    dict(type='FrameSelector', io_backend='disk'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Flip', flip_ratio=0),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=25,
        test_mode=True),
    dict(type='FrameSelector', io_backend='disk'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='TenCrop', crop_size=224),
    dict(type='Flip', flip_ratio=0),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
```

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

### Data loading

`SampleFrames`
- add: frame_inds, clip_len, frame_interval, num_clips, *total_frames

`DenseSampleFrames`
- add: frame_inds, clip_len, frame_interval, num_clips, *total_frames

`PyAVDecode`
- add: imgs, original_shape
- update: *frame_inds

`DecordDecode`
- add: imgs, original_shape
- update: *frame_inds

`OpenCVDecode`
- add: imgs, original_shape
- update: *frame_inds

`FrameSelector`
- add: imgs, original_shape
- update: *frame_inds

### Pre-processing

`RandomCrop`
- add: crop_bbox, img_shape
- update: imgs

`RandomResizedCrop`
- add: crop_bbox, img_shape
- update: imgs

`MultiScaleCrop`
- add: crop_bbox, img_shape, scales
- update: imgs

`Resize`
- add: img_shape, keep_ratio, scale_factor
- update: imgs

`Flip`
- add: flip, flip_direction
- update: imgs

`Normalize`
- add: img_norm_cfg
- update: imgs

`CenterCrop`
- add: crop_bbox, img_shape
- update: imgs

`ThreeCrop`
- add: crop_bbox, img_shape
- update: imgs

`TenCrop`
- add: crop_bbox, img_shape
- update: imgs

`MultiGroupCrop`
- add: crop_bbox, img_shape
- update: imgs

### Formatting

`ToTensor`
- update: specified by `keys`.

`ImageToTensor`
- update: specified by `keys`.

`Transpose`
- update: specified by `keys`.

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

It is **noteworthy** that the first key, commonly `imgs`, will be used as the main key to calculate the batch size.

`FormatShape`
- add: input_shape
- update: imgs

## Extend and Use Custom Pipelines

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

    ```python
    from mmaction.datasets import PIPELINES

    @PIPELINES.register_module()
    class MyTransform:

        def __call__(self, results):
            results['key'] = value
            return results
    ```

2. Import the new class.

    ```python
    from .my_pipeline import MyTransform
    ```

3. Use it in config files.

    ```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='DenseSampleFrames', clip_len=8, frame_interval=8, num_clips=1),
        dict(type='FrameSelector', io_backend='disk'),
        dict(type='MyTransform'),       # use a custom pipeline
        dict(type='Normalize', **img_norm_cfg),
        dict(type='FormatShape', input_format='NCTHW'),
        dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
        dict(type='ToTensor', keys=['imgs', 'label'])
    ]
    ```