## Introduction
The data pipeline is responsible for loading and converting data. Each
resulting data sample is a tuple of np.ndarrays.
For example, Faster R-CNN training uses samples of this format: `[(im,
im_info, im_id, gt_bbox, gt_class, is_crowd), (...)]`.
### Implementation
The data pipeline consists of four sub-systems: data parsing, image
pre-processing, data conversion and data feeding APIs.
Data samples are collected to form `data.Dataset`s, usually 3 sets are
needed for training, validation, and testing respectively.
First, `data.source` loads the data files into memory, then
`data.transform` processes them, and lastly, the batched samples
are fetched by `data.Reader`.
Sub-systems details:
1. Data parsing
Parses various data sources and creates `data.Dataset` instances. Currently,
following data sources are supported:
- COCO data source
Loads `COCO` type datasets with directory structures like this:
```
dataset/coco/
├── annotations
│ ├── instances_train2017.json
│ ├── instances_val2017.json
| ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000580008.jpg
| ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
| ...
```
- Pascal VOC data source
Loads `Pascal VOC` like datasets with directory structure like this:
```
data/pascalvoc/
├──Annotations
│ ├── i000050.jpg
│ ├── 003876.xml
| ...
├── ImageSets
│ ├──Main
└── train.txt
└── val.txt
└── test.txt
└── dog_train.txt
└── dog_trainval.txt
└── dog_val.txt
└── dog_test.txt
└── ...
│ ├──Layout
└──...
│ ├── Segmentation
└──...
├── JPEGImages
│ ├── 000050.jpg
│ ├── 003876.jpg
| ...
```
- Roidb data source
A generalized data source serialized as pickle files, which have the following
structure:
```python
(records, cname2id)
# `cname2id` is a `dict` which maps category name to class IDs
# and `records` is a list of dict of this structure:
{
'im_file': im_fname, # image file name
'im_id': im_id, # image ID
'h': im_h, # height of image
'w': im_w, # width of image
'is_crowd': is_crowd, # crowd marker
'gt_class': gt_class, # ground truth class
'gt_bbox': gt_bbox, # ground truth bounding box
'gt_poly': gt_poly, # ground truth segmentation
}
```
We provide a tool to generate roidb data sources. To convert `COCO` or `VOC`
like dataset, run this command:
```sh
# --type: the type of original data (xml or json)
# --annotation: the path of file, which contains the name of annotation files
# --save-dir: the save path
# --samples: the number of samples (default is -1, which mean all datas in dataset)
python ./tools/generate_data_for_training.py
--type=json \
--annotation=./annotations/instances_val2017.json \
--save-dir=./roidb \
--samples=-1
```
2. Image preprocessing
the `data.transform.operator` module provides operations such as image
decoding, expanding, cropping, etc. Multiple operators are combined to form
larger processing pipelines.
3. Data transformer
Transform a `data.Dataset` to achieve various desired effects, Notably: the
`data.transform.paralle_map` transformer accelerates image processing with
multi-threads or multi-processes. More transformers can be found in
`data.transform.transformer`.
4. Data feeding apis
To facilitate data pipeline building, we combine multiple `data.Dataset` to
form a `data.Reader` which can provide data for training, validation and
testing respectively. Users can simply call `Reader.[train|eval|infer]` to get
the corresponding data stream. Many aspect of the `Reader`, such as storage
location, preprocessing pipeline, acceleration mode can be configured with yaml
files.
The main APIs are as follows:
1. Data parsing
- `source/coco_loader.py`: COCO dataset parser. [source](../ppdet/data/source/coco_loader.py)
- `source/voc_loader.py`: Pascal VOC dataset parser. [source](../ppdet/data/source/voc_loader.py)
[Note] To use a non-default label list for VOC datasets, a `label_list.txt`
file is needed, one can use the provided label list
(`data/pascalvoc/ImageSets/Main/label_list.txt`) or generate a custom one (with `tools/generate_data_for_training.py`). Also, `use_default_label` option should
be set to `false` in the configuration file
- `source/loader.py`: Roidb dataset parser. [source](../ppdet/data/source/loader.py)
2. Operator
`transform/operators.py`: Contains a variety of data enhancement methods, including:
- `DecodeImage`: Read images in RGB format.
- `RandomFlipImage`: Horizontal flip.
- `RandomDistort`: Distort brightness, contrast, saturation, and hue.
- `ResizeImage`: Resize image with interpolation.
- `RandomInterpImage`: Use a random interpolation method to resize the image.
- `CropImage`: Crop image with respect to different scale, aspect ratio, and overlap.
- `ExpandImage`: Pad image to a larger size, padding filled with mean image value.
- `NormalizeImage`: Normalize image pixel values.
- `NormalizeBox`: Normalize the bounding box.
- `Permute`: Arrange the channels of the image and optionally convert image to BGR format.
- `MixupImage`: Mixup two images with given fraction[1](#vd).
[1] Please refer to [this paper](https://arxiv.org/pdf/1710.09412.pdf)。
`transform/arrange_sample.py`: Assemble the data samples needed by different models.
3. Transformer
`transform/post_map.py`: Transformations that operates on whole batches, mainly for:
- Padding whole batch to given stride values
- Resize images to Multi-scales
- Randomly adjust the image size of the batch data
`transform/transformer.py`: Data filtering batching.
`transform/parallel_map.py`: Accelerate data processing with multi-threads/multi-processes.
4. Reader
`reader.py`: Combine source and transforms, return batch data according to `max_iter`.
`data_feed.py`: Configure default parameters for `reader.py`.
### Usage
#### Canned Datasets
Preset for common datasets, e.g., `COCO` and `Pascal Voc` are included. In
most cases, user can simply use these canned dataset as is. Moreover, the
whole data pipeline is fully customizable through the yaml configuration files.
#### Custom Datasets
- Option 1: Convert the dataset to COCO or VOC format.
```sh
# a small utility (`tools/labelme2coco.py`) is provided to convert
# Labelme-annotated dataset to COCO format.
python ./tools/labelme2coco.py --json_input_dir ./labelme_annos/
--image_input_dir ./labelme_imgs/
--output_dir ./cocome/
--train_proportion 0.8
--val_proportion 0.2
--test_proportion 0.0
# --json_input_dir：The path of json files which are annotated by Labelme.
# --image_input_dir：The path of images.
# --output_dir：The path of coverted COCO dataset.
# --train_proportion：The train proportion of annatation data.
# --val_proportion：The validation proportion of annatation data.
# --test_proportion: The inference proportion of annatation data.
```
- Option 2:
1. Add `source/XX_loader.py` and implement the `load` function, following the
example of `source/coco_loader.py` and `source/voc_loader.py`.
2. Modify the `load` function in `source/loader.py` to make use of the newly
added data loader.
3. Modify `/source/__init__.py` accordingly.
```python
if data_cf['type'] in ['VOCSource', 'COCOSource', 'RoiDbSource']:
source_type = 'RoiDbSource'
# Replace the above code with the following code:
if data_cf['type'] in ['VOCSource', 'COCOSource', 'RoiDbSource', 'XXSource']:
source_type = 'RoiDbSource'
```
4. In the configure file, define the `type` of `dataset` as `XXSource`.
#### How to add data pre-processing？
- To add pre-processing operation for a single image, refer to the classes in
`transform/operators.py`, and implement the desired transformation with a new
class.
- To add pre-processing for a batch, one needs to modify the `build_post_map`
function in `transform/post_map.py`.