vendor/detectron2/docs/tutorials/data_loading.md

Dataloader

Dataloader is the component that provides data to models. A dataloader usually (but not necessarily) takes raw information from datasets, and process them into a format needed by the model.

How the Existing Dataloader Works

Detectron2 contains a builtin data loading pipeline. It's good to understand how it works, in case you need to write a custom one.

Detectron2 provides two functions build_detection_{train,test}_loader that create a default data loader from a given config. Here is how build_detection_{train,test}_loader work:

1. It takes the name of a registered dataset (e.g., "coco_2017_train") and loads a list[dict] representing the dataset items in a lightweight format. These dataset items are not yet ready to be used by the model (e.g., images are not loaded into memory, random augmentations have not been applied, etc.). Details about the dataset format and dataset registration can be found in datasets.
2. Each dict in this list is mapped by a function ("mapper"):
   • Users can customize this mapping function by specifying the "mapper" argument in build_detection_{train,test}_loader. The default mapper is DatasetMapper.
   • The output format of the mapper can be arbitrary, as long as it is accepted by the consumer of this data loader (usually the model). The outputs of the default mapper, after batching, follow the default model input format documented in Use Models.
   • The role of the mapper is to transform the lightweight representation of a dataset item into a format that is ready for the model to consume (including, e.g., read images, perform random data augmentation and convert to torch Tensors). If you would like to perform custom transformations to data, you often want a custom mapper.
3. The outputs of the mapper are batched (simply into a list).
4. This batched data is the output of the data loader. Typically, it's also the input of model.forward().

Write a Custom Dataloader

Using a different "mapper" with build_detection_{train,test}_loader(mapper=) works for most use cases of custom data loading. For example, if you want to resize all images to a fixed size for training, use:

import detectron2.data.transforms as T
from detectron2.data import DatasetMapper   # the default mapper
dataloader = build_detection_train_loader(cfg,
   mapper=DatasetMapper(cfg, is_train=True, augmentations=[
      T.Resize((800, 800))
   ]))
# use this dataloader instead of the default

If the arguments of the default DatasetMapper does not provide what you need, you may write a custom mapper function and use it instead, e.g.:

from detectron2.data import detection_utils as utils
 # Show how to implement a minimal mapper, similar to the default DatasetMapper
def mapper(dataset_dict):
    dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
    # can use other ways to read image
    image = utils.read_image(dataset_dict["file_name"], format="BGR")
    # See "Data Augmentation" tutorial for details usage
    auginput = T.AugInput(image)
    transform = T.Resize((800, 800))(auginput)
    image = torch.from_numpy(auginput.image.transpose(2, 0, 1))
    annos = [
        utils.transform_instance_annotations(annotation, [transform], image.shape[1:])
        for annotation in dataset_dict.pop("annotations")
    ]
    return {
       # create the format that the model expects
       "image": image,
       "instances": utils.annotations_to_instances(annos, image.shape[1:])
    }
dataloader = build_detection_train_loader(cfg, mapper=mapper)

If you want to change not only the mapper (e.g., in order to implement different sampling or batching logic), build_detection_train_loader won't work and you will need to write a different data loader. The data loader is simply a python iterator that produces the format that the model accepts. You can implement it using any tools you like.

No matter what to implement, it's recommended to check out API documentation of detectron2.data to learn more about the APIs of these functions.

Use a Custom Dataloader

If you use DefaultTrainer, you can overwrite its build_{train,test}_loader method to use your own dataloader. See the deeplab dataloader for an example.

If you write your own training loop, you can plug in your data loader easily.