multimodal/YOLOX/docs/cache.md

Cache Custom Data

The caching feature is specifically tailored for users with ample memory resources. However, we still offer the option to cache data to disk, but disk performance can vary and may not guarantee optimal user experience. Implementing custom dataset RAM caching is also more straightforward and user-friendly compared to disk caching. With a few simple modifications, users can expect to see a significant increase in training speed, with speeds nearly double that of non-cached datasets.

This page explains how to cache your own custom data with YOLOX.

0. Before you start

Step1 Clone this repo and follow the README to install YOLOX.

Stpe2 Read the Training on custom data tutorial to understand how to prepare your custom data.

1. Inheirit from CacheDataset

Step1 Create a custom dataset that inherits from the CacheDataset class. Note that whether inheriting from Dataset or CacheDataset , the __init__() method of your custom dataset should take the following keyword arguments: input_dimension, cache, and cache_type. Also, call super().__init__() and pass in input_dimension, num_imgs, cache, and cache_type as input, where num_imgs is the size of the dataset.

Step2 Implement the abstract function read_img(self, index, use_cache=True) of parent class and decorate it with @cache_read_img. This function takes an index as input and returns an image, and the returned image will be used for caching. It is recommended to put all repetitive and fixed post-processing operations on the image in this function to reduce the post-processing time of the image during training.

# CustomDataset.py
from yolox.data.datasets import CacheDataset, cache_read_img

class CustomDataset(CacheDataset):
    def __init__(self, input_dimension, cache, cache_type, *args, **kwargs):
        # Get the required keyword arguments of super().__init__()
        super().__init__(
            input_dimension=input_dimension,
            num_imgs=num_imgs,
            cache=cache,
            cache_type=cache_type
        )
        # ...

    @cache_read_img
    def read_img(self, index, use_cache=True):
        # get image ...
        # (optional) repetitive and fixed post-processing operations for image
        return image

2. Create your Exp file and return your custom dataset

Step1 Create a new class that inherits from the Exp class provided by the yolox_base.py. Override the get_dataset() and get_eval_dataset() method to return an instance of your custom dataset.

Step2 Implement your own get_evaluator method to return an instance of your custom evaluator.

# CustomeExp.py
from yolox.exp import Exp as MyExp

class Exp(MyExp):
    def get_dataset(self, cache, cache_type: str = "ram"):
        return CustomDataset(
            input_dimension=self.input_size,
            cache=cache,
            cache_type=cache_type
        )

    def get_eval_dataset(self):
        return CustomDataset(
            input_dimension=self.input_size,
        )

    def get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False):
        return CustomEvaluator(
            dataloader=self.get_eval_loader(batch_size, is_distributed, testdev=testdev, legacy=legacy),
            img_size=self.test_size,
            confthre=self.test_conf,
            nmsthre=self.nmsthre,
            num_classes=self.num_classes,
            testdev=testdev,
        )

(Optional) get_data_loader and get_eval_loader are now a default behavior in yolox_base.py and generally do not need to be changed. If you have to change get_data_loader, you need to add the following code at the beginning.

# CustomeExp.py
from yolox.exp import Exp as MyExp

class Exp(MyExp):
    def get_data_loader(self, batch_size, is_distributed, no_aug=False, cache_img: str = None):
        if self.dataset is None:
            with wait_for_the_master():
                assert cache_img is None
                self.dataset = self.get_dataset(cache=False, cache_type=cache_img)
        # ...

3. Cache to Disk

It's important to note that the cache_type can be "ram" or "disk", depending on where you want to cache your dataset. If you choose "disk", you need to pass in additional parameters to super().__init__() of CustomDataset: data_dir, cache_dir_name, path_filename.

• data_dir: the root directory of the dataset, e.g. /path/to/COCO.
• cache_dir_name: the name of the directory to cache to disk, for example "custom_cache", then the files cached to disk will be saved under /path/to/COCO/custom_cache.
• path_filename: a list of paths to the data relative to the data_dir, e.g. if you have data /path/to/COCO/train/1.jpg, /path/to/COCO/train/2.jpg, then path_filename = ['train/1.jpg', ' train/2.jpg'].