PicPac is an image streamer that feeds data to various deep learning frameworks for iterative training. It tries to solve the following two problems:
There lacks a unified image streamer for deep learning frameworks. Existing frameworks typically use generic storage backends like HDF5, leveldb/lmdb, etc. They either provide a thin adaptive layer for streaming, or rely on third party generic streaming library like fuel. Typically, changing a learning framework requires working out the data streaming mechanism afresh, and it is a challenge to make different frameworks see the same dataset the same way so as to fairly compare the performance of the down-stream processing.
It is a burden to manage different versions of the same image dataset for experimental purposes. When a framework does not support on-the-fly resizing, data splitting for cross validation or other preprocessing, one has to temporarily store the preprocessing results to the filesystem before feeding to the framework for learning. One will soon face the delimma of whether to spend time waiting for preprocessing, or two manage the ever growing versions of the same dataset. We observe that most CPU cores are free when training happens on GPU, so we make the design desicion to always store the raw data when possible, and do all kinds of preprocessing on-the-fly in parallel with the main training process.
We make our design decisions in favor of flexibility, and aim at small to medium datasets. We assume SSD storage, or memory that is big enough to hold the whole dataset. To stream extremely large datasets like the ImageNet with the sequential reading throughput of HDDs, see RecordIO or PicPoc.
A PicPac database is a single file. Below is how to import data for a classification problem.
picpac-import -f 2 data_dir path_to_db
The input format 2 assumes that data_dir has N subdirectories named 0, 1, ..., each containing training images for one category. See Importing Data for other input formats.
import picpac config = dict(loop=True, # restart from beginning when all data consumed # this leads to an endless loop. batch=16, # with batch > 0, images in the same batch must be of the same size # use resize_width/height if raw images have different sizes resize_width=256, resize_width=256, ) stream = picpac.ImageStream('path_to_db', **config) for images, labels, _ in stream: # do something with images and labels # images is 4-dim tensors in the shape of (batch, channel, rows, cols) # labels is 1-dim array by default pass
The streaming behavior can be tweaked with the config dict. See Configuration for all supported configuration parameters.