img2dataset 1.3.0

Easily turn a set of image urls to an image dataset

img2dataset

Easily turn a set of image urls to an image dataset.

Also supports saving captions for url+caption datasets.

Install

pip install img2dataset

Usage

First get some image url list. For example:

echo 'https://placekitten.com/200/305' >> myimglist.txt
echo 'https://placekitten.com/200/304' >> myimglist.txt
echo 'https://placekitten.com/200/303' >> myimglist.txt

Then, run the tool:

img2dataset --url_list=myimglist.txt --output_folder=output_folder --thread_count=64 --image_size=256

The tool will then automatically download the urls, resize them, and store them with that format:

• output_folder
  • 0
    • 0.jpg
    • 1.jpg
    • 2.jpg

or as this format if choosing webdataset:

• output_folder
  • 0.tar containing:
    • 0.jpg
    • 1.jpg
    • 2.jpg

with each number being the position in the list. The subfolders avoids having too many files in a single folder.

If captions are provided, they will be saved as 0.txt, 1.txt, ...

This can then easily be fed into machine learning training or any other use case.

API

This module exposes a single function download which takes the same arguments as the command line tool:

• url_list A file with the list of url of images to download, one by line (required)
• image_size The side to resize image to (default 256)
• output_folder The path to the output folder (default "images")
• processes_count The number of processes used for downloading the pictures. This is important to be high for performance. (default 1)
• thread_count The number of threads used for downloading the pictures. This is important to be high for performance. (default 256)
• resize_mode The way to resize pictures, can be no, border or keep_ratio (default border)
  • no doesn't resize at all
  • border will make the image image_size x image_size and add a border
  • keep_ratio will keep the ratio and make the smallest side of the picture image_size
• resize_only_if_bigger resize pictures only if bigger that the image_size (default False)
• output_format decides how to save pictures (default files)
  • files saves as a set of subfolder containing pictures
  • webdataset saves as tars containing pictures
• input_format decides how to load the urls (default txt)
  • txt loads the urls as a text file of url, one per line
  • csv loads the urls and optional caption as a csv
  • parquet loads the urls and optional caption as a parquet
• url_col the name of the url column for parquet and csv (default url)
• caption_col the name of the caption column for parquet and csv (default None)
• number_sample_per_shard the number of sample that will be downloaded in one shard (default 10000)

How to tweak the options

The default values should be good enough for small sized dataset. For larger ones, these tips may help you get the best performance:

• set the processes_count as the number of cores your machine has
• increase thread_count as long as your bandwidth and cpu are below the limits
• I advise to set output_format to webdataset if your dataset has more than 1M elements, it will be easier to manipulate few tars rather than million of files

Road map

This tool work as it. However in the future goals will include:

• support for multiple input files
• support for csv or parquet files as input
• benchmarks for 1M, 10M, 100M pictures

Architecture notes

This tool is designed to download pictures as fast as possible. This put a stress on various kind of resources. Some numbers assuming 1350 image/s:

• Bandwidth: downloading a thousand average image per second requires about 130MB/s
• CPU: resizing one image may take several milliseconds, several thousand per second can use up to 16 cores
• DNS querying: million of urls mean million of domains, default OS setting usually are not enough. Setting up a local bind9 resolver may be required
• Disk: if using resizing, up to 30MB/s write speed is necessary. If not using resizing, up to 130MB/s. Writing in few tar files make it possible to use rotational drives instead of a SSD.

With these information in mind, the design choice was done in this way:

• the list of urls is split in N shards. N is usually chosen as the number of cores
• N processes are started (using multiprocessing process pool)
  • each process starts M threads. M should be maximized in order to use as much network as possible while keeping cpu usage below 100%.
  • each of this thread download 1 image and returns it
  • the parent thread handle resizing (which means there is at most N resizing running at once, using up the cores but not more)
  • the parent thread saves to a tar file that is different from other process

This design make it possible to use the CPU resource efficiently by doing only 1 resize per core, reduce disk overhead by opening 1 file per core, while using the bandwidth resource as much as possible by using M thread per process.

Setting up a bind9 resolver

In order to keep the success rate high, it is necessary to use an efficient DNS resolver. I tried several options: systemd-resolved, dnsmaskq and bind9 and reached the conclusion that bind9 reaches the best performance for this use case. Here is how to set this up on ubuntu:

sudo apt install bind9
sudo vim /etc/bind/named.conf.options

Add this in options:
        recursive-clients 10000;
        resolver-query-timeout 30000;
        max-clients-per-query 10000;

sudo systemctl restart bind9

sudo vim /etc/resolv.conf

Put this content:
nameserver 127.0.0.1

This will make it possible to keep an high success rate while doing thousands of dns queries. You may also want to setup bind9 logging in order to check that few dns errors happen.

For development

Either locally, or in gitpod (do export PIP_USER=false there)

Setup a virtualenv:

python3 -m venv .env
source .env/bin/activate
pip install -e .

to run tests:

pip install -r requirements-test.txt

then

python -m pytest -v tests -s

Benchmarks

10000 image benchmark

cd tests
bash benchmark.sh

18M image benchmark

Download crawling at home first part, then:

cd tests
bash large_bench.sh

It takes 3.7h to download 18M pictures

1350 images/s is the currently observed performance. 4.8M images per hour, 116M images per 24h.