In the last year, we’ve seen that GPUs can be used as a computational resource to unlock work that's previously been bottlenecked in sectors like gaming, computer vision and blockchain. One of the most common GPU-accelerated high complexity works is artificial deep neural network training. But what happens if we try to use that computational resource for something other than machine learning models training?
Imagine GPUs being used to accelerate the three common stages of every data science project ETL - extraction, transform and load). How much speed acceleration could we get using GPUs instead of CPUs on image augmentation tasks?
Our Goal “Get a Better performance using GPU for Image Augmentation than using CPU”
This article will illustrate how GPU use can accelerate the transformation process in techniques like image augmentation. There are many CPU libraries to use on image augmentation such as imgaug and torchvision, but we decided to pursue the python library albumentations because of the sheer number of possible transformations that can manage as well as the performance of those compared to others.
After a closer look at the functions and implementation of the albumentations library, we found that it generally relies on two libraries—NumPy and OpenCV. Since our goal was to enable the use of the functions at GPU, we decided to use CuPy as a replacement for NumPy and OpenCV with Compute Unified Device Architecture (CUDA) support.
Experiment Setup
We are using our wrapper for the following image Augmentation functions:
HorizontalFlip, VerticalFlip, RandomTranspose, CLAHE, GaussianFilter, GaussNoise, MedianFilter, MotionFilter, RandomContrast, RandomBrightness, ShiftHSV, ShiftScaleRotate, Resize, Cutout, ElasticTransform, OpticalDistortion and GridDistortion
AWS Setup
We used an EC2 Instance of type G (g4dn.xlarge) for running this benchmark, which contains a GPU Tesla T4 and a 4 vCPU.
Docker Image
In order to use OpenCV in GPU, we had to build a modification of the following image https://github.com/datamachines/cuda_tensorflow_opencv. This image freed us from concerns over Nvidia Drivers, CUDA installation, and OpenCV compilation.
Datasets
For testing the functions mentioned in the experiment setup above, we use four different datasets:
- Image Net 2012 Dataset:
(http://www.image-net.org/challenges/LSVRC/2012/) This consists of 150,000 photographs, collected from Flickr and other search engines, mainly used for object recognition tasks. - ISIC 2020 Challenge:
(https://challenge2020.isic-archive.com/) This contains 33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. - HDR+ Burst Photography Dataset:(http://hdrplusdata.org/dataset.html) This has 4k resolution pictures taken from mobile cameras with dynamic range and low-light imaging.
- The last one is composed of about 16k (15360x8640 pixels) resolution wallpapers that we got from https://wall.alphacoders.com/by_resolution.php?w=15360&h=8640
Experiment Execution
The experiment effectively consisted of comparing similar function executions in CPU and GPU, using the Albumentations library and our wrapper San Cristobal for OpenCV.
The metric was “time execution per Image”, so every function mentioned before was executed on four different datasets of images in order to compare the behavior in several environments, with five repetitions in the calculation of each transformation.
Benchmark Results
These are the plots of the time execution against each image augmentation function for every dataset, taking into account that the Y-axis is in a logarithmic scale. You can check the unscaled results in the tables at the beginning of this section.
Comparing acceleration performance on GPU in ImageNet against 16K resolution images we can see that the GPU acceleration keeps increasing in most of the functions for high-resolution images (except RandomTranspose function), meaning it will take less time to apply all of those transformations
The most remarkable example is the ElasticTransform function, which took almost 0.015 seconds per image using GPU and approximately 142 seconds per image using CPU.
If we had a dataset with 10.000 high-resolution images (16K), we could save more than 13 days of computing time. That’s a huge financial incentive.
CPU Behaviour around all the data
GPU Behaviour around all the data
Even though there is an increase in the time it takes to execute most high-res images, in 14 out of 17 cases there was a positive improvement compared to CPU behavior.
Main difficulties and ongoing challenges
- We weren’t able to find large datasets with high-resolution images
- Lack of GPU Memory
- Excessive time between uploading and downloading data from GPU memory to RAM for visualization
Conclusion
Based on our work, it's clearly beneficial to use GPU processing to accelerate image augmentation with OpenCV – even if we don’t have one of the latest and most powerful GPUs. Additionally, we can use cloud services like AWS to improve performance and minimize execution time.
For datasets with small images, like ISIC and ImageNet, we can conclude that there is an insignificant difference between CPU and GPU.
More than half of the functions have differences that are less than 0.001 seconds, and 30% are less or around 0.01s. For a very complex Elastic Transformation, there was a difference of 0.2s.
For almost half of the image transformations, the CPU performs better.
For 4k and 16k datasets, we can see the real performance of GPU.
For 13 functions we have improvements of 47% to 100%. Gaussian Filter gets better performance on CPU for ImageNet, ISIC, and 4k, but the difference between the CPU performance for each dataset is decreasing, and for 16k we have 22% better performance on GPU. Median Filter performs better on CPU, most likely due to inefficient implementation, and there is space for improvement. CPU performs far better for Random Transpose, and CPU is better for Vertical Flip, which is quite interesting because Horizontal Flip performs better on GPU.
Next Steps
We noticed that some of the transformations were faster using other libraries like CuPy or Numba, or using other implementations. We want to get better results for VerticalFlip, RandomTranspose, and MedianFilter functions. Next we’ll want to measure the performance of some of the image augmentation functions that were not as good as we expected. After that, we’ll use other methods or decorators from Numba or even implement high performance functions by inserting CUDA Kernels inside our Python code.
There is a possibility to parallelize even more the transformation using a Dask Cluster Multiple GPU and Multi-Node.
Notes
These results were for the configuration mentioned in the Experiment Setup section. It is possible if you try to re-run the benchmarks on your own on another setup you are going to get different results.
