“Differentiable programming for image processing and deep learning in halide” by Li, Gharbi, Adams, Durand and Ragan-Kelley

  • ©Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, and Jonathan Ragan-Kelley



Entry Number: 139


    Differentiable programming for image processing and deep learning in halide

Session/Category Title: Pipelines and Languages for the GPU




    Gradient-based optimization has enabled dramatic advances in computational imaging through techniques like deep learning and nonlinear optimization. These methods require gradients not just of simple mathematical functions, but of general programs which encode complex transformations of images and graphical data. Unfortunately, practitioners have traditionally been limited to either hand-deriving gradients of complex computations, or composing programs from a limited set of coarse-grained operators in deep learning frameworks. At the same time, writing programs with the level of performance needed for imaging and deep learning is prohibitively difficult for most programmers.We extend the image processing language Halide with general reverse-mode automatic differentiation (AD), and the ability to automatically optimize the implementation of gradient computations. This enables automatic computation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. A key challenge is to structure the gradient code to retain parallelism. We define a simple algorithm to automatically schedule these pipelines, and show how Halide’s existing scheduling primitives can express and extend the key AD optimization of “checkpointing.”Using this new tool, we show how to easily define new neural network layers which automatically compile to high-performance GPU implementations, and how to solve nonlinear inverse problems from computational imaging. Finally, we show how differentiable programming enables dramatically improving the quality of even traditional, feed-forward image processing algorithms, blurring the distinction between classical and deep methods.


    1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.Google Scholar
    2. Jonathan T Barron and Ben Poole. 2016. The fast bilateral solver. In European Conference on Computer Vision. Springer, 617–632.Google ScholarCross Ref
    3. James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. 2010. Theano: a CPU and GPU Math Expression Compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy).Google ScholarCross Ref
    4. Christian Bischof, Alan Carle, George Corliss, and Andreas Griewank. 1992. ADIFOR: Automatic Differentiation in a Source Translator Environment. In Papers from the International Symposium on Symbolic and Algebraic Computation (ISSAC ’92). 294–302. Google ScholarDigital Library
    5. Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. 2011. Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs. In The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarDigital Library
    6. Jiawen Chen, Sylvain Paris, and Frédo Durand. 2007. Real-time Edge-aware Image Processing with the Bilateral Grid. ACM Trans. Graph. (Proceedings of SIGGRAPH) 26, 3, Article 103 (July 2007). Google ScholarDigital Library
    7. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. 2014. cuDNN: Efficient Primitives for Deep Learning. arXiv preprint arXiv:1410.0759 (2014).Google Scholar
    8. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.Google Scholar
    9. Zachary Devito, Michael Mara, Michael Zollhöfer, Gilbert Bernstein, Jonathan Ragan-Kelley, Christian Theobalt, Pat Hanrahan, Matthew Fisher, and Matthias Niessner. 2017. Opt: A Domain Specific Language for Non-Linear Least Squares Optimization in Graphics and Imaging. ACM Trans. Graph. 36, 5, Article 171 (Oct. 2017), 27 pages. Google ScholarDigital Library
    10. Martin A. Fischler and Robert C. Bolles. 1981. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 24, 6 (June 1981), 381–395. Google ScholarDigital Library
    11. Horacio E. Fortunato and Manuel M. Oliveira. 2014. Fast high-quality non-blind deconvolution using sparse adaptive priors. The Visual Computer 30, 6–8 (2014), 661–671. Google ScholarDigital Library
    12. Leon A Gatys, Alexanders Ecker, and Matthias Bethge. 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2414–2423.Google ScholarCross Ref
    13. Michaël Gharbi, Gaurav Chaurasia, Sylvain Paris, and Frédo Durand. 2016. Deep Joint Demosaicking and Denoising. ACM Trans. Graph. (Proceedings of SIGGRAPH Asia) 35, 6, Article 191 (Nov. 2016), 12 pages. Google ScholarDigital Library
    14. Michaël Gharbi, Jiawen Chen, Jonathan T Barron, Samuel W Hasinoff, and Frédo Durand. 2017. Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. (Proceedings of SIGGRAPH) 36, 4 (2017), 118. Google ScholarDigital Library
    15. Mark Girolami and Ben Calderhead. 2011. Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73, 2 (2011), 123–214.Google ScholarCross Ref
    16. Andreas Griewank, David Juedes, and Jean Utke. 1996. Algorithm 755: ADOL-C: A Package for the Automatic Differentiation of Algorithms Written in C/C++. ACM Trans. Math. Softw. 22, 2 (June 1996), 131–167. Google ScholarDigital Library
    17. Andreas Griewank and Shawn Reese. 1991. On the Calculation of Jacobian Matrices by the Markowitz Rule. In Automatic Differentiation of Algorithms: Theory Implementation, and Application, Andreas Griewank and George F. Corliss (Eds.). 126–135.Google Scholar
    18. Andreas Griewank and Andrea Walther. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (second ed.). Society for Industrial and Applied Mathematics. Google ScholarDigital Library
    19. Brian Guenter. 2007. Efficient Symbolic Differentiation for Graphics Applications. ACM Trans. Graph. (Proceedings of SIGGRAPH) 26, 3 (July 2007). Google ScholarDigital Library
    20. Laurent Hascoet and Valérie Pascual. 2013. The Tapenade Automatic Differentiation Tool: Principles, Model, and Specification. ACM Trans. Math. Softw. 39, 3, Article 20 (May 2013), 43 pages. Google ScholarDigital Library
    21. Felix Heide, Steven Diamond, Matthias Niessner, Jonathan Ragan-Kelley, Wolfgang Heidrich, and Gordon Wetzstein. 2016. ProxImaL: Efficient Image Optimization Using Proximal Algorithms. ACM Trans. Graph. (Proceedings of SIGGRAPH) 35, 4, Article 84 (July 2016), 15 pages. Google ScholarDigital Library
    22. Felix Heide, Markus Steinberger, Yun-Ta Tsai, Mushfiqur Rouf, Dawid Pająk, Dikpal Reddy, Orazio Gallo, Jing Liu, Wolfgang Heidrich, Karen Egiazarian, Jan Kautz, and Kari Pulli. 2014. FlexISP: A Flexible Camera Image Processing Framework. ACM Trans. Graph. (Proceedings of SIGGRAPH) 33, 6, Article 231 (Nov. 2014), 13 pages. Google ScholarDigital Library
    23. Keigo Hirakawa and Thomas W Parks. 2005. Adaptive homogeneity-directed demosaicing algorithm. IEEE Trans. Image Processing 14, 3 (2005), 360–369. Google ScholarDigital Library
    24. Robin J. Hogan. 2014. Fast Reverse-Mode Automatic Differentiation Using Expression Templates in C++. ACM Trans. Math. Softw. 40, 4, Article 26 (July 2014), 16 pages. Google ScholarDigital Library
    25. Berthold KP Horn and Brian G Schunck. 1981. Determining optical flow. Artificial intelligence 17, 1–3 (1981), 185–203. Google ScholarDigital Library
    26. Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2016. Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Transactions on Graphics (TOG) 35, 4 (2016), 110. Google ScholarDigital Library
    27. E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox. 2017. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
    28. Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 2017–2025. Google ScholarDigital Library
    29. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22Nd ACM International Conference on Multimedia (MM ’14). 675–678. Google ScholarDigital Library
    30. Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
    31. Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiri Matas. 2017. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. arXiv preprint arXiv:1711.07064 (2017).Google Scholar
    32. Leslie Lamport. 1975. The Hyperplane Method for an Array Computer. In Proceedings of the Sagamore Computer Conference on Parallel Processing. 113–131. Google ScholarDigital Library
    33. Gunther Lange. 1957. Gauss type photographic objective containing two outer collective and two inner dispersive members. U.S. Patent 2,799,207 A.Google Scholar
    34. Seppo Linnainmaa. 1970. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master’s thesis. Univ. Helsinki.Google Scholar
    35. David G. Lowe. 2004. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 2 (Nov. 2004), 91–110. Google ScholarDigital Library
    36. Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala. 2017. Deep Photo Style Transfer. arXiv preprint arXiv:1703.07511 (2017).Google Scholar
    37. Ravi Teja Mullapudi, Andrew Adams, Dillon Sharlet, Jonathan Ragan-Kelley, and Kayvon Fatahalian. 2016. Automatically Scheduling Halide Image Processing Pipelines. ACM Trans. Graph. (Proceedings of SIGGRAPH) 35, 4, Article 83 (July 2016), 11 pages. Google ScholarDigital Library
    38. Ravi Teja Mullapudi, Vinay Vasista, and Uday Bondhugula. 2015. PolyMage: Automatic Optimization for Image Processing Pipelines. SIGARCH Comput. Archit. News 43, 1 (March 2015), 429–443.Google ScholarDigital Library
    39. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).Google Scholar
    40. Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris, Marc Levoy, Saman Amarasinghe, and Frédo Durand. 2012. Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines. ACM Trans. Graph. (Proceedings of SIGGRAPH) 31, 4, Article 32 (July 2012), 12 pages. Google ScholarDigital Library
    41. Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe. 2013. Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines. SIGPLAN Not. 48, 6 (June 2013), 519–530. Google ScholarDigital Library
    42. S. Roth and M.J. Black. 2005. Fields of Experts: A framework for learning image priors. In IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2. 860–867. Google ScholarDigital Library
    43. Leonid I Rudin, Stanley Osher, and Emad Fatemi. 1992. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60, 1–4 (1992), 259–268. Google ScholarDigital Library
    44. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. Chapter Learning Internal Representations by Error Propagation, 318–362. Google ScholarDigital Library
    45. Patricia Suriana, Andrew Adams, and Shoaib Kamil. 2017. Parallel Associative Reductions in Halide. In Proceedings of the 2017 International Symposium on Code Generation and Optimization (CGO ’17). Google ScholarCross Ref
    46. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2017. Deep Image Prior. arXiv preprint arXiv:1711.10925 (2017).Google Scholar
    47. Yu. M. Volin and G. M. Ostrovskii. 1985. Automatic computation of derivatives with the use of the multilevel differentiating technique — I: Algorithmic basis. Computers and Mathematics with Applications 11 (1985), 1099–1114.Google ScholarCross Ref
    48. Paul J Werbos. 1982. Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization. Springer, 762–770.Google Scholar
    49. Alexander B Wiltschko, Bart van MerriÃńnboer, and Dan Moldovan. 2017. Tangent: automatic differentiation using source code transformation in Python.Google Scholar
    50. Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, and Jiaya Jia. 2015. Deep edge-aware filters. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 1669–1678. Google ScholarDigital Library
    51. Yuting Yang, Sam Prestwood, and Connelly Barnes. 2016. VizGen: Accelerating Visual Computing Prototypes in Dynamic Languages. ACM Trans. Graph. (Proceedings of SIGGRAPH Asia) 35, 6, Article 206 (Nov. 2016), 13 pages. Google ScholarDigital Library
    52. Dong Yu, Adam Eversole, Mike Seltzer, Kaisheng Yao, Oleksii Kuchaiev, Yu Zhang, Frank Seide, Zhiheng Huang, Brian Guenter, Huaming Wang, Jasha Droppo, Geoffrey Zweig, Chris Rossbach, Jie Gao, Andreas Stolcke, Jon Currey, Malcolm Slaney, Guoguo Chen, Amit Agarwal, Chris Basoglu, Marko Padmilac, Alexey Kamenev, Vladimir Ivanov, Scott Cypher, Hari Parthasarathi, Bhaskar Mitra, Baolin Peng, and Xuedong Huang. 2014. An Introduction to Computational Networks and the Computational Network Toolkit. Technical Report.Google Scholar
    53. Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing (2017). Google ScholarDigital Library
    54. Richard Zhang, Phillip Isola, and Alexei A Efros. 2016. Colorful image colorization. In European Conference on Computer Vision. Springer, 649–666.Google ScholarCross Ref
    55. Barbara Zitova and Jan Flusser. 2003. Image registration methods: a survey. Image and vision computing 21, 11 (2003), 977–1000.Google Scholar

ACM Digital Library Publication: