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【14-C-7】コンピュータビジョンを支える深層学習技術の新潮流

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Developers Summit 2019【14-C-7】鮫島様の講演資料です。

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【14-C-7】コンピュータビジョンを支える深層学習技術の新潮流

  1. 1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Masaki Samejima Machine Learning Solutions Architect, Amazon Web Services Japan. 2019.2.14 Developers Summit 2019
  2. 2. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda • • • •
  3. 3. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  4. 4. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • Demographic Data Facial Landmarks Sentiment Expressed Image Quality General Attributes
  5. 5. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 2012 SuperVision[1] ILSVRC2012 [1] A. Krizhevsky, et al., Imagenet classification with deep convolutional neural networks, NIPS 2012. [2] R Girshick, et al., Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2014. [3] I.J. Goodfellow, et al., Generative Adversarial Nets, NIPS 2014. [4] V. Badrinarayanan, et al, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. PAMI 2017 2014 R-CNN[2] Pascal VOC GAN[3] SegNet[4] 2015
  6. 6. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://gluon-cv.mxnet.io/model_zoo/classification.html senet_154 resnet_v1d resnet_v1c resnet_v1b resnet_v1 densenet darknet VGG resnet_v2 mobilenet mobilenetv2 0.80 0.75 0.70 Accuracy 1000 2000 #sample/sec.3000 4000 • ImageNet 80% • V100 GPU
  7. 7. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://gluon-cv.mxnet.io/model_zoo/detection.html mAP 10 100 #sample/sec. 40 35 30 yolo3 faster_rcnn ssd • (IoU ) mAP 30-40% •
  8. 8. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://gluon-cv.mxnet.io/model_zoo/segmentation.html 0 10 20 30 40 50 60 70 80 90 100 fcn_resnet101 psp_resnet101 deeplab_resnet101 fcn_resnet101 psp_resnet101 deeplab_resnet101 deeplab_resnet152 COCO VOC IoU
  9. 9. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 3 [1] [1] B. Tekin, et al., Real-Time Seamless Single Shot 6D Object Pose Prediction, CVPR 2018. [2] R. Girdhar, et al., Detect-and-Track: Efficient Pose Estimation in Videos, CVPR 2018. [3] L. Chen, et al., MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features, CVPR 2018. [2] [3]
  10. 10. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. GANNoise Text-to-image [3] (and Image-to-text)[1] [2] [1] P. Isola, et al., Image-to-Image Translation with Conditional Adversarial Nets, CVPR 2017. [2] C. Ledig, et al., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, CVPR 2017. [3] S. Reed, et al., Generative Adversarial Text to Image Synthesis, ICML 2016.
  11. 11. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Saliency ( ) [1] [1] N. Liu, et al., PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection, CVPR 2018. [2] Z. Li, et al., MegaDepth: Learning Single-View Depth Prediction from Internet Photos, CVPR 2018. [2]
  12. 12. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 0 2 4 6 8 10 12 14 16 18 20 1 2 3 4 5 6 7 8 9 1011121314151617181920 ID [1] O. Vinyals, et al., Matching Networks for One Shot Learning, arXiv:1606.04080 • • [1]
  13. 13. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Deep Learning • X. Yuan, et al., Adversarial Examples: Attacks and Defenses for Deep Learning, IEEE Trans Neural Netw Learn Syst. 2019.
  14. 14. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  15. 15. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • • • ONNX AutoML Define-by-run
  16. 16. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • TensorFlow models TF slim GluonCV ChainerCV PyTorchCV
  17. 17. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ResNet (Gluon vs MXNet) num_unit = len(units) assert(num_unit == num_stages) data = mx.sym.Variable(name='data') if dtype == 'float32': data = mx.sym.identity(data=data, name='id') else: if dtype == 'float16': data = mx.sym.Cast(data=data, dtype=np.float16) data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') (nchannel, height, width) = image_shape if height <= 32: # such as cifar10 body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1), no_bias=True, name="conv0", workspace=workspace) else: # often expected to be 224 such as imagenet body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') for i in range(num_stages): body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, workspace=workspace, memonger=memonger) for j in range(units[i]-1): body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2), bottle_neck=bottle_neck, workspace=workspace, memonger=memonger) bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1') MXNet from mxnet.gluon.model_zoo import vision resnet18 = vision.resnet18_v1() Gluon
  18. 18. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ONNX (Open Neural Network Exchange) MXNet Caffe2 PyTorch TF CNTKCoreML Tensor RT NGraph SNPE • ONNX ONNX •
  19. 19. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ONNX Protocol Buffers • • • API Protocol Buffers Graph Operator Tensor, … Operator Definitions ONNX Python API
  20. 20. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Define-and-run Define-by-run • Define-and-run • • TensorFlow, MXNet • Define-by-run • • Chainer PyTorch, TensorFlow, MXNet
  21. 21. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Define-and-run Define-by-run Define-and-run Define-by-run def our_function(A, B): C = A + B return C A = Load_Data_A() B = Load_Data_B() result = our_function(A, B) A = placeholder() B = placeholder() C = A + B our_function = compile(inputs=[A, B], outputs =[C]) A = Load_Data_A() B = Load_Data_B() result = our_function(A, B) https://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html Define Run Define, Run
  22. 22. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Define-by-run Define-and-run Define-by-run def our_function(A, B): C = A + B return C A = Load_Data_A() B = Load_Data_B() result = our_function(A, B) A = placeholder() B = placeholder() C = A + B our_function = compile(inputs=[A, B], outputs =[C]) A = Load_Data_A() B = Load_Data_B() result = our_function(A, B)
  23. 23. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AutoML • • , etc. D. Bayor, et al., TFX: A TensorFlow-Based Production-Scale Machine Learning Platform, KDD 2017.
  24. 24. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AutoML • AutoML • ICML 2014 AutoML * • • • Meta-Learning, Learning to learn * https://sites.google.com/site/automlwsicml14/
  25. 25. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AutoML
  26. 26. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AutoML Amazon Forecast User CSV file 1. S3 2. Forecast 3. Forecast 4.
  27. 27. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  28. 28. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • Model Server Interpretable ML
  29. 29. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model Server • • Model Server • • REST/RPC Model Server Mobile client Deploy REST/RPC
  30. 30. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TensorFlow Serving [1] C. Olston, et al., TensorFlow-Serving: Flexible, High-Performance ML Serving, NIPS 2017. • Controller, Synchronizer Serving job • Router Serving job
  31. 31. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. MXNet Model Server https://aws.amazon.com/jp/blogs/news/model-server-for-apache-mxnet-v1-0-released/ • REST API • MMS 1.0 1,000 MMS 1.0 MMS 0.4
  32. 32. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • • • AWS, SageMaker Neo • Nvidia, TensorRT Raspberry Pi ResNet18 Mobilenet 11.5x 2.2x
  33. 33. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. SageMaker Neo / TVM • Operator Fusion • Data Layout Transformation 4x4 4x4 • Tensor Expression and Schedule Space • Nested Parallelism with Cooperation • etc… T. Chen, et al., TVM: An Automated End-to-End Optimizing Compiler for Deep Learning, OSDI 2018.
  34. 34. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TensorRT • Layer & Tensor Fusion 1 • FP16 and INT8 Precision Calibration FP32 FP16 INT8 • Kernel Auto-Tuning • Dynamic Tensor Memory • Multi Stream Execution https://devblogs.nvidia.com/tensorrt-3-faster-tensorflow-inference/
  35. 35. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Interpretable ML: : SVM GBT C. Molnar, Interpretable Machine Learning, https://christophm.github.io/interpretable-ml-book/ >900< 900 < 2000 km2 > 2000 km2
  36. 36. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Interpretable ML for computer vision • • M.T. Ribeiro, et al., Anchors: High-Precision Model-Agnostic Explanations, AAAI 2018.
  37. 37. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  38. 38. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • • • 1 1 • • AWS Inferentia • Intel Nervana
  39. 39. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine Learning on FPGA • FPGA • AWS F1 instance Amazon Machine Image • Loop tiling [1] [1] C. Zhang, et al., Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks, FPGA 2015.
  40. 40. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • GPU
  41. 41. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  42. 42. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  43. 43. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • • AutoML AI •
  44. 44. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://amzn.to/aws_dev

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