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Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search, CVPR 2019


Achieving good speed and accuracy trade-off on target platform is very important in deploying deep neural networks. Most existing automatic architecture search approaches only pursue high performance but ignores such an important factor. In this work, we propose an algorithm "Partial Order Pruning" to prune architecture search space with partial order assumption, quickly lift the boundary of speed/accuracy trade-off on target platform, and automatically search the architecture with the best speed and accuracy trade-off. Our algorithm explicitly take profile information about the inference speed on target platform into consideration. With the proposed algorithm, we present several "Dongfeng(东风)" networks that provide high accuracy and fast inference speed on various application GPU platforms. By further searching decoder architecture, our DF-Seg real-time segmentation models yields state-of-the-art speed/accuracy trade-off on both embedded device and high-end GPU.


1.We conduct backbone architecture searching experiments on TX2:

模型(Model) ImageNet Val. Top-1 Acc. Lat.(ms)/224x224/batch_size=1 GPU Mem./224x224/batch_size=50 GPU Mem./1024x1024/batch_size=1 FLOPS
东风一(DF1) 69.78% 2.5 1863 1042 746M
东风二(DF2) 73.92% 5.0 2867 1567 1.77G
东风二甲(DF2A) 76.00% 6.5 4844 2356 1.97G
ResNet-18 69.0% 4.4 2784 1397 1.8G
ResNet-50 75.3% 10.6 9914 4488 3.8G
ResNet-101 76.4% - 14900 6813 7.6G

Comparing to ResNet18/50/101, our Dongfeng networks achieve similar accuracy with much lower latency on target platform. We have evaluated the GPU memory consumptions of inferencing with different backbone networks, in BVLC_CAFFE. Our Dongfeng networks consume much less GPU memory. Specifically, our DF2A has a similar accuracy with ResNet-50/101, but consumes 2x~3x less GPU memory. Faster inference and Less GPU memory consumption are of great importance in deploying, and also enables researcher to implement more fancy, sophisticated algorithm.

2.With our Dongfeng backbone network (searched on TX2), we conduct decoder architecture search experiments on both TX2 and 1080Ti:

Model Cityscapes mIoU (Val/Test) FPS(TX2/TensorRT-3.0.4) FPS(1080Ti/TensorRT-3.0.4) FPS(1080Ti/TensorRT-3.0.4) FPS(Titan X/Caffe)
Resolution 1024x2048 640x384 1024x2048 1024x1024 1024x2048
DFlite-Seg-d8 71.7/- - 157.4 263.4 45.7
DF1-Seg-d8 72.4/71.4 92.7 136.9 232.6 40.2
DFlite-Seg 73.4/- - 118.4 202.5 33.8
DF1-Seg 74.1/73.0 71.4 106.1 182.1 30.7
DF2-Seg1 75.9/74.8 44.9 67.2 - 20.5
DF2-Seg2 76.9/75.3 39.9 56.3 - 17.7

3.Dongfeng models are designed for GPU platforms. We further conduct backbone and decoder architecture searching experiments on Snapdragon 845 CPU platform:

Model Cityscapes mIoU (Val/Test) FPS(Snapdragon 845)
Resolution 1024x2048 640x384
PL1A-Seg 68.7/69.1 52.0


欢迎使用“东风”系列模型,万事俱备,只欠东风! Everything is ready, all you need is Dongfeng!

1.Please set weight_decay to 0.0001 during finetuning, otherwise performance will be negatively affected.

2.I would recommond using DF2 rather than DF2A in dense prediction. Although its accracy on ImageNet Val. is lower, DF2 provides better segmentation accuracy in our preliminary experiments. I attribute this to the larger receptive field of DF2.












If Dongfeng networks are useful for your research, please consider citing:

author = {Xin Li,
	Yiming Zhou,
	Zheng Pan,
	Jiashi Feng},
title = {Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}


Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search






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