yu4u / conv-benchmark

Evaluating efficiency of several types of convolutions

Benchmarking different convolutions

Evaluating efficiency of several types of convolutions.

Keras implementation

PyTorch implementation

Results

Summary

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5	conv3x3dilated
Keras CPU	6.736	14.133	14.043	7.184	43.700	118.898	49.442
Keras GPU	1.135	1.525	1.440	1.556	1.571	2.848	2.008
PyTorch CPU	6.956	17.209	16.916	16.480	50.636	133.781	111.480
PyTorch GPU	0.102	0.180	0.186	1.951	0.230	1.024	0.484

Keras CPU

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5	conv3x3dilated
processing time [sec]	6.736	14.133	14.043	7.184	43.700	118.898	49.442
vs 3x3	0.154	0.323	0.321	0.164	1.000	2.721	1.131
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778	1.000

Keras GPU

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5	conv3x3dilated
processing time [sec]	1.135	1.525	1.440	1.556	1.571	2.848	2.008
vs 3x3	0.722	0.971	0.916	0.990	1.000	1.812	1.278
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778	1.000

PyTorch CPU

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5	conv3x3dilated
processing time [sec]	6.956	17.209	16.916	16.480	50.636	133.781	111.480
vs 3x3	0.137	0.340	0.334	0.325	1.000	2.642	2.202
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778	1.000

PyTorch GPU

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5
processing time [sec]	0.102	0.173	0.169	3.786	0.230	1.108
vs 3x3	0.441	0.750	0.733	16.447	1.000	4.816
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778

cudnn.benchmark = True

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5
processing time [sec]	0.096	0.173	0.169	1.716	0.229	0.984
vs 3x3	0.418	0.753	0.735	7.485	1.000	4.291
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778

cudnn.benchmark = True, cudnn.fastest = True

	conv1x1	conv3x1	conv1x3	conv3x3sep	conv3x3	conv5x5	conv3x3dilated
processing time [sec]	0.102	0.180	0.186	1.951	0.230	1.024	0.484
vs 3x3	0.444	0.780	0.809	8.464	1.000	4.446	2.101
theoretical complexity	0.111	0.333	0.333	0.016	1.000	2.778	1.000

References

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in Proc. of CVPR, 2016.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," in arXiv:1704.04861, 2017.

F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in Proc. of CVPR, 2017.