Popular Computer Vision Model Benchmarks Batch Size = 8, Image = 3 x 224 x 224 (IF NOTHING SPECIFIED / CPU USED) Batch Size = 4, Image = 3 x 224 x 224
GPU USED --- Titan 1080Ti 12 GB Model Framework Forward Pass Backward Pass Total Time Inference VGG16 Pytorch 0.4.1 0.0245 s 0.0606 s 0.0852 s 0.0234 s Flux 0.6.8+ 0.0287 s 0.0760 s 0.1047 s 0.0288 s VGG16 BN Pytorch 0.4.1 0.0271 s 0.0672 s 0.0943 s 0.0273 s Flux 0.6.8+ 0.0333 s 0.0818 s 0.1151 s 0.0327 s VGG19 Pytorch 0.4.1 0.0281 s 0.0741 s 0.1021 s 0.0280 s Flux 0.6.8+ 0.0355 s 0.0923 s 0.1278 s 0.0356 s VGG19 BN Pytorch 0.4.1 0.0321 s 0.0812 s 0.1134 s 0.0325 s Flux 0.6.8+ 0.0377 s 0.0965 s 0.1342 s 0.0371 s Resnet18 Pytorch 0.4.1 0.0064 s 0.0125 s 0.0190 s 0.0050 s Flux 0.6.8+ 0.0079 s 0.0218 s 0.0297 s 0.0079 s Resnet34 Pytorch 0.4.1 0.0092 s 0.0216 s 0.0307 s 0.0092 s Flux 0.6.8+ 0.0137 s 0.0313 s 0.0450 s 0.0151 s Resnet50 Pytorch 0.4.1 0.0155 s 0.0351 s 0.0506 s 0.0152 s Flux 0.6.8+ 0.0205 s 0.1795 s 0.2000 s - Resnet101 Pytorch 0.4.1 0.0297 s 0.0379 s 0.0676 s 0.0298 s Flux 0.6.8+ 0.0215 s 0.0616 s 0.0831 s 0.0208 s Resnet152 Pytorch 0.4.1 0.0431 s 0.05337 s 0.0965 s 0.0429 s Flux 0.6.8+ 0.0308 s 0.0807 s 0.1115 s 0.0298 s
CPU USED --- Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz Model Framework Forward Pass Backward Pass Total Time Inference VGG16 Pytorch 0.4.1 6.6024 s 9.4336 s 16.036 s 6.4216 s Flux 0.6.8+ 10.458 s 10.245 s 20.703 s 10.111 s VGG16 BN Pytorch 0.4.1 7.0793 s 9.0536 s 16.132 s 6.7909 s Flux 0.6.8+ 29.633 s 18.649 s 49.282 s 24.047 s VGG19 Pytorch 0.4.1 8.3075 s 10.899 s 19.207 s 8.0593 s Flux 0.6.8+ 12.226 s 12.457 s 24.683 s 12.029 s VGG19 BN Pytorch 0.4.1 8.7794 s 12.739 s 21.519 s 8.4044 s Flux 0.6.8+ 28.518 s 21.464 s 49.982 s 22.649 s
Individual Layer Benchmarks Conv3x3/1 = Conv2d, 3x3 Kernel, 1x1 Padding, 1x1 Stride Conv5x5/1 = Conv2d, 5x5 Kernel, 2x2 Padding, 1x1 Stride Conv3x3/2 = Conv2d, 3x3 Kernel, 1x1 Padding, 2x2 Stride Conv5x5/2 = Conv2d, 5x5 Kernel, 2x2 Padding, 2x2 Stride Dense = 1024 => 512 BatchNorm = BatchNorm2d GPU USED --- Titan 1080Ti 12 GB Layer Framework Forward Pass Backward Pass Total Time Conv3x3/1 Pytorch 0.4.1 0.2312 ms 0.5359 ms 0.7736 ms Flux 0.6.8+ 0.1984 ms 0.7640 ms 0.9624 ms Conv5x5/1 Pytorch 0.4.1 0.2667 ms 0.5345 ms 0.8299 ms Flux 0.6.8+ 0.2065 ms 0.8075 ms 1.014 ms Conv3x3/2 Pytorch 0.4.1 0.1170 ms 0.2203 ms 0.3376 ms Flux 0.6.8+ 0.0927 ms 0.5988 ms 0.6915 ms Conv5x5/2 Pytorch 0.4.1 0.1233 ms 0.2162 ms 0.3407 ms Flux 0.6.8+ 0.0941 ms 0.6515 ms 0.7456 ms Dense Pytorch 0.4.1 0.0887 ms 0.1523 ms 0.2411 ms Flux 0.6.8+ 0.0432 ms 0.2044 ms 0.2476 ms BatchNorm Pytorch 0.4.1 0.1096 ms 0.1999 ms 0.3095 ms Flux 0.6.8+ 0.2211 ms 0.2849 ms 0.5060 ms
To reproduce the benchmarks checkout Flux 0.6.8+avik-pal/cudnn_batchnorm and CuArraysmaster .
Since the Batchnorm GPU is broken for Flux 0.6.8+ master so we cannot perform the benchmarks using that.