├── .gitignore ├── LICENSE ├── README.md ├── analyze_cnn_benchmark_results.py ├── cnn_benchmark.lua ├── convert_model.lua ├── outputs ├── 1080Ti │ ├── 176422.json │ ├── 430986.json │ ├── 470497.json │ ├── 658244.json │ ├── 881357.json │ └── 976860.json ├── 1080Ti_cudnn51 │ ├── 101362.json │ ├── 202606.json │ ├── 267406.json │ ├── 311738.json │ ├── 453685.json │ ├── 474549.json │ ├── 644777.json │ ├── 677177.json │ └── 957923.json ├── 1080_cudnn50 │ ├── 11393.json │ ├── 234830.json │ ├── 243870.json │ ├── 327519.json │ ├── 422317.json │ ├── 443109.json │ ├── 46471.json │ ├── 470467.json │ ├── 473403.json │ ├── 511688.json │ ├── 533635.json │ ├── 561535.json │ ├── 592844.json │ ├── 593930.json │ ├── 595867.json │ ├── 601028.json │ ├── 638525.json │ ├── 640169.json │ ├── 670759.json │ ├── 745969.json │ ├── 882582.json │ ├── 898191.json │ ├── 899060.json │ ├── 926972.json │ ├── 934124.json │ ├── 934144.json │ ├── 94136.json │ ├── 957290.json │ └── 988163.json ├── 1080_cudnn51 │ ├── 179512.json │ ├── 325985.json │ ├── 432032.json │ ├── 510304.json │ ├── 598494.json │ ├── 624722.json │ ├── 784576.json │ └── 805366.json ├── cpu │ ├── 189638.json │ ├── 308550.json │ ├── 378556.json │ ├── 541076.json │ ├── 629699.json │ ├── 655060.json │ ├── 704333.json │ ├── 731542.json │ └── 864633.json ├── titan_xm_cudnn4 │ ├── 153412.json │ ├── 224180.json │ ├── 373687.json │ ├── 382014.json │ ├── 44953.json │ ├── 629022.json │ ├── 724419.json │ ├── 736736.json │ ├── 955986.json │ └── 959133.json ├── titan_xm_cudnn50 │ ├── 122538.json │ ├── 149024.json │ ├── 185837.json │ ├── 593076.json │ ├── 717778.json │ ├── 718808.json │ ├── 75762.json │ ├── 883354.json │ ├── 885142.json │ └── 907527.json ├── titan_xm_cudnn51 │ ├── 107545.json │ ├── 270716.json │ ├── 272114.json │ ├── 296977.json │ ├── 311261.json │ ├── 329084.json │ ├── 34966.json │ ├── 365430.json │ ├── 402717.json │ ├── 461634.json │ ├── 539546.json │ ├── 545185.json │ ├── 66175.json │ ├── 785346.json │ ├── 794544.json │ ├── 803718.json │ ├── 816588.json │ ├── 943134.json │ └── 988380.json ├── titan_xp_cudnn50 │ ├── 232057.json │ ├── 264135.json │ ├── 285570.json │ ├── 481954.json │ ├── 485149.json │ ├── 816352.json │ ├── 830288.json │ ├── 838864.json │ ├── 959228.json │ └── 98112.json └── titan_xp_cudnn51 │ ├── 114516.json │ ├── 194600.json │ ├── 434880.json │ ├── 440838.json │ ├── 50941.json │ ├── 586889.json │ ├── 600209.json │ ├── 636953.json │ ├── 70037.json │ ├── 750624.json │ ├── 750844.json │ ├── 813670.json │ ├── 846628.json │ ├── 865701.json │ ├── 879577.json │ ├── 880068.json │ ├── 939670.json │ ├── 962635.json │ └── 971506.json ├── run_cnn_benchmarks.py └── utils.lua /.gitignore: -------------------------------------------------------------------------------- 1 | models 2 | *.swp 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 Justin Johnson 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # cnn-benchmarks 2 | 3 | Benchmarks for popular convolutional neural network models on CPU and different GPUs, with and without cuDNN. 4 | 5 | Some general conclusions from this benchmarking: 6 | 7 | - **Pascal Titan X > GTX 1080**: Across all models, the Pascal Titan X is **1.31x to 1.43x** faster than the GTX 1080 and **1.47x to 1.60x** faster than the Maxwell Titan X. This is without a doubt the best card you can get for deep learning right now. 8 | - **GTX 1080 > Maxwell Titan X**: Across all models, the GTX 1080 is **1.10x to 1.15x** faster than the Maxwell Titan X. 9 | - **ResNet > VGG**: ResNet-50 is faster than VGG-16 and more accurate than VGG-19 (7.02 vs 9.0); ResNet-101 is about the same speed as VGG-19 but much more accurate than VGG-16 (6.21 vs 9.0). 10 | - **Always use cuDNN**: On the Pascal Titan X, cuDNN is **2.2x to 3.0x** faster than nn; on the GTX 1080, cuDNN is **2.0x to 2.8x** faster than nn; on the Maxwell Titan X, cuDNN is **2.2x to 3.0x** faster than nn. 11 | - **GPUs are critical**: The Pascal Titan X with cuDNN is **49x to 74x** faster than dual Xeon E5-2630 v3 CPUs. 12 | 13 | All benchmarks were run in Torch. 14 | The GTX 1080 and Maxwell Titan X benchmarks were run on a machine with dual 15 | Intel Xeon E5-2630 v3 processors (8 cores each plus hyperthreading means 32 16 | threads) and 64GB RAM running Ubuntu 14.04 with the CUDA 8.0 Release Candidate. 17 | The Pascal Titan X benchmarks were run on a machine with an Intel Core i5-6500 18 | CPU and 16GB RAM running Ubuntu 16.04 with the CUDA 8.0 Release Candidate. 19 | The GTX 1080 Ti benchmarks were run on a machine with an Intel Core i7-7700 CPU 20 | and 64GB RAM running Ubuntu 16.04 with the CUDA 8.0 release. 21 | 22 | We benchmark all models with a minibatch size of 16 and an image size of 224 x 224; 23 | this allows direct comparisons between models, and allows all but the ResNet-200 model 24 | to run on the GTX 1080, which has only 8GB of memory. 25 | 26 | The following models are benchmarked: 27 | 28 | |Network|Layers|Top-1 error|Top-5 error|Speed (ms)|Citation| 29 | |---|---:|---:|---:|---:|---| 30 | |[AlexNet](#alexnet)|8|42.90|19.80|14.56|[[1]](#alexnet-paper)| 31 | |[Inception-V1](#inception-v1)|22|-|10.07|39.14|[[2]](#inception-v1-paper)| 32 | |[VGG-16](#vgg-16)|16|27.00|8.80|128.62|[[3]](#vgg-paper)| 33 | |[VGG-19](#vgg-19)|19|27.30|9.00|147.32|[[3]](#vgg-paper)| 34 | |[ResNet-18](#resnet-18)|18|30.43|10.76|31.54|[[4]](#resnet-cvpr)| 35 | |[ResNet-34](#resnet-34)|34|26.73|8.74|51.59|[[4]](#resnet-cvpr)| 36 | |[ResNet-50](#resnet-50)|50|24.01|7.02|103.58|[[4]](#resnet-cvpr)| 37 | |[ResNet-101](#resnet-101)|101|22.44|6.21|156.44|[[4]](#resnet-cvpr)| 38 | |[ResNet-152](#resnet-152)|152|22.16|6.16|217.91|[[4]](#resnet-cvpr)| 39 | |[ResNet-200](#resnet-200)|200|21.66|5.79|296.51|[[5]](#resnet-eccv)| 40 | 41 | Top-1 and Top-5 error are single-crop error rates on the ILSVRC 2012 Validation set, 42 | except for VGG-16 and VGG-19 which instead use dense prediction on a 256x256 image. 43 | This gives the VGG models a slight advantage, but I was unable to find single-crop error 44 | rates for these models. All models perform better when using more than one crop at test-time. 45 | 46 | Speed is the total time for a forward and backward pass on a Pascal Titan X with cuDNN 5.1. 47 | 48 | You can download the model files used for benchmarking [here](https://drive.google.com/open?id=0Byvt-AfX75o1STUxZTFpMU10djA) (2.1 GB); 49 | these were converted from Caffe or Torch checkpoints using the `convert_model.lua` script. 50 | 51 | We use the following GPUs for benchmarking: 52 | 53 | |GPU|Memory|Architecture|CUDA Cores|FP32 TFLOPS|Release Date| 54 | |---|---|---|---:|---:|---| 55 | |[Pascal Titan X](http://www.geforce.com/hardware/10series/titan-x-pascal)|12GB GDDRX5|Pascal|3584|10.16|August 2016| 56 | |[GTX 1080](http://www.geforce.com/hardware/10series/geforce-gtx-1080)|8GB GDDRX5|Pascal|2560|8.87|May 2016| 57 | |[GTX 1080 Ti](https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti/)|11GB GDDRX5|Pascal|3584|10.6|March 2017| 58 | |[Maxwell Titan X](http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-titan-x)|12GB GDDR5|Maxwell|3072|6.14|March 2015| 59 | 60 | 61 | ## AlexNet 62 | (input 16 x 3 x 224 x 224) 63 | 64 | We use the [BVLC AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) from Caffe. 65 | 66 | AlexNet uses grouped convolutions; this was a strategy to allow model parallelism over two GTX 580 67 | GPUs, which had only 3GB of memory each. Grouped convolutions are no longer commonly used, and are 68 | not even implemented by the [torch/nn](https://github.com/torch/nn) backend; therefore we can only 69 | benchmark AlexNet using cuDNN. 70 | 71 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 72 | |---|---|---:|---:|---:| 73 | |GTX 1080 Ti|5.1.10|4.31|9.58|13.89| 74 | |Pascal Titan X|5.1.05|5.04|9.52|14.56| 75 | |Pascal Titan X|5.0.05|5.32|10.90|16.23| 76 | |GTX 1080|5.1.05|7.00|13.74|20.74| 77 | |Maxwell Titan X|5.1.05|7.09|14.76|21.85| 78 | |GTX 1080|5.0.05|7.35|15.73|23.08| 79 | |Maxwell Titan X|5.0.05|7.55|17.78|25.33| 80 | |Maxwell Titan X|4.0.07|8.03|17.91|25.94| 81 | 82 | 83 | ## Inception-V1 84 | (input 16 x 3 x 224 x 224) 85 | 86 | We use the Torch implementation of Inception-V1 from 87 | [soumith/inception.torch](https://github.com/soumith/inception.torch). 88 | 89 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 90 | |---|---|---:|---:|---:| 91 | |GTX 1080 Ti|5.1.10|11.50|25.37|36.87| 92 | |Pascal Titan X|5.1.05|12.06|27.08|39.14| 93 | |Pascal Titan X|5.0.05|11.94|28.39|40.33| 94 | |GTX 1080|5.0.05|16.08|40.08|56.16| 95 | |Maxwell Titan X|5.1.05|19.29|42.69|61.98| 96 | |Maxwell Titan X|5.0.05|19.27|46.41|65.68| 97 | |Maxwell Titan X|4.0.07|21.04|49.41|70.45| 98 | |GTX 1080 Ti|None|56.34|85.30|141.64| 99 | |Pascal Titan X|None|57.46|85.90|143.36| 100 | |GTX 1080|None|63.03|102.31|165.34| 101 | |Maxwell Titan X|None|91.31|140.81|232.12| 102 | 103 | 104 | ## VGG-16 105 | (input 16 x 3 x 224 x 224) 106 | 107 | This is Model D in [[3]](#vgg-paper) used in the ILSVRC-2014 competition, 108 | [available here](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md). 109 | 110 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 111 | |---|---|---:|---:|---:| 112 | |GTX 1080 Ti|5.1.10|41.23|86.91|128.14| 113 | |Pascal Titan X|5.1.05|41.59|87.03|128.62| 114 | |Pascal Titan X|5.0.05|46.16|111.23|157.39| 115 | |GTX 1080|5.1.05|59.37|123.42|182.79| 116 | |Maxwell Titan X|5.1.05|62.30|130.48|192.78| 117 | |GTX 1080|5.0.05|67.27|166.17|233.43| 118 | |Maxwell Titan X|5.0.05|75.80|186.47|262.27| 119 | |Maxwell Titan X|4.0.07|111.99|226.69|338.69| 120 | |Pascal Titan X|None|98.15|260.38|358.53| 121 | |GTX 1080|None|143.73|379.09|522.82| 122 | |Maxwell Titan X|None|172.61|415.87|588.47| 123 | |CPU: Dual Xeon E5-2630 v3|None|3101.76|5393.72|8495.48| 124 | 125 | 126 | 127 | ## VGG-19 128 | (input 16 x 3 x 224 x 224) 129 | 130 | This is Model E in [[3]](#vgg-paper) used in the ILSVRC-2014 competition, 131 | [available here](https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md). 132 | 133 | 134 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 135 | |---|---|---:|---:|---:| 136 | |Pascal Titan X|5.1.05|48.09|99.23|147.32| 137 | |GTX 1080 Ti|5.1.10|48.15|100.04|148.19| 138 | |Pascal Titan X|5.0.05|55.75|134.98|190.73| 139 | |GTX 1080|5.1.05|68.95|141.44|210.39| 140 | |Maxwell Titan X|5.1.05|73.66|151.48|225.14| 141 | |GTX 1080|5.0.05|79.79|202.02|281.81| 142 | |Maxwell Titan X|5.0.05|93.47|229.34|322.81| 143 | |Maxwell Titan X|4.0.07|139.01|279.21|418.22| 144 | |Pascal Titan X|None|121.69|318.39|440.08| 145 | |GTX 1080|None|176.36|453.22|629.57| 146 | |Maxwell Titan X|None|215.92|491.21|707.13| 147 | |CPU: Dual Xeon E5-2630 v3|None|3609.78|6239.45|9849.23| 148 | 149 | 150 | 151 | ## ResNet-18 152 | (input 16 x 3 x 224 x 224) 153 | 154 | This is the 18-layer model described in [[4]](#resnet-cvpr) and implemented in 155 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 156 | 157 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 158 | |---|---|---:|---:|---:| 159 | |Pascal Titan X|5.1.05|10.14|21.40|31.54| 160 | |GTX 1080 Ti|5.1.10|10.45|22.34|32.78| 161 | |Pascal Titan X|5.0.05|10.06|23.08|33.13| 162 | |GTX 1080|5.1.05|14.62|29.32|43.94| 163 | |GTX 1080|5.0.05|14.84|32.68|47.52| 164 | |Maxwell Titan X|5.1.05|16.87|34.55|51.42| 165 | |Maxwell Titan X|5.0.05|17.08|37.79|54.87| 166 | |Maxwell Titan X|4.0.07|21.54|42.26|63.80| 167 | |Pascal Titan X|None|34.76|61.64|96.40| 168 | |GTX 1080 Ti|None|50.04|65.99|116.03| 169 | |GTX 1080|None|42.94|79.17|122.10| 170 | |Maxwell Titan X|None|55.82|96.01|151.82| 171 | |CPU: Dual Xeon E5-2630 v3|None|847.46|1348.33|2195.78| 172 | 173 | 174 | 175 | ## ResNet-34 176 | (input 16 x 3 x 224 x 224) 177 | 178 | This is the 34-layer model described in [[4]](#resnet-cvpr) and implemented in 179 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 180 | 181 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 182 | |---|---|---:|---:|---:| 183 | |GTX 1080 Ti|5.1.10|16.71|34.60|51.31| 184 | |Pascal Titan X|5.1.05|17.01|34.58|51.59| 185 | |Pascal Titan X|5.0.05|16.91|38.67|55.58| 186 | |GTX 1080|5.1.05|24.50|47.59|72.09| 187 | |GTX 1080|5.0.05|24.76|55.00|79.76| 188 | |Maxwell Titan X|5.1.05|27.33|52.90|80.23| 189 | |Maxwell Titan X|5.0.05|28.79|63.19|91.98| 190 | |Maxwell Titan X|4.0.07|40.12|76.00|116.11| 191 | |Pascal Titan X|None|66.56|106.42|172.98| 192 | |GTX 1080 Ti|None|86.30|109.43|195.73| 193 | |GTX 1080|None|82.71|137.42|220.13| 194 | |Maxwell Titan X|None|108.95|166.19|275.13| 195 | |CPU: Dual Xeon E5-2630 v3|None|1530.01|2435.20|3965.21| 196 | 197 | 198 | ## ResNet-50 199 | (input 16 x 3 x 224 x 224) 200 | 201 | This is the 50-layer model described in [[4]](#resnet-cvpr) and implemented in 202 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 203 | 204 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 205 | |---|---|---:|---:|---:| 206 | |GTX 1080 Ti|5.1.10|34.14|67.06|101.21| 207 | |Pascal Titan X|5.1.05|35.03|68.54|103.58| 208 | |Pascal Titan X|5.0.05|35.03|70.76|105.78| 209 | |GTX 1080|5.1.05|50.64|99.18|149.82| 210 | |GTX 1080|5.0.05|50.76|103.35|154.11| 211 | |Maxwell Titan X|5.1.05|55.75|103.87|159.62| 212 | |Maxwell Titan X|5.0.05|56.30|109.75|166.05| 213 | |Maxwell Titan X|4.0.07|62.03|116.81|178.84| 214 | |Pascal Titan X|None|87.62|158.96|246.58| 215 | |GTX 1080 Ti|None|99.90|177.58|277.47| 216 | |GTX 1080|None|109.79|201.40|311.18| 217 | |Maxwell Titan X|None|137.14|247.65|384.79| 218 | |CPU: Dual Xeon E5-2630 v3|None|2477.61|4149.64|6627.25| 219 | 220 | 221 | 222 | ## ResNet-101 223 | (input 16 x 3 x 224 x 224) 224 | 225 | This is the 101-layer model described in [[4]](#resnet-cvpr) and implemented in 226 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 227 | 228 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 229 | |---|---|---:|---:|---:| 230 | |GTX 1080 Ti|5.1.10|52.18|102.08|154.26| 231 | |Pascal Titan X|5.1.05|53.38|103.06|156.44| 232 | |Pascal Titan X|5.0.05|53.28|108.20|161.48| 233 | |GTX 1080|5.1.05|77.59|148.21|225.80| 234 | |GTX 1080|5.0.05|77.39|158.19|235.58| 235 | |Maxwell Titan X|5.1.05|87.76|159.73|247.49| 236 | |Maxwell Titan X|5.0.05|88.45|172.12|260.57| 237 | |Maxwell Titan X|4.0.07|108.96|189.93|298.90| 238 | |Pascal Titan X|None|161.55|257.57|419.11| 239 | |GTX 1080 Ti|None|162.03|266.77|428.81| 240 | |GTX 1080|None|203.19|322.48|525.67| 241 | |Maxwell Titan X|None|260.48|453.45|713.93| 242 | |CPU: Dual Xeon E5-2630 v3|None|4414.91|6891.33|11306.24| 243 | 244 | 245 | 246 | ## ResNet-152 247 | (input 16 x 3 x 224 x 224) 248 | 249 | This is the 152-layer model described in [[4]](#resnet-cvpr) and implemented in 250 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 251 | 252 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 253 | |---|---|---:|---:|---:| 254 | |GTX 1080 Ti|5.1.10|73.52|142.02|215.54| 255 | |Pascal Titan X|5.1.05|75.45|142.47|217.91| 256 | |Pascal Titan X|5.0.05|75.12|150.08|225.20| 257 | |GTX 1080|5.1.05|109.32|204.98|314.30| 258 | |GTX 1080|5.0.05|109.64|218.62|328.26| 259 | |Maxwell Titan X|5.1.05|124.04|221.41|345.45| 260 | |Maxwell Titan X|5.0.05|124.88|240.16|365.03| 261 | |Maxwell Titan X|4.0.07|150.90|268.64|419.54| 262 | |Pascal Titan X|None|238.04|371.40|609.43| 263 | |GTX 1080 Ti|None|225.36|368.42|593.79| 264 | |GTX 1080|None|299.05|461.67|760.72| 265 | |Maxwell Titan X|None|382.39|583.83|966.22| 266 | |CPU: Dual Xeon E5-2630 v3|None|6572.17|10300.61|16872.78| 267 | 268 | 269 | ## ResNet-200 270 | (input 16 x 3 x 224 x 224) 271 | 272 | This is the 200-layer model described in [[5]](#resnet-eccv) and implemented in 273 | [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). 274 | 275 | Even with a batch size of 16, the 8GB GTX 1080 did not have enough memory to run 276 | the model. 277 | 278 | |GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)| 279 | |---|---|---:|---:|---:| 280 | |Pascal Titan X|5.1.05|104.74|191.77|296.51| 281 | |Pascal Titan X|5.0.05|104.36|201.92|306.27| 282 | |Maxwell Titan X|5.0.05|170.03|320.80|490.83| 283 | |Maxwell Titan X|5.1.05|169.62|383.80|553.42| 284 | |Maxwell Titan X|4.0.07|203.52|356.35|559.87| 285 | |Pascal Titan X|None|314.77|519.72|834.48| 286 | |Maxwell Titan X|None|497.57|953.94|1451.51| 287 | |CPU: Dual Xeon E5-2630 v3|None|8666.43|13758.73|22425.16| 288 | 289 | ## Citations 290 | 291 | 292 | [1] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." NIPS 2012 293 |
294 | 295 | 296 | [2] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, 297 | Dragomir Anguelov, Dumitru Erhan, Andrew Rabinovich. 298 | "Going Deeper with Convolutions." CVPR 2015. 299 |
300 | 301 | 302 | [3] Karen Simonyan and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." ICLR 2015 303 |
304 | 305 | 306 | [4] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep Residual Learning for Image Recognition." CVPR 2016. 307 |
308 | 309 | 310 | [5] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Identity Mappings in Deep Residual Networks." ECCV 2016. 311 | -------------------------------------------------------------------------------- /analyze_cnn_benchmark_results.py: -------------------------------------------------------------------------------- 1 | import json, os, argparse, itertools, math 2 | from collections import defaultdict 3 | 4 | 5 | parser = argparse.ArgumentParser() 6 | parser.add_argument('--results_dir', default='outputs') 7 | parser.add_argument('--include_std', default=0) 8 | args = parser.parse_args() 9 | 10 | 11 | # Maps the cuDNN version reported by torch.cudnn to a more friendly string 12 | cudnn_map = { 13 | 5005: '5.0.05', 14 | 5105: '5.1.05', 15 | 5110: '5.1.10', 16 | 4007: '4.0.07', 17 | 'none': 'None', 18 | } 19 | 20 | # Maps the GPU name reported by the driver to a more friendly string 21 | gpu_name_map = { 22 | 'Tesla P100-SXM2-16GB': 'Tesla P100', 23 | 'TITAN X': 'Pascal Titan X', 24 | 'GeForce GTX TITAN X': 'Maxwell Titan X', 25 | 'GeForce GTX 1080': 'GTX 1080', 26 | 'GeForce GTX 1080 Ti': 'GTX 1080 Ti', 27 | 'cpu': 'CPU: Dual Xeon E5-2630 v3', 28 | } 29 | 30 | 31 | def main(args): 32 | # Load all the results 33 | results = [] 34 | for dirpath, dirnames, fns in os.walk(args.results_dir): 35 | for fn in fns: 36 | if not fn.endswith('.json'): continue 37 | with open(os.path.join(dirpath, fn), 'r') as f: 38 | results.append(json.load(f)) 39 | 40 | all_values = defaultdict(set) 41 | keyed_results = {} 42 | 43 | for result in results: 44 | gpu_name = result['gpu_name'] 45 | cudnn_version = result['cudnn_version'] 46 | model = result['opt']['model_t7'] 47 | 48 | batch_size = result['opt']['batch_size'] 49 | im_width = result['opt']['image_width'] 50 | im_height = result['opt']['image_height'] 51 | input_size = '%d x 3 x %d x %d' % (batch_size, im_height, im_width) 52 | 53 | model = os.path.splitext(os.path.basename(model))[0] 54 | keyed_results[(gpu_name, cudnn_version, model)] = result 55 | 56 | all_values['gpu_name'].add(gpu_name) 57 | all_values['cudnn_version'].add(cudnn_version) 58 | all_values['model'].add(model) 59 | all_values['input_size'].add(input_size) 60 | 61 | for k, vs in all_values.iteritems(): 62 | print k 63 | for v in vs: 64 | print ' %s' % v 65 | 66 | markdown_tables = {} 67 | 68 | for model in all_values['model']: 69 | for input_size in all_values['input_size']: 70 | table_header = '|GPU|cuDNN|Forward (ms)|Backward (ms)|Total (ms)|' 71 | table_header2 = '|---|---|---:|---:|---:|' 72 | table_lines = {} 73 | for gpu_name in all_values['gpu_name']: 74 | for cudnn_version in all_values['cudnn_version']: 75 | k = (gpu_name, cudnn_version, model) 76 | if k not in keyed_results: continue 77 | result = keyed_results[k] 78 | 79 | cudnn_str = cudnn_map[cudnn_version] 80 | cudnn_str = cudnn_map.get(cudnn_version, cudnn_version) 81 | gpu_str = gpu_name_map.get(gpu_name, gpu_name) 82 | 83 | f_mean = mean(result['forward_times']) * 1000 84 | f_std = std(result['forward_times']) * 1000 85 | b_mean = mean(result['backward_times']) * 1000 86 | b_std = std(result['backward_times']) * 1000 87 | t_mean = mean(result['total_times']) * 1000 88 | t_std = std(result['total_times']) * 1000 89 | 90 | if args.include_std == 1: 91 | f_str = '%.2f += %.2f' % (f_mean, f_std) 92 | b_str = '%.2f += %.2f' % (b_mean, b_std) 93 | t_str = '%.2f += %.2f' % (t_mean, t_std) 94 | else: 95 | f_str = '%.2f' % f_mean 96 | b_str = '%.2f' % b_mean 97 | t_str = '%.2f' % t_mean 98 | table_lines[t_mean] = '|%s|%s|%s|%s|%s|' % ( 99 | gpu_str, cudnn_str, f_str, b_str, t_str) 100 | 101 | table_lines = [table_lines[k] for k in sorted(table_lines)] 102 | table_lines = [table_header, table_header2] + table_lines 103 | model_batch_str = '%s (input %s)' % (model, input_size) 104 | markdown_tables[model_batch_str] = table_lines 105 | 106 | for model, table_lines in markdown_tables.iteritems(): 107 | print model 108 | for line in table_lines: 109 | print line 110 | print 111 | 112 | 113 | def mean(xs): 114 | return float(sum(xs)) / len(xs) 115 | 116 | 117 | def std(xs): 118 | m = mean(xs) 119 | diffs = [x - m for x in xs] 120 | var = sum(d ** 2.0 for d in diffs) / (len(xs) - 1) 121 | return math.sqrt(var) 122 | 123 | 124 | if __name__ == '__main__': 125 | args = parser.parse_args() 126 | main(args) 127 | 128 | -------------------------------------------------------------------------------- /cnn_benchmark.lua: -------------------------------------------------------------------------------- 1 | require 'torch' 2 | require 'nn' 3 | 4 | local utils = require 'utils' 5 | 6 | 7 | local cmd = torch.CmdLine() 8 | -- Model options 9 | cmd:option('-model_t7', 'models/vgg16.t7') 10 | cmd:option('-image_height', 224) 11 | cmd:option('-image_width', 224) 12 | cmd:option('-batch_size', 16) 13 | 14 | -- Benchmark options 15 | cmd:option('-num_passes', 10) 16 | 17 | -- Backend options 18 | cmd:option('-gpu', 0) 19 | cmd:option('-use_cudnn', 1) 20 | 21 | -- Output options 22 | cmd:option('-output_json', 'outputs/cnn_out.json') 23 | 24 | local opt = cmd:parse(arg) 25 | local dtype, use_cudnn, gpu_name, cudnn_version = utils.setup_gpu(opt) 26 | print('Loading model from ' .. opt.model_t7) 27 | local model = torch.load(opt.model_t7) 28 | utils.restore_gradients(model) 29 | model:training() 30 | model:type(dtype) 31 | if use_cudnn then 32 | cudnn.convert(model, cudnn) 33 | end 34 | 35 | 36 | local forward_times = {} 37 | local backward_times = {} 38 | local N, C = opt.batch_size, 3 39 | local H, W = opt.image_height, opt.image_width 40 | for t = 1, opt.num_passes + 1 do 41 | local msg = 'Running iteration %d / %d' 42 | print(string.format(msg, t - 1, opt.num_passes)) 43 | 44 | local x = torch.randn(N, C, H, W):type(dtype) 45 | utils.sync() 46 | local forward_time = utils.timeit(function() model:forward(x) end) 47 | if t > 1 then 48 | -- The first pass does not count since it will allocate 49 | -- a bunch of memory 50 | table.insert(forward_times, forward_time) 51 | end 52 | 53 | local dout = torch.randn(#model.output):type(dtype) 54 | utils.sync() 55 | local backward_time = utils.timeit(function() model:backward(x, dout) end) 56 | if t > 1 then 57 | table.insert(backward_times, backward_time) 58 | end 59 | end 60 | 61 | forward_times = torch.DoubleTensor(forward_times) 62 | backward_times = torch.DoubleTensor(backward_times) 63 | local total_times = forward_times + backward_times 64 | 65 | local msg = '%f += %f' 66 | print('Forward:') 67 | print(string.format(msg, forward_times:mean(), forward_times:std())) 68 | print('Backward:') 69 | print(string.format(msg, backward_times:mean(), backward_times:std())) 70 | print('Total:') 71 | print(string.format(msg, total_times:mean(), backward_times:std())) 72 | 73 | local json_data = { 74 | opt = opt, 75 | forward_times = forward_times:totable(), 76 | backward_times = backward_times:totable(), 77 | total_times = total_times:totable(), 78 | gpu_name = gpu_name, 79 | cudnn_version = cudnn_version, 80 | } 81 | utils.write_json(opt.output_json, json_data) 82 | -------------------------------------------------------------------------------- /convert_model.lua: -------------------------------------------------------------------------------- 1 | require 'torch' 2 | require 'cutorch' 3 | require 'nn' 4 | require 'cunn' 5 | require 'loadcaffe' 6 | require 'cudnn' 7 | local utils = require 'utils' 8 | 9 | local cmd = torch.CmdLine() 10 | cmd:option('-input_prototxt', '') 11 | cmd:option('-input_caffemodel', '') 12 | cmd:option('-input_t7', '') 13 | cmd:option('-output_t7', '') 14 | cmd:option('-backend', 'nn') 15 | cmd:option('-clear_gradients', 1) 16 | local opt = cmd:parse(arg) 17 | 18 | 19 | if (opt.input_prototxt == '') == (opt.input_t7 == '') then 20 | error('Must provide one of -input_prototxt or -input_t7') 21 | end 22 | 23 | local model = nil 24 | if opt.input_prototxt ~= '' then 25 | if opt.input_caffemodel == '' then 26 | error('Must provide both -input_prototxt and -input_caffemodel') 27 | end 28 | model = loadcaffe.load(opt.input_prototxt, opt.input_caffemodel, opt.backend) 29 | elseif opt.input_t7 then 30 | model = torch.load(opt.input_t7) 31 | end 32 | local backend_map = {nn=nn, cudnn=cudnn} 33 | cudnn.convert(model, backend_map[opt.backend]) 34 | model:float() 35 | model:clearState() 36 | if opt.clear_gradients == 1 then 37 | utils.clear_gradients(model) 38 | end 39 | torch.save(opt.output_t7, model) 40 | -------------------------------------------------------------------------------- /outputs/1080Ti/176422.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.086354970932007,0.086237192153931,0.086359977722168,0.08631706237793,0.08633017539978,0.086178064346313,0.086344957351685,0.086236953735352,0.08635401725769,0.086253881454468],"total_times":[0.19582080841064,0.19563317298889,0.19581007957458,0.1958372592926,0.19567322731018,0.19553518295288,0.19580006599426,0.19569396972656,0.19577312469482,0.19569778442383],"gpu_name":"GeForce GTX 1080 Ti","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-34.t7","batch_size":16,"output_json":"outputs\/176422.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.10946583747864,0.10939598083496,0.10945010185242,0.10952019691467,0.1093430519104,0.10935711860657,0.10945510864258,0.10945701599121,0.10941910743713,0.10944390296936]} -------------------------------------------------------------------------------- /outputs/1080Ti/430986.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.054296016693115,0.051362991333008,0.049355983734131,0.049355030059814,0.049371004104614,0.049302101135254,0.04933500289917,0.049325942993164,0.049317836761475,0.049340009689331],"total_times":[0.12485218048096,0.1182599067688,0.11456799507141,0.11465191841125,0.11467385292053,0.1145920753479,0.11471104621887,0.11469483375549,0.11458277702332,0.11469411849976],"gpu_name":"GeForce GTX 1080 Ti","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-18.t7","batch_size":16,"output_json":"outputs\/430986.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.070556163787842,0.066896915435791,0.06521201133728,0.06529688835144,0.065302848815918,0.065289974212646,0.065376043319702,0.065368890762329,0.065264940261841,0.065354108810425]} -------------------------------------------------------------------------------- /outputs/1080Ti/470497.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.225252866745,0.22533297538757,0.22540307044983,0.22533202171326,0.22530102729797,0.22519087791443,0.22543215751648,0.22517108917236,0.22553396224976,0.22567200660706],"total_times":[0.59362196922302,0.59355187416077,0.59378218650818,0.59349894523621,0.59355902671814,0.59341883659363,0.59386229515076,0.59366226196289,0.59416890144348,0.59472608566284],"gpu_name":"GeForce GTX 1080 Ti","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-152.t7","batch_size":16,"output_json":"outputs\/470497.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.36836910247803,0.36821889877319,0.36837911605835,0.36816692352295,0.36825799942017,0.3682279586792,0.36843013763428,0.36849117279053,0.36863493919373,0.36905407905579]} -------------------------------------------------------------------------------- /outputs/1080Ti/658244.json: 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-------------------------------------------------------------------------------- /outputs/1080Ti/976860.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.16191983222961,0.16189694404602,0.16198897361755,0.16185712814331,0.16212606430054,0.16215515136719,0.16207385063171,0.16214108467102,0.16209292411804,0.16206789016724],"total_times":[0.42863988876343,0.42879796028137,0.42856597900391,0.42868494987488,0.42884206771851,0.42888307571411,0.42886877059937,0.42897605895996,0.42878389358521,0.42902398109436],"gpu_name":"GeForce GTX 1080 Ti","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-101.t7","batch_size":16,"output_json":"outputs\/976860.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.26672005653381,0.26690101623535,0.26657700538635,0.26682782173157,0.26671600341797,0.26672792434692,0.26679491996765,0.26683497428894,0.26669096946716,0.26695609092712]} 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-------------------------------------------------------------------------------- /outputs/1080Ti_cudnn51/453685.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.0043320655822754,0.004302978515625,0.0043110847473145,0.0043001174926758,0.004302978515625,0.004288911819458,0.0043439865112305,0.0042991638183594,0.0043160915374756,0.0043010711669922],"total_times":[0.013919115066528,0.013903856277466,0.013913154602051,0.013905048370361,0.01386284828186,0.013881921768188,0.013935089111328,0.01383900642395,0.013842105865479,0.013866186141968],"gpu_name":"GeForce GTX 1080 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-------------------------------------------------------------------------------- /outputs/titan_xp_cudnn51/865701.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.075244903564453,0.076074123382568,0.075314998626709,0.075268030166626,0.075250864028931,0.075294971466064,0.075273990631104,0.075304985046387,0.076148986816406,0.07527494430542],"total_times":[0.21753096580505,0.21831011772156,0.21762895584106,0.21741199493408,0.21825289726257,0.21755504608154,0.21840906143188,0.2179548740387,0.21848607063293,0.21757984161377],"gpu_name":"TITAN 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{"forward_times":[0.3199188709259,0.3141770362854,0.31249809265137,0.3136248588562,0.32422113418579,0.31225609779358,0.31298303604126,0.31201887130737,0.31307506561279,0.31291389465332],"total_times":[0.83949995040894,0.83622622489929,0.83131694793701,0.83960485458374,0.84244728088379,0.83208107948303,0.83131098747253,0.83056974411011,0.83123707771301,0.8305549621582],"gpu_name":"TITAN X","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-200.t7","batch_size":16,"output_json":"outputs\/titan_xp_cudnn51\/879577.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.51958107948303,0.52204918861389,0.51881885528564,0.52597999572754,0.518226146698,0.51982498168945,0.51832795143127,0.51855087280273,0.51816201210022,0.51764106750488]} -------------------------------------------------------------------------------- /outputs/titan_xp_cudnn51/880068.json: 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/outputs/titan_xp_cudnn51/939670.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.034669876098633,0.034687042236328,0.034734964370728,0.034639835357666,0.034732103347778,0.034833192825317,0.0346519947052,0.034627199172974,0.034680843353271,0.035356044769287],"total_times":[0.096118927001953,0.097100257873535,0.096163988113403,0.096238851547241,0.09619402885437,0.09631609916687,0.096839904785156,0.096081256866455,0.09608793258667,0.096840858459473],"gpu_name":"TITAN X","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-18.t7","batch_size":16,"output_json":"outputs\/titan_xp_cudnn51\/939670.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.06144905090332,0.062413215637207,0.061429023742676,0.061599016189575,0.061461925506592,0.061482906341553,0.062187910079956,0.061454057693481,0.061407089233398,0.061484813690186]} -------------------------------------------------------------------------------- /outputs/titan_xp_cudnn51/962635.json: -------------------------------------------------------------------------------- 1 | {"forward_times":[0.053352832794189,0.05338191986084,0.053401947021484,0.053405046463013,0.053335905075073,0.053375959396362,0.053392171859741,0.053406953811646,0.053378105163574,0.053378105163574],"total_times":[0.15637493133545,0.15648484230042,0.15647482872009,0.15646910667419,0.15639591217041,0.15645408630371,0.15644311904907,0.15647673606873,0.15641117095947,0.15643501281738],"gpu_name":"TITAN 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{"forward_times":[0.088304996490479,0.087491989135742,0.08752703666687,0.087547779083252,0.087496995925903,0.087574005126953,0.087591171264648,0.087502002716064,0.087582111358643,0.087571144104004],"total_times":[0.24716711044312,0.24631786346436,0.24626302719116,0.24717688560486,0.24615502357483,0.24634504318237,0.24644136428833,0.24704813957214,0.24640727043152,0.24643301963806],"gpu_name":"TITAN X","opt":{"gpu":0,"num_passes":10,"model_t7":"models\/resnets\/resnet-50.t7","batch_size":16,"output_json":"outputs\/titan_xp_cudnn51\/971506.json","image_height":224,"image_width":224,"use_cudnn":0},"cudnn_version":"none","backward_times":[0.15886211395264,0.15882587432861,0.15873599052429,0.15962910652161,0.15865802764893,0.15877103805542,0.15885019302368,0.15954613685608,0.15882515907288,0.15886187553406]} -------------------------------------------------------------------------------- /run_cnn_benchmarks.py: -------------------------------------------------------------------------------- 1 | import os, json, itertools, random, argparse 2 | 3 | 4 | DEFAULT_MODELS = ','.join([ 5 | 'models/alexnet/alexnet.t7', 6 | 'models/vgg16/vgg16.t7', 7 | 'models/vgg19/vgg19.t7', 8 | 'models/googlenet/googlenet-v1.t7', 9 | 'models/resnets/resnet-18.t7', 10 | 'models/resnets/resnet-34.t7', 11 | 'models/resnets/resnet-50.t7', 12 | 'models/resnets/resnet-101.t7', 13 | 'models/resnets/resnet-152.t7', 14 | 'models/resnets/resnet-200.t7', 15 | ]) 16 | 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--gpus', default='0') 19 | parser.add_argument('--models', default=DEFAULT_MODELS) 20 | parser.add_argument('--batch_sizes', default='16') 21 | parser.add_argument('--use_cudnns', default='0,1') 22 | parser.add_argument('--output_dir', default='outputs') 23 | 24 | 25 | def main(args): 26 | factors = { 27 | 'gpu': [int(g) for g in args.gpus.split(',')], 28 | 'model_t7': args.models.split(','), 29 | 'batch_size': [int(b) for b in args.batch_sizes.split(',')], 30 | 'use_cudnn': [int(c) for c in args.use_cudnns.split(',')], 31 | } 32 | 33 | base_command = 'th cnn_benchmark.lua' 34 | 35 | output_jsons = set() 36 | for vals in itertools.product(*factors.values()): 37 | lua_args = dict(zip(factors.keys(), vals)) 38 | while True: 39 | lua_args['output_json'] = '/%d.json' % random.randint(1, 100000) 40 | rand_id = random.randint(1, 1000000) 41 | lua_args['output_json'] = os.path.join(args.output_dir, '%d.json' % rand_id) 42 | if lua_args['output_json'] not in output_jsons: break 43 | output_jsons.add(lua_args['output_json']) 44 | 45 | command = base_command 46 | for k, v in lua_args.iteritems(): 47 | command = '%s -%s %s' % (command, k, v) 48 | 49 | print command 50 | os.system(command) 51 | 52 | 53 | if __name__ == '__main__': 54 | args = parser.parse_args() 55 | main(args) 56 | 57 | -------------------------------------------------------------------------------- /utils.lua: -------------------------------------------------------------------------------- 1 | local cjson = require 'cjson' 2 | 3 | local M = {} 4 | 5 | 6 | function M.setup_gpu(opt) 7 | local dtype = 'torch.FloatTensor' 8 | local use_cudnn = false 9 | local gpu_name = 'cpu' 10 | local cudnn_version = 'none' 11 | if opt.gpu >= 0 then 12 | require 'cutorch' 13 | require 'cunn' 14 | cutorch.setDevice(opt.gpu + 1) 15 | gpu_name = cutorch.getDeviceProperties(opt.gpu + 1).name 16 | local msg = 'Running on GPU %d (%s)' 17 | print(string.format(msg, opt.gpu, gpu_name)) 18 | dtype = 'torch.CudaTensor' 19 | if opt.use_cudnn == 1 then 20 | require 'cudnn' 21 | use_cudnn = true 22 | cudnn.benchmark = true 23 | cudnn_version = cudnn.version 24 | print('Using cuDNN version ' .. tostring(cudnn.version)) 25 | end 26 | else 27 | print('Running on CPU') 28 | end 29 | return dtype, use_cudnn, gpu_name, cudnn_version 30 | end 31 | 32 | 33 | function M.sync() 34 | if cutorch then cutorch.synchronize() end 35 | end 36 | 37 | 38 | function M.timeit(f) 39 | M.sync() 40 | local timer = torch.Timer() 41 | f() 42 | M.sync() 43 | return timer:time().real 44 | end 45 | 46 | 47 | function M.clear_gradients(m) 48 | if torch.isTypeOf(m, nn.Container) then 49 | m:applyToModules(M.clear_gradients) 50 | end 51 | if m.weight and m.gradWeight then 52 | m.gradWeight = m.gradWeight.new() 53 | end 54 | if m.bias and m.gradBias then 55 | m.gradBias = m.gradBias.new() 56 | end 57 | end 58 | 59 | 60 | function M.restore_gradients(m) 61 | if torch.isTypeOf(m, nn.Container) then 62 | m:applyToModules(M.restore_gradients) 63 | end 64 | if m.weight and m.gradWeight then 65 | m.gradWeight = m.gradWeight.new(#m.weight):zero() 66 | end 67 | if m.bias and m.gradBias then 68 | m.gradBias = m.gradBias.new(#m.bias):zero() 69 | end 70 | end 71 | 72 | 73 | function M.read_json(path) 74 | local file = assert(io.open(path, 'r')) 75 | local text = file:read() 76 | local info = cjson.decode(file:read()) 77 | file:read() 78 | return info 79 | end 80 | 81 | 82 | function M.write_json(path, data) 83 | local file = assert(io.open(path, 'w')) 84 | file:write(cjson.encode(data)) 85 | file:close() 86 | end 87 | 88 | 89 | return M 90 | --------------------------------------------------------------------------------