算法剪枝-基于Tensorflow实现的迭代剪枝-算法优化-附项目源码-优质项目实战.zip

## TensorFlow implementation of "Iterative Pruning" ## File descriptions and usages model_ckpt_dense: original model<br> model_ckpt_dense_pruned: 90% pruned-only model<br> model_ckpt_sparse_retrained: 90% pruned and retrained model<br> #### Python package requirements ```bash sudo apt-get install python-scipy python-numpy python-matplotlib ``` To regenerate these sparse model, edit ```config.py``` first as your threshold configuration, and then run training with second (pruning and retraining) and third (generate sparse form of weight data) round options. ```bash ./train.py -2 -3 ``` To inference single image (seven.png) and measure its latency, ```bash ./deploy_test.py -d -m model_ckpt_dense ./deploy_test_sparse.py -d -m model_ckpt_sparse_retrained ``` To test dense model, ```bash ./deploy_test.py -t -m model_ckpt_dense ./deploy_test.py -t -m model_ckpt_dense_pruned ./deploy_test.py -t -m model_ckpt_dense_retrained ``` To draw histogram that shows the weight distribution, ```bash # After running train.py (it generates .dat files) ./draw_histogram.py ``` ## Performance Results are currently somewhat mediocre or degraded due to indirection and additional storage overhead originated from sparse matrix form. Also, it may because model size is too small. (12.49MB) #### Storage overhead Baseline: 12.49 MB<br> 10 % pruned: 21.86 MB<br> 20 % pruned: 19.45 MB<br> 30 % pruned: 17.05 MB<br> 40 % pruned: 14.64 MB<br> 50 % pruned: 12.23 MB<br> 60 % pruned: 9.83 MB<br> 70 % pruned: 7.42 MB<br> 80 % pruned: 5.02 MB<br> 90 % pruned: 2.61 MB<br> #### CPU performance (5 times averaged) CPU: Intel Core i5-2500 @ 3.3 GHz, LLC size: 6 MB <img src=http://younghwanoh.github.io/images/cpu-desktop.png alt=http://younghwanoh.github.io/images/cpu-desktop.png> Baseline: 0.01118040085 s<br> 10 % pruned: 1.919299984 s<br> 20 % pruned: 0.2325239658 s<br> 30 % pruned: 0.2111079693 s<br> 40 % pruned: 0.1982570648 s<br> 50 % pruned: 0.1691776752 s<br> 60 % pruned: 0.1305227757 s<br> 70 % pruned: 0.116039753 s<br> 80 % pruned: 0.103564167 s<br> 90 % pruned: 0.1058168888 s<br> #### GPU performance (5 times averaged) GPU: Nvidia Geforce GTX650 @ 1.058 GHz, LLC size: 256 KB <img src=http://younghwanoh.github.io/images/gpu-desktop.png alt=http://younghwanoh.github.io/images/gpu-desktop.png> Baseline: 0.1475181845 s<br> 10 % pruned: 0.2954540253 s<br> 20 % pruned: 0.2665398121 s<br> 30 % pruned: 0.2585638046 s<br> 40 % pruned: 0.2090051651 s<br> 50 % pruned: 0.1995279789 s<br> 60 % pruned: 0.1815193653 s<br> 70 % pruned: 0.1436806202 s<br> 80 % pruned: 0.135668993 s<br> 90 % pruned: 0.1218701839 s<br>