Starting Pruning Loop - 2.0 English

Vitis AI Optimizer User Guide (UG1333)

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2.0 English

Once the ana command has executed successfully, you can start pruning the model. prune is very similar to ana and uses the same configuration file:

./vai_p_darknet pruner prune pruning/cfg pruning/yolov3-voc_final.weights

The pruning tool prunes the model according to the value of threshold and generates an output as follows. After loading the model, the tool shows the pruning rate for each layer and the output weights file. Finally, it automatically evaluates the mAP of the pruned model. You can see that the mAP after pruning is 0.494531, much lower than the original one. Finetuning, which is the next step, is used to recover the accuracy. If you want to follow the iterative pruning with a small step size, change the threshold option in the main cfg to a smaller value and run again.

Start pruning ...
pruning slot: 0,
prune main layer 0
  kernel_batch:2, rate:0.3000, keep_num:24
pruning slot: 1,3,
prune main layer 1
  kernel_batch:2, rate:0.3000, keep_num:46
  prune slave layer 3
  prune related layer 2
  kernel_batch:2, rate:0.3000, keep_num:24
pruning slot: 102,
prune main layer 102
  kernel_batch:2, rate:0.5000, keep_num:128
  prune related layer 101
  kernel_batch:2, rate:0.5000, keep_num:64
Saving weights to pruning/weights.prune
calculate map
Process   100 on GPU 0
Process   200 on GPU 0
Process  4800 on GPU 0
Process  4900 on GPU 0
AP for class 0 = 0.501017
AP for class 1 = 0.711958
AP for class 18 = 0.621339
AP for class 19 = 0.472648
mAP : 0.494531 
Total Detection Time: 158.943884 Seconds