vai_p_caffe Usage - 2.0 English

Vitis AI Optimizer User Guide (UG1333)

Document ID
Release Date
2.0 English

The following arguments are available when running vai_p_caffe:

Table 1. vai_p_caffe Arguments
Argument Attribute Default Description
config required “” The configuration file path.
config required “” The configuration file path.
config required “” The configuration file path.
model required “” Baseline model definition protocol buffer text file
weights required “” Model weights file path.
output optional “” The output transformed weights.
Table 2. vai_p_caffe Configuration File Parameters
Argument Type Attribute Default Description
workspace string required None Directory for saving output files.
gpu string optional “0” GPU device IDs used for compression and fine-tuning, separated by ‘,’.
test_iter int optional 100 The number of iterations to run in test phase.
acc_name string required None The accuracy measure of interest. This parameter is the layer_top of the layer used to evaluate network performance. If the network has multiple evaluation metrics, choose the one that you think is most important. For classification tasks, this parameter may be top-1 or top-5 accuracy; for detection tasks, this parameter is generally mAP; for segmentation tasks, typically the layer for calculating mIOU is set here.
model string required None The model definition protocol buffer text file. If there are two different model definition files for training and testing, it is recommended to merge them into a single file.
weights string required None The trained weights to compress.
solver string required None The solver definition protocol buffer text file.
rate float optional None The expected model pruning ratio.
method enum optional REGULAR Pruning method to be used. Currently REGULAR is the only valid value.
ssd_ap_version string optional None The ap_version setting for SSD network compression. Must be one of 11point, MaxIntegral and Integral.
exclude repeated optional None Used to exclude some layers from pruning. You can use this parameter to prevent specified convolutional layers from being pruned.
kernel_batch int optional 2 The number of output channels is a multiple of this value after pruning.