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BMN模型和AttentionLSTM模型量化压缩和蒸馏方案 #1876
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飞桨没有提供这两个模型的量化压缩何蒸馏方案,可以参考其它模型来做,基础原理是一样的。
可以使用飞桨接口 paddle.flops 计算flops(计算量);
辛苦上传下日志,我们确认下具体触发了什么压缩算法? |
压缩脚本使用的是:PaddleVideo/deploy/slim/quant_post_static.py 压缩配置文件内容如下: PIPELINE: inference_model_dir: "/home/jetion/Action/apps/cpr/export_models/ppTSM_server" model_name: "ppTSM" 静态离线量化日志如下: Preparation stage, Run batch:| | 0/10 Sampling stage, Run batch:| | 0/10 Adding quant op with weight:| | 0/202 Adding quant activation op:| | 0/422
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1、BMN模型和AttentionLSTM模型是否有量化压缩和蒸馏的方案?训练完成的AttentionLSTM模型的模型参数和计算量如何计算?
2、模型使用自动压缩,参数文件大小没有变化,如何处理这种情况?
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