trt2022 / mst-plus-plus-tensorrt Goto Github PK
View Code? Open in Web Editor NEW:poodle: :poodle: :poodle: TensorRT 2022复赛方案: 首个基于Transformer的图像重建模型MST++的TensorRT模型推断优化
License: Apache License 2.0
:poodle: :poodle: :poodle: TensorRT 2022复赛方案: 首个基于Transformer的图像重建模型MST++的TensorRT模型推断优化
License: Apache License 2.0
很感谢你们的分享~干货很多,有个小疑问想请教下:
Transpose+Reshape
结构,就和你使用nsight sys展示的一样,这其实已经合并多余的reshape/transpose吧,这个和直接使用API的方式合并,性能有差别吗?希望可以和大佬交流下!
@DataXujing Hi,请问是否尝试过使用TensorRT-API方式从量化训练导出的Q/DQ-onnx模型构建trt_int8 engine?
我尝试直接使用trtexec对Q/DQ-onnx进行转换,nsight分析发现推理过程transpose+reshape耗时占用高,想结合api的方式优化这部分性能,可否给一些建议?多谢~
你好,大神。请教一下,TensorRT 的 动态shape 推理相对静态shape,显存是不是一定会增加?如果是,有没有方法优化这个显存占用增加的现象。谢谢
BaiduDisk link is not available
请问,大佬知道 dynamic shape 下的 getWorkspaceSize 如何写吗?静态的很好判断,但目前个人自定义的算子卡在了这里,还望大佬指点!
Could you please share it again? Thanks!
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