When configuring TensorRT backend for int8, both int8 and fp16 is enabled about mmdeploy

[grimoire]

It is OK to enable both of them. TensorRT will choose a higher-precision kernel if it results in overall lower runtime, or if no low-precision implementation exists.
Read this for more details.

[tehkillerbee]

@grimoire That makes sense however Nvidia states "..There are three precision flags: FP16, INT8, and TF32, and they may be enabled independently.." so are you really supposed to enable more than one at the same time? Well if it works, I guess it is fine. In any case, I will test this further and see what happens on my Jetson AGX Xavier.

Slightly off-topic. In the link you sent it states that "..TensorRT will still choose a higher-precision kernel if it results in overall lower runtime..."
However, if I enable FP16 when using a GPU architecture that does not use it (eg. my Quadro P2000), a warning is given:

[TRT] [W] Half2 support requested on hardware without native FP16 support, performance will be negatively affected.

Naturally, the default FP32 would've been the fastest - but the FP16 is still used instead. So does TensorRT actually pick the fastest one in this case?

[grimoire]

According to my experiment, enabling both flags is slightly faster than int8 or fp16 only, so I guess TensorRT would do some optimization about the precision.

And I guess TensorRT will use fp16 if the layer does not support int8(whatever device). That's why the performance would be bad on the device without fp16 support.

[tehkillerbee]

@grimoire I see, that is good to know. In that case, I think we can close this issue.