By default TF32 is already used, so the TensorCores on your device will already be utilized, which might not leave a lot of performance benefits left for AMP. This issue with the related double post might be interesting for you.
In any case, you could create profiles with a visual profiler such as Nsight Systems and check for other bottlenecks, such as data loading, which might be the current bottleneck.
Also refer to the Performance Tuning Guide 2 and, if possible, you could also try out the latest CUDA + cudnn versions by building from source, which could yield additional performance improvements.