INT8EntropyCalibrator2 implicit quantization superseded by explicit quantization

Description

Hi,

I have been using the INT8 Entropy Calibrator 2 for INT8 quantization in Python and it’s been working well (TensorRT 10.0.1). The example of how I use the INT8 Entropy Calibrator 2 can be found in the official TRT GitHub repo (TensorRT/samples/python/efficientdet/build_engine.py at release/10.0 · NVIDIA/TensorRT · GitHub)

The warning I’ve been getting starting with TensorRT 10.1 is that the INT8 Entropy Calibrator 2 implicit quantization has been deprecated and superseded by explicit quantization.

I’ve read the official document on the difference between the implicit and explicit quantization processes (Developer Guide :: NVIDIA Deep Learning TensorRT Documentation) and they seem to work differently. The explicit quantization seems to expect a network to have QuantizeLayer and DequantizeLayer layers which my networks don’t. The implicit quantization can be used when those layers are not present in a network. Therefore, I am confused about how the implicit quantization can be superseded by the explicit quantization since they seem to work differently.

So, my question is what needs to be modified in the standard INT8 Calibrator 2 quantization method (TensorRT/samples/python/efficientdet/build_engine.py at release/10.0 · NVIDIA/TensorRT · GitHub) for the deprecation warning not to show up ? Or what is the proper way to implement the INT8 Calibrator 2 implicit quantization now that the current one is deprecated ? Couldn’t find any example using a newer TensorRT version (10.1 and up)

Thank you!

Environment

TensorRT Version: 10.1

NVIDIA GPU: 3090

Operating System: Windows 10

Python Version: 3.9.19

Start with samples/python/efficientdet/build_engine.py from the release/10.0 branch and compare it with the TensorRT Developer Guide section on implicit and explicit quantization. Determine whether the sample or accompanying documentation needs updating for TensorRT 10.1 and later, and define completion as a clear, current example or explanation that resolves the deprecation warning.