  The tradeoff between performance and inference speed is critical for
practical applications. Architecture reparameterization obtains better
tradeoffs and it is becoming an increasingly popular ingredient in modern
convolutional neural networks. Nonetheless, its quantization performance is
usually too poor to deploy (more than 20% top-1 accuracy drop on ImageNet) when
INT8 inference is desired. In this paper, we dive into the underlying mechanism
of this failure, where the original design inevitably enlarges quantization
error. We propose a simple, robust, and effective remedy to have a
quantization-friendly structure that also enjoys reparameterization benefits.
Our method greatly bridges the gap between INT8 and FP32 accuracy for RepVGG.
Without bells and whistles, the top-1 accuracy drop on ImageNet is reduced
within 2% by standard post-training quantization. Moreover, our method also
achieves similar FP32 performance as RepVGG. Extensive experiments on detection
and semantic segmentation tasks verify its generalization.
