  Efficient inference for object detection networks is a major challenge on
edge devices. Post-Training Quantization (PTQ), which transforms a
full-precision model into low bit-width directly, is an effective and
convenient approach to reduce model inference complexity. But it suffers severe
accuracy drop when applied to complex tasks such as object detection. PTQ
optimizes the quantization parameters by different metrics to minimize the
perturbation of quantization. The p-norm distance of feature maps before and
after quantization, Lp, is widely used as the metric to evaluate perturbation.
For the specialty of object detection network, we observe that the parameter p
in Lp metric will significantly influence its quantization performance. We
indicate that using a fixed hyper-parameter p does not achieve optimal
quantization performance. To mitigate this problem, we propose a framework,
DetPTQ, to assign different p values for quantizing different layers using an
Object Detection Output Loss (ODOL), which represents the task loss of object
detection. DetPTQ employs the ODOL-based adaptive Lp metric to select the
optimal quantization parameters. Experiments show that our DetPTQ outperforms
the state-of-the-art PTQ methods by a significant margin on both 2D and 3D
object detectors. For example, we achieve
31.1/31.7(quantization/full-precision) mAP on RetinaNet-ResNet18 with 4-bit
weight and 4-bit activation.
