Learning to Upsample by Learning to Sample
Wenze Liu Hao Lu Hongtao Fu Zhiguo Cao
We present DySample, an ultra-lightweight and effective dynamic upsampler.
While impressive performance gains have been witnessed from recent kernel-based
dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much
workload, mostly due to the time-consuming dynamic convolution and the
additional sub-network used to generate dynamic kernels. Further, the need for
high-res feature guidance of FADE and SAPA somehow limits their application
scenarios. To address these concerns, we bypass dynamic convolution and
formulate upsampling from the perspective of point sampling, which is more
resource-efficient and can be easily implemented with the standard built-in
function in PyTorch. We first showcase a naive design, and then demonstrate how
to strengthen its upsampling behavior step by step towards our new upsampler,
DySample. Compared with former kernel-based dynamic upsamplers, DySample
requires no customized CUDA package and has much fewer parameters, FLOPs, GPU
memory, and latency. Besides the light-weight characteristics, DySample
outperforms other upsamplers across five dense prediction tasks, including
semantic segmentation, object detection, instance segmentation, panoptic
segmentation, and monocular depth estimation. Code is available at
https://github.com/tiny-smart/dysample.