pytorch/vision

Add uint8 support for interpolate and grid-sample in PyTorch

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#2,289 创建于 2020年6月5日

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 (4 评论) (6 反应) (1 负责人)Python (15,050 star) (6,858 fork)batch import
help wantedhigh prioritymodule: transforms

描述

🚀 Feature

With the addition of torch tensor support for the transforms (following https://github.com/pytorch/vision/issues/1375), there are two operators that will require extra attention in order for them to be as efficient as the PIL implementations.

Indeed, while we can implement resize via torch.nn.functional.interpolate and rotate via torch.nn.functional.grid_sample, those operators for now only support floating-point types, so for now we need to perform a .float() -> interpolate() -> byte() in order to maintain compatibility, which is wasteful.

It would be great if they could be extended to support uint8 (and maybe other integer types) as well.

A first PR adding support for uint8 to nearest mode interpolate has been sent in https://github.com/pytorch/pytorch/pull/35029

Something to keep in mind: the interpolate function is under optimization in https://github.com/pytorch/pytorch/pull/34864, so this should be kept in mind to avoid conflicts.

贡献者指南

技术栈
pythonpytorch
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
4
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
over 1 week
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
blocked
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
Basic PyTorch knowledgeFamiliarity with C++ and CUDAUnderstanding of interpolation algorithms
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
15
研究方向
The issue requests adding uint8 support to torch.nn.functional.interpolate and grid sample. A preliminary PR for nearest mode interpolate exists at https://github.com/pytorch/pytorch/pull/35029. Additionally, there is ongoing optimization in PR #34864. Primary files to modify are in `aten/src/ATen/native/` for CPU kernels and `aten/src/ATen/native/cuda/` for GPU. The current code path involves a float conversion, so the fix would add integer type dispatch in the kernel implementations.