mirror of https://github.com/open-mmlab/mmcv.git
[Docs] Fix mdformat version to support python3.6 (#2014)
* [Docs] Refine documentation * fix mdformat version to support python3.6 * remove ruby dependency from CI * fix invalid linkspull/1813/head^2
Zaida Zhou
GitHub
parent
834d5978df
commit
11d8554cf9
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@ -17,10 +17,6 @@ jobs:
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python-version: 3.7
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- name: Install pre-commit hook
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run: |
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# markdownlint requires ruby >= 2.7
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sudo apt-add-repository ppa:brightbox/ruby-ng -y
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sudo apt-get update
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sudo apt-get install -y ruby2.7
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pip install pre-commit
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pre-commit install
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- name: Linting
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@ -30,12 +30,12 @@ repos:
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hooks:
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- id: codespell
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- repo: https://github.com/executablebooks/mdformat
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rev: 0.7.14
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rev: 0.7.9
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hooks:
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- id: mdformat
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args: ["--number"]
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additional_dependencies:
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- mdformat-gfm
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- mdformat-openmmlab
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- mdformat_frontmatter
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- linkify-it-py
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- repo: https://github.com/myint/docformatter
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@ -4,27 +4,27 @@ This document is used as a reference for English-Chinese terminology translation
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该文档用作中英文翻译对照参考。
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| English | 中文 |
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| :---------------: | :----: |
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| annotation | 标注 |
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| backbone | 主干网络 |
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| benchmark | 基准测试 |
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| English | 中文 |
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| :---------------: | :----------: |
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| annotation | 标注 |
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| backbone | 主干网络 |
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| benchmark | 基准测试 |
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| checkpoint | 模型权重文件 |
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| classifier | 分类器 |
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| cls_head | 分类头 |
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| decoder | 解码器 |
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| detector | 检测器 |
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| encoder | 编码器 |
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| finetune | 微调 |
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| ground truth | 真实标签 |
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| hook | 钩子 |
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| localizer | 定位器 |
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| neck | 模型颈部 |
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| pipeline | 流水线 |
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| recognizer | 识别器 |
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| register | 注册器 |
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| schedule | 调整 |
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| scheduler | 调度器 |
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| segmentor | 分割器 |
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| tensor | 张量 |
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| training schedule | 训练策略 |
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| classifier | 分类器 |
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| cls_head | 分类头 |
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| decoder | 解码器 |
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| detector | 检测器 |
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| encoder | 编码器 |
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| finetune | 微调 |
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| ground truth | 真实标签 |
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| hook | 钩子 |
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| localizer | 定位器 |
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| neck | 模型颈部 |
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| pipeline | 流水线 |
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| recognizer | 识别器 |
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| register | 注册器 |
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| schedule | 调整 |
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| scheduler | 调度器 |
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| segmentor | 分割器 |
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| tensor | 张量 |
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| training schedule | 训练策略 |
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@ -107,7 +107,7 @@ Take custom operator `soft_nms` for example.
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2. Add source `soft_nms.cpp` to ONNX Runtime source directory `mmcv/ops/csrc/onnxruntime/cpu/`
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3. Register `soft_nms` operator in [onnxruntime_register.cpp](../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)
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3. Register `soft_nms` operator in [onnxruntime_register.cpp](../../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)
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```c++
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#include "soft_nms.h"
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@ -133,4 +133,4 @@ Take custom operator `soft_nms` for example.
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### References
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- [How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime](https://github.com/onnx/tutorials/blob/master/PyTorchCustomOperator/README.md)
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- [How to add a custom operator/kernel in ONNX Runtime](https://github.com/microsoft/onnxruntime/blob/master/docs/AddingCustomOp.md)
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- [How to add a custom operator/kernel in ONNX Runtime](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html)
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@ -2,55 +2,55 @@
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<!-- TOC -->
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- [ONNX Runtime自定义算子](#onnx-runtime%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90)
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- [ONNX Runtime自定义算子](#onnx-runtime自定义算子)
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- [SoftNMS](#softnms)
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- [描述](#%E6%8F%8F%E8%BF%B0)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0)
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- [输入](#%E8%BE%93%E5%85%A5)
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- [输出](#%E8%BE%93%E5%87%BA)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F)
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- [描述](#描述)
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- [模型参数](#模型参数)
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- [输入](#输入)
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- [输出](#输出)
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- [类型约束](#类型约束)
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- [RoIAlign](#roialign)
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- [描述](#%E6%8F%8F%E8%BF%B0-1)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-1)
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- [输入](#%E8%BE%93%E5%85%A5-1)
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- [输出](#%E8%BE%93%E5%87%BA-1)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-1)
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- [描述](#描述-1)
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- [模型参数](#模型参数-1)
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- [输入](#输入-1)
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- [输出](#输出-1)
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- [类型约束](#类型约束-1)
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- [NMS](#nms)
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- [描述](#%E6%8F%8F%E8%BF%B0-2)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-2)
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- [输入](#%E8%BE%93%E5%85%A5-2)
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- [输出](#%E8%BE%93%E5%87%BA-2)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-2)
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- [描述](#描述-2)
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- [模型参数](#模型参数-2)
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- [输入](#输入-2)
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- [输出](#输出-2)
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- [类型约束](#类型约束-2)
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- [grid_sampler](#grid_sampler)
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- [描述](#%E6%8F%8F%E8%BF%B0-3)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-3)
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- [输入](#%E8%BE%93%E5%85%A5-3)
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- [输出](#%E8%BE%93%E5%87%BA-3)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-3)
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- [描述](#描述-3)
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- [模型参数](#模型参数-3)
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- [输入](#输入-3)
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- [输出](#输出-3)
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- [类型约束](#类型约束-3)
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- [CornerPool](#cornerpool)
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- [描述](#%E6%8F%8F%E8%BF%B0-4)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-4)
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- [输入](#%E8%BE%93%E5%85%A5-4)
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- [输出](#%E8%BE%93%E5%87%BA-4)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-4)
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- [描述](#描述-4)
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- [模型参数](#模型参数-4)
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- [输入](#输入-4)
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- [输出](#输出-4)
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- [类型约束](#类型约束-4)
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- [cummax](#cummax)
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- [描述](#%E6%8F%8F%E8%BF%B0-5)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-5)
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- [输入](#%E8%BE%93%E5%85%A5-5)
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- [输出](#%E8%BE%93%E5%87%BA-5)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-5)
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- [描述](#描述-5)
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- [模型参数](#模型参数-5)
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- [输入](#输入-5)
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- [输出](#输出-5)
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- [类型约束](#类型约束-5)
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- [cummin](#cummin)
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- [描述](#%E6%8F%8F%E8%BF%B0-6)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-6)
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- [输入](#%E8%BE%93%E5%85%A5-6)
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- [输出](#%E8%BE%93%E5%87%BA-6)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-6)
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- [描述](#描述-6)
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- [模型参数](#模型参数-6)
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- [输入](#输入-6)
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- [输出](#输出-6)
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- [类型约束](#类型约束-6)
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- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
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- [描述](#%E6%8F%8F%E8%BF%B0-7)
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- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-7)
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- [输入](#%E8%BE%93%E5%85%A5-7)
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- [输出](#%E8%BE%93%E5%87%BA-7)
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- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-7)
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- [描述](#描述-7)
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- [模型参数](#模型参数-7)
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- [输入](#输入-7)
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- [输出](#输出-7)
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- [类型约束](#类型约束-7)
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<!-- TOC -->
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@ -62,13 +62,13 @@
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ------- | --------------- | -------------------------------------------------- |
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| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
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| `float` | `sigma` | 高斯方法的超参数 |
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| `float` | `min_score` | NMS的score阈值 |
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| 类型 | 参数名 | 描述 |
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| ------- | --------------- | ------------------------------------------------------- |
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| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
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| `float` | `sigma` | 高斯方法的超参数 |
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| `float` | `min_score` | NMS的score阈值 |
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| `int` | `method` | NMS的计算方式, (0: `naive`, 1: `linear`, 2: `gaussian`) |
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| `int` | `offset` | 用来计算候选框的宽高(x2 - x1 + offset)。可选值0或1 |
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| `int` | `offset` | 用来计算候选框的宽高(x2 - x1 + offset)。可选值0或1 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ------- | ---------------- | ----------------------------------- |
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| `int` | `output_height` | roi特征的输出高度 |
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| `int` | `output_width` | roi特征的输出宽度 |
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| `float` | `spatial_scale` | 输入检测框的缩放系数 |
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| `int` | `sampling_ratio` | 输出的采样率。`0`表示使用密集采样 |
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| `str` | `mode` | 池化方式。 `avg`或`max` |
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| 类型 | 参数名 | 描述 |
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| ------- | ---------------- | ------------------------------------------------------- |
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| `int` | `output_height` | roi特征的输出高度 |
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| `int` | `output_width` | roi特征的输出宽度 |
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| `float` | `spatial_scale` | 输入检测框的缩放系数 |
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| `int` | `sampling_ratio` | 输出的采样率。`0`表示使用密集采样 |
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| `str` | `mode` | 池化方式。 `avg`或`max` |
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| `int` | `aligned` | 如果`aligned=1`，则像素会进行-0.5的偏移以达到更好的对齐 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ------- | --------------- | ----------------------------------- |
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| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
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| `int` | `offset` | 用来计算候选框的宽高(x2 - x1 + offset)。可选值0或1 |
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| 类型 | 参数名 | 描述 |
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| ------- | --------------- | ------------------------------------------------------- |
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| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
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| `int` | `offset` | 用来计算候选框的宽高(x2 - x1 + offset)。可选值0或1 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ----- | -------------------- | ------------------------------------------------------------------------------------------------ |
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| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
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| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
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| 类型 | 参数名 | 描述 |
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| ----- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
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| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
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| `int` | `align_corners` | 如果`align_corners=1`，则极值(`-1`和`1`)会被当做输入边缘像素的中心点。如果`align_corners=0`，则它们会被看做是边缘像素的边缘点,减小分辨率对采样的影响 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ----- | ------ | --------------------------------------------------- |
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| 类型 | 参数名 | 描述 |
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| ----- | ------ | -------------------------------------------------------- |
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| `int` | `mode` | 池化模式。(0: `top`, 1: `bottom`, 2: `left`, 3: `right`) |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ----- | ----- | --------- |
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| `int` | `dim` | 进行累计计算的维度 |
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| 类型 | 参数名 | 描述 |
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| ----- | ------ | ------------------ |
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| `int` | `dim` | 进行累计计算的维度 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| ----- | ----- | --------- |
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| `int` | `dim` | 进行累计计算的维度 |
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| 类型 | 参数名 | 描述 |
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| ----- | ------ | ------------------ |
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| `int` | `dim` | 进行累计计算的维度 |
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#### 输入
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#### 模型参数
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| 类型 | 参数名 | 描述 |
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| -------------- | ------------------- | -------------------------------------- |
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| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
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| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
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| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
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| `int` | `deformable_groups` | 可变偏移量的分组，通常置位1即可 |
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| 类型 | 参数名 | 描述 |
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| -------------- | ------------------- | ------------------------------------------------------------- |
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| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
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| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
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| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
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| `int` | `deformable_groups` | 可变偏移量的分组，通常置位1即可 |
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| `int` | `groups` | 卷积分组数，`input_channel`会根据这个值被分为数个分组进行计算 |
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#### 输入
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@ -15,16 +15,16 @@
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|
||||
### MMCV已支持的算子
|
||||
|
||||
| 算子 | CPU | GPU | MMCV版本 |
|
||||
| :------------------------------------------------------------------------------: | :-: | :-: | :----: |
|
||||
| [SoftNMS](onnxruntime_custom_ops.md#softnms) | Y | N | 1.2.3 |
|
||||
| [RoIAlign](onnxruntime_custom_ops.md#roialign) | Y | N | 1.2.5 |
|
||||
| [NMS](onnxruntime_custom_ops.md#nms) | Y | N | 1.2.7 |
|
||||
| [grid_sampler](onnxruntime_custom_ops.md#grid_sampler) | Y | N | 1.3.1 |
|
||||
| [CornerPool](onnxruntime_custom_ops.md#cornerpool) | Y | N | 1.3.4 |
|
||||
| [cummax](onnxruntime_custom_ops.md#cummax) | Y | N | 1.3.4 |
|
||||
| [cummin](onnxruntime_custom_ops.md#cummin) | Y | N | 1.3.4 |
|
||||
| [MMCVModulatedDeformConv2d](onnxruntime_custom_ops.md#mmcvmodulateddeformconv2d) | Y | N | 1.3.12 |
|
||||
| 算子 | CPU | GPU | MMCV版本 |
|
||||
| :------------------------------------------------------------------------------: | :-: | :-: | :------: |
|
||||
| [SoftNMS](onnxruntime_custom_ops.md#softnms) | Y | N | 1.2.3 |
|
||||
| [RoIAlign](onnxruntime_custom_ops.md#roialign) | Y | N | 1.2.5 |
|
||||
| [NMS](onnxruntime_custom_ops.md#nms) | Y | N | 1.2.7 |
|
||||
| [grid_sampler](onnxruntime_custom_ops.md#grid_sampler) | Y | N | 1.3.1 |
|
||||
| [CornerPool](onnxruntime_custom_ops.md#cornerpool) | Y | N | 1.3.4 |
|
||||
| [cummax](onnxruntime_custom_ops.md#cummax) | Y | N | 1.3.4 |
|
||||
| [cummin](onnxruntime_custom_ops.md#cummin) | Y | N | 1.3.4 |
|
||||
| [MMCVModulatedDeformConv2d](onnxruntime_custom_ops.md#mmcvmodulateddeformconv2d) | Y | N | 1.3.12 |
|
||||
|
||||
### 如何编译ONNX Runtime自定义算子？
|
||||
|
||||
|
|
@ -100,7 +100,7 @@ onnx_results = sess.run(None, {'input' : input_data})
|
|||
|
||||
2. 在ONNX Runtime源码目录`mmcv/ops/csrc/onnxruntime/cpu/`下添加算子实现`soft_nms.cpp`
|
||||
|
||||
3. 在[onnxruntime_register.cpp](../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)中注册实现的算子`soft_nms`
|
||||
3. 在[onnxruntime_register.cpp](../../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)中注册实现的算子`soft_nms`
|
||||
|
||||
```c++
|
||||
#include "soft_nms.h"
|
||||
|
|
@ -126,4 +126,4 @@ onnx_results = sess.run(None, {'input' : input_data})
|
|||
### 引用
|
||||
|
||||
- [How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime](https://github.com/onnx/tutorials/blob/master/PyTorchCustomOperator/README.md)
|
||||
- [How to add a custom operator/kernel in ONNX Runtime](https://github.com/microsoft/onnxruntime/blob/master/docs/AddingCustomOp.md)
|
||||
- [How to add a custom operator/kernel in ONNX Runtime](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html)
|
||||
|
|
|
|||
|
|
@ -2,61 +2,61 @@
|
|||
|
||||
<!-- TOC -->
|
||||
|
||||
- [TensorRT自定义算子](#tensorrt%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90)
|
||||
- [TensorRT自定义算子](#tensorrt自定义算子)
|
||||
- [MMCVRoIAlign](#mmcvroialign)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0)
|
||||
- [输入](#%E8%BE%93%E5%85%A5)
|
||||
- [输出](#%E8%BE%93%E5%87%BA)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F)
|
||||
- [描述](#描述)
|
||||
- [模型参数](#模型参数)
|
||||
- [输入](#输入)
|
||||
- [输出](#输出)
|
||||
- [类型约束](#类型约束)
|
||||
- [ScatterND](#scatternd)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-1)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-1)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-1)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-1)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-1)
|
||||
- [描述](#描述-1)
|
||||
- [模型参数](#模型参数-1)
|
||||
- [输入](#输入-1)
|
||||
- [输出](#输出-1)
|
||||
- [类型约束](#类型约束-1)
|
||||
- [NonMaxSuppression](#nonmaxsuppression)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-2)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-2)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-2)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-2)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-2)
|
||||
- [描述](#描述-2)
|
||||
- [模型参数](#模型参数-2)
|
||||
- [输入](#输入-2)
|
||||
- [输出](#输出-2)
|
||||
- [类型约束](#类型约束-2)
|
||||
- [MMCVDeformConv2d](#mmcvdeformconv2d)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-3)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-3)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-3)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-3)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-3)
|
||||
- [描述](#描述-3)
|
||||
- [模型参数](#模型参数-3)
|
||||
- [输入](#输入-3)
|
||||
- [输出](#输出-3)
|
||||
- [类型约束](#类型约束-3)
|
||||
- [grid_sampler](#grid_sampler)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-4)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-4)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-4)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-4)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-4)
|
||||
- [描述](#描述-4)
|
||||
- [模型参数](#模型参数-4)
|
||||
- [输入](#输入-4)
|
||||
- [输出](#输出-4)
|
||||
- [类型约束](#类型约束-4)
|
||||
- [cummax](#cummax)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-5)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-5)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-5)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-5)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-5)
|
||||
- [描述](#描述-5)
|
||||
- [模型参数](#模型参数-5)
|
||||
- [输入](#输入-5)
|
||||
- [输出](#输出-5)
|
||||
- [类型约束](#类型约束-5)
|
||||
- [cummin](#cummin)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-6)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-6)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-6)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-6)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-6)
|
||||
- [描述](#描述-6)
|
||||
- [模型参数](#模型参数-6)
|
||||
- [输入](#输入-6)
|
||||
- [输出](#输出-6)
|
||||
- [类型约束](#类型约束-6)
|
||||
- [MMCVInstanceNormalization](#mmcvinstancenormalization)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-7)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-7)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-7)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-7)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-7)
|
||||
- [描述](#描述-7)
|
||||
- [模型参数](#模型参数-7)
|
||||
- [输入](#输入-7)
|
||||
- [输出](#输出-7)
|
||||
- [类型约束](#类型约束-7)
|
||||
- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
|
||||
- [描述](#%E6%8F%8F%E8%BF%B0-8)
|
||||
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-8)
|
||||
- [输入](#%E8%BE%93%E5%85%A5-8)
|
||||
- [输出](#%E8%BE%93%E5%87%BA-8)
|
||||
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-8)
|
||||
- [描述](#描述-8)
|
||||
- [模型参数](#模型参数-8)
|
||||
- [输入](#输入-8)
|
||||
- [输出](#输出-8)
|
||||
- [类型约束](#类型约束-8)
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
|
|
@ -68,13 +68,13 @@
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | ---------------- | ----------------------------------- |
|
||||
| `int` | `output_height` | roi特征的输出高度 |
|
||||
| `int` | `output_width` | roi特征的输出宽度 |
|
||||
| `float` | `spatial_scale` | 输入检测框的缩放系数 |
|
||||
| `int` | `sampling_ratio` | 输出的采样率。`0`表示使用密集采样 |
|
||||
| `str` | `mode` | 池化方式。 `avg`或`max` |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | ---------------- | ------------------------------------------------------- |
|
||||
| `int` | `output_height` | roi特征的输出高度 |
|
||||
| `int` | `output_width` | roi特征的输出宽度 |
|
||||
| `float` | `spatial_scale` | 输入检测框的缩放系数 |
|
||||
| `int` | `sampling_ratio` | 输出的采样率。`0`表示使用密集采样 |
|
||||
| `str` | `mode` | 池化方式。 `avg`或`max` |
|
||||
| `int` | `aligned` | 如果`aligned=1`，则像素会进行-0.5的偏移以达到更好的对齐 |
|
||||
|
||||
#### 输入
|
||||
|
|
@ -147,12 +147,12 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | ---------------------------- | ----------------------------------------------------------------------------- |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | ---------------------------- | -------------------------------------------------------------------------------------------- |
|
||||
| `int` | `center_point_box` | 0 - 候选框的格式为\[y1, x1, y2, x2\]， 1-候选框的格式为\[x_center, y_center, width, height\] |
|
||||
| `int` | `max_output_boxes_per_class` | 每一类最大的输出检测框个数。默认为0，输出检测框个数等于输入候选框数 |
|
||||
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
|
||||
| `float` | `score_threshold` | 用来判断候选框是否合法的阈值 |
|
||||
| `int` | `max_output_boxes_per_class` | 每一类最大的输出检测框个数。默认为0，输出检测框个数等于输入候选框数 |
|
||||
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
|
||||
| `float` | `score_threshold` | 用来判断候选框是否合法的阈值 |
|
||||
| `int` | `offset` | 检测框长宽计算方式为(x2 - x1 + offset)，可选值0或1 |
|
||||
|
||||
#### 输入
|
||||
|
|
@ -185,13 +185,13 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| -------------- | ------------------ | ------------------------------------------------------- |
|
||||
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
|
||||
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
|
||||
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
|
||||
| `int` | `deformable_group` | 可变偏移量的分组 |
|
||||
| `int` | `group` | 卷积分组数，`input_channel`会根据这个值被分为数个分组进行计算 |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
|
||||
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
|
||||
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
|
||||
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
|
||||
| `int` | `deformable_group` | 可变偏移量的分组 |
|
||||
| `int` | `group` | 卷积分组数，`input_channel`会根据这个值被分为数个分组进行计算 |
|
||||
| `int` | `im2col_step` | 可变卷积使用im2col计算卷积。输入与偏移量会以im2col_step为步长分块计算，减少临时空间的使用量。 |
|
||||
|
||||
#### 输入
|
||||
|
|
@ -224,10 +224,10 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | -------------------- | ------------------------------------------------------------------------------------------------ |
|
||||
| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
|
||||
| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
|
||||
| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
|
||||
| `int` | `align_corners` | 如果`align_corners=1`，则极值(`-1`和`1`)会被当做输入边缘像素的中心点。如果`align_corners=0`，则它们会被看做是边缘像素的边缘点,减小分辨率对采样的影响 |
|
||||
|
||||
#### 输入
|
||||
|
|
@ -258,9 +258,9 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | ----- | --------- |
|
||||
| `int` | `dim` | 进行累计计算的维度 |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | ------ | ------------------ |
|
||||
| `int` | `dim` | 进行累计计算的维度 |
|
||||
|
||||
#### 输入
|
||||
|
||||
|
|
@ -290,9 +290,9 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | ----- | --------- |
|
||||
| `int` | `dim` | 进行累计计算的维度 |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ----- | ------ | ------------------ |
|
||||
| `int` | `dim` | 进行累计计算的维度 |
|
||||
|
||||
#### 输入
|
||||
|
||||
|
|
@ -322,8 +322,8 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | --------- | ----------------- |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| ------- | --------- | ---------------------------- |
|
||||
| `float` | `epsilon` | 用来避免除0错误。默认为1e-05 |
|
||||
|
||||
#### 输入
|
||||
|
|
@ -356,12 +356,12 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
|
|||
|
||||
#### 模型参数
|
||||
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| -------------- | ------------------- | -------------------------------------- |
|
||||
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
|
||||
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
|
||||
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
|
||||
| `int` | `deformable_groups` | 可变偏移量的分组，通常置位1即可 |
|
||||
| 类型 | 参数名 | 描述 |
|
||||
| -------------- | ------------------- | ------------------------------------------------------------- |
|
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| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
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| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
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| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
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| `int` | `deformable_groups` | 可变偏移量的分组，通常置位1即可 |
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| `int` | `groups` | 卷积分组数，`input_channel`会根据这个值被分为数个分组进行计算 |
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#### 输入
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@ -24,17 +24,17 @@
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### MMCV中的TensorRT插件列表
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| ONNX算子 | TensorRT插件 | MMCV版本 |
|
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| :-----------------------: | :-----------------------------------------------------------------------------: | :----: |
|
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| MMCVRoiAlign | [MMCVRoiAlign](./tensorrt_custom_ops.md#mmcvroialign) | 1.2.6 |
|
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| ScatterND | [ScatterND](./tensorrt_custom_ops.md#scatternd) | 1.2.6 |
|
||||
| NonMaxSuppression | [NonMaxSuppression](./tensorrt_custom_ops.md#nonmaxsuppression) | 1.3.0 |
|
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| MMCVDeformConv2d | [MMCVDeformConv2d](./tensorrt_custom_ops.md#mmcvdeformconv2d) | 1.3.0 |
|
||||
| grid_sampler | [grid_sampler](./tensorrt_custom_ops.md#grid-sampler) | 1.3.1 |
|
||||
| cummax | [cummax](./tensorrt_custom_ops.md#cummax) | 1.3.5 |
|
||||
| cummin | [cummin](./tensorrt_custom_ops.md#cummin) | 1.3.5 |
|
||||
| MMCVInstanceNormalization | [MMCVInstanceNormalization](./tensorrt_custom_ops.md#mmcvinstancenormalization) | 1.3.5 |
|
||||
| MMCVModulatedDeformConv2d | [MMCVModulatedDeformConv2d](./tensorrt_custom_ops.md#mmcvmodulateddeformconv2d) | master |
|
||||
| ONNX算子 | TensorRT插件 | MMCV版本 |
|
||||
| :-----------------------: | :-----------------------------------------------------------------------------: | :------: |
|
||||
| MMCVRoiAlign | [MMCVRoiAlign](./tensorrt_custom_ops.md#mmcvroialign) | 1.2.6 |
|
||||
| ScatterND | [ScatterND](./tensorrt_custom_ops.md#scatternd) | 1.2.6 |
|
||||
| NonMaxSuppression | [NonMaxSuppression](./tensorrt_custom_ops.md#nonmaxsuppression) | 1.3.0 |
|
||||
| MMCVDeformConv2d | [MMCVDeformConv2d](./tensorrt_custom_ops.md#mmcvdeformconv2d) | 1.3.0 |
|
||||
| grid_sampler | [grid_sampler](./tensorrt_custom_ops.md#grid-sampler) | 1.3.1 |
|
||||
| cummax | [cummax](./tensorrt_custom_ops.md#cummax) | 1.3.5 |
|
||||
| cummin | [cummin](./tensorrt_custom_ops.md#cummin) | 1.3.5 |
|
||||
| MMCVInstanceNormalization | [MMCVInstanceNormalization](./tensorrt_custom_ops.md#mmcvinstancenormalization) | 1.3.5 |
|
||||
| MMCVModulatedDeformConv2d | [MMCVModulatedDeformConv2d](./tensorrt_custom_ops.md#mmcvmodulateddeformconv2d) | master |
|
||||
|
||||
注意
|
||||
|
||||
|
|
|
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Loading…
Reference in New Issue