From b51ccae3b21ced3dd93fd9a67917047bddb4f672 Mon Sep 17 00:00:00 2001 From: baominghelly <41820386+baominghelly@users.noreply.github.com> Date: Wed, 21 Feb 2024 14:03:20 +0800 Subject: [PATCH] fix broken link in docs (#216) Co-authored-by: Haojie Wang --- docs/SUPPORT_MATRIX_CN.md | 9 +++++---- docs/USER_GUIDE_CN.md | 21 +++++++++++---------- 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/docs/SUPPORT_MATRIX_CN.md b/docs/SUPPORT_MATRIX_CN.md index f812b355..2720f2ed 100644 --- a/docs/SUPPORT_MATRIX_CN.md +++ b/docs/SUPPORT_MATRIX_CN.md @@ -2,6 +2,7 @@ ## 目录 + - [环境支持](#环境支持) - [神经网络支持](#神经网络支持) - [技术支持](#技术支持) @@ -19,10 +20,10 @@ 目前已经验证过的神经网络模型有 -- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx) -- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/vision/classification/densenet-121/model/densenet-12.onnx) -- [x] [Inception-2](https://github.com/onnx/models/blob/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx) -- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx) +- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx) +- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/validated/vision/classification/densenet-121/model/densenet-12.onnx) +- [x] [Inception-2](https://github.com/onnx/models/blob/main/validated/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx) +- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx) ## 技术支持 diff --git a/docs/USER_GUIDE_CN.md b/docs/USER_GUIDE_CN.md index e6a12c31..974579b9 100644 --- a/docs/USER_GUIDE_CN.md +++ b/docs/USER_GUIDE_CN.md @@ -3,9 +3,10 @@ ## 目录 - [使用方法](#使用方法) -- [python-前端应用指南](#python-前端应用指南) - - [导入-onnx-模型](#导入-onnx-模型) - - [导出-onnx-模型](#导出-onnx-模型) +- [python 前端应用指南](#python-前端应用指南) + - [导入 onnx 模型](#导入-onnx-模型) + - [优化](#优化) + - [导出 onnx 模型](#导出-onnx-模型) - [执行推理](#执行推理) - [样例代码](#样例代码) - [技术支持](#技术支持) @@ -13,7 +14,7 @@ ## 使用方法 -项目管理功能已写到 [Makefile](Makefile)，支持下列功能： +项目管理功能已写到 [Makefile](../Makefile)，支持下列功能： - 编译项目：`make`/`make build` - 清理生成文件：`make clean` @@ -38,10 +39,10 @@ 支持的模型： -- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx) -- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/vision/classification/densenet-121/model/densenet-12.onnx) -- [x] [Inception-2](https://github.com/onnx/models/blob/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx) -- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx) +- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx) +- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/validated/vision/classification/densenet-121/model/densenet-12.onnx) +- [x] [Inception-2](https://github.com/onnx/models/blob/main/validated/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx) +- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx) ```python import onnx @@ -96,7 +97,7 @@ for name, tensor in stub.inputs.items(): print(name, tensor.shape(), tensor) ``` -对于 [resnet18-v2-7.onnx](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx)，会打印出： +对于 [resnet18-v2-7.onnx](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx)，会打印出： ```plaintext data [1, 3, 224, 224] @@ -137,7 +138,7 @@ for name, tensor in stub.outputs.items(): ### 样例代码 -您可以参照[./example/Resnet/resnet.py](./example/ResNet/resnet.py)的样例代码进行了解，并尝试运行。在这个文件中，我们使用了 Pytorch 构建了 resnet 网络。您可以查阅该脚本使用方式： +您可以参照[resnet.py](https://github.com/wanghailu0717/NNmodel/blob/main/ResNet/resnet.py)的样例代码进行了解，并尝试运行。在这个文件中，我们使用了 Pytorch 构建了 resnet 网络。您可以查阅该脚本使用方式： ```python python resnet.py -h