fix docs

2023-04-04 13:20:58 +08:00 · 2023-04-04 13:20:58 +08:00 · 23dcedc7cc
parent f76d8c44a5
commit 23dcedc7cc
10 changed files with 31 additions and 31 deletions
--- a/.github/ISSUE_TEMPLATE/1-bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/1-bug-report.yml
@ -9,7 +9,7 @@ body:
    label: Checklist
    options:
    - label: I have searched related issues but cannot get the expected help.
-    - label: 2. I have read the [FAQ documentation](https://github.com/open-mmlab/mmdeploy/tree/1.x/docs/en/faq.md) but cannot get the expected help.
+    - label: 2. I have read the [FAQ documentation](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/faq.md) but cannot get the expected help.
    - label: 3. The bug has not been fixed in the latest version.
 - type: textarea
  attributes:
--- a/docs/en/04-supported-codebases/mmcls.md
+++ b/docs/en/04-supported-codebases/mmcls.md
@ -35,7 +35,7 @@ If your target platform is **Ubuntu 18.04 or later version**, we encourage you t
 [scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.

 ```shell
-git clone --recursive -b 1.x https://github.com/open-mmlab/mmdeploy.git
+git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
 export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
@ -48,7 +48,7 @@ If neither **I** nor **II** meets your requirements, [building mmdeploy from sou

 ## Convert model

-You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/tools/deploy.py) to convert mmcls models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/1.x/docs/en/02-how-to-run/convert_model.md#usage).
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/main/tools/deploy.py) to convert mmcls models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/02-how-to-run/convert_model.md#usage).

 The command below shows an example about converting `resnet18` model to onnx model that can be inferred by ONNX Runtime.

@ -70,7 +70,7 @@ python tools/deploy.py \
    --dump-info
 ```

-It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmcls) of all supported backends for mmclassification. The config filename pattern is:
+It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmcls) of all supported backends for mmclassification. The config filename pattern is:

 ```
 classification_{backend}-{precision}_{static | dynamic}_{shape}.py
@ -81,7 +81,7 @@ classification_{backend}-{precision}_{static | dynamic}_{shape}.py
 - **{static | dynamic}:** static shape or dynamic shape
 - **{shape}:** input shape or shape range of a model

-Therefore, in the above example, you can also convert `resnet18` to other backend models by changing the deployment config file `classification_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmcls), e.g., converting to tensorrt-fp16 model by `classification_tensorrt-fp16_dynamic-224x224-224x224.py`.
+Therefore, in the above example, you can also convert `resnet18` to other backend models by changing the deployment config file `classification_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmcls), e.g., converting to tensorrt-fp16 model by `classification_tensorrt-fp16_dynamic-224x224-224x224.py`.

 ```{tip}
 When converting mmcls models to tensorrt models, --device should be set to "cuda"
--- a/docs/en/04-supported-codebases/mmdet.md
+++ b/docs/en/04-supported-codebases/mmdet.md
@ -35,7 +35,7 @@ If your target platform is **Ubuntu 18.04 or later version**, we encourage you t
 [scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.

 ```shell
-git clone --recursive -b 1.x https://github.com/open-mmlab/mmdeploy.git
+git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
 export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
@ -48,7 +48,7 @@ If neither **I** nor **II** meets your requirements, [building mmdeploy from sou

 ## Convert model

-You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/tools/deploy.py) to convert mmdet models to the specified backend models. Its detailed usage can be learned from [here](../02-how-to-run/convert_model.md).
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/main/tools/deploy.py) to convert mmdet models to the specified backend models. Its detailed usage can be learned from [here](../02-how-to-run/convert_model.md).

 The command below shows an example about converting `Faster R-CNN` model to onnx model that can be inferred by ONNX Runtime.

@ -68,7 +68,7 @@ python tools/deploy.py \
    --dump-info
 ```

-It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet) of all supported backends for mmdetection, under which the config file path follows the pattern:
+It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmdet) of all supported backends for mmdetection, under which the config file path follows the pattern:

 ```
 {task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py
@ -90,7 +90,7 @@ It is crucial to specify the correct deployment config during model conversion.

 - **{shape}:** input shape or shape range of a model

-Therefore, in the above example, you can also convert `faster r-cnn` to other backend models by changing the deployment config file `detection_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet/detection), e.g., converting to tensorrt-fp16 model by `detection_tensorrt-fp16_dynamic-320x320-1344x1344.py`.
+Therefore, in the above example, you can also convert `faster r-cnn` to other backend models by changing the deployment config file `detection_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmdet/detection), e.g., converting to tensorrt-fp16 model by `detection_tensorrt-fp16_dynamic-320x320-1344x1344.py`.

 ```{tip}
 When converting mmdet models to tensorrt models, --device should be set to "cuda"
@ -185,7 +185,7 @@ for index, bbox, label_id in zip(indices, bboxes, labels):
 cv2.imwrite('output_detection.png', img)
 ```

-Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo).
+Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo).

 ## Supported models

--- a/docs/en/04-supported-codebases/mmocr.md
+++ b/docs/en/04-supported-codebases/mmocr.md
@ -40,7 +40,7 @@ If your target platform is **Ubuntu 18.04 or later version**, we encourage you t
 [scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.

 ```shell
-git clone --recursive -b 1.x https://github.com/open-mmlab/mmdeploy.git
+git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
 export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
@ -53,9 +53,9 @@ If neither **I** nor **II** meets your requirements, [building mmdeploy from sou

 ## Convert model

-You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/tools/deploy.py) to convert mmocr models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/1.x/docs/en/02-how-to-run/convert_model.md#usage).
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/main/tools/deploy.py) to convert mmocr models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/02-how-to-run/convert_model.md#usage).

-When using `tools/deploy.py`, it is crucial to specify the correct deployment config. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmocr) of all supported backends for mmocr, under which the config file path follows the pattern:
+When using `tools/deploy.py`, it is crucial to specify the correct deployment config. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmocr) of all supported backends for mmocr, under which the config file path follows the pattern:

 ```
 {task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py
@ -113,7 +113,7 @@ python tools/deploy.py \
    --dump-info
 ```

-You can also convert the above models to other backend models by changing the deployment config file `*_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmocr), e.g., converting `dbnet` to tensorrt-fp32 model by `text-detection/text-detection_tensorrt-_dynamic-320x320-2240x2240.py`.
+You can also convert the above models to other backend models by changing the deployment config file `*_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmocr), e.g., converting `dbnet` to tensorrt-fp32 model by `text-detection/text-detection_tensorrt-_dynamic-320x320-2240x2240.py`.

 ```{tip}
 When converting mmocr models to tensorrt models, --device should be set to "cuda"
@ -230,7 +230,7 @@ texts = recognizer(img)
 print(texts)
 ```

-Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo).
+Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo).

 ## Supported models

--- a/docs/en/04-supported-codebases/mmpose.md
+++ b/docs/en/04-supported-codebases/mmpose.md
@ -35,7 +35,7 @@ If your target platform is **Ubuntu 18.04 or later version**, we encourage you t
 [scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.

 ```shell
-git clone --recursive -b 1.x https://github.com/open-mmlab/mmdeploy.git
+git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
 export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
@ -48,7 +48,7 @@ If neither **I** nor **II** meets your requirements, [building mmdeploy from sou

 ## Convert model

-You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/tools/deploy.py) to convert mmpose models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/1.x/docs/en/02-how-to-run/convert_model.md#usage).
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/main/tools/deploy.py) to convert mmpose models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/02-how-to-run/convert_model.md#usage).

 The command below shows an example about converting `hrnet` model to onnx model that can be inferred by ONNX Runtime.

@ -67,7 +67,7 @@ python tools/deploy.py \
    --show
 ```

-It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmpose) of all supported backends for mmpose. The config filename pattern is:
+It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmpose) of all supported backends for mmpose. The config filename pattern is:

 ```
 pose-detection_{backend}-{precision}_{static | dynamic}_{shape}.py
@ -78,7 +78,7 @@ pose-detection_{backend}-{precision}_{static | dynamic}_{shape}.py
 - **{static | dynamic}:** static shape or dynamic shape
 - **{shape}:** input shape or shape range of a model

-Therefore, in the above example, you can also convert `hrnet` to other backend models by changing the deployment config file `pose-detection_onnxruntime_static.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmpose), e.g., converting to tensorrt model by `pose-detection_tensorrt_static-256x192.py`.
+Therefore, in the above example, you can also convert `hrnet` to other backend models by changing the deployment config file `pose-detection_onnxruntime_static.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmpose), e.g., converting to tensorrt model by `pose-detection_tensorrt_static-256x192.py`.

 ```{tip}
 When converting mmpose models to tensorrt models, --device should be set to "cuda"
--- a/docs/zh_cn/01-how-to-build/rockchip.md
+++ b/docs/zh_cn/01-how-to-build/rockchip.md
@ -105,7 +105,7 @@ python tools/deploy.py \

 - YOLOV3 & YOLOX

-将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)
+将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/main/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)

 ```python
 # yolov3, yolox for rknn-toolkit and rknn-toolkit2
@ -154,7 +154,7 @@ partition_config = dict(

 - RetinaNet & SSD & FSAF with rknn-toolkit2

-将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)。使用 rknn-toolkit 的用户则不用。
+将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/main/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)。使用 rknn-toolkit 的用户则不用。

 ```python
 # retinanet, ssd and fsaf for rknn-toolkit2
--- a/docs/zh_cn/conf.py
+++ b/docs/zh_cn/conf.py
@ -106,7 +106,7 @@ html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
 # documentation.
 #
 html_theme_options = {
-    'logo_url': 'https://mmdeploy.readthedocs.io/zh_CN/1.x/',
+    'logo_url': 'https://mmdeploy.readthedocs.io/zh_CN/latest/',
    'menu': [{
        'name': 'GitHub',
        'url': 'https://github.com/open-mmlab/mmdeploy'
--- a/docs/zh_cn/get_started.md
+++ b/docs/zh_cn/get_started.md
@ -268,7 +268,7 @@ for index, bbox, label_id in zip(indices, bboxes, labels):
 cv2.imwrite('output_detection.png', img)
 ```

-更多示例，请查阅[这里](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo/python)。
+更多示例，请查阅[这里](https://github.com/open-mmlab/mmdeploy/tree/main/demo/python)。

 #### C++ API

@ -322,9 +322,9 @@ target_link_libraries(${name} PRIVATE mmdeploy ${OpenCV_LIBS})
 ```

 编译时，使用 -DMMDeploy_DIR，传入MMDeloyConfig.cmake所在的路径。它在预编译包中的sdk/lib/cmake/MMDeloy下。
-更多示例，请查阅[此处](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo/csrc/cpp)。
+更多示例，请查阅[此处](https://github.com/open-mmlab/mmdeploy/tree/main/demo/csrc/cpp)。

-对于 C API、C# API、Java API 的使用方法，请分别阅读代码[C demos](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo/csrc/c)， [C# demos](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo/csharp) 和 [Java demos](https://github.com/open-mmlab/mmdeploy/tree/1.x/demo/java)。
+对于 C API、C# API、Java API 的使用方法，请分别阅读代码[C demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo/csrc/c)， [C# demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo/csharp) 和 [Java demos](https://github.com/open-mmlab/mmdeploy/tree/main/demo/java)。
 我们将在后续版本中详细讲述它们的用法。

 #### 加速预处理（实验性功能）
--- a/docs/zh_cn/switch_language.md
+++ b/docs/zh_cn/switch_language.md
@ -1,3 +1,3 @@
-## <a href='https://mmdeploy.readthedocs.io/en/1.x/'>English</a>
+## <a href='https://mmdeploy.readthedocs.io/en/latest/'>English</a>

-## <a href='https://mmdeploy.readthedocs.io/zh_CN/1.x/'>简体中文</a>
+## <a href='https://mmdeploy.readthedocs.io/zh_CN/latest/'>简体中文</a>
--- a/docs/zh_cn/tutorial/02_challenges.md
+++ b/docs/zh_cn/tutorial/02_challenges.md
@ -1,6 +1,6 @@
 # 第二章：解决模型部署中的难题

-在[第一章](https://mmdeploy.readthedocs.io/zh_CN/1.x/tutorial/01_introduction_to_model_deployment.html)中，我们部署了一个简单的超分辨率模型，一切都十分顺利。但是，上一个模型还有一些缺陷——图片的放大倍数固定是 4，我们无法让图片放大任意的倍数。现在，我们来尝试部署一个支持动态放大倍数的模型，体验一下在模型部署中可能会碰到的困难。
+在[第一章](https://mmdeploy.readthedocs.io/zh_CN/latest/tutorial/01_introduction_to_model_deployment.html)中，我们部署了一个简单的超分辨率模型，一切都十分顺利。但是，上一个模型还有一些缺陷——图片的放大倍数固定是 4，我们无法让图片放大任意的倍数。现在，我们来尝试部署一个支持动态放大倍数的模型，体验一下在模型部署中可能会碰到的困难。

 ## 模型部署中常见的难题

@ -10,7 +10,7 @@
 - 新算子的实现。深度学习技术日新月异，提出新算子的速度往往快于 ONNX 维护者支持的速度。为了部署最新的模型，部署工程师往往需要自己在 ONNX 和推理引擎中支持新算子。
 - 中间表示与推理引擎的兼容问题。由于各推理引擎的实现不同，对 ONNX 难以形成统一的支持。为了确保模型在不同的推理引擎中有同样的运行效果，部署工程师往往得为某个推理引擎定制模型代码，这为模型部署引入了许多工作量。

-我们会在后续教程详细讲述解决这些问题的方法。如果对前文中 ONNX、推理引擎、中间表示、算子等名词感觉陌生，不用担心，可以阅读[第一章](https://mmdeploy.readthedocs.io/zh_CN/1.x/tutorial/01_introduction_to_model_deployment.html)，了解有关概念。
+我们会在后续教程详细讲述解决这些问题的方法。如果对前文中 ONNX、推理引擎、中间表示、算子等名词感觉陌生，不用担心，可以阅读[第一章](https://mmdeploy.readthedocs.io/zh_CN/latest/tutorial/01_introduction_to_model_deployment.html)，了解有关概念。

 现在，让我们对原来的 SRCNN 模型做一些小的修改，体验一下模型动态化对模型部署造成的困难，并学习解决该问题的一种方法。

@ -38,7 +38,7 @@ def init_torch_model():

 现在，假设我们要做一个超分辨率的应用。我们的用户希望图片的放大倍数能够自由设置。而我们交给用户的，只有一个 .onnx 文件和运行超分辨率模型的应用程序。我们在不修改 .onnx 文件的前提下改变放大倍数。

-因此，我们必须修改原来的模型，令模型的放大倍数变成推理时的输入。在[第一章](https://mmdeploy.readthedocs.io/zh_CN/1.x/tutorial/01_introduction_to_model_deployment.html)中的 Python 脚本的基础上，我们做一些修改，得到这样的脚本：
+因此，我们必须修改原来的模型，令模型的放大倍数变成推理时的输入。在[第一章](https://mmdeploy.readthedocs.io/zh_CN/latest/tutorial/01_introduction_to_model_deployment.html)中的 Python 脚本的基础上，我们做一些修改，得到这样的脚本：

 ```python
 import torch
@ -75,7 +75,7 @@ def init_torch_model():
    torch_model = SuperResolutionNet()

    # Please read the code about downloading 'srcnn.pth' and 'face.png' in
-    # https://mmdeploy.readthedocs.io/zh_CN/1.x/tutorial/01_introduction_to_model_deployment.html#pytorch
+    # https://mmdeploy.readthedocs.io/zh_CN/latest/tutorial/01_introduction_to_model_deployment.html#pytorch
    state_dict = torch.load('srcnn.pth')['state_dict']

    # Adapt the checkpoint