modify the links & fix some typos (#1150)

* modify the links & fix some typos

* typo fixed

docs/en/02-how-to-run/convert_model.md

@@ -35,20 +35,20 @@ python ./tools/deploy.py \
 - `deploy_cfg` : The deployment configuration of mmdeploy for the model, including the type of inference framework, whether quantize, whether the input shape is dynamic, etc. There may be a reference relationship between configuration files, `mmdeploy/mmcls/classification_ncnn_static.py` is an example.
 - `model_cfg` : Model configuration for algorithm library, e.g. `mmclassification/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py`, regardless of the path to mmdeploy.
 - `checkpoint` : torch model path. It can start with http/https, see the implementation of `mmcv.FileClient` for details.
-- `img` : The path to the image or point cloud file used for testing during model conversion.
-- `--test-img` : The path of image file that used to test model. If not specified, it will be set to `None`.
-- `--work-dir` : The path of work directory that used to save logs and models.
-- `--calib-dataset-cfg` : Only valid in int8 mode. Config used for calibration. If not specified, it will be set to `None` and  use "val" dataset in model config for calibration.
-- `--device` : The device used for model conversion. If not specified, it will be set to `cpu`, for trt use `cuda:0` format.
+- `img` : The path to the image or point cloud file used for testing during the model conversion.
+- `--test-img` : The path of the image file that is used to test the model. If not specified, it will be set to `None`.
+- `--work-dir` : The path of the work directory that is used to save logs and models.
+- `--calib-dataset-cfg` : Only valid in int8 mode. The config used for calibration. If not specified, it will be set to `None` and use the "val" dataset in the model config for calibration.
+- `--device` : The device used for model conversion. If not specified, it will be set to `cpu`. For trt, use `cuda:0` format.
 - `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
 - `--show` : Whether to show detection outputs.
 - `--dump-info` : Whether to output information for SDK.
 
 ### How to find the corresponding deployment config of a PyTorch model
 
-1. Find model's codebase folder in `configs/`. Example, convert a yolov3 model you need to find `configs/mmdet` folder.
-2. Find model's task folder in `configs/codebase_folder/`. Just like yolov3 model, you need to find `configs/mmdet/detection` folder.
-3. Find deployment config file in `configs/codebase_folder/task_folder/`. Just like deploy yolov3 model you can use `configs/mmdet/detection/detection_onnxruntime_dynamic.py`.
+1. Find the model's codebase folder in `configs/`.  For converting a yolov3 model, you need to check `configs/mmdet` folder.
+2. Find the model's task folder in `configs/codebase_folder/`. For a yolov3 model, you need to check `configs/mmdet/detection` folder.
+3. Find the deployment config file in `configs/codebase_folder/task_folder/`. For deploying a yolov3 model to the onnx backend, you could use `configs/mmdet/detection/detection_onnxruntime_dynamic.py`.
 
 ### Example
 

docs/zh_cn/02-how-to-run/convert_model.md

@@ -11,8 +11,6 @@
     - [示例](#示例)
   - [如何评测模型](#如何评测模型)
   - [各后端已支持导出的模型列表](#各后端已支持导出的模型列表)
-  - [注意事项](#注意事项)
-  - [问答](#问答)
 
 <!-- TOC -->
 
@@ -20,15 +18,15 @@
 
 注意:
 
-- 现在已支持的后端包括 [ONNX Runtime](https://mmdeploy.readthedocs.io/en/latest/backends/onnxruntime.html) ，[TensorRT](https://mmdeploy.readthedocs.io/en/latest/backends/tensorrt.html) ，[ncnn](https://mmdeploy.readthedocs.io/en/latest/backends/ncnn.html) ，[PPLNN](https://mmdeploy.readthedocs.io/en/latest/backends/pplnn.html), [OpenVINO](https://mmdeploy.readthedocs.io/en/latest/backends/openvino.html)。
-- 现在已支持的代码库包括 [MMClassification](https://mmdeploy.readthedocs.io/en/latest/codebases/mmcls.html) ，[MMDetection](https://mmdeploy.readthedocs.io/en/latest/codebases/mmdet.html) ，[MMSegmentation](https://mmdeploy.readthedocs.io/en/latest/codebases/mmseg.html) ，[MMOCR](https://mmdeploy.readthedocs.io/en/latest/codebases/mmocr.html) ，[MMEditing](https://mmdeploy.readthedocs.io/en/latest/codebases/mmedit.html)。
+- 现在已支持的后端包括 [ONNXRuntime](../05-supported-backends/onnxruntime.md) ，[TensorRT](../05-supported-backends/tensorrt.md) ，[ncnn](../05-supported-backends/ncnn.md) ，[PPLNN](../05-supported-backends/pplnn.md) ，[OpenVINO](../05-supported-backends/openvino.md)。
+- 现在已支持的代码库包括 [MMClassification](../04-supported-codebases/mmcls.md) ，[MMDetection](../04-supported-codebases/mmdet.md) ，[MMSegmentation](../04-supported-codebases/mmseg.md) ，[MMOCR](../04-supported-codebases/mmocr.md) ，[MMEditing](../04-supported-codebases/mmedit.md)。
 
 ## 如何将模型从pytorch形式转换成其他后端形式
 
 ### 准备工作
 
-1. 安装您的目标后端。 您可以参考 [ONNXRuntime-install](https://mmdeploy.readthedocs.io/en/latest/backends/onnxruntime.html) ，[TensorRT-install](https://mmdeploy.readthedocs.io/en/latest/backends/tensorrt.html) ，[ncnn-install](https://mmdeploy.readthedocs.io/en/latest/backends/ncnn.html) ，[PPLNN-install](https://mmdeploy.readthedocs.io/en/latest/backends/pplnn.html), [OpenVINO-install](https://mmdeploy.readthedocs.io/en/latest/backends/openvino.html)。
-2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/install.md)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://mmocr.readthedocs.io/en/latest/install.html)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/master/docs/zh_cn/install.md)。
+1. 安装您的目标后端。 您可以参考 [ONNXRuntime-install](../05-supported-backends/onnxruntime.md) ，[TensorRT-install](../05-supported-backends/tensorrt.md) ，[ncnn-install](../05-supported-backends/ncnn.md) ，[PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md)。
+2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/install.md)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://mmocr.readthedocs.io/zh_CN/latest/install.html)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/master/docs/zh_cn/install.md)。
 
 ### 使用方法
 

mmdeploy/backend/ncnn/onnx2ncnn.py

@@ -22,12 +22,12 @@ def get_output_model_file(onnx_path: str,
     """Returns the path to the .param, .bin file with export result.
 
     Args:
-        onnx_path (str): The path to the onnx model.
-        work_dir (str|None): The path to the directory for saving the results.
-            Defaults to `None`, which means use the directory of onnx_path.
+        onnx_path (str): The path of the onnx model.
+        work_dir (str|None): The path of the directory for saving the results.
+            Defaults to `None`, which means using the directory of onnx_path.
 
     Returns:
-        List[str]: The path to the files where the export result will be
+        List[str]: The path of the files where the export result will be
             located.
     """
     if work_dir is None:
@@ -44,7 +44,7 @@ def from_onnx(onnx_model: Union[onnx.ModelProto, str],
     """Convert ONNX to ncnn.
 
     The inputs of ncnn include a model file and a weight file. We need to use
-    a executable program to convert the `.onnx` file to a `.param` file and
+    an executable program to convert the `.onnx` file to a `.param` file and
     a `.bin` file. The output files will save to work_dir.
 
     Example: