Generate supported-backends markdown table

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

@@ -202,3 +202,45 @@ And the output look like this:
 |  Max   |   1.689    | 591.983 |
 +--------+------------+---------+
 ```
+
+## generate_md_table
+
+This tool can be used to generate supported-backends markdown table.
+
+### Usage
+
+```shell
+python tools/generate_md_table.py \
+    ${YML_FILE} \
+    ${OUTPUT} \
+    --backends ${BACKENDS}
+```
+
+### Description of all arguments
+
+- `yml_file:` input yml config path
+- `output:`  output markdown file path
+- `--backends:` output backends list. If not specified, it will be set 'onnxruntime' 'tensorrt' 'torchscript' 'pplnn' 'openvino' 'ncnn'.
+
+### Example:
+
+Generate backends markdown table from mmocr.yml
+
+```shell
+python tools/generate_md_table.py tests/regression/mmocr.yml tests/regression/mmocr.md --backends  onnxruntime tensorrt torchscript pplnn openvino ncnn
+```
+
+And the output look like this:
+
+| model                                                                                | task            | onnxruntime | tensorrt | torchscript | pplnn | openvino | ncnn |
+| :----------------------------------------------------------------------------------- | :-------------- | :---------: | :------: | :---------: | :---: | :------: | :--: |
+| [DBNet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/dbnet)         | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [DBNetpp](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/dbnetpp)     | TextDetection   |      Y      |    Y     |      N      |   N   |    Y     |  Y   |
+| [PANet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/panet)         | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [PSENet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/psenet)       | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [TextSnake](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/textsnake) | TextDetection   |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [MaskRCNN](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/maskrcnn)   | TextDetection   |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [CRNN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/crnn)         | TextRecognition |      Y      |    Y     |      Y      |   Y   |    N     |  Y   |
+| [SAR](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/sar)           | TextRecognition |      Y      |    N     |      Y      |   N   |    N     |  N   |
+| [SATRN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/satrn)       | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [ABINet](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/abinet)     | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |

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

@@ -202,3 +202,45 @@ python tools/profiler.py \
 |  Max   |   1.689    | 591.983 |
 +--------+------------+---------+
 ```
+
+## generate_md_table
+
+生成mmdeploy支持的后端表。
+
+### 用法
+
+```shell
+python tools/generate_md_table.py \
+    ${YML_FILE} \
+    ${OUTPUT} \
+    --backends ${BACKENDS}
+```
+
+### 参数说明
+
+- `yml_file:`  输入 yml 配置路径
+- `output:` 输出markdown文件路径
+- `--backends:` 要输出的后端，默认为 onnxruntime tensorrt torchscript pplnn openvino ncnn
+
+### 使用举例
+
+从 mmocr.yml 生成mmdeploy支持的后端表
+
+```shell
+python tools/generate_md_table.py tests/regression/mmocr.yml tests/regression/mmocr.md --backends onnxruntime tensorrt torchscript pplnn openvino ncnn
+```
+
+输出：
+
+| model                                                                                | task            | onnxruntime | tensorrt | torchscript | pplnn | openvino | ncnn |
+| :----------------------------------------------------------------------------------- | :-------------- | :---------: | :------: | :---------: | :---: | :------: | :--: |
+| [DBNet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/dbnet)         | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [DBNetpp](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/dbnetpp)     | TextDetection   |      Y      |    Y     |      N      |   N   |    Y     |  Y   |
+| [PANet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/panet)         | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [PSENet](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/psenet)       | TextDetection   |      Y      |    Y     |      Y      |   Y   |    Y     |  Y   |
+| [TextSnake](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/textsnake) | TextDetection   |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [MaskRCNN](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/maskrcnn)   | TextDetection   |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [CRNN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/crnn)         | TextRecognition |      Y      |    Y     |      Y      |   Y   |    N     |  Y   |
+| [SAR](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/sar)           | TextRecognition |      Y      |    N     |      Y      |   N   |    N     |  N   |
+| [SATRN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/satrn)       | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+| [ABINet](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/abinet)     | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |

docs/zh_cn/tutorial/03_pytorch2onnx.md

@@ -280,7 +280,7 @@ ONNX 算子的定义情况，都可以在官方的[算子文档](https://github.
 
 ### PyTorch 对 ONNX 算子的映射
 
-在 PyTorch 中，和 ONNX 有关的定义全部放在 [torch.onnx 目录](https://github.com/pytorch/pytorch/tree/master/torch/onnx)中，如下图所示：
+在 PyTorch 中，和 ONNX 有关的定义全部放在 [torch.onnx 目录](https://github.com/pytorch/pytorch/tree/main/torch/onnx)中，如下图所示：
 
 ![image](https://user-images.githubusercontent.com/47652064/163531700-ddf994e5-6989-483c-a1a3-f1b50dfd84f0.png)
 

docs/zh_cn/tutorial/04_onnx_custom_op.md

@@ -233,7 +233,7 @@ def symbolic(g,
 
 在这个符号函数中，我们以刚刚搜索到的算子输入参数作为符号函数的输入参数，并只用 `input` 和 `offset` 来构造一个简单的 ONNX 算子。
 
-这段代码中，最令人疑惑的就是装饰器 `@parse_args` 了。简单来说，TorchScript 算子的符号函数要求标注出每一个输入参数的类型。比如"v"表示 Torch 库里的 `value` 类型，一般用于标注张量，而"i"表示 int 类型，"f"表示 float 类型，"none"表示该参数为空。具体的类型含义可以在 [torch.onnx.symbolic_helper.py](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_helper.py)中查看。这里输入参数中的 `input, weight, offset, mask, bias` 都是张量，所以用"v"表示。后面的其他参数同理。我们不必纠结于 `@parse_args`的原理，根据实际情况对符号函数的参数标注类型即可。
+这段代码中，最令人疑惑的就是装饰器 `@parse_args` 了。简单来说，TorchScript 算子的符号函数要求标注出每一个输入参数的类型。比如"v"表示 Torch 库里的 `value` 类型，一般用于标注张量，而"i"表示 int 类型，"f"表示 float 类型，"none"表示该参数为空。具体的类型含义可以在 [torch.onnx.symbolic_helper.py](https://gthub.com/pytorch/pytorch/blob/main/torch/onnx/symbolic_helper.py)中查看。这里输入参数中的 `input, weight, offset, mask, bias` 都是张量，所以用"v"表示。后面的其他参数同理。我们不必纠结于 `@parse_args`的原理，根据实际情况对符号函数的参数标注类型即可。
 
 有了符号函数后，我们通过如下的方式注册符号函数：
 

tests/regression/mmaction.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmaction2/tree/main
   codebase_dir: ../mmaction2
   checkpoint_force_download: False
   images:

tests/regression/mmcls.yml

@@ -1,5 +1,6 @@
 globals:
-  codebase_dir: ../mmclassification
+  repo_url: https://github.com/open-mmlab/mmpretrain/tree/main
+  codebase_dir: ../mmpretrain
   checkpoint_force_download: False
   images:
     img_snake: &img_snake ../mmclassification/demo/demo.JPEG

tests/regression/mmdet.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmdetection/tree/main
   codebase_dir: ../mmdetection
   checkpoint_force_download: False
   images:

tests/regression/mmdet3d.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmdetection3d/tree/main
   codebase_dir: ../mmdetection3d
   checkpoint_force_download: False
   images:

tests/regression/mmedit.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmediting/tree/main
   codebase_dir: ../mmediting
   checkpoint_force_download: False
   images:

tests/regression/mmocr.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmocr/tree/main
   codebase_dir: ../mmocr
   checkpoint_force_download: False
   images:

tests/regression/mmpose.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmpose/tree/main
   codebase_dir: ../mmpose
   checkpoint_force_download: False
   images:

tests/regression/mmrotate.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmrotate/tree/main
   codebase_dir: ../mmrotate
   checkpoint_force_download: False
   images:

tests/regression/mmseg.yml

@@ -1,4 +1,5 @@
 globals:
+  repo_url: https://github.com/open-mmlab/mmsegmentation/tree/main
   codebase_dir: ../mmsegmentation
   checkpoint_force_download: False
   images:

tools/generate_md_table.py

@@ -0,0 +1,81 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import yaml
+from mmengine import Config
+
+from mmdeploy.utils import get_backend, get_task_type, load_config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='from yaml export markdown table')
+    parser.add_argument('yml_file', help='input yml config path')
+    parser.add_argument('output', help='output markdown file path')
+    parser.add_argument(
+        '--backends',
+        nargs='+',
+        help='backends you want to generate',
+        default=[
+            'onnxruntime', 'tensorrt', 'torchscript', 'pplnn', 'openvino',
+            'ncnn'
+        ])
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    assert osp.exists(args.yml_file), f'File not exists: {args.yml_file}'
+    output_dir, _ = osp.split(args.output)
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)
+    header = ['model', 'task'] + args.backends
+    aligner = [':--'] * 2 + [':--:'] * len(args.backends)
+
+    def write_row_f(writer, row):
+        writer.write('|' + '|'.join(row) + '|\n')
+
+    print(f'Processing{args.yml_file}')
+    with open(args.yml_file, 'r') as reader, open(args.output, 'w') as writer:
+        config = yaml.load(reader, Loader=yaml.FullLoader)
+        config = Config(config)
+        write_row_f(writer, header)
+        write_row_f(writer, aligner)
+        repo_url = config.globals.repo_url
+        for i in range(len(config.models)):
+            name = config.models[i].name
+            model_configs = config.models[i].model_configs
+            pipelines = config.models[i].pipelines
+            config_url = osp.join(repo_url, model_configs[0])
+            config_url, _ = osp.split(config_url)
+            support_backends = {b: 'N' for b in args.backends}
+            deploy_config = [
+                pipelines[i].deploy_config for i in range(len(pipelines))
+            ]
+            cfg = [
+                load_config(deploy_config[i])
+                for i in range(len(deploy_config))
+            ]
+            task = [
+                get_task_type(cfg[i][0]).value
+                for i in range(len(deploy_config))
+            ]
+            backend_type = [
+                get_backend(cfg[i][0]).value
+                for i in range(len(deploy_config))
+            ]
+            for i in range(len(deploy_config)):
+                support_backends[backend_type[i]] = 'Y'
+            support_backends = [support_backends[i] for i in args.backends]
+            model_name = f'[{name}]({config_url})'
+            row = [model_name, task[i]] + support_backends
+
+            write_row_f(writer, row)
+        print(f'Save to {args.output}')
+
+
+if __name__ == '__main__':
+    main()