diff --git a/docs/en/02-how-to-run/useful_tools.md b/docs/en/02-how-to-run/useful_tools.md index a402f2113d..c18a0e3048 100644 --- a/docs/en/02-how-to-run/useful_tools.md +++ b/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 | diff --git a/docs/zh_cn/02-how-to-run/useful_tools.md b/docs/zh_cn/02-how-to-run/useful_tools.md index 2f08cefa9f..ae746f4fe0 100644 --- a/docs/zh_cn/02-how-to-run/useful_tools.md +++ b/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 | diff --git a/docs/zh_cn/tutorial/03_pytorch2onnx.md b/docs/zh_cn/tutorial/03_pytorch2onnx.md index 1a30c9c9b1..680b7d979b 100644 --- a/docs/zh_cn/tutorial/03_pytorch2onnx.md +++ b/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) diff --git a/docs/zh_cn/tutorial/04_onnx_custom_op.md b/docs/zh_cn/tutorial/04_onnx_custom_op.md index a3f515f363..54ebe9e728 100644 --- a/docs/zh_cn/tutorial/04_onnx_custom_op.md +++ b/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://github.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： diff --git a/requirements/codebases.txt b/requirements/codebases.txt index 2d1a8db3cf..6ae0c4a35b 100644 --- a/requirements/codebases.txt +++ b/requirements/codebases.txt @@ -5,4 +5,4 @@ mmedit>=1.0.0rc2 mmocr>=1.0.0rc4 mmpose>=1.0.0rc0 mmrotate>=1.0.0rc0 -mmsegmentation @ git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x +mmsegmentation>=1.0.0 diff --git a/tests/regression/mmaction.yml b/tests/regression/mmaction.yml index aed2656968..2356244524 100644 --- a/tests/regression/mmaction.yml +++ b/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: diff --git a/tests/regression/mmcls.yml b/tests/regression/mmcls.yml index a4b5a905e9..50e5642a89 100644 --- a/tests/regression/mmcls.yml +++ b/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 diff --git a/tests/regression/mmdet.yml b/tests/regression/mmdet.yml index 947dfe93cc..8790ec1a52 100644 --- a/tests/regression/mmdet.yml +++ b/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: diff --git a/tests/regression/mmdet3d.yml b/tests/regression/mmdet3d.yml index dc476aed7a..3cacd7e655 100644 --- a/tests/regression/mmdet3d.yml +++ b/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: diff --git a/tests/regression/mmedit.yml b/tests/regression/mmedit.yml index 7af7113f73..0213b65213 100644 --- a/tests/regression/mmedit.yml +++ b/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: diff --git a/tests/regression/mmocr.yml b/tests/regression/mmocr.yml index 2270f66ec1..640d38ee18 100644 --- a/tests/regression/mmocr.yml +++ b/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: diff --git a/tests/regression/mmpose.yml b/tests/regression/mmpose.yml index 6e93db1c7a..b0796432cc 100644 --- a/tests/regression/mmpose.yml +++ b/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: diff --git a/tests/regression/mmrotate.yml b/tests/regression/mmrotate.yml index 2ecb1a6cf6..153c69b6e0 100644 --- a/tests/regression/mmrotate.yml +++ b/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: diff --git a/tests/regression/mmseg.yml b/tests/regression/mmseg.yml index bf1a80e6f4..1b914013e0 100644 --- a/tests/regression/mmseg.yml +++ b/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: diff --git a/tools/generate_md_table.py b/tools/generate_md_table.py new file mode 100644 index 0000000000..ea2e028293 --- /dev/null +++ b/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()