218 lines
10 KiB
Markdown
218 lines
10 KiB
Markdown
# MMDetection 模型部署
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- [MMDetection 模型部署](#mmdetection-模型部署)
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- [安装](#安装)
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- [安装 mmdet](#安装-mmdet)
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- [安装 mmdeploy](#安装-mmdeploy)
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- [模型转换](#模型转换)
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- [模型规范](#模型规范)
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- [模型推理](#模型推理)
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- [后端模型推理](#后端模型推理)
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- [SDK 模型推理](#sdk-模型推理)
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- [模型支持列表](#模型支持列表)
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______________________________________________________________________
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[MMDetection](https://github.com/open-mmlab/mmdetection) ，又称 `mmdet`， 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
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## 安装
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### 安装 mmdet
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请参考[官网安装指南](https://mmdetection.readthedocs.io/en/3.x/get_started.html)。
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### 安装 mmdeploy
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mmdeploy 有以下几种安装方式:
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**方式一：** 安装预编译包
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请参考[安装概述](https://mmdeploy.readthedocs.io/zh_CN/latest/get_started.html#mmdeploy)
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**方式二：** 一键式脚本安装
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如果部署平台是 **Ubuntu 18.04 及以上版本**， 请参考[脚本安装说明](../01-how-to-build/build_from_script.md)，完成安装过程。
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比如，以下命令可以安装 mmdeploy 以及配套的推理引擎——`ONNX Runtime`.
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```shell
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git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
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cd mmdeploy
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python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
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export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
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export LD_LIBRARY_PATH=$(pwd)/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1/lib/:$LD_LIBRARY_PATH
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```
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**方式三：** 源码安装
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在方式一、二都满足不了的情况下，请参考[源码安装说明](../01-how-to-build/build_from_source.md) 安装 mmdeploy 以及所需推理引擎。
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## 模型转换
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你可以使用 [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/main/tools/deploy.py) 把 mmdet 模型一键式转换为推理后端模型。
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该工具的详细使用说明请参考[这里](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/02-how-to-run/convert_model.md#usage).
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以下，我们将演示如何把 `Faster R-CNN` 转换为 onnx 模型。
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```shell
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cd mmdeploy
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# download faster r-cnn model from mmdet model zoo
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mim download mmdet --config faster-rcnn_r50_fpn_1x_coco --dest .
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# convert mmdet model to onnxruntime model with dynamic shape
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python tools/deploy.py \
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configs/mmdet/detection/detection_onnxruntime_dynamic.py \
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faster-rcnn_r50_fpn_1x_coco.py \
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faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
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demo/resources/det.jpg \
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--work-dir mmdeploy_models/mmdet/ort \
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--device cpu \
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--show \
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--dump-info
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```
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转换的关键之一是使用正确的配置文件。项目中已内置了各后端部署[配置文件](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmdet)。
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文件的命名模式是：
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```
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{task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py
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```
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其中：
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- **{task}:** mmdet 中的任务
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mmdet 任务有2种：物体检测（detection）、实例分割（instance-seg）。例如，`RetinaNet`、`Faster R-CNN`、`DETR`等属于前者。`Mask R-CNN`、`SOLO`等属于后者。更多`模型-任务`的划分，请参考章节[模型支持列表](#模型支持列表)。
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**请务必**使用 `detection/detection_*.py` 转换检测模型，使用 `instance-seg/instance-seg_*.py` 转换实例分割模型。
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- **{backend}:** 推理后端名称。比如，onnxruntime、tensorrt、pplnn、ncnn、openvino、coreml 等等
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- **{precision}:** 推理精度。比如，fp16、int8。不填表示 fp32
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- **{static | dynamic}:** 动态、静态 shape
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- **{shape}:** 模型输入的 shape 或者 shape 范围
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在上例中，你也可以把 `Faster R-CNN` 转为其他后端模型。比如使用`detection_tensorrt-fp16_dynamic-320x320-1344x1344.py`，把模型转为 tensorrt-fp16 模型。
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```{tip}
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当转 tensorrt 模型时, --device 需要被设置为 "cuda"
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```
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## 模型规范
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在使用转换后的模型进行推理之前，有必要了解转换结果的结构。 它存放在 `--work-dir` 指定的路路径下。
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上例中的`mmdeploy_models/mmdet/ort`，结构如下：
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```
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mmdeploy_models/mmdet/ort
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├── deploy.json
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├── detail.json
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├── end2end.onnx
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└── pipeline.json
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```
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重要的是：
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- **end2end.onnx**: 推理引擎文件。可用 ONNX Runtime 推理
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- \***.json**: mmdeploy SDK 推理所需的 meta 信息
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整个文件夹被定义为**mmdeploy SDK model**。换言之，**mmdeploy SDK model**既包括推理引擎，也包括推理 meta 信息。
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## 模型推理
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## 后端模型推理
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以上述模型转换后的 `end2end.onnx` 为例，你可以使用如下代码进行推理：
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```python
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from mmdeploy.apis.utils import build_task_processor
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from mmdeploy.utils import get_input_shape, load_config
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import torch
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deploy_cfg = 'configs/mmdet/detection/detection_onnxruntime_dynamic.py'
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model_cfg = './faster-rcnn_r50_fpn_1x_coco.py'
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device = 'cpu'
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backend_model = ['./mmdeploy_models/mmdet/ort/end2end.onnx']
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image = './demo/resources/det.jpg'
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# read deploy_cfg and model_cfg
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deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
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# build task and backend model
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task_processor = build_task_processor(model_cfg, deploy_cfg, device)
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model = task_processor.build_backend_model(backend_model)
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# process input image
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input_shape = get_input_shape(deploy_cfg)
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model_inputs, _ = task_processor.create_input(image, input_shape)
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# do model inference
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with torch.no_grad():
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result = model.test_step(model_inputs)
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# visualize results
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task_processor.visualize(
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image=image,
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model=model,
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result=result[0],
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window_name='visualize',
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output_file='output_detection.png')
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```
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## SDK 模型推理
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你也可以参考如下代码，对 SDK model 进行推理：
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```python
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from mmdeploy_runtime import Detector
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import cv2
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img = cv2.imread('./demo/resources/det.jpg')
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# create a detector
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detector = Detector(model_path='./mmdeploy_models/mmdet/ort', device_name='cpu', device_id=0)
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# perform inference
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bboxes, labels, masks = detector(img)
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# visualize inference result
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indices = [i for i in range(len(bboxes))]
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for index, bbox, label_id in zip(indices, bboxes, labels):
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[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
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if score < 0.3:
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continue
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cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
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cv2.imwrite('output_detection.png', img)
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```
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除了python API，mmdeploy SDK 还提供了诸如 C、C++、C#、Java等多语言接口。
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你可以参考[样例](https://github.com/open-mmlab/mmdeploy/tree/main/demo)学习其他语言接口的使用方法。
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## 模型支持列表
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| Model | Task | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVINO |
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| :-------------------------------------------------------------------------------------------: | :------------------: | :---------: | :------: | :--: | :---: | :------: |
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| [ATSS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/atss) | ObjectDetection | Y | Y | N | N | Y |
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| [FCOS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fcos) | ObjectDetection | Y | Y | Y | N | Y |
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| [FoveaBox](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/foveabox) | ObjectDetection | Y | N | N | N | Y |
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| [FSAF](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fsaf) | ObjectDetection | Y | Y | Y | Y | Y |
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| [RetinaNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/retinanet) | ObjectDetection | Y | Y | Y | Y | Y |
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| [SSD](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/ssd) | ObjectDetection | Y | Y | Y | N | Y |
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| [VFNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/vfnet) | ObjectDetection | N | N | N | N | Y |
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| [YOLOv3](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolo) | ObjectDetection | Y | Y | Y | N | Y |
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| [YOLOX](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolox) | ObjectDetection | Y | Y | Y | N | Y |
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| [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn) | ObjectDetection | Y | Y | N | Y | Y |
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| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn) | ObjectDetection | Y | Y | Y | Y | Y |
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| [Faster R-CNN + DCN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn) | ObjectDetection | Y | Y | Y | Y | Y |
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| [GFL](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/gfl) | ObjectDetection | Y | Y | N | ? | Y |
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| [RepPoints](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/reppoints) | ObjectDetection | N | Y | N | ? | Y |
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| [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr) | ObjectDetection | Y | Y | N | ? | Y |
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| [CenterNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet) | Object Detection | Y | Y | N | ? | ? |
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| [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn) | InstanceSegmentation | Y | N | N | N | Y |
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| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/mask_rcnn) | InstanceSegmentation | Y | Y | N | N | Y |
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| [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/swin) | InstanceSegmentation | Y | Y | N | N | N |
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| [SOLO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo) | InstanceSegmentation | Y | N | N | N | Y |
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| [SOLOv2](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solov2) | InstanceSegmentation | Y | N | N | N | Y |
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