mmcls/mmdet模型部署至 TorchServe

官方教程：模型部署至 TorchServe — MMClassification 0.23.2 文档

接口说明：

serve/inference_api.md at master · pytorch/serve (github.com)

测试torchserve的分类模型是否健康

curl http://127.0.0.1:8080/ping

# return
{
  "status": "Healthy"
}

测试torchserve的检测模型是否健康

curl http://127.0.0.1:8083/ping

# return
{
  "status": "Healthy"
}

分类：

请求地址：

http://127.0.0.1:8080/predictions/cls_slice_resnet50

http://127.0.0.1:8080/predictions/cls_thyroid_resnet50

http://127.0.0.1:8080/predictions/cls_componente_resnet50
http://127.0.0.1:8080/predictions/cls_echoes_resnet50
http://127.0.0.1:8080/predictions/cls_edges_resnet50
http://127.0.0.1:8080/predictions/cls_strong_echoes_resnet50

返回结果：

{
"pred_label": 0,
"pred_score": 0.5629526972770691,
"pred_class": "horizontal"
}

python+requests

import requests

upload_url = "http://127.0.0.1:8080/predictions/cls_slice_resnet50"
file = {'data': open('F:\\imgs\\nodule2.jpg', 'rb')}
res = requests.post(upload_url, files=file)
print(res.json())

curl

curl http://127.0.0.1:8080/predictions/cls_slice_resnet50 -T torchserve/imgs/demo.jpg

检测：

请求地址：

http://127.0.0.1:8083/predictions/det_thyroid_yolox

返回结果：

[
{
"class_name": "motorcycle",
"bbox": [
449.106689453125,
267.3927307128906,
603.3954467773438,
414.5325622558594
],
"score": 0.8570554256439209
}
]

python+requests

import requests

upload_url = "http://127.0.0.1:8083/predictions/det_thyroid_yolox"
file = {'data': open('F:\\imgs\\nodule1.jpg', 'rb')}
res = requests.post(upload_url, files=file)
print(res.json())

curl

curl http://127.0.0.1:8083/predictions/det_thyroid_yolox -T torchserve/imgs/demo.jpg

mmcls

1. 转换 MMClassification 模型至 TorchServe

python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}

${MODEL_STORE} 需要是一个文件夹的绝对路径。

示例：

python tools/deployment/mmcls2torchserve.py configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py checkpoints/resnet50_8xb256-rsb-a1-600e_in1k.pth --output-folder ./checkpoints/ --model-name resnet50_8xb256-rsb-a1-600e_in1k

2. 构建 mmcls-serve docker 镜像

cd mmclassification
docker build -t mmcls-serve docker/serve/

修改mmcls/docker/serve/Dockerfile，为下面内容。主要是在 apt-get update 前面添加了下面几句：

RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
RUN apt-get clean
RUN rm /etc/apt/sources.list.d/cuda.list

第一行是添加国内源，第二行是修复nvidia更新密钥问题。更新 CUDA Linux GPG 存储库密钥 - NVIDIA 技术博客

RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
RUN apt-get clean
RUN rm /etc/apt/sources.list.d/cuda.list

⚠️ 不加上边几行会报错：

W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 InRelease' is no longer signed.

新的Dockerfile内容：

ARG PYTORCH="1.8.1"
ARG CUDA="10.2"
ARG CUDNN="7"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ARG MMCV="1.4.2"
ARG MMCLS="0.23.2"

ENV PYTHONUNBUFFERED TRUE

RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
RUN apt-get clean
RUN rm /etc/apt/sources.list.d/cuda.list

RUN apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
    ca-certificates \
    g++ \
    openjdk-11-jre-headless \
    # MMDet Requirements
    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
    && rm -rf /var/lib/apt/lists/*

ENV PATH="/opt/conda/bin:$PATH"
RUN export FORCE_CUDA=1

# TORCHSEVER
RUN pip install torchserve torch-model-archiver

# MMLAB
ARG PYTORCH
ARG CUDA
RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
RUN pip install mmcls==${MMCLS}

RUN useradd -m model-server \
    && mkdir -p /home/model-server/tmp

COPY entrypoint.sh /usr/local/bin/entrypoint.sh

RUN chmod +x /usr/local/bin/entrypoint.sh \
    && chown -R model-server /home/model-server

COPY config.properties /home/model-server/config.properties
RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store

EXPOSE 8080 8081 8082

USER model-server
WORKDIR /home/model-server
ENV TEMP=/home/model-server/tmp
ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
CMD ["serve"]

3. 运行 mmcls-serve 镜像

请参考官方文档基于 docker 运行 TorchServe.

为了使镜像能够使用 GPU 资源，需要安装 nvidia-docker。之后可以传递 --gpus 参数以在 GPU 上运。

示例：

docker run --rm \
--cpus 8 \
--gpus device=0 \							# 单GPU
-p8080:8080 -p8081:8081 -p8082:8082 \		# HTTP
-p7070:7070 -p7071:7071						# gRPC 
--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
mmcls-serve:latest

实例(启动服务)

docker run --rm --gpus 2 -p8080:8080 -p8081:8081 -p8082:8082 -p7072:7070 -p7073:7071 --mount type=bind,source=/home/xbsj/gy77/torchserve/torchserve_models/mmcls,target=/home/model-server/model-store mmcls-serve

备注

realpath ./checkpoints 是 “./checkpoints” 的绝对路径，你可以将其替换为你保存 TorchServe 模型的目录的绝对路径。

参考该文档了解关于推理 (8080)，管理 (8081) 和指标 (8082) 等 API 的信息。

4. 测试部署

curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG

示例：

curl http://127.0.0.1:8080/predictions/cls_thyroid_resnet50 -T torchserve/imgs/demo.jpg
# 或者
curl http://${IP地址}:8080/predictions/resnet50_8xb256-rsb-a1-600e_in1k -T demo/demo.JPEG

您应该获得类似于以下内容的响应：

{
  "pred_label": 65,
  "pred_score": 0.9548004269599915,
  "pred_class": "sea snake"
}

另外，你也可以使用 test_torchserver.py 来比较 TorchServe 和 PyTorch 的结果，并进行可视化。

python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]

示例：

python tools/deployment/test_torchserver.py demo/demo.JPEG configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py checkpoints/resnet50_8xb256-rsb-a1-600e_in1k.pth resnet50_8xb256-rsb-a1-600e_in1k

可视化结果展示：

docker终止运行的服务：

docker container ps			# 查看正在运行的容器
docker container stop ${container id}  # 根据容器id终止运行的容器

mmdet

1. 转换 MMDetection 模型至 TorchServe

python tools/deployment/mmdet2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}

${MODEL_STORE} 需要是一个文件夹的绝对路径。

示例：

python tools/deployment/mmdet2torchserve.py configs/yolox/yolox_s_8x8_300e_coco.py checkpoints/yolox_s_8x8_300e_coco.pth --output-folder ./checkpoints --model-name yolox_s_8x8_300e_coco

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_thyroid_resnet50.py ../torchserve/pytorch_models/cls_thyroid_resnet50.pth --output-folder ../torchserve/torchserve_models --model-name cls_thyroid_resnet50

2. 构建 mmdet-serve docker 镜像

cd mmdet
docker build -t mmdet-serve docker/serve/

修改mmdet/docker/serve/Dockerfile，为下面内容。主要是在 apt-get update 前面添加了下面几句：

第一行是添加国内源，第二行是修复nvidia更新密钥问题。更新 CUDA Linux GPG 存储库密钥 - NVIDIA 技术博客

RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
RUN apt-get clean
RUN rm /etc/apt/sources.list.d/cuda.list

⚠️ 不加上边几行会报错：

W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 InRelease' is no longer signed.

新的Dockerfile内容：

ARG PYTORCH="1.6.0"
ARG CUDA="10.1"
ARG CUDNN="7"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ARG MMCV="1.3.17"
ARG MMDET="2.23.0"

ENV PYTHONUNBUFFERED TRUE

RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
RUN apt-get clean
RUN rm /etc/apt/sources.list.d/cuda.list
RUN apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
    ca-certificates \
    g++ \
    openjdk-11-jre-headless \
    # MMDet Requirements
    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
    && rm -rf /var/lib/apt/lists/*

ENV PATH="/opt/conda/bin:$PATH"
RUN export FORCE_CUDA=1

# TORCHSEVER
RUN pip install torchserve torch-model-archiver nvgpu -i https://pypi.tuna.tsinghua.edu.cn/simple/

# MMLAB
ARG PYTORCH
ARG CUDA
RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html -i https://pypi.tuna.tsinghua.edu.cn/simple/"]
RUN pip install mmdet==${MMDET} -i https://pypi.tuna.tsinghua.edu.cn/simple/

RUN useradd -m model-server \
    && mkdir -p /home/model-server/tmp

COPY entrypoint.sh /usr/local/bin/entrypoint.sh

RUN chmod +x /usr/local/bin/entrypoint.sh \
    && chown -R model-server /home/model-server

COPY config.properties /home/model-server/config.properties
RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store

EXPOSE 8080 8081 8082


USER model-server
WORKDIR /home/model-server
ENV TEMP=/home/model-server/tmp
ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
CMD ["serve"]

3. 运行 mmdet-serve 镜像

请参考官方文档基于 docker 运行 TorchServe.

为了使镜像能够使用 GPU 资源，需要安装 nvidia-docker。之后可以传递 --gpus 参数以在 GPU 上运。

示例：

docker run --rm \
--cpus 8 \
--gpus device=0 \							# 单GPU
-p8080:8080 -p8081:8081 -p8082:8082 \		# HTTP
-p7070:7070 -p7071:7071						# gRPC 
--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
mmcls-serve:latest

实例，为了不跟 mmcls-serve 冲突，使用8083，8084，8085， 7072，7073端口。

docker run --rm --gpus 2 -p8083:8080 -p8084:8081 -p8085:8082 -p7072:7070 -p7073:7071 --mount type=bind,source=/home/xbsj/gy77/torchserve/torchserve_models/mmdet,target=/home/model-server/model-store mmdet-serve

4. 测试部署

curl http://127.0.0.1:8083/predictions/det_thyroid_yolox -T torchserve/imgs/demo.jpg

您应该获得类似于以下内容的响应：

[
  {
    "class_name": "car",
    "bbox": [
      481.5609130859375,
      110.4412612915039,
      522.7328491210938,
      130.5723876953125
    ],
    "score": 0.8954943418502808
  },
  {
    "class_name": "car",
    "bbox": [
      431.3537902832031,
      105.25204467773438,
      484.0513610839844,
      132.73513793945312
    ],
    "score": 0.8776198029518127
  },
  {
    "class_name": "car",
    "bbox": [
      294.16278076171875,
      117.66851806640625,
      379.8677978515625,
      149.80923461914062
    ],
    "score": 0.8764416575431824
  },
  {
    "class_name": "car",
    "bbox": [
      191.56170654296875,
      108.98323059082031,
      299.0423278808594,
      155.1902313232422
    ],
    "score": 0.8606226444244385
  },
  {
    "class_name": "car",
    "bbox": [
      398.29461669921875,
      110.82112884521484,
      433.4544677734375,
      133.10105895996094
    ],
    "score": 0.8603343963623047
  },
  {
    "class_name": "car",
    "bbox": [
      608.4430541992188,
      111.58413696289062,
      637.6807250976562,
      137.55311584472656
    ],
    "score": 0.8566091060638428
  },
  {
    "class_name": "car",
    "bbox": [
      589.808349609375,
      110.58977508544922,
      619.0382080078125,
      126.56522369384766
    ],
    "score": 0.7685313820838928
  },
  {
    "class_name": "car",
    "bbox": [
      167.66847229003906,
      110.8987045288086,
      211.2526092529297,
      140.1393585205078
    ],
    "score": 0.764432430267334
  },
  {
    "class_name": "car",
    "bbox": [
      0.3290290832519531,
      112.89485931396484,
      62.417659759521484,
      145.11058044433594
    ],
    "score": 0.7574507594108582
  },
  {
    "class_name": "car",
    "bbox": [
      268.7782287597656,
      105.21003723144531,
      328.5860290527344,
      127.79859924316406
    ],
    "score": 0.7260770201683044
  },
  {
    "class_name": "car",
    "bbox": [
      96.76626586914062,
      89.0433349609375,
      118.81390380859375,
      102.16648864746094
    ],
    "score": 0.6803644299507141
  },
  {
    "class_name": "car",
    "bbox": [
      571.3563232421875,
      110.22184753417969,
      592.4779052734375,
      126.81962585449219
    ],
    "score": 0.6668680906295776
  },
  {
    "class_name": "car",
    "bbox": [
      61.34038162231445,
      93.14757537841797,
      84.83381652832031,
      106.10137176513672
    ],
    "score": 0.6323376893997192
  },
  {
    "class_name": "bench",
    "bbox": [
      221.9741668701172,
      176.775146484375,
      456.5819091796875,
      382.6751708984375
    ],
    "score": 0.9417163729667664
  },
  {
    "class_name": "bench",
    "bbox": [
      372.16351318359375,
      134.79196166992188,
      433.37713623046875,
      189.78695678710938
    ],
    "score": 0.5323100686073303
  }

另外，你也可以使用 test_torchserver.py 来比较 TorchServe 和 PyTorch 的结果，并进行可视化。

python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]

示例：

python tools/deployment/test_torchserver.py demo/demo.jpg configs/yolox/yolox_s_8x8_300e_coco.py checkpoints/yolox_s_8x8_300e_coco.pth yolox_s_8x8_300e_coco

可视化展示：

docker终止运行的服务：

docker container ps			# 查看正在运行的容器
docker container stop ${container id}  # 根据容器id终止运行的容器

mmcls 多标签模型部署在torch serve

GitHub仓库：gy-7/mmcls_multi_label_torchserve (github.com)

各个文件说明：

cls_requests_demo：分类模型请求api服务的demo

det_requests_demo：检测模型请求api服务的demo

inference：要修改的inference代码

mmcls_handler：要修改的mmcls_handler代码

torchserve_log：过程中遇到的报错集合

1️⃣ 修改 mmcls_handler.py

我们首先要搞清楚，mmcls_handler.py 是转换 pytorch 模型为 torch serve 模型的时候用到的。转换过程中把里边的内容嵌入到转换完的 torch serve 模型里了。

我们主要修改的是 mmcls_handler 中 postprocess 的操作。将仓库中 mmcls_handler.py 文件内容覆盖掉mmclassification/tools/deployment/mmcls_handler.py。

2️⃣ 重新转换所有的模型：

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_componente_resnet50.py ../torchserve/pytorch_models/cls_componente_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_componente_resnet50

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_echoes_resnet50.py ../torchserve/pytorch_models/cls_echoes_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_echoes_resnet50

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_edges_resnet50.py ../torchserve/pytorch_models/cls_edges_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_edges_resnet50

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_slice_resnet50.py ../torchserve/pytorch_models/cls_slice_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_slice_resnet50

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_thyroid_resnet50.py ../torchserve/pytorch_models/cls_thyroid_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_thyroid_resnet50

python tools/deployment/mmcls2torchserve.py ../torchserve/pytorch_models/cls_strong_echoes_resnet50.py ../torchserve/pytorch_models/cls_strong_echoes_resnet50.pth --output-folder ../torchserve/torchserve_models/mmcls/ --model-name cls_strong_echoes_resnet50

3️⃣ 修改inference.py

inference.py 是调用api服务的时候，调用的接口。我们用docker 装的 torch serve服务，所以我们要修改容器里边的源码。

启动原先的服务，进入容器。

docker exec -it --user root 7f0f1ea9e3e8 /bin/bash

# 修改inference.py
vim /opt/conda/lib/python3.7/site-packages/mmcls/apis/inference.py 

# 保存镜像
docker commit -m "fix inference.py" 7f0f1ea9e3e8 mmcls-serve_multi_label:latest

4️⃣ 可以愉快的出来结果了

前五个是单标签，最后一个是多标签。

{'pred_label': 2, 'pred_score': 0.9856280088424683, 'pred_class': 'vertical'}
{'pred_label': 0, 'pred_score': 0.9774421453475952, 'pred_class': 'benign'}
{'pred_label': 4, 'pred_score': 0.6918501853942871, 'pred_class': 'componentes_4'}
{'pred_label': 2, 'pred_score': 0.5446202158927917, 'pred_class': 'echoes_2'}
{'pred_label': 1, 'pred_score': 0.4259072542190552, 'pred_class': 'edges_1'}
{'pred_label': [0, 0, 0, 0, 0], 'pred_score': [0.46634966135025024, 0.07801822572946548, 0.2685200273990631, 0.016055332496762276, 0.13444863259792328], 'pred_class': []}

posted @ 2022-08-22 15:20 gy77 阅读(430) 评论(0) 编辑收藏举报

会员力量，点亮园子希望

刷新页面返回顶部

一届书生

本博客所有内容以学习、研究和分享为主，如需转载，请标明作者和出处，并且是非商业用途，谢谢。

mmcls/mmdet模型部署至 TorchServe

mmcls/mmdet模型部署至 TorchServe

接口说明：

分类：

检测：

mmcls

1. 转换 MMClassification 模型至 TorchServe

2. 构建 mmcls-serve docker 镜像

3. 运行 mmcls-serve 镜像

4. 测试部署

mmdet

1. 转换 MMDetection 模型至 TorchServe

2. 构建 mmdet-serve docker 镜像

3. 运行 mmdet-serve 镜像

4. 测试部署

mmcls 多标签模型部署在torch serve

1️⃣ 修改 mmcls_handler.py

2️⃣ 重新转换所有的模型：

3️⃣ 修改inference.py

4️⃣ 可以愉快的出来结果了

公告