MMDeploy部署YOLOX-x模型_jeston nx yolox模型使用mmdeploy部署

环境搭建

• 本文初始环境为PyTorch 2.0.0、Python 3.8(ubuntu20.04)、Cuda 11.8

OpenMMLab基础环境

• 首先安装OpenMMLab基础环境，以下代码均在命令窗口下输入

pip install openmim
mim install mmcv-full
mim install mmcv
mim install mmdeploy_runtime

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -e . >> /dev/null

mkdir checkpoint
mkdir output
cd ..
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• mim install mmcv-full这一步需要大概15~30分钟，没报错就耐心等待。

MMDeploy环境

• 再次声明，下面代码均在命令窗中输入运行！！

git clone https://github.com/open-mmlab/mmdeploy.git --recursive
cd mmdeploy

# 备份镜像源
sudo cp /etc/apt/sources.list /etc/apt/sources.list.backup
# 更换镜像源
sudo sed -i "s@http://.*archive.ubuntu.com@https://mirrors.tuna.tsinghua.edu.cn@g" /etc/apt/sources.list
sudo sed -i "s@http://.*security.ubuntu.com@https://mirrors.tuna.tsinghua.edu.cn@g" /etc/apt/sources.list
sudo apt-get update

python tools/scripts/build_ubuntu_x64_ort.py
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• 更改环境变量，依然是在命令行输入

export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
export LD_LIBRARY_PATH=$(pwd)/build/lib:$(pwd)/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1/lib:$LD_LIBRARY_PATH
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• 可以使用脚本检查环境安装状态python tools/check_env.py

07/14 19:14:16 - mmengine - INFO - **********Environmental information**********
07/14 19:14:17 - mmengine - INFO - sys.platform: linux
07/14 19:14:17 - mmengine - INFO - Python: 3.8.10 (default, Jun  4 2021, 15:09:15) [GCC 7.5.0]
07/14 19:14:17 - mmengine - INFO - CUDA available: True
07/14 19:14:17 - mmengine - INFO - numpy_random_seed: 2147483648
07/14 19:14:17 - mmengine - INFO - GPU 0: NVIDIA GeForce RTX 2080 Ti
07/14 19:14:17 - mmengine - INFO - CUDA_HOME: /usr/local/cuda
07/14 19:14:17 - mmengine - INFO - NVCC: Cuda compilation tools, release 11.8, V11.8.89
07/14 19:14:17 - mmengine - INFO - GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
07/14 19:14:17 - mmengine - INFO - PyTorch: 2.0.0+cu118
07/14 19:14:17 - mmengine - INFO - PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

07/14 19:14:17 - mmengine - INFO - TorchVision: 0.15.1+cu118
07/14 19:14:17 - mmengine - INFO - OpenCV: 4.8.0
07/14 19:14:17 - mmengine - INFO - MMEngine: 0.7.2
07/14 19:14:17 - mmengine - INFO - MMCV: 2.0.0
07/14 19:14:17 - mmengine - INFO - MMCV Compiler: GCC 9.3
07/14 19:14:17 - mmengine - INFO - MMCV CUDA Compiler: 11.8
07/14 19:14:17 - mmengine - INFO - MMDeploy: 1.2.0+0a8cbe2
07/14 19:14:17 - mmengine - INFO - 

07/14 19:14:17 - mmengine - INFO - **********Backend information**********
07/14 19:14:17 - mmengine - INFO - tensorrt:    None
07/14 19:14:17 - mmengine - INFO - ONNXRuntime: 1.8.1
07/14 19:14:17 - mmengine - INFO - ONNXRuntime-gpu:     None
07/14 19:14:17 - mmengine - INFO - ONNXRuntime custom ops:      Available
07/14 19:14:17 - mmengine - INFO - pplnn:       None
07/14 19:14:17 - mmengine - INFO - ncnn:        None
07/14 19:14:17 - mmengine - INFO - snpe:        None
07/14 19:14:17 - mmengine - INFO - openvino:    None
07/14 19:14:17 - mmengine - INFO - torchscript: 2.0.0+cu118
07/14 19:14:17 - mmengine - INFO - torchscript custom ops:      NotAvailable
07/14 19:14:18 - mmengine - INFO - rknn-toolkit:        None
07/14 19:14:18 - mmengine - INFO - rknn-toolkit2:       None
07/14 19:14:18 - mmengine - INFO - ascend:      None
07/14 19:14:18 - mmengine - INFO - coreml:      None
07/14 19:14:18 - mmengine - INFO - tvm: None
07/14 19:14:18 - mmengine - INFO - vacc:        None
07/14 19:14:18 - mmengine - INFO - 

07/14 19:14:18 - mmengine - INFO - **********Codebase information**********
07/14 19:14:18 - mmengine - INFO - mmdet:       3.1.0
07/14 19:14:18 - mmengine - INFO - mmseg:       None
07/14 19:14:18 - mmengine - INFO - mmpretrain:  None
07/14 19:14:18 - mmengine - INFO - mmocr:       None
07/14 19:14:18 - mmengine - INFO - mmagic:      None
07/14 19:14:18 - mmengine - INFO - mmdet3d:     None
07/14 19:14:18 - mmengine - INFO - mmpose:      None
07/14 19:14:18 - mmengine - INFO - mmrotate:    None
07/14 19:14:18 - mmengine - INFO - mmaction:    None
07/14 19:14:18 - mmengine - INFO - mmrazor:     None
07/14 19:14:18 - mmengine - INFO - mmyolo:      None

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YOLO-x转ONNX格式

• 下载YOLO-x模型的预训练权重

cd ..
cd mmdetection

# 下载YOLOX-X权重
wget https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth -P checkpoint
cd ..
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• 使用转换脚本将YOLO-x模型转换为ONNX格式

python mmdeploy/tools/deploy.py \
    mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py \
    mmdetection/configs/yolox/yolox_x_8xb8-300e_coco.py \
    mmdetection/checkpoint/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth \
    mmdetection/demo/demo.jpg \
    --work-dir mmdeploy_models/mmdet/yolox \
    --device cpu \
    --show \
    --dump-info
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• 在经过上述代码后，工作目录下的文件组织信息为：

 - mmdeploy_models
     - mmdet
         - yolox
        	 - deploy.json
        	 - detail.json
        	 - end2end.onnx
        	 - pipeline.json
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• 其中，end2end.onnx: 推理引擎文件。可用ONNX Runtime推理。*.json: mmdeploy SDK推理所需的meta 信息。

模型推理

后端推理

from mmdeploy.apis.utils import build_task_processor
from mmdeploy.utils import get_input_shape, load_config
import torch

deploy_cfg = '/kaggle/working/mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = '/kaggle/working/mmdetection/configs/yolox/yolox_x_8xb8-300e_coco.py'
device = 'cpu'
backend_model = ['/kaggle/input/yolo-x-onnx-model/end2end.onnx']
image = '/kaggle/working/mmdetection/demo/demo.jpg'

deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)

task_processor = build_task_processor(model_cfg, deploy_cfg, device)
model = task_processor.build_backend_model(backend_model)

input_shape = get_input_shape(deploy_cfg)
model_inputs, _ = task_processor.create_input(image, input_shape)

with torch.no_grad():
    result = model.test_step(model_inputs)

task_processor.visualize(
    image=image,
    model=model,
    result=result[0],
    window_name='visualize',
    output_file='output_detection.png')
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SDK推理

from mmdeploy_runtime import Detector
import cv2
import numpy as np

_COLORS = np.array([
    0.000, 0.447, 0.741, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494,
    0.184, 0.556, 0.466, 0.674, 0.188, 0.301, 0.745, 0.933, 0.635, 0.078,
    0.184, 0.300, 0.300, 0.300, 0.600, 0.600, 0.600, 1.000, 0.000, 0.000,
    1.000, 0.500, 0.000, 0.749, 0.749, 0.000, 0.000, 1.000, 0.000, 0.000,
    0.000, 1.000, 0.667, 0.000, 1.000, 0.333, 0.333, 0.000, 0.333, 0.667,
    0.000, 0.333, 1.000, 0.000, 0.667, 0.333, 0.000, 0.667, 0.667, 0.000,
    0.667, 1.000, 0.000, 1.000, 0.333, 0.000, 1.000, 0.667, 0.000, 1.000,
    1.000, 0.000, 0.000, 0.333, 0.500, 0.000, 0.667, 0.500, 0.000, 1.000,
    0.500, 0.333, 0.000, 0.500, 0.333, 0.333, 0.500, 0.333, 0.667, 0.500,
    0.333, 1.000, 0.500, 0.667, 0.000, 0.500, 0.667, 0.333, 0.500, 0.667,
    0.667, 0.500, 0.667, 1.000, 0.500, 1.000, 0.000, 0.500, 1.000, 0.333,
    0.500, 1.000, 0.667, 0.500, 1.000, 1.000, 0.500, 0.000, 0.333, 1.000,
    0.000, 0.667, 1.000, 0.000, 1.000, 1.000, 0.333, 0.000, 1.000, 0.333,
    0.333, 1.000, 0.333, 0.667, 1.000, 0.333, 1.000, 1.000, 0.667, 0.000,
    1.000, 0.667, 0.333, 1.000, 0.667, 0.667, 1.000, 0.667, 1.000, 1.000,
    1.000, 0.000, 1.000, 1.000, 0.333, 1.000, 1.000, 0.667, 1.000, 0.333,
    0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000,
    0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000,
    0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000,
    1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000,
    0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000,
    0.000, 0.000, 0.000, 0.143, 0.143, 0.143, 0.286, 0.286, 0.286, 0.429,
    0.429, 0.429, 0.571, 0.571, 0.571, 0.714, 0.714, 0.714, 0.857, 0.857,
    0.857, 0.000, 0.447, 0.741, 0.314, 0.717, 0.741, 0.50, 0.5, 0
]).astype(np.float32).reshape(-1, 3)

coco_labels = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
    'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
    'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator',
    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]

img = cv2.imread('demo/resources/lqq.jpg')

# create a detector
detector = Detector(model_path='/root/mmdeploy_models/mmdet/yolox', device_name='cpu', device_id=0)
# perform inference
bboxes, labels, masks = detector(img)

# visualize inference result
indices = [i for i in range(len(bboxes))]
for index, bbox, label_id in zip(indices, bboxes, labels):
  [left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
  if score < 0.5:
    continue

  color = (_COLORS[label_id] * 255).astype(np.uint8).tolist()
  text = '{}:{:.1f}%'.format(coco_labels[label_id], score * 100)
  txt_color = (0, 0, 0) if np.mean(_COLORS[label_id]) > 0.5 else (255, 255,255)
  font = cv2.FONT_HERSHEY_SIMPLEX

  txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]

  # 框线粗细可以由rectangle的thickness参数设定
  cv2.rectangle(img, (left, top), (right, bottom), color,thickness=2)

  txt_bk_color = (_COLORS[label_id] * 255 * 0.7).astype(np.uint8).tolist()
  cv2.rectangle(img, (left, top + 1),
                (left + txt_size[0] + 1, top + int(1.5 * txt_size[1])),
                txt_bk_color, -1)

  # 标注字体大小可由fontScale设定，字体粗细可由thickness设定
  cv2.putText(img,
              text, (left, top + txt_size[1]),
              font,
              0.4,
              txt_color,
              thickness=1)

cv2.imwrite('output_detection.png', img)
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