基于Jupyter Notebook+yolov7+python实现的铁轨缺陷检测+源码（毕业设计&课程设计&项目开发）

# YOLOv7 on Triton Inference Server Instructions to deploy YOLOv7 as TensorRT engine to [Triton Inference Server](https://github.com/NVIDIA/triton-inference-server). Triton Inference Server takes care of model deployment with many out-of-the-box benefits, like a GRPC and HTTP interface, automatic scheduling on multiple GPUs, shared memory (even on GPU), dynamic server-side batching, health metrics and memory resource management. There are no additional dependencies needed to run this deployment, except a working docker daemon with GPU support. ## Export TensorRT See https://github.com/WongKinYiu/yolov7#export for more info. ```bash #install onnx-simplifier not listed in general yolov7 requirements.txt pip3 install onnx-simplifier # Pytorch Yolov7 -> ONNX with grid, EfficientNMS plugin and dynamic batch size python export.py --weights ./yolov7.pt --grid --end2end --dynamic-batch --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 # ONNX -> TensorRT with trtexec and docker docker run -it --rm --gpus=all nvcr.io/nvidia/tensorrt:22.06-py3 # Copy onnx -> container: docker cp yolov7.onnx <container-id>:/workspace/ # Export with FP16 precision, min batch 1, opt batch 8 and max batch 8 ./tensorrt/bin/trtexec --onnx=yolov7.onnx --minShapes=images:1x3x640x640 --optShapes=images:8x3x640x640 --maxShapes=images:8x3x640x640 --fp16 --workspace=4096 --saveEngine=yolov7-fp16-1x8x8.engine --timingCacheFile=timing.cache # Test engine ./tensorrt/bin/trtexec --loadEngine=yolov7-fp16-1x8x8.engine # Copy engine -> host: docker cp <container-id>:/workspace/yolov7-fp16-1x8x8.engine . ``` Example output of test with RTX 3090. ``` [I] === Performance summary === [I] Throughput: 73.4985 qps [I] Latency: min = 14.8578 ms, max = 15.8344 ms, mean = 15.07 ms, median = 15.0422 ms, percentile(99%) = 15.7443 ms [I] End-to-End Host Latency: min = 25.8715 ms, max = 28.4102 ms, mean = 26.672 ms, median = 26.6082 ms, percentile(99%) = 27.8314 ms [I] Enqueue Time: min = 0.793701 ms, max = 1.47144 ms, mean = 1.2008 ms, median = 1.28644 ms, percentile(99%) = 1.38965 ms [I] H2D Latency: min = 1.50073 ms, max = 1.52454 ms, mean = 1.51225 ms, median = 1.51404 ms, percentile(99%) = 1.51941 ms [I] GPU Compute Time: min = 13.3386 ms, max = 14.3186 ms, mean = 13.5448 ms, median = 13.5178 ms, percentile(99%) = 14.2151 ms [I] D2H Latency: min = 0.00878906 ms, max = 0.0172729 ms, mean = 0.0128844 ms, median = 0.0125732 ms, percentile(99%) = 0.0166016 ms [I] Total Host Walltime: 3.04768 s [I] Total GPU Compute Time: 3.03404 s [I] Explanations of the performance metrics are printed in the verbose logs. ``` Note: 73.5 qps x batch 8 = 588 fps @ ~15ms latency. ## Model Repository See [Triton Model Repository Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_repository.md#model-repository) for more info. ```bash # Create folder structure mkdir -p triton-deploy/models/yolov7/1/ touch triton-deploy/models/yolov7/config.pbtxt # Place model mv yolov7-fp16-1x8x8.engine triton-deploy/models/yolov7/1/model.plan ``` ## Model Configuration See [Triton Model Configuration Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#model-configuration) for more info. Minimal configuration for `triton-deploy/models/yolov7/config.pbtxt`: ``` name: "yolov7" platform: "tensorrt_plan" max_batch_size: 8 dynamic_batching { } ``` Example repository: ```bash $ tree triton-deploy/ triton-deploy/ └── models └── yolov7 ├── 1 │   └── model.plan └── config.pbtxt 3 directories, 2 files ``` ## Start Triton Inference Server ``` docker run --gpus all --rm --ipc=host --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v$(pwd)/triton-deploy/models:/models nvcr.io/nvidia/tritonserver:22.06-py3 tritonserver --model-repository=/models --strict-model-config=false --log-verbose 1 ``` In the log you should see: ``` +--------+---------+--------+ | Model | Version | Status | +--------+---------+--------+ | yolov7 | 1 | READY | +--------+---------+--------+ ``` ## Performance with Model Analyzer See [Triton Model Analyzer Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_analyzer.md#model-analyzer) for more info. Performance numbers @ RTX 3090 + AMD Ryzen 9 5950X Example test for 16 concurrent clients using shared memory, each with batch size 1 requests: ```bash docker run -it --ipc=host --net=host nvcr.io/nvidia/tritonserver:22.06-py3-sdk /bin/bash ./install/bin/perf_analyzer -m yolov7 -u 127.0.0.1:8001 -i grpc --shared-memory system --concurrency-range 16 # Result (truncated) Concurrency: 16, throughput: 590.119 infer/sec, latency 27080 usec ``` Throughput for 16 clients with batch size 1 is the same as for a single thread running the engine at 16 batch size locally thanks to Triton [Dynamic Batching Strategy](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#dynamic-batcher). Result without dynamic batching (disable in model configuration) considerably worse: ```bash # Result (truncated) Concurrency: 16, throughput: 335.587 infer/sec, latency 47616 usec ``` ## How to run model in your code Example client can be found in client.py. It can run dummy input, images and videos. ```bash pip3 install tritonclient[all] opencv-python python3 client.py image data/dog.jpg ```  ``` $ python3 client.py --help usage: client.py [-h] [-m MODEL] [--width WIDTH] [--height HEIGHT] [-u URL] [-o OUT] [-f FPS] [-i] [-v] [-t CLIENT_TIMEOUT] [-s] [-r ROOT_CERTIFICATES] [-p PRIVATE_KEY] [-x CERTIFICATE_CHAIN] {dummy,image,video} [input] positional arguments: {dummy,image,video} Run mode. 'dummy' will send an emtpy buffer to the server to test if inference works. 'image' will process an image. 'video' will process a video. input Input file to load from in image or video mode optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL Inference model name, default yolov7 --width WIDTH Inference model input width, default 640 --height HEIGHT Inference model input height, default 640 -u URL, --url URL Inference server URL, default localhost:8001 -o OUT, --out OUT Write output into file instead of displaying it -f FPS, --fps FPS Video output fps, default 24.0 FPS -i, --model-info Print model status, configuration and statistics -v, --verbose Enable verbose client output -t CLIENT_TIMEOUT, --client-timeout CLIENT_TIMEOUT Client timeout in seconds, default no timeout -s, --ssl Enable SSL encrypted channel to the server -r ROOT_CERTIFICATES, --root-certificates ROOT_CERTIFICATES File holding PEM-encoded root certificates, default none -p PRIVATE_KEY, --private-key PRIVATE_KEY File holding PEM-encoded private key, default is none -x CERTIFICATE_CHAIN, --certificate-chain CERTIFICATE_CHAIN File holding PEM-encoded certicate chain default is none ```