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| # Test speed | |
| This guidence explains how to reproduce speed results of YOLOv6. For fair comparision, the speed results do not contain the time cost of data pre-processing and NMS post-processing. | |
| ## 0. Prepare model | |
| Download the models you want to test from the latest release. | |
| ## 1. Prepare testing environment | |
| Refer to README, install packages corresponding to CUDA, CUDNN and TensorRT version. | |
| Here, we use Torch1.8.0 inference on V100 and TensorRT 7.2 on T4. | |
| ## 2. Reproduce speed | |
| #### 2.1 Torch Inference on V100 | |
| To get inference speed without TensorRT on V100, you can run the following command: | |
| ```shell | |
| python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6n.pt --task speed [--half] | |
| ``` | |
| - Speed results with batchsize = 1 are unstable in multiple runs, thus we do not provide the bs1 speed results. | |
| #### 2.2 TensorRT Inference on T4 | |
| To get inference speed with TensorRT in FP16 mode on T4, you can follow the steps below: | |
| First, export pytorch model as onnx format using the following command: | |
| ```shell | |
| python deploy/ONNX/export_onnx.py --weights yolov6n.pt --device 0 --batch [1 or 32] | |
| ``` | |
| Second, generate an inference trt engine and test speed using `trtexec`: | |
| ``` | |
| trtexec --onnx=yolov6n.onnx --workspace=1024 --avgRuns=1000 --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw | |
| ``` | |