5). 转化训练模型为torchscript版本的模型,采用了Detectron2所提供了工具export_model.py, 地址如下: https://github.com/facebookresearch/detectron2/tree/main/tools/deploy 这里要注意修改一下Detectron2的配置文件config.yaml, MODEL.WEIGHTS=./output/
--export-path \output\path\to\model-store \ # 输出路径 -f 这里简单说明一下,关于torch-model-archiver.exe的调用问题,不像UNIX系统,windows下无法直接调用torch-model-archiver,只能先找到其安装路径,然后通过python调用其exe传入参数的方式运行,下面同理。 启动服务: python torchserve.exe --start --model-...
geffnet.config.set_exportable(True) model = MobileV3Large.from_pretrained().eval() dummy_input = torch.randn(1, 3, 1024, 2048) input_names = ['input0'] output_names = ['output0'] model(dummy_input) print("Export PyTorch Fastseg model") torch.onnx.export(model, dummy_input, "fast...
build_model(cfg) DetectionCheckpointer(model).resume_or_load(cfg.MODEL.WEIGHTS) # 准备一个示例输入 input = {"image": torch.randn(1, 3, 800, 800)} # 根据你的模型输入调整尺寸 # 使用Caffe2Tracer进行转换 tracer = Caffe2Tracer(cfg, model, input) script_model = tracer.export_torchscript()...
export CMAKE_PREFIX_PATH=$HOME/.local/lib/python3.6/site-packages/torch/ mkdir build && cd build cmake -DTORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST .. && make # To run: ./caffe2_mask_rcnn --predict_net=./model.pb --init_net=./model_init.pb --input=input.jpg ...
copying detectron2\model_zoo\model_zoo.py -> build\lib.win-amd64-3.6\detectron2\model_zoo copying detectron2\model_zoo\__init__.py -> build\lib.win-amd64-3.6\detectron2\model_zoo creating build\lib.win-amd64-3.6\detectron2\solver ...
copying detectron2\model_zoo\model_zoo.py -> build\lib.win-amd64-3.6\detectron2\model_zoo copying detectron2\model_zoo\__init__.py -> build\lib.win-amd64-3.6\detectron2\model_zoo creating build\lib.win-amd64-3.6\detectron2\solver ...
你可以通过export DETECTRON2_DATASETS=/path/to/datasets为内置的数据集设置位置,如果不设置的话,对你的当前的工作目录来讲,默认的为./datasets, model zoo包含了使用这些内置数据集的配置和模型 COCO instance/keypoint detection的理想型数据集结构: coco/ annotations/ instances_{train,val}2017.json person_key...
exportDETECTRON2_MODEL_ZOO=/path/to/model_zoo 设置日志目录 DETECTRON2_LOGS环境变量用于指定日志文件的保存位置: exportDETECTRON2_LOGS=/path/to/logs 通过设置这些环境变量,Detectron2将能够更顺畅地运行,避免因路径问题导致的错误。 以上步骤详细介绍了如何在你的系统上安装和配置Detectron2。通过遵循这些指导,你...
We knew if we wanna use the model on TensorRT that we have to export the onnx model then converting onnx model to TensorRT engine. However, there are many functions in Detectron2 which were written byPython classso that we cannot export the model toonnx modelbecause ofPyt...