Please install the correct version of MMCV and MMDetection to avoid installation issues. pip install -v -e .[optional]). See more details in the Changelog. PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1. Example on nuScenes data using FCOS3D model: Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. Following the above instructions, mmdetection is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number). Supported CUDA versions include 10.2, 11.1, 11.3, and 11.4. The pre-build mmcv-full could be installed by running: (available versions could be found here). conda install pytorch torchvision -c pytorch Note: Make sure that your compilation CUDA version and runtime CUDA version match. The git commit id will be written to the version number with step d, e.g. open-mmlab / mmdetection3d Public master mmdetection3d/configs/pointpillars/README.md Go to file Cannot retrieve contributors at this time 78 lines (58 sloc) 18.5 KB Raw Blame PointPillars: Fast Encoders for Object Detection from Point Clouds PointPillars: Fast Encoders for Object Detection from Point Clouds Abstract Download and install Miniconda from the official website. Some dependencies are optional. This project is released under the Apache 2.0 license. pip install -v -e .[optional]). This requires manually specifying a find-url based on PyTorch version and its CUDA version. Example on ScanNet data using PointNet++ (SSG) model: Copyright 2020-2023, OpenMMLab. If you want to input a ply file, you can use the following function and convert it to bin format. Revision 9556958f. Create TriMesh from PolyMesh. The code can not be built for CPU only environment (where CUDA isnt available) for now. In this version, we update some of the model checkpoints after the refactor of coordinate systems. Step 4. Notice: If the metric you want to plot is calculated in the eval stage, you need to add the flag --mode eval. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. If you have point clouds in other format (off, obj, etc. We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. # evaluate PartA2 and second on KITTI according to Car_3D_moderate_strict, # evaluate PointPillars for car and 3 classes on KITTI according to Car_3D_moderate_strict, 1: Inference and train with existing models and standard datasets, Tutorial 8: MMDetection3D model deployment, 1. 1 comment SimonDoll commented on Dec 9, 2020 ZwwWayne added the usage label on Dec 11, 2020 ZwwWayne closed this as completed on Dec 11, 2020 We have supported spconv2.0. The output is expected to be like the following. Note: All the about 300+ models, methods of 40+ papers in 2D detection supported by MMDetection can be trained or used in this codebase. However, the whole process is highly customizable. We provide guidance for quick run with existing dataset and with customized dataset for beginners. Support multi-modality/single-modality detectors out of box. It is a part of the OpenMMLab project developed by MMLab. ***_points.obj and ***_pred.obj in single-modality 3D detection task) will be saved in ${SHOW_DIR}. Install PyTorch and torchvision following the official instructions. It is recommended that you run step d each time you pull some updates from github. The main results are as below. Check the official docs for running TorchServe with docker. Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. a. Install PyTorch following official instructions, e.g. MMDection3D works on Linux, Windows (experimental support) and macOS and requires the following packages: CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible). Create a conda environment and activate it. Then you can use the converted bin file to generate demo. Users could refer to them for our approach to converting data format. Convert the model from MMDetection3D to TorchServe. Readme The program supports drawing six training result and the most important evaluation tool:PR curve (only for VOC now) loss_rpn_bbox loss_rpn_cls loss_bbox loss_cls There are two steps to finetune a model on a new dataset. If you build PyTorch from source instead of installing the prebuilt pacakge, input shape is (1, 40000, 4). Please see getting_started.md for the basic usage of MMDetection3D. You may open an issue on GitHub if no solution is found. e.g. Install PyTorch following official instructions, e.g. There are also tutorials for learning configuration systems, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and Waymo dataset. The pre-trained models can be downloaded from model zoo. Here is an example of building the model and test given point clouds. Currently we support single-modality 3D detection and 3D segmentation on all the datasets, multi-modality 3D detection on KITTI and SUN RGB-D, as well as monocular 3D detection on nuScenes. To verify the data consistency and the effect of data augmentation, you can also add --aug flag to visualize the data after data augmentation using the command as below: If you also want to show 2D images with 3D bounding boxes projected onto them, you need to find a config that supports multi-modality data loading, and then change the --task args to multi_modality-det. The pre-trained models can be downloaded from model zoo. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIAs website, and its version should match the CUDA version of PyTorch. The pre-trained models can be downloaded from model zoo. We currently only support FLOPs calculation of single-stage models with single-modality input (point cloud or image). Convert the model from MMDetection3D to TorchServe python tools/deployment/mmdet3d2torchserve.py $ {CONFIG_FILE} $ {CHECKPOINT_FILE} \ --output-folder $ {MODEL_STORE} \ --model-name $ {MODEL_NAME} Note: $ {MODEL_STORE} needs to be an absolute path to a folder. Larger number could reduce the preparation time as images are processed in parallel. Otherwise, you can follow these steps for the preparation. Please refer to model_deployment.md for more details. We will support two-stage and multi-modality models in the future. The version will also be saved in trained models. When installing PyTorch, you need to specify the version of CUDA. It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc. If you find this project useful in your research, please consider cite: We appreciate all contributions to improve MMDetection3D. Step 1. The train and test scripts already modify the PYTHONPATH to ensure the script use the MMDetection3D in the current directory. The master branch works with PyTorch 1.3+. To get the full dataset, please use --version v1.0-train v1.0-val v1.0-mini. ResNet models to PyTorch style. It is This function can also be used for data preprocessing for training ply data. Unifies interfaces of all components based on. It is also convenient to modify them to use as scripts like nuImages converter. Otherwise, you should refer to the step-by-step installation instructions in the next section. See this table for more information. Some dependencies are optional. PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2. 2. you can install it before installing MMCV. If you are not clear on which to choose, follow our recommendations: For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must. Modify the configs as will be discussed in this tutorial. Please refer to CONTRIBUTING.md for the contributing guideline. Please replace {cu_version} and {torch_version} in the url to your desired one. We recommend that users follow our best practices to install MMDetection3D. tools/data_converter/ contains tools for converting datasets to other formats. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. This function can also be used for data preprocessing for training ply data. You can omit the --gpus argument in order to run on the CPU. MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. We also support Minkowski Engine as a sparse convolution backend. We provide a Dockerfile to build an image. Clone the MMDetection3D repository. conda create --name mmdeploy python=3 .8 -y conda activate mmdeploy Step 2. Install build requirements and then install MMDetection3D. trimesh.load ('/path/to/file.obj') or trimesh.load_mesh ('/path/to/file.obj'), the object class returned is Scene, which is incompatible with repair.fix_winding (mesh), only Trimesh object are accepted. Here is a full script for setting up mmdetection with conda. Major features Support multi-modality/single-modality detectors out of box 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install Code and models for the best vision-only method, FCOS3D, have been released. trimesh .scene.cameras Camera Camera.K Camera.__init__ Camera.angles Camera.copy Camera.focal Camera.fov Camera.look_at Camera.resolution Camera.to_rays camera_to_rays look_at ray_pixel_coords trimesh .scene.lighting lighting.py DirectionalLight DirectionalLight.name DirectionalLight.color DirectionalLight.intensity. The required versions of MMCV and MMDetection for different versions of MMDetection3D are as below. Welcome to MMDetection3D's documentation! To test a 3D detector on multi-modality data (typically point cloud and image), simply run: where the ANNOTATION_FILE should provide the 3D to 2D projection matrix. Documentation: https://mmdetection3d.readthedocs.io/. Note If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next section. Step 1. Example on KITTI data using MVX-Net model: Example on SUN RGB-D data using ImVoteNet model: To test a monocular 3D detector on image data, simply run: where the ANNOTATION_FILE should provide the 3D to 2D projection matrix (camera intrinsic matrix). The final output filename will be faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth. MMDetection style. For nuScenes dataset, we also support nuImages dataset. FLOPs are related to the input shape while parameters are not. In the nuScenes 3D detection challenge of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results. You may well use the result for simple Note: This tool is still experimental and we do not guarantee that the If you are running test in remote server without GUI, the online visualization is not supported, you can set show=False to only save the output results in {SHOW_DIR}. Create a conda virtual environment and activate it. point_cloud) # visualize the results and save the results in 'results' folder model.show_results(data, result, out_dir= 'my_results') . You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long. will only install the minimum runtime requirements. Note that if you set the flag --show, the prediction result will be displayed online using Open3D. All the about 300+ models, methods of 40+ papers, and modules supported in MMDetection can be trained or used in this codebase. imports. Like MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it. --out-dir: the output directory of annotations and semantic masks, defaults to ./data/nuimages/annotations/. Pre-trained models can be downloaded from model zoo. The models that are not supported by other codebases are marked by . Step 0. Are you sure you want to create this branch? Please refer to getting_started.md for installation. We provide several demo scripts to test a single sample. Add support for the new dataset following Tutorial 2: Customize Datasets. filename. This can be used to separate different annotations processed in different time for study. You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long. A tag already exists with the provided branch name. Revision 9556958f. Example on KITTI data using SECOND model: Example on SUN RGB-D data using VoteNet model: Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. It is recommended that you run step d each time you pull some updates from github. Plot the classification loss of some run. compute the hash of the checkpoint file and append the hash id to the The compatibilities of models are broken due to the unification and simplification of coordinate systems. you can use more CUDA versions such as 9.0. c. Install MMCV. Optionally, you could also build the full version from source: Optionally, you could also build MMDetection from source in case you want to modify the code: f.Install build requirements and then install MMDetection3D. The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in ${OUT_DIR}/PCD_NAME. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. conda create -n open-mmlab python=3 .7 -y conda activate open-mmlab b. Users can use the following commands to install spconv2.0: Where xxx is the CUDA version in the environment. You signed in with another tab or window. We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. Assuming that you already have CUDA 11.0 installed, here is a full script for quick installation of MMDetection3D with conda. It trains faster than other codebases. If you would like to use opencv-python-headless instead of opencv-python, OpenMMLab's next-generation platform for general 3D object detection. Note: Make sure that your compilation CUDA version and runtime CUDA version match. --version: the version of the dataset, defaults to v1.0-mini. Waymo converter is used to reorganize waymo raw data like KITTI style. You can use test_torchserver.py to compare result of torchserver and pytorch. An example is showed below, You can simply browse different datasets using different configs, e.g. Copyright 2020-2023, OpenMMLab. you can install it before installing MMCV. You can use tools/misc/browse_dataset.py to show loaded data and ground-truth online and save them on the disk. Read the docs about the Inference (8080), Management (8081) and Metrics (8082) APis. Create a conda environment and activate it. Details can be found in benchmark.md. visualizing the ScanNet dataset in 3D semantic segmentation task, And browsing the nuScenes dataset in monocular 3D detection task. The width/height are minused by 1 when calculating the anchors' centers and corners to meet the V1.x coordinate system. Copyright 2020-2023, OpenMMLab Legacy anchor generator used in MMDetection V1.x. MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI. For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3. The version will also be saved in trained models. To convert the nuImages dataset into COCO format, please use the command below: --data-root: the root of the dataset, defaults to ./data/nuimages. More demos about single/multi-modality and indoor/outdoor 3D detection can be found in demo. MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. This constuctor creates a triangle/tetrahedron mesh from a . Plot the classification and regression loss of some run, and save the figure to a pdf. In order to do an end-to-end model deployment, MMDeploy requires Python 3.6+ and PyTorch 1.5+. Specifically, open ***_points.obj to see the input point cloud and open ***_pred.obj to see the predicted 3D bounding boxes. Or you can use 3D visualization software such as the MeshLab to open these files under ${SHOW_DIR} to see the 3D detection output. will only install the minimum runtime requirements. tools/model_converters/regnet2mmdet.py convert keys in pycls pretrained RegNet models to To see the prediction results during evaluation, you can run the following command. A brand new version of MMDetection v1.1.0rc0 was released in 1/9/2022: Find more new features in 1.1.x branch. Note that you need to install pandas and plyfile before using this script. When show is enabled, Open3D will be used to visualize the results online. Note that you need to install pandas and plyfile before using this script. If the user has installed spconv2.0, the code will use spconv2.0 first, which will take up less GPU memory than using the default mmcv spconv. 1 mmdetection3d 1.1 docker 1.1.1 docker 1 2 . To visualize the results with Open3D backend, you can run the following command. E.g. Step 0. In order to serve an MMDetection3D model with TorchServe, you can follow the steps: Note: ${MODEL_STORE} needs to be an absolute path to a folder. Revision e3662725. You can also compute the average training speed. Note Difference to the V2.0 anchor generator: The center offset of V1.x anchors are set to be 0.5 rather than 0. Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. Copyright 2020-2023, OpenMMLab. Built upon the new training engine and MMDet 3.x, MMDet3D 1.1 unifies the interfaces of dataset, models, evaluation, and visualization with faster training and testing speed. number is absolutely correct. Get Started Prerequisites Installation Demo Demo Model Zoo Model Zoo Data Preparation Dataset Preparation Exist Data and Model 1: Inference and train with existing models and standard datasets New Data and Model 2: Train with customized datasets Supported Tasks LiDAR-Based 3D Detection E.g. Create a conda virtual environment and activate it. 1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install How can I force it to load and return a Trimesh object or parse the Scene object to Trimesh object?.. You can use any other data following our pre-processing steps. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. To test a 3D segmentor on point cloud data, simply run: The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in ${OUT_DIR}/PCD_NAME. It is a part of the OpenMMLab project developed by MMLab. Compare the bbox mAP of two runs in the same figure. The git commit id will be written to the version number with step d, e.g. i.e., the specified version of cudatoolkit in conda install command. Here is a full script for setting up MMdetection3D with conda. You can use tools/analysis_tools/get_flops.py in MMDetection3D, a script adapted from flops-counter.pytorch, to compute the FLOPs and params of a given model. After running this command, plotted results including input data and the output of networks visualized on the input (e.g. Install MMDetection3D a. You can plot loss/mAP curves given a training log file. Please refer to FAQ for frequently asked questions. MMDetection works on Linux, Windows and macOS. For more details please refer to spconv v2.x. MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. Results and models are available in the model zoo. Parameters Important: Be sure to remove the ./build folder if you reinstall mmdet with a different CUDA/PyTorch version. This allows the inference and results generation to be done in remote server and the users can open them on their host with GUI. See Customize Installation section for more information. scatter GPUtrain_step val_step batch Detector train_step val_step . Run pip install seaborn first to install the dependency. You can check the supported CUDA version for precompiled packages on the PyTorch website. Revision 9556958f. The visualization results including a point cloud, an image, predicted 3D bounding boxes and their projection on the image will be saved in ${OUT_DIR}/PCD_NAME. Major features Support multi-modality/single-modality detectors out of box For example, using CUDA 10.2, the command will be pip install cumm-cu102 && pip install spconv-cu102. We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. A standard data protocol defines and unifies the common keys across different datasets. Please stay tuned for MoCa. Optionally, you could also build MMDetection from source in case you want to modify the code: Optionally, you could also build MMSegmentation from source in case you want to modify the code: Step 3. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new 3D detectors. We provide a Dockerfile to build an image. Step 2. 2. Linux or macOS (Windows is not currently officially supported), CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible). As for offline visualization, you will have two options. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+. Now MMDeploy has supported some MMDetection3D model deployment. MMDet3D 1.1.0rc0 is the first version of MMDetection3D 1.1, a part of the OpenMMLab 2.0 projects. Notice: The visualization API is a little unstable since we plan to refactor these parts together with MMDetection in the future. We provide lots of useful tools under tools/ directory. The default 0.6.0+2e7045c. If you perform evaluation with an interval of ${INTERVAL}, you need to add the args --interval ${INTERVAL}. Valid keys for the extras field are: all, tests, build, and optional. If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next section. See its README for detailed instructions on how to convert the checkpoint. Issues and PRs are welcome! Note: This tool is still experimental now, only SECOND is supported to be served with TorchServe. To see the prediction results of trained models, you can run the following command. 0.6.0+2e7045c. Pre-trained models can be downloaded from model zoo. By exploring. If you would like to use opencv-python-headless instead of opencv-python, We compare the number of samples trained per second (the higher, the better). Important: Be sure to remove the ./build folder if you reinstall mmdet with a different CUDA/PyTorch version. This tutorial provides instruction for users to use the models provided in the Model Zoo for other datasets to obtain better performance. 1: Inference and train with existing models and standard datasets, Tutorial 8: MMDetection3D model deployment. If you dont have a monitor, you can remove the --online flag to only save the visualization results and browse them offline. Notice: Once specifying --output-dir, the images of views specified by users will be saved when pressing _ESC_ in open3d window. In order to serve an MMDetection3D model with TorchServe, you can follow the steps: 1. For now, most models are benchmarked with similar performance, though few models are still being benchmarked. In order to run it on the GPU, you need to install nvidia-docker. MIM solves such dependencies automatically and makes the installation easier. tools/model_converters/publish_model.py helps users to prepare their model for publishing. Support indoor/outdoor 3D detection out of box. In order to serve an MMDetection model with TorchServe, you can follow the steps: 1. When updating the version of MMDetection3D, please also check the compatibility doc to be aware of the BC-breaking updates introduced in each version. Introduction We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. To install MMCV with pip instead of MIM, please follow MMCV installation guides. open-mmlabmmdetectionmmsegmentationmmsegmentationmmdetectionmmsegmentationmmdetection mmsegmentation mmsegmentationdata . Before you upload a model to AWS, you may want to. Following the above instructions, MMDetection3D is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number). ), you can use trimesh to convert them into ply. Well support more models in the future. See more details and examples in PR #744. Simply running pip install -v -e . Please refer to changelog.md for details and release history. You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long. MMDeploy has supported some MMDetection3d model deployment. After running this command, you will obtain the input data, the output of networks and ground-truth labels visualized on the input (e.g. # package mmcv-full will be installed after this step, # build an image with PyTorch 1.6, CUDA 10.1, # install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest), 'configs/votenet/votenet_8x8_scannet-3d-18class.py', 'checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth', # build the model from a config file and a checkpoint file, # test a single image and show the results, # visualize the results and save the results in 'results' folder, 1: Inference and train with existing models and standard datasets. comparisons, but double check it before you adopt it in technical reports or papers. We also provide scripts to visualize the dataset without inference. The anchors' corners are quantized. For example, to install the latest mmcv-full with CUDA 11 and PyTorch 1.7.0, use the following command: See here for different versions of MMCV compatible to different PyTorch and CUDA versions. To test a 3D detector on point cloud data, simply run: The visualization results including a point cloud and predicted 3D bounding boxes will be saved in ${OUT_DIR}/PCD_NAME, which you can open using MeshLab. Users can also install it by building from the source. Some operators are not counted into FLOPs like GN and custom operators. If necessary please follow original installation guide or use pip: The code can not be built for CPU only environment (where CUDA isnt available) for now. If C++/CUDA codes are modified, then this step is compulsory. Then you can use the converted bin file to generate demo. MMDection3D works on Linux, Windows (experimental support) and macOS and requires the following packages: Python 3.6+ PyTorch 1.3+ CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible) GCC 5+ MMCV Note If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next section. ***_points.obj, ***_pred.obj, ***_gt.obj, ***_img.png and ***_pred.png in multi-modality detection task) in ${SHOW_DIR}. mmcv-full is necessary since MMDetection3D relies on MMDetection, CUDA ops in mmcv-full are required. a part of the OpenMMLab project developed by MMLab. Download and install Miniconda from the official website. Simply running pip install -v -e . To browse the KITTI dataset, you can run the following command. tools/detectron2pytorch.py in MMDetection could convert keys in the original detectron pretrained visualize training result for mmdetection Sep 03, 2019 1 min read mmdetection_visualize_v1 It's a very simple version for visualizing the training result produced by mmdetection. Please make sure the GPU driver satisfies the minimum version requirements. # build an image with PyTorch 1.6, CUDA 10.1, # install latest PyTorch prebuilt with the default prebuilt CUDA version (usually the latest), 1: Inference and train with existing models and standard datasets, Tutorial 8: MMDetection3D model deployment. In this section we demonstrate how to prepare an environment with PyTorch. Faster training and testing speed with more strong baselines. Step 0. b. We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. Valid keys for the extras field are: all, tests, build, and optional. mmdetection3d kitti Mmdetection3d3DKITTIKITTImmdetection3dkittiMini KITTIKITTI Mini KITTI_Coding-CSDN . Since MMDetection 2.0, the config system supports to inherit configs such that the users can focus on the modification. Install PyTorch and torchvision following the official instructions. To use the default MMDetection3D installed in the environment rather than that you are working with, you can remove the following line in those scripts, We provide a demo script to test a single sample. --nproc: number of workers for data preparation, defaults to 4. More details could be referred to the doc for dataset preparation and README for nuImages dataset. To test a single-modality 3D detection on point cloud scenes: If you want to input a ply file, you can use the following function and convert it to bin format. Convert model from MMDetection to TorchServe python tools/deployment/mmdet2torchserve.py $ {CONFIG_FILE} $ {CHECKPOINT_FILE} \ --output-folder $ {MODEL_STORE} \ --model-name $ {MODEL_NAME} Note: $ {MODEL_STORE} needs to be an absolute path to a folder. MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Refer to mmcv.cnn.get_model_complexity_info() for details. If C++/CUDA codes are modified, then this step is compulsory. For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight. Download and install Miniconda from the official website. We appreciate all the contributors as well as users who give valuable feedbacks. The Double Head R-CNN mainly uses a new DoubleHeadRoIHead and a new DoubleConvFCBBoxHead, the arguments are set according to the __init__ function of each module. Otherwise, you can follow these steps for the preparation. However, it is not a must. tools/misc/print_config.py prints the whole config verbatim, expanding all its Add new loss If you have some issues during the installation, please first view the FAQ page. --extra-tag: extra tag of the annotations, defaults to nuimages. 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