# Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch.
MMDection3D works on Linux, Windows (experimental support) and macOS and requires the following packages:

- Python 3.6+
- PyTorch 1.6+
- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
- GCC 5+
- [MMEngine](https://mmengine.readthedocs.io/zh_CN/latest/#installation)
- [MMCV](https://mmcv.readthedocs.io/zh_CN/latest/#installation)

```{note}
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
```

**Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).

**Step 1.** Create a conda environment and activate it.

```shell
# We recommend to install python=3.8 since the waymo-open-dataset-tf-2-6-0 requires python>=3.7
# If you want to install python<3.7, make sure to install waymo-open-dataset-tf-2-x-0 (x<=4)
conda create --name openmmlab python=3.8 -y
conda activate openmmlab
```

**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.

On GPU platforms:

```shell
conda install pytorch torchvision -c pytorch
```

On CPU platforms:

```shell
conda install pytorch torchvision cpuonly -c pytorch
```

# Installation

We recommend that users follow our best practices to install MMDetection3D. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information.

## Best Practices

Assuming that you already have CUDA 11.0 installed, here is a full script for quick installation of MMDetection3D with conda.
Otherwise, you should refer to the step-by-step installation instructions in the next section.

```shell
pip install openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc0'
mim install 'mmdet>=3.0.0rc0'
git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
cd mmdetection3d
pip install -e .
```

**Step 0.** Install [MMEngine](https://github.com/open-mmlab/mmengine) and [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).

```shell
pip install -U openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc0'
```

**Step 1.** Install [MMDetection](https://github.com/open-mmlab/mmdetection).

```shell
mim install 'mmdet>=3.0.0rc0'
```

Optionally, you could also build MMDetection from source in case you want to modify the code:

```shell
git clone https://github.com/open-mmlab/mmdetection.git -b dev-3.x
# "-b dev-3.x" means checkout to the `dev-3.x` branch.
cd mmdetection
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
```

**Step 2.** Clone the MMDetection3D repository.

```shell
git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
# "-b dev-1.x" means checkout to the `dev-1.x` branch.
cd mmdetection3d
```

**Step 3.** Install build requirements and then install MMDetection3D.

```shell
pip install -v -e .  # or "python setup.py develop"
```

Note:

1. The git commit id will be written to the version number with step 4, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
   It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

   > Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.

   ```shell
   pip uninstall mmdet3d
   rm -rf ./build
   find . -name "*.so" | xargs rm
   ```

2. 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).

3. If you would like to use `opencv-python-headless` instead of `opencv-python`,
   you can install it before installing MMCV.

4. Some dependencies are optional. Simply running `pip install -v -e .` will only install the minimum runtime requirements. 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. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.

   We have supported spconv2.0. 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. Users can use the following commands to install spconv2.0:

   ```bash
   pip install cumm-cuxxx
   pip install spconv-cuxxx
   ```

   Where xxx is the CUDA version in the environment.

   For example, using CUDA 10.2, the command will be `pip install cumm-cu102 && pip install spconv-cu102`.

   Supported CUDA versions include 10.2, 11.1, 11.3, and 11.4. Users can also install it by building from the source. For more details please refer to [spconv v2.x](https://github.com/traveller59/spconv).

   We also support Minkowski Engine as a sparse convolution backend. If necessary please follow original [installation guide](https://github.com/NVIDIA/MinkowskiEngine#installation) or use `pip`:

   ```shell
   conda install openblas-devel -c anaconda
   pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=/opt/conda/include" --install-option="--blas=openblas"
   ```

5. The code can not be built for CPU only environment (where CUDA isn't available) for now.

## Verification

### Verify with point cloud demo

We provide several demo scripts to test a single sample. Pre-trained models can be downloaded from [model zoo](model_zoo.md). To test a single-modality 3D detection on point cloud scenes:

```shell
python demo/pcd_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}]
```

Examples:

```shell
python demo/pcd_demo.py demo/data/kitti/000008.bin configs/second/second_hv-secfpn_8xb6-80e_kitti-3d-car.py checkpoints/second_hv-secfpn_8xb6-80e_kitti-3d-car_20200620_230238-393f000c.pth
```

If you want to input a `ply` file, you can use the following function and convert it to `bin` format. Then you can use the converted `bin` file to generate demo.
Note that you need to install `pandas` and `plyfile` before using this script. This function can also be used for data preprocessing for training `ply data`.

```python
import numpy as np
import pandas as pd
from plyfile import PlyData

def convert_ply(input_path, output_path):
    plydata = PlyData.read(input_path)  # read file
    data = plydata.elements[0].data  # read data
    data_pd = pd.DataFrame(data)  # convert to DataFrame
    data_np = np.zeros(data_pd.shape, dtype=np.float)  # initialize array to store data
    property_names = data[0].dtype.names  # read names of properties
    for i, name in enumerate(
            property_names):  # read data by property
        data_np[:, i] = data_pd[name]
    data_np.astype(np.float32).tofile(output_path)
```

Examples:

```python
convert_ply('./test.ply', './test.bin')
```

If you have point clouds in other format (`off`, `obj`, etc.), you can use `trimesh` to convert them into `ply`.

```python
import trimesh

def to_ply(input_path, output_path, original_type):
    mesh = trimesh.load(input_path, file_type=original_type)  # read file
    mesh.export(output_path, file_type='ply')  # convert to ply
```

Examples:

```python
to_ply('./test.obj', './test.ply', 'obj')
```

More demos about single/multi-modality and indoor/outdoor 3D detection can be found in [demo](user_guides/inference.md).

## Customize Installation

### CUDA Versions

When installing PyTorch, you need to specify the version of CUDA. 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.
- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.

Please make sure the GPU driver satisfies the minimum version requirements. See [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information.

```{note}
Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.
```

### Install MMEngine without MIM

To install MMEngine with pip instead of MIM, please follow [MMEngine installation guides](https://mmengine.readthedocs.io/en/latest/get_started/installation.html).

For example, you can install MMEngine by the following command.

```shell
pip install mmengine
```

### Install MMCV without MIM

MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.

To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.

For example, the following command install mmcv built for PyTorch 1.10.x and CUDA 11.3.

```shell
pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
```

### Using MMDetection3D with Docker

We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection3d/blob/master/docker/Dockerfile) to build an image.

```shell
# build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmdetection3d -f docker/Dockerfile .
```

Run it with

```shell
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection3d/data mmdetection3d
```

### A from-scratch setup script

Here is a full script for setting up MMdetection3D with conda.

```shell
# We recommend to install python=3.8 since the waymo-open-dataset-tf-2-6-0 requires python>=3.7
# If you want to install python<3.7, make sure to install waymo-open-dataset-tf-2-x-0 (x<=4)
conda create -n open-mmlab python=3.8 -y
conda activate open-mmlab

# install latest PyTorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y

# install mmengine and mmcv
pip install openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc0'

# install mmdetection
mim install 'mmdet>=3.0.0rc0'

# install mmdetection3d
git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
cd mmdetection3d
pip install -e .
```

## Trouble shooting

If you have some issues during the installation, please first view the [FAQ](notes/faq.md) page.
You may [open an issue](https://github.com/open-mmlab/mmdetection3d/issues/new/choose) on GitHub if no solution is found.