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[Enhance] update the installation and faq doc (#1545) 

* update the installation and faq doc

* update the version requirements in installation documents and polish the installation script

* change pip3 to pip
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docs/en/faq.md
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...@@ -4,6 +4,36 @@ We list some potential troubles encountered by users and developers, along with ...@@ -4,6 +4,36 @@ We list some potential troubles encountered by users and developers, along with
## MMCV/MMDet/MMDet3D Installation ## MMCV/MMDet/MMDet3D Installation
- Compatibility issue between MMCV, MMDetection, MMSegmentation and MMDection3D; "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."
The required versions of MMCV, MMDetection and MMSegmentation for different versions of MMDetection3D are as below. Please install the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues.
| MMDetection3D version | MMDetection version | MMSegmentation version | MMCV version |
| :-------------------: | :---------------------: | :--------------------: | :------------------------: |
| master | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.7.0 |
| v1.0.0rc3 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.7.0 |
| v1.0.0rc2 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.7.0 |
| v1.0.0rc1 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.5.0 |
| v1.0.0rc0 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
| 0.18.1 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
| 0.18.0 | mmdet>=2.19.0, <=3.0.0 | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
| 0.17.3 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.17.2 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.17.1 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.17.0 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.16.0 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.15.0 | mmdet>=2.14.0, <=3.0.0 | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0 |
| 0.14.0 | mmdet>=2.10.0, <=2.11.0 | mmseg==0.14.0 | mmcv-full>=1.3.1, <=1.4.0 |
| 0.13.0 | mmdet>=2.10.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.4.0 |
| 0.12.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.4.0 |
| 0.11.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.3.0 |
| 0.10.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.3.0 |
| 0.9.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.2.4, <=1.3.0 |
| 0.8.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.1.5, <=1.3.0 |
| 0.7.0 | mmdet>=2.5.0, <=2.11.0 | Not required | mmcv-full>=1.1.5, <=1.3.0 |
| 0.6.0 | mmdet>=2.4.0, <=2.11.0 | Not required | mmcv-full>=1.1.3, <=1.2.0 |
| 0.5.0 | 2.3.0 | Not required | mmcv-full==1.0.5 |
- If you faced the error shown below when importing open3d: - If you faced the error shown below when importing open3d:
`OSError: /lib/x86_64-linux-gnu/libm.so.6: version 'GLIBC_2.27' not found` `OSError: /lib/x86_64-linux-gnu/libm.so.6: version 'GLIBC_2.27' not found`
......
docs/en/getting_started.md
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# Prerequisites # 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:
- Linux or macOS (Windows is in experimental support)
- Python 3.6+ - Python 3.6+
- PyTorch 1.3+ - PyTorch 1.3+
- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible) - CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
- GCC 5+ - GCC 5+
- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation) - [MMCV](https://mmcv.readthedocs.io/en/latest/#installation)
The required versions of MMCV, MMDetection and MMSegmentation for different versions of MMDetection3D are as below. Please install the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues. ```{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.
| MMDetection3D version | MMDetection version | MMSegmentation version | MMCV version |
| :-------------------: | :----------------------: | :---------------------: | :-------------------------: |
| master | mmdet>=2.24.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.6.0 |
| v1.0.0rc2 | mmdet>=2.24.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.6.0 |
| v1.0.0rc1 | mmdet>=2.19.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.5.0 |
| v1.0.0rc0 | mmdet>=2.19.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
| 0.18.1 | mmdet>=2.19.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
| 0.18.0 | mmdet>=2.19.0, \<=3.0.0 | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
| 0.17.3 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.17.2 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.17.1 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.17.0 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.16.0 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.15.0 | mmdet>=2.14.0, \<=3.0.0 | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0 |
| 0.14.0 | mmdet>=2.10.0, \<=2.11.0 | mmseg==0.14.0 | mmcv-full>=1.3.1, \<=1.4.0 |
| 0.13.0 | mmdet>=2.10.0, \<=2.11.0 | Not required | mmcv-full>=1.2.4, \<=1.4.0 |
| 0.12.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.2.4, \<=1.4.0 |
| 0.11.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.2.4, \<=1.3.0 |
| 0.10.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.2.4, \<=1.3.0 |
| 0.9.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.2.4, \<=1.3.0 |
| 0.8.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.1.5, \<=1.3.0 |
| 0.7.0 | mmdet>=2.5.0, \<=2.11.0 | Not required | mmcv-full>=1.1.5, \<=1.3.0 |
| 0.6.0 | mmdet>=2.4.0, \<=2.11.0 | Not required | mmcv-full>=1.1.3, \<=1.2.0 |
| 0.5.0 | 2.3.0 | Not required | mmcv-full==1.0.5 |
# Installation
## Install MMDetection3D
### Quick installation instructions script
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
conda create -n open-mmlab python=3.7 pytorch=1.9 cudatoolkit=11.0 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet
mim install mmsegmentation
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip3 install -e .
```
### Step-by-step installation instructions
**a. Create a conda virtual environment and activate it.**
```shell
conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab
```
**b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).**
```shell
conda install pytorch torchvision -c pytorch
``` ```
Note: Make sure that your compilation CUDA version and runtime CUDA version match. **Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).
You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
`E.g. 1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install **Step 1.** Create a conda environment and activate it.
PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.
```shell ```shell
conda install pytorch==1.5.0 cudatoolkit=10.1 torchvision==0.6.0 -c pytorch conda create --name openmmlab python=3.8 -y
conda activate openmmlab
``` ```
`E.g. 2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install **Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
```shell On GPU platforms:
conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
```
If you build PyTorch from source instead of installing the prebuilt package,
you can use more CUDA versions such as 9.0.
**c. Install [MMCV](https://mmcv.readthedocs.io/en/latest/).**
*mmcv-full* is necessary since MMDetection3D relies on MMDetection, CUDA ops in *mmcv-full* are required.
`e.g.` The pre-build *mmcv-full* could be installed by running: (available versions could be found [here](https://mmcv.readthedocs.io/en/latest/#install-with-pip))
```shell ```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html conda install pytorch torchvision -c pytorch
``` ```
Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example, to install the latest `mmcv-full` with `CUDA 11` and `PyTorch 1.7.0`, use the following command: On CPU platforms:
```shell ```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html conda install pytorch torchvision cpuonly -c pytorch
``` ```
mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well. # Installation
```shell 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.
# We can ignore the micro version of PyTorch
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7/index.html
```
See [here](https://github.com/open-mmlab/mmcv#install-with-pip) for different versions of MMCV compatible to different PyTorch and CUDA versions. ## Best Practices
Optionally, you could also build the full version from source: 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 ```shell
git clone https://github.com/open-mmlab/mmcv.git pip install openmim
cd mmcv mim install mmcv-full
MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step mim install mmdet
cd .. mim install mmsegmentation
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -e .
``` ```
Or directly run **Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
```shell **Step 1.** Install [MMDetection](https://github.com/open-mmlab/mmdetection).
pip install mmcv-full
```
**d. Install [MMDetection](https://github.com/open-mmlab/mmdetection).**
```shell ```shell
pip install mmdet pip install mmdet
...@@ -139,12 +67,12 @@ Optionally, you could also build MMDetection from source in case you want to mod ...@@ -139,12 +67,12 @@ Optionally, you could also build MMDetection from source in case you want to mod
```shell ```shell
git clone https://github.com/open-mmlab/mmdetection.git git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection cd mmdetection
git checkout v2.19.0 # switch to v2.19.0 branch git checkout v2.24.0 # switch to v2.24.0 branch
pip install -r requirements/build.txt pip install -r requirements/build.txt
pip install -v -e . # or "python setup.py develop" pip install -v -e . # or "python setup.py develop"
``` ```
**e. Install [MMSegmentation](https://github.com/open-mmlab/mmsegmentation).** **Step 2.** Install [MMSegmentation](https://github.com/open-mmlab/mmsegmentation).
```shell ```shell
pip install mmsegmentation pip install mmsegmentation
...@@ -159,14 +87,14 @@ git checkout v0.20.0 # switch to v0.20.0 branch ...@@ -159,14 +87,14 @@ git checkout v0.20.0 # switch to v0.20.0 branch
pip install -e . # or "python setup.py develop" pip install -e . # or "python setup.py develop"
``` ```
**f. Clone the MMDetection3D repository.** **Step 3.** Clone the MMDetection3D repository.
```shell ```shell
git clone https://github.com/open-mmlab/mmdetection3d.git git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d cd mmdetection3d
``` ```
**g.Install build requirements and then install MMDetection3D.** **Step 4.** Install build requirements and then install MMDetection3D.
```shell ```shell
pip install -v -e . # or "python setup.py develop" pip install -v -e . # or "python setup.py develop"
...@@ -175,20 +103,20 @@ pip install -v -e . # or "python setup.py develop" ...@@ -175,20 +103,20 @@ pip install -v -e . # or "python setup.py develop"
Note: Note:
1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. 1. The git commit id will be written to the version number with step d, 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. 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. > Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.
```shell ```shell
pip uninstall mmdet3d pip uninstall mmdet3d
rm -rf ./build rm -rf ./build
find . -name "*.so" | xargs rm 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). 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`, 3. If you would like to use `opencv-python-headless` instead of `opencv-python`,
you can install it before installing MMCV. 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`. 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`.
...@@ -207,67 +135,17 @@ Note: ...@@ -207,67 +135,17 @@ Note:
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`: 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 ```shell
conda install openblas-devel -c anaconda 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" 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. 5. The code can not be built for CPU only environment (where CUDA isn't available) for now.
## Another option: Docker Image
We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection3d/blob/master/docker/Dockerfile) to build an image. ## Verification
```shell ### Verify with point cloud demo
# 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
conda create -n open-mmlab python=3.7 -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 mmcv
pip install mmcv-full
# install mmdetection
pip install git+https://github.com/open-mmlab/mmdetection.git
# install mmsegmentation
pip install git+https://github.com/open-mmlab/mmsegmentation.git
# install mmdetection3d
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -v -e .
```
## Using multiple MMDetection3D versions
The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMDetection3D in the current directory.
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
```shell
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
```
# 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: 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:
...@@ -282,7 +160,7 @@ python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_secon ...@@ -282,7 +160,7 @@ python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_secon
``` ```
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. 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`. 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 ```python
import numpy as np import numpy as np
...@@ -325,24 +203,76 @@ to_ply('./test.obj', './test.ply', 'obj') ...@@ -325,24 +203,76 @@ to_ply('./test.obj', './test.ply', 'obj')
More demos about single/multi-modality and indoor/outdoor 3D detection can be found in [demo](demo.md). More demos about single/multi-modality and indoor/outdoor 3D detection can be found in [demo](demo.md).
## High-level APIs for testing point clouds ## Customize Installation
### Synchronous interface ### 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:
Here is an example of building the model and test given point clouds. - 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.
```python 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.
from mmdet3d.apis import init_model, inference_detector
```{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 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-full built for PyTorch 1.10.x and CUDA 11.3.
```shell
pip install mmcv-full -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.
config_file = 'configs/votenet/votenet_8x8_scannet-3d-18class.py' ```shell
checkpoint_file = 'checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth' # build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmdetection3d -f docker/Dockerfile .
```
# build the model from a config file and a checkpoint file Run it with
model = init_model(config_file, checkpoint_file, device='cuda:0')
# test a single image and show the results ```shell
point_cloud = 'test.bin' docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection3d/data mmdetection3d
result, data = inference_detector(model, point_cloud)
# visualize the results and save the results in 'results' folder
model.show_results(data, result, out_dir='results')
``` ```
### A from-scratch setup script
Here is a full script for setting up MMdetection3D with conda.
```shell
conda create -n open-mmlab python=3.7 -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 mmcv
pip install mmcv-full
# install mmdetection
pip install git+https://github.com/open-mmlab/mmdetection.git
# install mmsegmentation
pip install git+https://github.com/open-mmlab/mmsegmentation.git
# install mmdetection3d
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -v -e .
```
## Trouble shooting
If you have some issues during the installation, please first view the [FAQ](faq.md) page.
You may [open an issue](https://github.com/open-mmlab/mmdetection3d/issues/new/choose) on GitHub if no solution is found.
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