1. Installation environment
Here are some of the more important versions:
Graphics card: 4090&2080ti
CUDA=11.1
python=3.8
torch=1.9.0+cu111
torchvision=0.10.0+cu111
spconv-cu111=2.1.25
numpy=1.21.0
number=0.48.0
mmdet=2.25.0
mmdet3d=0.17.1
mmcv-full=1.3.18
The installation process basically follows MMDET3D, just pay attention to some installation package versions:
2. Environment construction
1. Configure mmdet3d environment
(1) Create a virtual environment (no need to install packages, Anconda automatically installs python):
conda create -n deepinteraction python=3.8
(2) Activate the environment
conda activate deepinteraction
(3) Install torch and torchvision
Installation instructions: Local installation is recommended, otherwise pip will accidentally install the cpu version.
①Download file
Link:
Baidu Cloud : Find the following folder, which contains the torch and torchvision installation packages .
②Installation
Use the pip command to implement local installation :
pip install torch-1.9.0+cu111-cp38-cp38-linux_x86_64.whl torchvision-0.10.0+cu111-cp38-cp38-linux_x86_64.whl
③Test
First enter python to enter the environment and enter the following commands:
import torch # 如正常则静默
torch.__version__ #查看torch版本
import torchvision
torchvision.__version__ #查看torchvision版本
torch.cuda.is_available() #正常的话返回“True”
a = torch.Tensor([1.]) # 如正常则静默
a.cuda() # 如正常则返回"tensor([ 1.], device='cuda:0')"
from torch.backends import cudnn # 如正常则静默
cudnn.is_acceptable(a.cuda()) # 如正常则返回 "True"The display works normally:
④Official download website
If you don’t want to use Baidu Cloud, here is the official download website:
torch download website: https://download.pytorch.org/whl/torch_stable.html
torch 1.9.0+cu111:https://download.pytorch.org/whl/cu111/torch-1.9.0%2Bcu111-cp38-cp38-linux_x86_64.whl
torchvision 0.10.0+cu111:https://download.pytorch.org/whl/cu111/torchvision-0.10.0%2Bcu111-cp38-cp38-linux_x86_64.whl
(4) Install mmcv-full 1.3.18
①pip installation
pip install mmcv-full==1.3.18 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
You cannot just use the pip install mmcv-full command to install it here. You need to add your own cuda information and torch information later.
(5) Install mmsegmentation
Installation instructions: Only mmdet3d needs to be installed.
①pip installation
pip install mmsegmentation==0.14.1
②Local installation
Go to the /mmsegmentation-master folder and execute:
pip install -r requirements.txt
python setup.py develop
(6) Install mmdet
Installation instructions: If you want to run mmdet instead of mmdet3d, it is recommended to install it locally to facilitate data running later.
①pip install mmdet
pip install mmdet==2.14.0
②Install mmdet locally
Go to the /mmdetection folder and execute:
pip install -r requirements.txt
python setup.py develop
(7) Install mmdet3d
①Official installation instructions
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v0.17.1
python setup.py install
②Local installation
Enter the /mmdetection3d-master folder
implement:
pip install -r requirements
python setup.py develop
(8) Install detectron2
The Dynamic Conv module in detectron2 is used. The instructions are as follows:
python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
2. Download and configure the data set
Official website method:
Dataset Preparation — MMDetection3D 1.2.0 documentation 
https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html By unzipping the dataset into a folder, and then executing the instructions:
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes
The converted results are as follows:
3. Pre-training weights
Copy the pretrained weights to the pretrained/ folder:
3. Train & Test
# train with 8 GPUs
tools/dist_train.sh projects/configs/nuscenes/Fusion_0075_refactor.py 8
# test with 8 GPUs
tools/dist_test.sh projects/configs/nuscenes/Fusion_0075_refactor.py ${CHECKPOINT_FILE} 8 --eval=bbox
Change the number of GPUs according to your needs.
4. Encountering problems
1、ModuleNotFoundError: No module named 'numba.errors'
Solution: The numba version is too high and needs to be installed to 0.48.0:
pip install numba==0.48.0
2、RuntimeError: Address already in use xxx
Problem description: The port is occupied
solution:
In the file /home/xd/xyy/DeepInteraction-main/tools/dist_train.sh
You can specify the port number of the terminal command. Add --port 12345 after running the terminal command. The port number can be specified arbitrarily, and only five digits are enough.
3、KeyError: 'SwinTransformer is already registered in models'
Solution: Just comment out from .swin_transformer import SwinTransformer in /DeepInteraction-main/projects/mmdet3d_plugin/__init__.py
reference:
FastBEV reproduces Ubuntu_A confused insect's blog-CSDN blog
4、from numba.errors import NumbaPerformanceWarning
Solution: Install numba==1.48 as required
5、AttributeError: 'ConfigDict' object has no attribute 'device'
Solution:
meta['config'] = cfg.pretty_textAdd the following code below tools/train.py :
cfg.device='cuda'reference:
6、 mmcv/_ext.cpython-38-x86_64-linux-gnu.so: undefined symbol: _Z27points_in_boxes_cpu_forwardN2at6TensorES0_S0_
Solution:
pip install mmcv-full==1.3.18 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
You cannot just use the pip install mmcv-full command to install it here. You need to add your own cuda information and torch information later.