BEVFuison測試全過程記錄

cuda版本10.1

pytorch：
根據cuda版本選擇pytorch版本：

1. 創建conda虛擬環境：

conda create -y --name mmcv python=3.8
conda activate mmcv

2. 安裝依賴庫：

pytorch:

conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch

mmcv:

pip install -U openmim
mim install mmcv-full==1.4.0

mmdet:

mim install mmdet==2.20.0

mpi4py:

pip install mpi4py==3.0.3

pillow:

pip install pillow==8.4.0

tqdm:

pip install tqdm

torchpack:

pip install torchpack

nuscenes-devkit:

pip install nuscenes-devkit

安裝Open MPI 4.0.4：

官網下載openmpi-4.0.4.tar.gz
解壓openmpi-4.0.4.tar.gz

cd openmpi-4.0.4
./configure --prefix=/usr/local CC=gcc CXX=g++ FC=gfortran
sudo make all install

查看是否安裝成功

which mpirun

如果顯示路徑，如：/usr/local/bin/mpirun，則說明安裝成功。

3. 安裝BEVFusion

cd ../bevfusion
python setup.py develop

報錯：
nvcc fatal : Unsupported gpu architecture ‘compute_80’
ninja: build stopped: subcommand failed.
原因：
我的顯卡是RTX2060 算力是7.5，故不支持compute_80(算力8.0), compute_86（算力8.6）
解決辦法：
注釋setup.py中的第24，25行，即：
#“-gencode=arch=compute_80,code=sm_80”,
#“-gencode=arch=compute_86,code=sm_86”,

重新執行：

python setup.py develop

Installed /home/zhkai/BEV/bevfusion
Processing dependencies for mmdet3d==0.0.0
Finished processing dependencies for mmdet3d==0.0.0

安裝成功！

4.數據準備

參考：NuScenes Dataset for 3D Object Detection

cd ../mmdetection3d
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

報錯：

ImportError: libcudart.so.10.2: cannot open shared object file: No such file or directory

原因：mmcv1.6.2版本與cuda10.1不匹配

解決辦法：重裝mmcv

mim install mmcv-full==1.6.0

重新執行：

python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

報錯：

======
Loading NuScenes tables for version v1.0-trainval...
23 category,
8 attribute,
4 visibility,
64386 instance,
12 sensor,
10200 calibrated_sensor,
2631083 ego_pose,
68 log,
850 scene,
34149 sample,
2631083 sample_data,
1166187 sample_annotation,
4 map,
Done loading in 37.483 seconds.
======
Reverse indexing ...
Done reverse indexing in 8.1 seconds.
======
total scene num: 850
exist scene num: 850
train scene: 700, val scene: 150
[>>>>                     ] 6743/34149, 14.6 task/s, elapsed: 461s, ETA:  1875sTraceback (most recent call last):File "tools/create_data.py", line 250, in <module>nuscenes_data_prep(File "tools/create_data.py", line 74, in nuscenes_data_prepnuscenes_converter.create_nuscenes_infos(
...
FileNotFoundError: file "./data/nuscenes/samples/LIDAR_TOP/n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532707811049131.pcd.bin" does not exist

原因：我只下載了trainval01、trainval02的數據，不是完整的訓練數據

修改代碼：
參考：https://blog.csdn.net/weixin_38362784/article/details/111397440

文件：mmdetection3d/tools/data_converter/nuscenes_converter.py第167行
函數：_fill_trainval_infos

# for sample in mmcv.track_iter_progress(nusc.sample):
for sample in mmcv.track_iter_progress(nusc.sample[0:6743]):

這里為什么是6743？
這只是我自己的推測，因為從上一步的報錯內容可知，nusc.sample是索引到6743的時候報錯的。

重新執行：

python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

======
Loading NuScenes tables for version v1.0-trainval...
23 category,
8 attribute,
4 visibility,
64386 instance,
12 sensor,
10200 calibrated_sensor,
2631083 ego_pose,
68 log,
850 scene,
34149 sample,
2631083 sample_data,
1166187 sample_annotation,
4 map,
Done loading in 37.468 seconds.
======
Reverse indexing ...
Done reverse indexing in 8.4 seconds.
======
total scene num: 850
exist scene num: 850
train scene: 700, val scene: 150
[>>>>>>>>>>>>>>>>>>>>>>>>>>] 6743/6743, 14.6 task/s, elapsed: 461s, ETA:     0s
train sample: 5512, val sample: 1231
======
Loading NuScenes tables for version v1.0-trainval...
23 category,
8 attribute,
4 visibility,
64386 instance,
12 sensor,
10200 calibrated_sensor,
2631083 ego_pose,
68 log,
850 scene,
34149 sample,
2631083 sample_data,
1166187 sample_annotation,
4 map,
Done loading in 39.969 seconds.
======
Reverse indexing ...
Done reverse indexing in 8.4 seconds.
======
[>>>>>>>>>>>>>>>>>>>>>>>>>>] 5512/5512, 4.8 task/s, elapsed: 1149s, ETA:     0s
======
Loading NuScenes tables for version v1.0-trainval...
23 category,
8 attribute,
4 visibility,
64386 instance,
12 sensor,
10200 calibrated_sensor,
2631083 ego_pose,
68 log,
850 scene,
34149 sample,
2631083 sample_data,
1166187 sample_annotation,
4 map,
Done loading in 41.562 seconds.
======
Reverse indexing ...
Done reverse indexing in 8.1 seconds.
======
[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1231/1231, 4.2 task/s, elapsed: 292s, ETA:     0s
Create GT Database of NuScenesDataset
[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5512/5512, 5.9 task/s, elapsed: 934s, ETA:     0s
load 12323 truck database infos
load 30252 pedestrian database infos
load 58001 car database infos
load 535 movable_object.debris database infos
load 9545 traffic_cone database infos
load 2365 motorcycle database infos
load 252 static_object.bicycle_rack database infos
load 509 movable_object.pushable_pullable database infos
load 11 vehicle.emergency.ambulance database infos
load 2610 construction_vehicle database infos
load 4037 trailer database infos
load 20002 barrier database infos
load 1963 bicycle database infos
load 1713 bus database infos
load 84 vehicle.emergency.police database infos
load 43 human.pedestrian.stroller database infos
load 149 animal database infos
load 12 human.pedestrian.wheelchair database infos
load 9 human.pedestrian.personal_mobility database infos
======
Loading NuScenes tables for version v1.0-test...
23 category,
8 attribute,
4 visibility,
0 instance,
12 sensor,
1800 calibrated_sensor,
462901 ego_pose,
15 log,
150 scene,
6008 sample,
462901 sample_data,
0 sample_annotation,
4 map,
Done loading in 8.141 seconds.
======
Reverse indexing ...
Done reverse indexing in 1.0 seconds.
======
total scene num: 150
exist scene num: 150
test scene: 150
[>>>>>>>>>>>>>>>>>>>>>>>>>>] 6008/6008, 328.9 task/s, elapsed: 18s, ETA:     0s
test sample: 6008
======
Loading NuScenes tables for version v1.0-test...
23 category,
8 attribute,
4 visibility,
0 instance,
12 sensor,
1800 calibrated_sensor,
462901 ego_pose,
15 log,
150 scene,
6008 sample,
462901 sample_data,
0 sample_annotation,
4 map,
Done loading in 5.318 seconds.
======
Reverse indexing ...
Done reverse indexing in 0.9 seconds.
======
[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 6008/6008, 9.9 task/s, elapsed: 609s, ETA:     0s

成功！

5. Evaluation

下載預訓練模型

./tools/download_pretrained.sh

在這里插入圖片描述

Then, you will be able to run：
參考：https://github.com/mit-han-lab/bevfusion

如果是單顆GPU，那就得對tools/test.py的做一定的修改：

#注釋掉dist.init()
# dist.init()#修改torch.cuda.set_device
# torch.cuda.set_device(dist.local_rank())torch.cuda.set_device(0)#修改distributed
# distributed = Truedistributed = False

然后執行，例如：

python tools/test.py configs/nuscenes/seg/fusion-bev256d2-lss.yaml pretrained/bevfusion-seg.pth --eval map

6. Training

To be continued…

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