heshuting555 / pading Goto Github PK
View Code? Open in Web Editor NEW[CVPR-2023] Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
License: MIT License
[CVPR-2023] Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
License: MIT License
你好,代码什么时候公开?
Hi,thank you for your brilliant work.May i ask how to reproduce the log here using the open source models ? Looking forward to your reply.
你好,在尝试编译项目的时候除了如标题的错误,具体如图所示:
我这边安装的环境如下:
Package Version
----------------------- ------------------
absl-py 1.4.0
antlr4-python3-runtime 4.9.3
appdirs 1.4.4
black 21.4b2
cachetools 5.3.1
certifi 2023.5.7
charset-normalizer 3.1.0
click 8.1.3
cloudpickle 2.2.1
contourpy 1.1.0
cycler 0.11.0
detectron2 0.6+cu111
fonttools 4.40.0
future 0.18.3
fvcore 0.1.5.post20221221
google-auth 2.21.0
google-auth-oauthlib 1.0.0
grpcio 1.56.0
hydra-core 1.3.2
idna 3.4
importlib-metadata 6.7.0
importlib-resources 5.12.0
iopath 0.1.9
kiwisolver 1.4.4
Markdown 3.4.3
MarkupSafe 2.1.3
matplotlib 3.7.1
mypy-extensions 1.0.0
numpy 1.25.0
oauthlib 3.2.2
omegaconf 2.3.0
packaging 23.1
pathspec 0.11.1
Pillow 9.5.0
pip 23.1.2
portalocker 2.7.0
protobuf 4.23.3
pyasn1 0.5.0
pyasn1-modules 0.3.0
pycocotools 2.0.6
pydot 1.4.2
pyparsing 3.1.0
python-dateutil 2.8.2
PyYAML 6.0
regex 2023.6.3
requests 2.31.0
requests-oauthlib 1.3.1
rsa 4.9
setuptools 67.8.0
six 1.16.0
tabulate 0.9.0
tensorboard 2.13.0
tensorboard-data-server 0.7.1
termcolor 2.3.0
toml 0.10.2
torch 1.9.0+cu111
torchaudio 0.9.0
torchvision 0.10.0+cu111
tqdm 4.65.0
typing_extensions 4.6.3
urllib3 1.26.16
Werkzeug 2.3.6
wheel 0.38.4
yacs 0.1.8
zipp 3.15.0
请问这边能给点解决建议吗
您好,请问是否可以分享一下已经训练好的模型权重和预测文件呢,想看一下测试的效果
您好,我尝试使用了实例分割的模型参数并成功实现了推理的过程,并得到了各项性能指标,但我应该如何得到该模型下的分割图片呢。抱歉,我在该领域是个新手,希望您能帮忙答疑解惑,谢谢!
我在华为昇腾服务器(没有gpu,使用的是NPU)上配置环境中,因为没有cuda环境而无法运行make.sh文件,请问我该怎么修改采用继续呢?
Thank you for your excellent work first!
When I reproduced your project, I found some discrepancies between the results and those presented in the paper, I ensured the file structure described in ReadMe and tried running "CUDA_VISIBLE_DEVICES=0 python train_net.py --config-file configs/ panoptic-segmentation/PADing.yaml --num-gpus 1 MODEL.WEIGHTS pretrained_weight_panoptic.pth", and the program executed successfully. If I understand correctly, the following should be the result in Table 1:
[09/25 03:31:55] PADing.evaluation.panoptic_evaluation_gzero INFO: Panoptic Evaluation Results:
PQ | SQ | RQ | #categories | |
---|---|---|---|---|
All | 34.972 | 72.119 | 41.642 | 133 |
Things | 42.356 | 77.451 | 49.989 | 80 |
Stuff | 23.827 | 64.071 | 29.042 | 53 |
SEEN | 37.847 | 74.862 | 45.048 | 119 |
UNSEEN | 10.536 | 48.809 | 12.694 | 14 |
I'm not sure if there's some step I'm not doing right? I found at the beginning of the log
[09/24 15:46:05] fvcore.common.checkpoint WARNING: Some model parameters or buffers are not found in the checkpoint.
, and I'm not sure if it causes the results to be misaligned? Please feel free to let me know if there is anything I can provide. Thanks again for your work and for any help you can provide is appreciated.
I trained the model with the hyperparameters in PADing.yaml file but got a much worse result than the result reported in the paper.
I trained using pretrained_weight_panoptic.pth on the Panoptic data and get the result as follows:
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: Task: panoptic_seg
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: PQ,SQ,RQ,PQ_th,SQ_th,RQ_th,PQ_st,SQ_st,RQ_st,PQ_se,SQ_se,RQ_se,PQ_un,SQ_un,RQ_un
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: 34.0248,71.6245,40.5138,41.6926,77.9330,49.1312,22.4507,62.1021,27.5063,37.0349,74.3694,44.0742,8.4391,48.2921,10.2500
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: Task: bbox
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: 32.4348,47.4895,34.3758,14.6653,34.2537,49.9620
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: Task: segm
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: 30.9822,48.1893,32.5781,11.5876,33.2543,51.1710
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: Task: sem_seg
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC,unseen_IoU,seen_IoU,unseen_acc,seen_acc
[11/15 15:13:38] d2.evaluation.testing INFO: copypaste: 41.5571,36.6851,57.7758,51.8384,8.8335,45.4070,21.7921,62.0091
I suspect this might be due to the hyperparameter issues. Currently, there are several misalignments between the hyperparameters in PADing.yaml and the values stated in the paper. May I ask how to select the parameters?
Best
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