Closed hustmf closed 6 years ago
Traceback (most recent call last):
File "examples/softmax_loss.py", line 217, in <module>
main(parser.parse_args())
File "examples/softmax_loss.py", line 147, in main
trainer.train(epoch, train_loader, optimizer)
File "/workspace/gjc/anaconda2/envs/py35/lib/python3.5/site-packages/open_reid-0.2.0-py3.5.egg/reid/trainers.py", line 31, in train
File "/workspace/gjc/anaconda2/envs/py35/lib/python3.5/site-packages/open_reid-0.2.0-py3.5.egg/reid/trainers.py", line 73, in _forward
File "/workspace/gjc/anaconda2/envs/py35/lib/python3.5/site-packages/open_reid-0.2.0-py3.5.egg/reid/evaluation_metrics/classification.py", line 17, in accuracy
TypeError: sum received an invalid combination of arguments - got (keepdim=bool, dim=int, ), but expected one of:
* no arguments
* (int dim)
When I ran example in readme on server, error above happened. @Cysu @zydou
update your pytorch version
@Linranran Thank you!
@Linranran Many thanks!
Hi, I'm trying to do a reid project with a new dataset. But the size of the images are different from each other. So what should I do to train with such dataset? Just convert it to the unified format? What should I do to split them into "trainval,gallery,query"? I only have one dataset with person id label. and camera id wasn't provided. And when I run the example in readme on a server with gpu, error happened.
TypeError: sum received an invalid combination of arguments -got(dim=int, keepdim=bool) but expected one of no arguments *(int dim)
It happened at reid/evaluation_metrics/classification.py line 17. I haven't solved it. But I have tried cpu version on my laptop by modifying some part of the code(deletecuda()
and letpin_memory=False
), then I got train data of epoch[0] like CMC score. Although I stopped training due to the bad performance of laptop, could the data printed verify that I can run with cpu? Will the change that I made in the code have influence on the training result? After training epoch[0],I gotMean AP:4.5%
CMC Scores allshots cuhk03 market1501
top-1 0.5% 3.5% 0.5%
top-5 5.0% 10.0% 5.0%
top-10 10.5% 21.0% 10.5%
It's quite low. My pc can't support continuing training. Will the result be better by training rest data? Or the bad result is due to my change to the code? Could someone tell me? Another problem is that how can I get the probability ranking of each image in the gallery if I use certain image as query?