Segmentation fault (core dumped) about deep-person-reid HOT 8 CLOSED

I meet segmentation problem under python 2.7.14 pytorch 0.5.0a0.
I find it is distmat.addmm_(1, -2, qf, gf.t()) that causes the program crash.
And my work-around is change

    m, n = qf.size(0), gf.size(0)
    distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
              torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    distmat.addmm_(1, -2, qf, gf.t())
    distmat = distmat.numpy()

to

qf = qf.numpy()
gf = gf.numpy()
from scipy.spatial.distance import cdist
distmat = cdist(qf, gf)

I am not sure what problem you meet, hope it help. (And I am wondering whether it is a bug of pytorch 0.5.0a0)

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Can you provide more detailed info? (e.g. python version, which script/command you are using)

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不要意思，python 2.7.13 pytorch 0.4.0 torchvision0.2.1
我是执行的
python train_img_model_xent_htri.py -d market1501 -a resnet50 --max-epoch 60 --train-batch 32 --test-batch 32 --stepsize 20 --eval-step 5 --save-dir log/resnet50-xent-market1501 --gpu-devices 2
出现的问题
Epoch: [5][50/93] Time 0.131 (0.143) Data 0.002 (0.015) Loss 6.8060 (6.7926)
Epoch: [5][60/93] Time 0.126 (0.141) Data 0.002 (0.013) Loss 6.9414 (6.8131)
Epoch: [5][70/93] Time 0.141 (0.141) Data 0.002 (0.012) Loss 6.8047 (6.8204)
Epoch: [5][80/93] Time 0.129 (0.140) Data 0.002 (0.010) Loss 6.7707 (6.8251)
Epoch: [5][90/93] Time 0.141 (0.139) Data 0.001 (0.010) Loss 6.8328 (6.8303)
==> Test
Extracted features for query set, obtained 3368-by-2048 matrix
Extracted features for gallery set, obtained 15913-by-2048 matrix
==> BatchTime(s)/BatchSize(img): 0.013/32
Segmentation fault (core dumped)
我可以训练就是在训练的过程中，如果验证的话，就会出现这个问题

from deep-person-reid.

hmm... i guess your python code crashed, but no idea why it happened on your machine, perhaps this, this, and this would help? try those answers and let me know.

from deep-person-reid.

@luzai @KaiyangZhou 非常感谢，按照你的方式已经把问题解决了
qf = qf.numpy()
gf = gf.numpy()
from scipy.spatial.distance import cdist
distmat = cdist(qf, gf)

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python train_img_model_xent_htri.py -d market1501 -a resnet50 --max-epoch 60 --train-batch 32 --test-batch 32 --stepsize 20 --eval-step 20 --save-dir log/resnet50-xent-market1501 --gpu-devices 2
不过我使用完之后
mAP: 59.8%
CMC curve
Rank-1 : 77.0%
Rank-5 : 90.6%
Rank-10 : 93.8%
Rank-20 : 96.3%
==> Best Rank-1 77.0%, achieved at epoch 60
感觉效果不好啊
我想问一下，怎么看各种 script/command所对应的超参数是什么？

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you can do python train_img_model_xent_htri.py -h or check the code for more info

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好好，谢谢

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