Ssd.pytorch: Pytorch version:

[xinyu1214]

Pytorch version:

>>> import torch
>>> print(torch.__version__)
1.1.0

Python version:

Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux

multibox_loss.py:

Switch the two lines 97,98:
loss_c = loss_c.view(num, -1)
loss_c[pos] = 0 # filter out pos boxes for now

Change line114 
N = num_pos.data.sum() -> N = num_pos.data.sum().double()

and change the following two lines to: 
loss_l = loss_l.double()
loss_c = loss_c.double()

train.py

loss_l.data[0] >> loss_l.data 
loss_c.data[0] >> loss_c.data 
loss.data[0] >> loss.data

And here is my output:

timer: 11.9583 sec.
iter 0 || Loss: 11728.9388 || timer: 0.2955 sec.
iter 10 || Loss: nan || timer: 0.2843 sec.
iter 20 || Loss: nan || timer: 0.2890 sec.
iter 30 || Loss: nan || timer: 0.2934 sec.
iter 40 || Loss: nan || timer: 0.2865 sec.
iter 50 || Loss: nan || timer: 0.2855 sec.
iter 60 || Loss: nan || timer: 0.2889 sec.
iter 70 || Loss: nan || timer: 0.2857 sec.
iter 80 || Loss: nan || timer: 0.2843 sec.
iter 90 || Loss: nan || timer: 0.2835 sec.
iter 100 || Loss: nan || timer: 0.2846 sec.
iter 110 || Loss: nan || timer: 0.2946 sec.
iter 120 || Loss: nan || timer: 0.2860 sec.
iter 130 || Loss: nan || timer: 0.2846 sec.
iter 140 || Loss: nan || timer: 0.2962 sec.
iter 150 || Loss: nan || timer: 0.2989 sec.
iter 160 || Loss: nan || timer: 0.2857 sec.

_Originally posted by @kleinash in https://github.com/amdegroot/ssd.pytorch/issues/173#issuecomment-526295317_

[Wangxy1202]

hi~ which version cuda ?

[HongChow]

Pytorch version:

>>> import torch
>>> print(torch.__version__)
1.1.0

Python version:

Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux

multibox_loss.py:

Switch the two lines 97,98:
loss_c = loss_c.view(num, -1)
loss_c[pos] = 0 # filter out pos boxes for now

Change line114 
N = num_pos.data.sum() -> N = num_pos.data.sum().double()

and change the following two lines to: 
loss_l = loss_l.double()
loss_c = loss_c.double()

train.py

loss_l.data[0] >> loss_l.data 
loss_c.data[0] >> loss_c.data 
loss.data[0] >> loss.data

And here is my output:

timer: 11.9583 sec.
iter 0 || Loss: 11728.9388 || timer: 0.2955 sec.
iter 10 || Loss: nan || timer: 0.2843 sec.
iter 20 || Loss: nan || timer: 0.2890 sec.
iter 30 || Loss: nan || timer: 0.2934 sec.
iter 40 || Loss: nan || timer: 0.2865 sec.
iter 50 || Loss: nan || timer: 0.2855 sec.
iter 60 || Loss: nan || timer: 0.2889 sec.
iter 70 || Loss: nan || timer: 0.2857 sec.
iter 80 || Loss: nan || timer: 0.2843 sec.
iter 90 || Loss: nan || timer: 0.2835 sec.
iter 100 || Loss: nan || timer: 0.2846 sec.
iter 110 || Loss: nan || timer: 0.2946 sec.
iter 120 || Loss: nan || timer: 0.2860 sec.
iter 130 || Loss: nan || timer: 0.2846 sec.
iter 140 || Loss: nan || timer: 0.2962 sec.
iter 150 || Loss: nan || timer: 0.2989 sec.
iter 160 || Loss: nan || timer: 0.2857 sec.

_Originally posted by @kleinash in #173 (comment)_

fixed?

[up2m]

I have the same problem，do you solve it?

[jmu201521121021]

@Summar-skyI solved it by reducing the learning rate.

[chnzhero]

@Summar-skyI solved it by reducing the learning rate.

it works, thx