ssd.pytorch
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Published
20 hours ago •
amdegroot
amdegroot
Pytorch version:
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
Pytorch version:
>>> import torch >>> print(torch.__version__) 1.1.0Python version:
Python 3.6.7 (default, Oct 22 2018, 11:32:17) [GCC 8.2.0] on linuxmultibox_loss.py:
Switch the two lines 97,98: loss_c = loss_c.view(num, -1) loss_c[pos] = 0 # filter out pos boxes for nowChange 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.dataAnd 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?
