Open DW1HH opened 5 years ago
@DW1HH The reason that leads to the error is the model file contain the in_place operation, you need trun off the switch of in_place in models/fssd_vgg.py
@DW1HH The reason that leads to the error is the model file contain the in_place operation, you need trun off the switch of in_place in models/fssd_vgg.py
sorry, i also meet this problem,when i train voc2007, could you tell how to fix it carefully(细致的=-=)?
like this?
`
Loading base network...
Initializing weights...
train.py:98: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaimingnormal.
init.kaiming_normal(m.state_dict()[key], mode='fan_out')
Loading Dataset...
Training FSSD_VGG on VOC0712
avg_loss_list:
[0.0]
/home/rw/anaconda3/envs/pytorch0.4.0/lib/python3.5/site-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/home/rw/anaconda3/envs/pytorch0.4.0/lib/python3.5/site-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
warnings.warn(warning.format(ret))
Traceback (most recent call last):
File "train.py", line 262, in
`
i replace fssd_vgg.py ` for k, v in enumerate(self.extras):
x = F.relu(v(x))
` and it works ,but found L: nan C: nan S: nan,so i think what i do is wrong
I got the same issue. After changed inplace=False, it returned nan C: nan S: nan
while training
i replace fssd_vgg.py
for k, v in enumerate(self.extras): # x = F.relu(v(x), inplace=True) x = F.relu(v(x))
and it works ,but found L: nan C: nan S: nan,so i think what i do is wrong
hi,my friend, have you solved this problem? Those Usewarings can be corrected easily. if you are still trouble with them, you can contact me for the solutions.
i replace fssd_vgg.py
for k, v in enumerate(self.extras): # x = F.relu(v(x), inplace=True) x = F.relu(v(x))
and it works ,but found L: nan C: nan S: nan,so i think what i do is wronghi,my friend, have you solved this problem? Those Usewarings can be corrected easily. if you are still trouble with them, you can contact me for the solutions.
Hello, I also encountered a similar problem, how did you solve it? Thank you @chaomartin @naviocean @rw1995
I got the same issue. After changed inplace=False, it returned
nan C: nan S: nan
while training
i have got this problem too, have you solve it yet?
Traceback (most recent call last): File "train.py", line 268, in
train()
File "train.py", line 233, in train
loss.backward()
File "/home/huhuai/anaconda3/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/huhuai/anaconda3/lib/python3.6/site-packages/torch/autograd/init.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation