Comments (7)
Did you build Caffe with cuDNN library? I think without it, Caffe uses much more GPU memory when applying the convolutional layers. That is probably why you run out of GPU memory. Could you check it out?
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@gidariss Yes, I did compile with cudnn. I noticed that after run the first network (rec), it used 6G memory, and when running the second network ,the error showed up. Do I need to free GPU memory after the first network ? How?
from locnet.
@litingfeng No, you do not need to free GPU memory after the first network. What you can do is in the demo_LocNet_object_detection_pipeline.m script to change the lines 90 and 91 from:
model_obj_rec_max_rois_num_in_gpu = 500;
model_obj_loc_max_rois_num_in_gpu = 400;
to
model_obj_rec_max_rois_num_in_gpu = 200;
model_obj_loc_max_rois_num_in_gpu = 200;
I just tried it and I manage to run the demo on a 6Gbyte GPU. Could you tried as well and let me know?
Spyros
from locnet.
@gidariss I gitted a new one, but it still can't work. I even tried 50,100, all run out of memory. When I was running , I checked GPU usage with nvidia-smi
. It turned out there was indeed 11408MB has been used. Thank you for your patience.
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Later today, I tried script_test_object_detection_pipeline_PASCAL.m
. It works without any modification. So strange.
from locnet.
It seems that in demo, you did not caffe.reset_all();
after each network.
from locnet.
@litingfeng Regarding the script_test_object_detection_pipeline_PASCAL.m
, it uses a single model (either the recognition or the localization model) at a time and that is why you do not have any problem running it. However, the LocNet_object_detection()
function, which is used in the demo, uses both models simultaneously. Do you mean that you placed caffe.reset_all()
calls inside the LocNet_object_detection()
function?
As I said, I did not have any problem running the demo on a 6Gbyte GPU. So it is strangle that in your case it cannot run in 11Gbyte GPU.
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Related Issues (9)
- how to train the model on my own dataset, the size of the pictures in the dataset are not the same
- Fine tuning
- when I run the demo_LocNet_object_detection_pipeline.m follow the read.me, matlab crashed
- cuda8.0 cudnn v5 or latter can not compile the caffe with use cudnn
- a symbolic link of Caffe
- caffe_LocNet --- Makefile:553: recipe for target '.build_release/src/caffe/net.o' failed
- Matlab crush when running 'demo_LocNet_object_detection_pipeline.m'
- Download the pre-computed Edge Box and Selective Search proposals of PASCAL images
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