Setting GLOG_minloglevel=1 to prevent output in shell from Caffe

python neural-network deep-learning caffe glog
pir picture pir · Apr 22, 2015 · Viewed 14.8k times · Source

I'm using Caffe, which is printing a lot of output to the shell when loading the neural net.
I'd like to suppress that output, which supposedly can be done by setting GLOG_minloglevel=1 when running the Python script. I've tried doing that using the following code, but I still get all the output from loading the net. How do I suppress the output correctly?

os.environ["GLOG_minloglevel"] = "1"
net = caffe.Net(model_file, pretrained, caffe.TEST)
os.environ["GLOG_minloglevel"] = "0"

Answer

Shai picture Shai · Apr 22, 2015

To supress the output level you need to increase the loglevel to at least 2

 os.environ['GLOG_minloglevel'] = '2'

The levels are

0 - debug
1 - info (still a LOT of outputs)
2 - warnings
3 - errors

Update:
Since this flag is global to caffe, it must be set prior to importing of caffe package (as pointed out by jbum). Once the flag is set and caffe is imported the behavior of the GLOG tool cannot be changed.

Related questions

How to feed caffe multi label data in HDF5 format?

I want to use caffe with a vector label, not integer. I have checked some answers, and it seems HDF5 is a better way. But then I'm stucked with error like: accuracy_layer.cpp:34] Check failed: outer_num_ * inner_num_ == …

Read More
Extracting weights from .caffemodel without caffe installed in Python

Is there a relatively simple way to extract weights in Python from one of the many pretrained models in Caffe Zoo WITHOUT CAFFE (nor pyCaffe)? i.e. parsing .caffemodel to hdf5/numpy or whatever format that can be read by …

Read More
Generating LMDB for Caffe

I am trying to build a deep learning model for Saliency analysis using caffe (I am using the python wrapper). But I am unable to understand how to generate the lmdb data structure for this purpose. I have gone through …

Read More