Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Check failed: new_queue_pairs_.size() == 0 #3394

Open
paraggoel14 opened this issue Nov 28, 2015 · 5 comments
Open

Check failed: new_queue_pairs_.size() == 0 #3394

paraggoel14 opened this issue Nov 28, 2015 · 5 comments

Comments

@paraggoel14
Copy link

Hi,

I am running the ImageNet example from the website, and while running caffe, I run into this error after it reads in the mean file:

data_reader.cpp:98] Check failed: new_queue_pairs_.size() == 0 (1 vs. 0)

I am not sure what to make of this error. What can be the possible reason behind this?

@seanbell
Copy link

seanbell commented Dec 2, 2015

The documentation for the error might answer your question:

// Check no additional readers have been created. This can happen if
// more than one net is trained at a time per process, whether single
// or multi solver. It might also happen if two data layers have same
// name and same source.
CHECK_EQ(new_queue_pairs_.size(), 0);

@etienne87
Copy link

Hi, i have the same error... my configuration is like this :

layer {
name: "image0" type: "Data"
top: "data"
data_param {
source: "dataXXX/image.lmdb"
batch_size: 32
backend: LMDB
}
}

layer {
name: "label0" type: "Data"
top: "label"
data_param {
source: "dataXXX/label.lmdb"
batch_size: 32
backend: LMDB
}
}

... rest is definition of the convnet...

What is the problem? should I specify include{phase: TRAIN} somewhere?

@ultrai
Copy link

ultrai commented Feb 11, 2016

Same error with similar data blob for image segmentation

@karunaahuja
Copy link

Same error. Any quick fix? It is very urgent.

@lukeyeager
Copy link
Contributor

If your data layers look like @etienne87's above, then you need to mark one as your TRAIN data and another as your TEST data. Look at the reference networks for guidance:

https://github.com/BVLC/caffe/blob/rc3/models/bvlc_alexnet/train_val.prototxt#L7-L28
https://github.com/BVLC/caffe/blob/rc3/models/bvlc_googlenet/train_val.prototxt#L7-L30
https://github.com/BVLC/caffe/blob/rc3/examples/mnist/lenet_train_test.prototxt#L7-L26

layer {
  name: "training_data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  # ...
}
layer {
  name: "validation_data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  # ...
}

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

6 participants