ysymyth / ec-nl Goto Github PK

Hi,

I am trying to replicate your codebase. The Readme instruction says we should put the files downloaded from the image_features google drive folder into ./ec-pretrain/data. I created a directory called ec-pretrain/data and added the files there but whenever I run train.py for the for the Emergent Communication Game, I receive this error below:

_Entering Main
Traceback (most recent call last):
File "train.py", line 73, in
data = torch.load(feat_path)
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 579, in load
with _open_file_like(f, 'rb') as opened_file:
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 230, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 211, in init
super(open_file, self).init(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: './data/coco.pt'

Then I thought maybe I should put the data in ./data/ instead of ./ec-pretrain/data but I still get the same error.

I was wondering if I have done something wrong?

Thanks and looking forward to hearing from you.

How can I train the model (EC game - used for generating the emergent language) on the multiple GPUs?

What are the necessary changes that need to be made to the code?

I really appreciate any help you can provide.

Would it be possible license this code under a free software license (https://opensource.org/licenses)? Currently, there is no license displayed in this project, so it is not clear if incorporating this code into other projects is allowed. Thanks.

Hello @ysymyth ,
If I have understood correctly, during each epoch out of all the N (=total images in the train dataset) only a set of n (=batch size*number of distracting images) is used as listener images here. And n' (=batch size) is used as speaker images here. Hence it is a possibility that some images might be used repetitively and some may even not be used. How can we make sure that each of the image in training data is used as a speaker image at least once?