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tomMoral / dicodile

Experiments for "Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals"
https://tommoral.github.io/dicodile/
BSD 3-Clause "New" or "Revised" License
18 stars 9 forks source link

Dicodile text example #21

Closed hndgzkn closed 3 years ago

hndgzkn commented 3 years ago

Add a draft example on using dicodile on a text image generated with letters PAMI of text_size=5000.

As the job runs too long, the artifacts can be viewed from https://github.com/hndgzkn/dicodile/actions/runs/624302432

codecov[bot] commented 3 years ago

Codecov Report

Merging #21 (a9baef0) into main (85a6310) will increase coverage by 0.12%. The diff coverage is 100.00%.

Impacted file tree graph

@@            Coverage Diff             @@
##             main      #21      +/-   ##
==========================================
+ Coverage   74.15%   74.28%   +0.12%     
==========================================
  Files          41       41              
  Lines        2573     2586      +13     
==========================================
+ Hits         1908     1921      +13     
  Misses        665      665
Flag Coverage Δ
unittests 74.28% <100.00%> (+0.12%) :arrow_up:

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Impacted Files Coverage Δ
dicodile/data/_text.py 0.00% <ø> (ø)
dicodile/data/images.py 76.92% <100.00%> (+12.21%) :arrow_up:
dicodile/data/tests/test_images.py 100.00% <100.00%> (ø)

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agramfort commented 3 years ago

@hndgzkn can you share an image of the results? thx

hndgzkn commented 3 years ago

@hndgzkn can you share an image of the results? thx

dict_image

text_image

agramfort commented 3 years ago

looks great !

hndgzkn commented 3 years ago

I'd like to document text image generation dependencies:

Shall I document it in Installation section of README.rst?

agramfort commented 3 years ago

I think this image was produced once for the paper and for illustration purposes.

I don't think it's really meant to be advertised. I would expect to just have one image downloaded for illustration.

hndgzkn commented 3 years ago

In that case:

agramfort commented 3 years ago

I would keep the code somewhere to replicate the image and yes store the image available for download.

basically like we do it for mandrill

hndgzkn commented 3 years ago

I am trying to use gitlfs to push the file, however it is not possible due to pushing-large-files-to-forks.

@agramfort Do you have any suggestion?

I would keep the code somewhere to replicate the image and yes store the image available for download. basically like we do it for mandrill …

agramfort commented 3 years ago

@hndgzkn are you suggesting to add the file to the main github repo? I would not add any big binary file to the source code repo.

I would put the file somewhere on the web eg in https://figshare.com/ https://zenodo.org/

hndgzkn commented 3 years ago

this looks good to me. A few nitpicks.

Also, maybe add a print for the full size of the image when loading it?

Initially I had it, but removed as you can hardly see anything.

whole_image

If you prefer, I can add it.

May be you didn't mean that?

tomMoral commented 3 years ago

May be you didn't mean that?

Yeah, I like the zoomed one, it is much clearer. I was mentionning adding print(X_orginal.shape), so the user see the scale of the problem.

hndgzkn commented 3 years ago

I was mentionning adding print(X_orginal.shape), so the user see the scale of the problem.

I print the shape for X after reshaping and also I print the shape of dictionary D and D_init as well thinking that it would be also good to see the expected shape for dicodile. If you prefer X_original, I'll change it.

tomMoral commented 3 years ago

I print the shape for X after reshaping and also I print the shape of dictionary D and D_init as well thinking that it would be also good to see the expected shape for dicodile. If you prefer X_original, I'll change it.

that is perfect like this. Merging! thanks @hndgzkn :tada:

agramfort commented 3 years ago

🎉