dmitryulyanov / fast-neural-doodle Goto Github PK
View Code? Open in Web Editor NEWFaster neural doodle
License: MIT License
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very impressive work!
wow, you work is definitely amazing! the result is even better than neural-style.
i tried these code and it work fine on my mbp. there comes a question to me, for any style image i picked from other place, is there any method to generate style mask from it automatically or i must "draw" mask manually?
Run This on EC2 GPU
Hey Dmitry,
I was wondering - how would I go about installing this onto a GPU server, and pass images into the script from an external source (i,e; upload from website, etc;).
Thanks,
Daniel
Training a model fails
Hi, I tried to run the command from the tutorial for model training, but it failed with the following error:
CUDA_VISIBLE_DEVICES=0 th feedforward_neural_doodle.lua -model_name skip_noise_4 -masks_hdf5 data/starry/gen_doodles.hdf5 -batch_size 4 -num_mask_noise_times 0 -num_noise_channels 0 -learning_rate 1e-1 -half false
/root/torch/install/bin/luajit: /root/torch/install/share/lua/5.1/hdf5/group.lua:312: HDF5Group:read() - no such child 'style_img' for [HDF5Group 33554432 /]
stack traceback:
[C]: in function 'error'
/root/torch/install/share/lua/5.1/hdf5/group.lua:312: in function 'read'
feedforward_neural_doodle.lua:49: in main chunk
[C]: in function 'dofile'
/root/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: at 0x00406670
any ideas why hdf5 might fail with such error?
Support for style copy
Hello Dmitry, this is very nice. I appreciate the speed your implementation provide.
I have a question, do you intend to add support for simple style copy instead of doodle based on mask file? This is a simpler alternative that I found to provide better results at times.
Regards
ValueError: Incorrect number of features. Got 4 features, expected 3
ubuntu@ip-Address:~/fast-neural-doodle$ python get_mask_hdf5.py --n_colors=5 --style_image=Monet2.jpg --style_mask=Monet2_sem2_r.png --target_mask=MonetNew2_sem2.png
Traceback (most recent call last):
File "get_mask_hdf5.py", line 45, in <module>
labels_target = kmeans.predict(target_flatten.astype(float))
File "/usr/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 889, in predict
X = self._check_test_data(X)
File "/usr/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 800, in _check_test_data
n_features, expected_n_features))
ValueError: Incorrect number of features. Got 4 features, expected 3
ubuntu@ip-Address:~/fast-neural-doodle$
ubuntu@ip-Address:~/fast-neural-doodle$ python get_mask_hdf5.py --n_colors=3 --style_image=Monet2.jpg --style_mask=Monet2_sem2_r.png --target_mask=MonetNew2_sem2.png
Traceback (most recent call last):
File "get_mask_hdf5.py", line 45, in <module>
labels_target = kmeans.predict(target_flatten.astype(float))
File "/usr/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 889, in predict
X = self._check_test_data(X)
File "/usr/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 800, in _check_test_data
n_features, expected_n_features))
ValueError: Incorrect number of features. Got 4 features, expected 3
ubuntu@ip-Address:~/fast-neural-doodle$
There are 5 distinct color regions on my masks.
Better than the real thing
Based on some of my testing I have to say that your implementation is actually producing better results that the original neural-doodle: https://twitter.com/netputing/status/710119813847207937
th fast_neural_doodle.lua -masks_hdf5 masks.hdf5 not working on ubuntu 16
/home/naman/torch/install/share/lua/5.1/trepl/init.lua:389: /home/naman/torch/install/share/lua/5.1/hdf5/ffi.lua:71: Unsupported HDF5 version: 1.10.1
stack traceback:
[C]: in function 'error'
/home/naman/torch/install/share/lua/5.1/trepl/init.lua:389: in function 'require'
fast_neural_doodle.lua:5: in main chunk
[C]: in function 'dofile'
...aman/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50
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