dmitryulyanov / online-neural-doodle Goto Github PK

Hi,
in line 137 of feedforward_neural_doodle.py, I don't quite understand why should the input masks be divided by 10 as in inputs_batch:copy(masks_batch_/10)?
Thanks in advance! and thanks for the great repo :)

Any plans to allow openCL support? (Given that other neural art style algorithms in Torch support it).

Thanks!

Hello, you wrote that you tested your code with NVIDIA Tesla K40m. How long will it take to learn example with starry night using this GPU?

Hello, I'm trying to figure out how to get this running on CPU since I don't have nvidia..

I'm able to run python generate.py --n_jobs 1 --n_colors 4 --style_image data/starry/style.png --style_mask data/starry/style_mask.png --out_hdf5 data/starry/gen_doodles.hdf5 successfully (have some DataConversionWarning but I guess it's no big deal), but I'm unable to run the 'learn a network' command since it complain about no luarocks module found for cunn,

I also tried to run with -backend nn and -backend nn|cudnn :
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 -backend nn -half false
but it still complains about module 'cunn' not found

When I execute

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

I get the followingh result:

[libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message. If the message turns out to be larger than 1073741824 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 574671192
Successfully loaded data/pretrained/VGG_ILSVRC_19_layers.caffemodel
conv1_1: 64 3 3 3
conv1_2: 64 64 3 3
conv2_1: 128 64 3 3
conv2_2: 128 128 3 3
conv3_1: 256 128 3 3
conv3_2: 256 256 3 3
conv3_3: 256 256 3 3
conv3_4: 256 256 3 3
conv4_1: 512 256 3 3
conv4_2: 512 512 3 3
conv4_3: 512 512 3 3
conv4_4: 512 512 3 3
conv5_1: 512 512 3 3
conv5_2: 512 512 3 3
conv5_3: 512 512 3 3
conv5_4: 512 512 3 3
fc6: 1 1 25088 4096
fc7: 1 1 4096 4096
fc8: 1 1 4096 1000
Setting up style layer 2 : relu1_1
Replacing max pooling at layer 5 with average pooling
Setting up style layer 7 : relu2_1
Replacing max pooling at layer 10 with average pooling
Setting up style layer 12 : relu3_1
Replacing max pooling at layer 19 with average pooling
Setting up style layer 21 : relu4_1
Replacing max pooling at layer 28 with average pooling
Setting up style layer 30 : relu5_1
Optimize
THCudaCheck FAIL file=/tmp/luarocks_cutorch-scm-1-7288/cutorch/lib/THC/generic/THCStorage.cu line=41 error=2 : out of memory
/home/andrew/torch/install/bin/luajit: /home/andrew/torch/install/share/lua/5.1/nn/Container.lua:67:
In 3 module of nn.Sequential:
In 1 module of nn.Sequential:
/home/andrew/torch/install/share/lua/5.1/nn/THNN.lua:109: cuda runtime error (2) : out of memory at /tmp/luarocks_cutorch-scm-1-7288/cutorch/lib/THC/generic/THCStorage.cu:41
stack traceback:
[C]: in function 'v'
/home/andrew/torch/install/share/lua/5.1/nn/THNN.lua:109: in function 'SpatialReplicationPadding_updateGradInput'
...h/install/share/lua/5.1/nn/SpatialReplicationPadding.lua:41: in function 'updateGradInput'
/home/andrew/torch/install/share/lua/5.1/nn/Module.lua:31: in function </home/andrew/torch/install/share/lua/5.1/nn/Module.lua:29>
[C]: in function 'xpcall'
/home/andrew/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/andrew/torch/install/share/lua/5.1/nn/Sequential.lua:88: in function </home/andrew/torch/install/share/lua/5.1/nn/Sequential.lua:78>
[C]: in function 'xpcall'
/home/andrew/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/home/andrew/torch/install/share/lua/5.1/nn/Sequential.lua:84: in function 'backward'
feedforward_neural_doodle.lua:167: in function 'opfunc'
/home/andrew/torch/install/share/lua/5.1/optim/adam.lua:33: in function 'optim_method'
feedforward_neural_doodle.lua:199: in main chunk
[C]: in function 'dofile'
...drew/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: at 0x00406670

I'm running with multiple GTX 980s, so GPU m,emory should not be an issue.

I have tried running with both -backend cudnn and -backen nn with no difference to the outcome.

I have been able to run the fast-neural-doodle project with no problems on my machine, so prerequisites such as python, torch and cuda appear to have been set up correctly.

Any idea of the cause of this problem?

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?

I want to generate images using CPU (don't want to train). How do I modify the code to do this?
Seeing the results on likemo.net isn't an option for me.

Hey,
super cool repo :)
One part I don't quite understand is in feedforward_neural_doodle.lua Line 151:

local gradFull = descriptor_net:backward(out, nil)

Don't quite understand what this means? if the error there is nil what does this backward do? Thanks a lot and looking forward to your reply!