Issues w/ running on Windows

[ad48hp]

File "D:\Cancer\neural-dream\neural_dream\models\googlenet\bvlc_googlenet.py", line 352, in forward pool5_7x7_s1 = F.avg_pool2d(inception_5b_output, kernel_size=(7, 7), stride=(1, 1), padding=(0,), ceil_mode=False, count_include_pad=False) RuntimeError: Given input size: (1024x4x4). Calculated output size: (1024x-2x-2). Output size is too small

Tried with multiple images, image-size & models..

Would be nice if you could get it working as it's quite moar challenging to get Caffe run on Windows currently..

[ProGamerGov]

@ad48hp That issue occurs when the smallest octave image size is too small for the model. A quick test with the GoogleNet Caffe models shows that they require both height and width to higher than 223px.

This will result in an error because one of Octave 1's dimensions is than 223px:

Performing 2 octaves with the following image sizes:
  Octave 1 image size: 208x278
  Octave 2 image size: 348x464

For the example images, I used -num_octaves 2 -octave_scale 0.6 -image_size 512, which resulted in Octave 1's smallest dimension being greater than 223:

Performing 2 octaves with the following image sizes:
  Octave 1 image size: 230x307
  Octave 2 image size: 384x512

Octaves as I understand are pretty much the same thing as the multiscale resolution technique used with style transfer. The -octave_scale value tells the code how much to reduce the image in size, and the -num_octaves tells the code how many times to do that.

[ad48hp]

Seems to work now, though not sure why, but the DeepDreaming is incredibly slow with this one..
On the Caffe linux version, image took few seconds to process, here one octave takes minutes.. (both on CPU)

Could ya look into that ?

This is the script i tried..
Dreamify

[ProGamerGov]

@ad48hp I'll look into it. Have you tried using -backend mkl when using the CPU at all? That should make it faster.

[ad48hp]

Seems to not go faster.
Also, what's the learning_rate parameter doing here, isn't that supposed to be used only for training phase ?

Can't yet find that one in the dreamify script..

[ProGamerGov]

The -learning_rate parameter is the same thing as the 'step size' on other DeepDream projects. On Dreamify, it's called step_size. It's the size of the 'jump' or 'step' towards the network's goal. If it's too high you can overshoot, and if it's too low then it will take forever to reach the goal.

If the learning rate is set too high, the image will look like this:

If the learning rate is set too low, then very little if any change will occur on the output image.

Different models can require different learning rates, and you may have to play around with the values to find one that works. PyTorch VGG models for instance will require a really low learning rate, like 0.0055 or something like that. Caffe models tend to work better with learning rates of around 1-5.

[ad48hp]

Okay, seems to be running fine now, maybe the speed gets higher when it's run few times..
I have a request, if ya don't mind, it's quite challenging maybe yet for me to orientate in other people's codes, so it might take some time for me to implement it..

The code i wrote for myself back then was able to pick-up w/ the zooming where it last ended, so let's say i wanted 1000 pictures to be generated, but on 437 i stopped it, and then when i runned it again it continued with 438..

Could ya implement it here, boi ?

[ProGamerGov]

@ad48hp For the moment you can try this: https://github.com/ProGamerGov/neural-dream/tree/output-image-name. The -output_start_num parameter will let you start the output number at a number of your choosing. I'll probably delete the branch and merge it into the master branch when I have more time, but for the moment all you need is the neural_dream.py file from the branch I linked.

I made the -output_start_num parameter start at the number that you give it, as I wasn't sure if it was better to have it be -output_start_num + 1. It's possible that I may change how it works.

[ad48hp]

How that script worked is that it used path.isfile to search for the last image, and then use it..
Only if no images were present in the output directory, it used the input image..

Check this script yet

[ad48hp]

On Windows, CUDA backend writes "RuntimeError: error in LoadLibraryA"..

I wrote..
'import ctypes
ctypes.cdll.LoadLibrary('caffe2_nvrtc.dll')'
in neural_dream.py after 'import torch'
and now it seems to work..

Src

[ad48hp]

Also, i was able to reproduce the error i wrote you about! ^^
https://github.com/ad48hp/TemporaryFiles/tree/master/NeuralDream

python neural_dream.py -content_image steve-halama-paxhSiyjndU.png -model_file models/googlenet_places205.pth -init image -gpu 0 -num_iterations 600 -octave_iter 50 -octave_scale 0.4 -image_size 576 -learning_rate 6 -zoom 98 -backend cudnn -output_image output2/t.png

Can you reproduce the results ?

[ProGamerGov]

@ad48hp The parameters don't seem to raise that PyTorch error that you posted above. The content image you are using seems extremely bright, and using -adjust_contrast 99.999 seems to change that, but changes the output look a bit.

[ad48hp]

I meant, i was able to reproduce the issue i wrote about here, oddly enough with the Places205 model path as well..

Img.

Iteration 98

Iteration 201

What's weird is that at the beggining, i wasn't getting those shapes at all and now consistently.. Isn't there some sort of cache that keeps ruining it (i tried to remove the __pycache folders, didn't changed anything so far)..

[ProGamerGov]

The learning rate that you are using seems like it might be a bit too high. Lowering it to 1.6 resulted in this output at 29 iterations:

Using -lap_scale 4, and a learning rate of 1.6, I got this at 11 iterations:

I've found that the best results sometimes require taking things slow initially, because the network has more time to build the details without overshooting.

[ad48hp]

That sucessfully fixed the problem with Places205 !
Sadly, the Places365 seems to do the same problem..

python neural_dream.py -content_image steve-halama-paxhSiyjndU.png -model_file models/googlenet_places365.pth -init image -gpu 0 -num_iterations 600 -octave_iter 24 -octave_scale 0.4 -image_size 576 -learning_rate 0.66 -zoom 98 -backend cudnn -output_image output22/t.png

Iteration 18

Iteration 36

[ProGamerGov]

@ad48hp I've found that results like those are normally caused by the inputs and parameters that you have chosen. The content image you are using may be affecting the results. You can try adding -channel_mode avg -channels 10 or -channel_mode weak -channels 10 -lap_scale 4 and potentially use a learning rate of around 1.5 if you want.