abhiskk / fast-neural-style Goto Github PK

Hello. train doesn't run:

danusya@werewolf:~/fast-neural-style$ python3 neural_style/neural_style.py train --dataset ~/dataset/ --style-image ~/Caleido/Color_Kaleidoscope_2017-6-24_15141.png  --vgg-model-dir caleido_vgg --save-model-dir caleido_model --epochs 2 --cuda 0
/usr/local/lib/python3.5/dist-packages/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
  "please use transforms.Resize instead.")
Traceback (most recent call last):
  File "neural_style/neural_style.py", line 210, in <module>
    main()
  File "neural_style/neural_style.py", line 204, in main
    train(args)
  File "neural_style/neural_style.py", line 50, in train
    style = utils.preprocess_batch(style)
  File "/home/danusya/fast-neural-style/neural_style/utils.py", line 59, in preprocess_batch
    (r, g, b) = torch.chunk(batch, 3)
ValueError: not enough values to unpack (expected 3, got 2)

Hi
1、Can you explain what's different of the image_size of "content and style" when Train and Eval?
2、How to set output_image_size when Eval? (if your default setting is 1080,can you show me where you set in your code?)
3、Really thanks for your work

What is the purpose of this line in your transforms block for the input :

transforms.Lambda(lambda x: x.mul(255))

Aren't the input and style images already in the range 0-255? Wouldn't this cause an overflow?

I am getting a runtime error w.r.t tensor size while trying out the toy example.

RuntimeError: The expanded size of the tensor (1080) must match the existing size (32) at non-singleton dimension 2. at /opt/conda/conda-bld/pytorch_1502001039157/work/torch/lib/TH/generic/THTensor.c:308

I am using Python 2.7 and used conda install pytorch torchvision -c soumith to install pytorch

Stack trace

File "~/envs/py27/lib/python2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__ result = self.forward(*input, **kwargs) File "~/fast-neural-style/neural_style/transformer_net.py", line 131, in forward mean = torch.mean(t, 2).unsqueeze(2).expand_as(x) File "~/envs/py27/lib/python2.7/site-packages/torch/autograd/variable.py", line 725, in expand_as return Expand.apply(self, (tensor.size(),)) File "~/envs/py27/lib/python2.7/site-packages/torch/autograd/_functions/tensor.py", line 111, in forward result = i.expand(*new_size)

Hi,

I'm trying to train new styles images on Windows (anaconda, pytorch 0.3).
Using vgg.t7 file gives me the following error messages:

... ...
File "c:\Anaconda3\envs\py36torch\lib\site-packages\torch\utils\serialization\read_lua_file.py", line 572, in read_table
v = self.read()
File "c:\Anaconda3\envs\py36torch\lib\site-packages\torch\utils\serialization\read_lua_file.py", line 598, in read
"corrupted.".format(typeidx))
torch.utils.serialization.read_lua_file.T7ReaderException: unknown type id 1056626884. The file may be corrupted.

I assume this is due to linux/windows compatibility and would like to try with vgg16.weight file.

Thank you for your code.

The scale changed after updating to the latest PyTorch release. This involved changing code for loading the Vgg model.

Look into the Vgg code maybe use the Vgg model from torchvision.models.

When I run download_styling_models.sh, it gets error：
--2018-03-16 09:26:26-- (try: 2) https://www.dropbox.com/s/gtwnyp9n49lqs7t/saved-models.zip?dl=1
Connecting to www.dropbox.com (www.dropbox.com)|162.125.82.1|:443... failed: Connection timed out.
Connecting to www.dropbox.com (www.dropbox.com)|2620:100:6032:1::a27d:5201|:443... failed: Cannot assign requested address.
Retrying.

Is there any other way to download it?
Thanks！

def tensor_save_rgbimage(tensor, filename, cuda=False):
    if cuda:
        img = tensor.clone().cpu().clamp(0, 255).numpy()
    else:
        img = tensor.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype('uint8')
    img = Image.fromarray(img)
    img.save(filename)


def tensor_save_bgrimage(tensor, filename, cuda=False):
    (b, g, r) = torch.chunk(tensor, 3)
    tensor = torch.cat((r, g, b))
    tensor_save_rgbimage(tensor, filename, cuda)

it seems like if you use function tensor_save_bgrimage to save a tensor, it will first transform from BGR to RGB and then transform from RGB to GBR in the function tensor_save_rgbimage?

Hi, I found there are two differences with jcjohnson's code

1. There is no Tanh after the output conv layer. But in the paper, section 3.1, it said there is a tanh layer after the output conv layer.
2. In the jcjohnson's code, the style image is also scaled to the content image size (256). But here the style image is directly taken into vgg16 without resizing.

Why and what are the influences on the result?

When I run download_styling_models.sh, it gets error: failed: Connection refused. I couldn't download the pretrained model from the website. Any ideas? Thanks!

Takes about 9s on an image. Looks like it's not using the GPU

When I run your code in Pytorch 0.4.0,it will be this error:
RuntimeError: The expanded size of the tensor (700) must match the existing size (32) at non-singleton dimension 2

I think it just because of the version of Pytorch ,Can you fix it,thank you?

DOVAN$ python neural_style/neural_style.py eval --content-image </Users/DOVAN/Desktop/WechatIMG128.jpeg> --model </Users/DOVAN/deeprely/fast-neural-style/saved-models/starry-night.pth> --output-image </Users/DOVAN/Desktop/test> --cuda 0
Python error: is a directory, cannot continue

epoch_2_Sat_Oct_21_17:31:07_2017_1.0_5.0.model.tar.gz
i have trained a model, program has finished successfully, yet evaluating the model seems to produce a blank screen.
i have attached the model.
https://www.dropbox.com/s/r9fxsy129he7rd1/epoch_2_Sat_Oct_21_17%3A31%3A07_2017_1.0_5.0.model.tar.gz?dl=0
please help ):

Hi,
I am using p2.xlarge in AWS (NVIDIA K80 GPU, 61GB RAM , 128GB Space). To check it out I was using COCO's val2017(5K images). It downloaded the vgg-model file and it has been blank for a very long while.
0.How much time does it take to get trained for 5000 images?
1.Also when I am trying to kill the python process with "kill -9 PID", it is not getting killed, is it because of the multi threading ? How do I end the training process in between ?

--> I am able to run 'eval' option using cuda 1 and it takes about 1-2 seconds to generate output images. So I am assuming all the package installations are fine on my side.

Please help me resolve the issues.

Hi, when adding Tanh() at the end of the transformer model, I got error during the training.
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
Is there a way to fix this problem? Thanks!

Hi, I found the vgg16 loss network returns [relu1_2, relu2_2, relu3_3, relu4_3], and in your neural_style.py,

y = transformer(x)
features_y = vgg(y)
f_xc_c = Variable(features_xc[1].data, requires_grad=False)
content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c)

the features_y is a list of loss corresponding to [relu1_2, relu2_2, relu3_3, relu4_3], the index for relu1_2, relu2_2, relu3_3 and relu4_3 is 0, 1, 2, and 3, right? In the paper, it used relu3_3 as content loss, so, here should it be the following?
f_xc_c = Variable(features_xc[2].data, requires_grad=False) content_loss = args.content_weight * mse_loss(features_y[2], f_xc_c)
Thanks.