When i tested your pre-trained model, i met the problem called "CUDA error: an illegal memory access was encountered", can you provide the version of your CUDA, cudnn and pytorch

Hi, could i apply this method to image colorization and remove the temporal consistency loss? BTW, how to get the pairs.txt/pairs_mid.txt/pairs_bad.txt used in videoloader_imagenet.py?

video colorization very good and very impressive . but render image low size 768x432 . and video size also same. how to in increase the image size and video size. thank you..

The original training command is python --data_root [root of video samples] \ --data_root_imagenet [root of image samples] \ --gpu_ids [gpu ids] \ Maybe it should be python train.py --data_root [root of video samples] \ --data_root_imagenet [root of image samples] \ --gpu_ids [gpu ids] \ ?

There seems a bug ofr feature centering with x_features - y_features.mean which I think should be x_features - x_features.mean https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization/blob/37639748f12dfecbb0a3fe265b533887b5fe46ce/models/ContextualLoss.py#L100

Thanks for your great work! I have a question when i run test.py. Why don't you extract the feature of inference image out of the for loop. I haven't found any difference.

Hello, I am running a 4gb nvidia GPU. Is that enough for inference? I try to run on ubuntu 18.04 as well as windows but always get a Out of memory error eventually. Sometimes happen after 2nd image and sometimes after 5th. This is 1080p video.

Could you please release a tiny illustrative training dataset, such that the preparation of a custom training data can be easily followed. Currently, it is not easy to prepare a custom training data by reading the train.py. or could you please give a further explanation of the following fields? ( image1_name, image2_name, reference_video_name, reference_video_name1, reference_name1, reference_name2, reference_name3, reference_name4, reference_name5, reference_gt1, reference_gt2, reference_gt3, ) Thank you very much.

Getting a runtime error when running the test cells at the 'first we visualize the input video'. I'm not good with code, but this is the first time I've experienced this issue with this wonderful program. No cells execute after this error. I've attached a screenshot.

At present, after training, it is found that the generated test image is effective, but the color saturation is very low. Is it because of the colored model or other reasons? I'm looking forward to your reply！！！

Hello, I read in the paper that "we train the network for 10 epichs with a batch size of 40 pairs of video frames. " Is it effective after only 10 iterations? Is your data 768 videos, 25 frames per video? I only train one video at present, epoch=40, but I find that it has little effect. What may be the reason?

I was working with the Colab program and there appears to be important models / files missing. As a result the program has ceased to function. I've brough to the designers attention so hopefully will be resolved.

Hi !

Trying out test.py results in the following error:

Traceback (most recent call last): File "test.py", line 26, in <module> torch.cuda.set_device(0) File "C:\Users\natha\anaconda3\envs\ColorVid\lib\site-packages\torch\cuda\__init__.py", line 311, in set_device torch._C._cuda_setDevice(device) AttributeError: module 'torch._C' has no attribute '_cuda_setDevice'

I tried installing pytorch manually using their tool https://pytorch.org/get-started/locally/ (with CUDA 11.6) but that doesn't resolve the issue.

Can someone help me understand what is going on ? Thanks !!

Thanks for your outstanding work! I have some questions when I read it.

1. What are the settings when you test this video model on image colorization which used for comparing with other image colorization methods?
2. Could you please give me a url about your video testset (116 video clips collected from Videvo)? Thanks again for your attention.

https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization/blob/37639748f12dfecbb0a3fe265b533887b5fe46ce/lib/TestTransforms.py#L341 it seems a repeated define of call

Processing 4x3 video 912x720 outputs cropped and downscaled 16x9 768x432. Playing around "python test.py --image-size [image-size] " doesn't help My be I don't properly specify an arguments? So, what the the proper use of --image-size [image-size] in order to get 912x720? Greatly appreciate for suggesting.