When running evaluate. py, the error "ModuleNotFoundError: No module named 'model.i3d'" has occurred“. Could the author please provide information about model.i3d, I look forward to your reply, thank you very much.

Hi, thanks for your sharing. 关于您提供的 prepared stationary masks中，我注意到STTN方法中将Davis训练集90个场景的sample都用于evaluation，而您只从中选择了50个场景制作了mask. 请问这是出于什么原因考虑呢？是否是处于效率考虑，增加的场景不会影响evaluation结果？

另外，我还注意到在对比实验中，您直接使用了其他对比方法在STTN工作中的evaluation结果，您是否使用相同的验证集进行了验证并得到了相同的结果呢？

谢谢您的回答

how can i find the stationary test mask for the two datasets (for PSNR)? if you can release the masks and the results that your model generate, that would be better!!!

The YouTube Vos data set is trained according to the requirements in the paper, and the trained model always reports errors. Looking forward to your reply, thank you!

Hey,

I am having trouble running the evaluate.py script.

Could you please specify which Davis dataset has been used/ is supposed to be downloaded?

Thanks in advance

I used your dataset and your training function on a single Tesla V100 with 32GB memory. But, I find over 5 batchsize, my memory is not enough, I don't know why. Cause you illustrate your batchsize is 8 and use V100 as well.

Thank you very much.

Best wishes.

Hey,

another issue with the evaluation script is that in evaluate.py line 260 the model's state_dict is loaded: model.load_state_dict(data['netG']) but there is no netG key to the dictionary.

I've downloaded the pertained fuseformer.pth swell as the i3d_rgb_imagenet.pt

what could be the reason for the missing dict key?

Thanks in advance :)

I see the code, trainer.py, where netD needs to be loaded with pretrained data, but I cannot find the relevant file in your repository? Could you provide a way to access the weight file? Thank you.

hi，thank you for your work !

in the test.py:

ref_ids = get_ref_index(f, neighbor_ids, video_length)

selected_imgs = imgs[:1, neighbor_ids+ref_ids :, :, :]

for i in range(len(neighbor_ids)):  
            idx = neighbor_ids[i]
            img = np.array(pred_img[i]).astype(
                np.uint8)*binary_masks[idx] + frames[idx] * (1-binary_masks[idx])
            if comp_frames[idx] is None:
                comp_frames[idx] = img
            else:
                comp_frames[idx] = comp_frames[idx].astype(
                    np.float32)*0.5 + img.astype(np.float32)*0.5

What is the purpose of the reference frames?

你好，感谢您的工作。我有一个关于学习率的问题。我注意到您文章中写到 initial learning rate is 0.01，之后分别在200k，400k和450k时reduce by factor of 10 请问这样的设计是出于什么考虑呢？ 我还注意到代码中您的学习率设置是与STTN一致的：initial learning rate is 0.0001，reduce at 400k by factor of 10 您是否测试过这二者的区别？ 希望得到您的解答！！！

Thanks for the excellent work! In the paper, DAVIS is split into 90 video clips for training and 60 clips for testing. However, there are only 50 video clip for DAVIS evaluation in the prepared stationary masks. Is there something missing in this folder? I would be very grateful if you could answer my questions. Best wishes.

The code seems to be only work on a specific input resolution which the model trained on. If I want to test it with dimension other than 432x240, I can but I have to train the model with that specific dimension. right?