xiaoming-yu / dmit Goto Github PK

Hi, thanks for sharing your codes.
I came across that you added something like our mode-seeking loss in your codes, but you mentioned it neither in your Github project nor your paper.
Could you please cite our Github project or our paper in your paper, thanks!

were there any attempts to train a city landscape?
Could you give any guidance on resources needed to achieve the mountain landscape ?
is it 8 x GPUs v100 for a week or two?

page 7 table1 , so the way to get LPIPS is real image & generated image or generated image & generated image
Higher means further/more different. Lower means more similar.

I saw in #2 you said you were busy with another project, and it would come out sometime before November, but it is now a week into November, do you know when it might be released?

Hello, I implemented it according to the method described in the paper, but the result is different from the one shown in the paper. Could you please share your implementation and I can find out where I misunderstood.THX！

In the page.16 of your paper dmit, a lpips of real data is listed in a table, but in my opinion there is no pairs in real data, so how do you get this value.
Thank you and hoping for you reply.

When training using a slightly modified season_transfer code (see my fork), everything quickly becomes NaN on my dataset:

(epoch: 1, iters: 20, time: 1.415) , D_rand_prior: 3.942, G_rand_prior: 0.426, D_rand_enc: 3.968, G_rand_enc: 0.356, D_content: 1.079, G_content: 0.930, errRec: 2.804, errKl: 0.005, errStyle: 0.600, errContent: 1.191, errCyc: 3.077, errDiv: 0.298
(epoch: 1, iters: 40, time: 1.413) , D_rand_prior: 3.905, G_rand_prior: 0.371, D_rand_enc: 3.860, G_rand_enc: 0.659, D_content: 1.675, G_content: 2.137, errRec: 4.170, errKl: 0.003, errStyle: 0.836, errContent: 0.912, errCyc: 4.638, errDiv: 0.621
(epoch: 1, iters: 60, time: 1.409) , D_rand_prior: 3.911, G_rand_prior: 0.255, D_rand_enc: 3.873, G_rand_enc: 0.415, D_content: 0.725, G_content: 0.183, errRec: 3.414, errKl: 0.033, errStyle: 0.749, errContent: 0.760, errCyc: 3.771, errDiv: 0.406
(epoch: 1, iters: 80, time: 1.414) , D_rand_prior: 3.803, G_rand_prior: 0.625, D_rand_enc: 3.784, G_rand_enc: 0.628, D_content: 0.579, G_content: 0.180, errRec: 5.343, errKl: 0.018, errStyle: 0.949, errContent: 0.653, errCyc: 5.572, errDiv: 0.617
(epoch: 1, iters: 100, time: 1.416) , D_rand_prior: 3.807, G_rand_prior: 0.434, D_rand_enc: 3.769, G_rand_enc: 0.575, D_content: 0.616, G_content: 0.819, errRec: 3.402, errKl: 0.030, errStyle: 0.615, errContent: 0.664, errCyc: 4.447, errDiv: 0.444
(epoch: 1, iters: 120, time: 1.409) , D_rand_prior: nan, G_rand_prior: nan, D_rand_enc: nan, G_rand_enc: nan, D_content: nan, G_content: nan, errRec: nan, errKl: nan, errStyle: nan, errContent: nan, errCyc: nan, errDiv: nan
(epoch: 1, iters: 140, time: 1.408) , D_rand_prior: nan, G_rand_prior: nan, D_rand_enc: nan, G_rand_enc: nan, D_content: nan, G_content: nan, errRec: nan, errKl: nan, errStyle: nan, errContent: nan, errCyc: nan, errDiv: nan
(epoch: 1, iters: 160, time: 1.400) , D_rand_prior: nan, G_rand_prior: nan, D_rand_enc: nan, G_rand_enc: nan, D_content: nan, G_content: nan, errRec: nan, errKl: nan, errStyle: nan, errContent: nan, errCyc: nan, errDiv: nan
(epoch: 1, iters: 180, time: 1.409) , D_rand_prior: nan, G_rand_prior: nan, D_rand_enc: nan, G_rand_enc: nan, D_content: nan, G_content: nan, errRec: nan, errKl: nan, errStyle: nan, errContent: nan, errCyc: nan, errDiv: nan

Are there any hyperparams you recommend tweaking to fix this? (My dataset has one domain of vector drawings, and one of images, if that's important)

Hey there, there is a bug when one wants to continue to train a model. Simply adding --continue_train flag in the command line cannot succeed.

To continue training, one has to

1. Go to /models/base_model.py, modify the line 64-65. Remove the quotation marks around net_name.
2. And add the following code snippet immediately after line 65:

          for state in net_optimizer.state.values():
                for k, v in state.items():
                    if isinstance(v, torch.Tensor):
                        state[k] = v.cuda()

For the second step, it is my temporary solution. Without it, one might encounter a warning like: RuntimeError: expected device cpu but got device cuda:0. Though it can solve the error, I think there should be a more elegant way existing.

In the semantic image synthesis task, how to select datas to calculate FID, PSNR and SSIM indicators

/DMIT/models/base_model.py", line 246, in update_model
errDec_total.backward(retain_graph=True)
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [8, 256]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

Anybody has this question?

Nice paper!
Could you please release the code asap?